From 03372927271ab86ab89e284d5ff71e3758bd9364 Mon Sep 17 00:00:00 2001 From: Casa Admin Date: Fri, 2 Feb 2024 08:28:55 -0500 Subject: [PATCH] Generated from sources --- ...ic.time_mosaic_cube_eph_pcwdT_restart.json | 2 +- ...pplyModes.time_applycal_cal_only_mode.json | 2 +- ...plyModes.time_applycal_flag_only_mode.json | 2 +- ...s.time_applycal_flag_only_strict_mode.json | 2 +- ....ApplyModes.time_applycal_strict_mode.json | 2 +- ...pplycal.CalFlag.time_applycal_calflag.json | 2 +- ...l.CalLibrary.time_applycal_callibrary.json | 2 +- ...cal.CalWeight.time_applycal_calweight.json | 2 +- ....FlagBackup.time_applycal_flag_backup.json | 2 +- ...cal.GainField.time_applycal_gainfield.json | 2 +- ..._applycal.Interp.time_applycal_interp.json | 2 +- ..._applycal.Parang.time_applycal_parang.json | 2 +- ...ionTypes.time_applycal_select_antenna.json | 2 +- ...ctionTypes.time_applycal_select_field.json | 2 +- ...tionTypes.time_applycal_select_intent.json | 2 +- ...ectionTypes.time_applycal_select_scan.json | 2 +- ...lectionTypes.time_applycal_select_spw.json | 2 +- ...nTypes.time_applycal_select_timerange.json | 2 +- ...ionTypes.time_applycal_select_uvrange.json | 2 +- ..._applycal.SpwMap.time_applycal_spwmap.json | 2 +- ...inTable.time_applycal_basic_gaintable.json | 2 +- ...pplycal.TrialMode.time_applycal_trial.json | 2 +- ...intMode.time_antint_low_threshold_spw.json | 2 +- ...Bandpass.time_bandpass_clip_timerange.json | 2 +- ...CalibrationTsys.time_tsys_clip_fparam.json | 2 +- ...ata.ClipMode.time_clip_corrected_data.json | 2 +- ...gdata.ClipMode.time_clip_with_timeavg.json | 2 +- ...ElevationMode.time_elevation_interval.json | 2 +- ...ta.ListFileMode.time_list_from_3files.json | 2 +- ...gdata.ListMode.time_list_bandpassflag.json | 2 +- ...h_flagdata.ListMode.time_list_summary.json | 2 +- ...nch_flagdata.ListMode.time_list_tbuff.json | 2 +- ...data.RflagMode.time_rflag_numpy_types.json | 2 +- ..._flagdata.Selections.time_sel_abs_wvr.json | 2 +- ...data.Selections.time_sel_autocorr_wvr.json | 2 +- ...hadowMode.time_shadow_absent_antennas.json | 2 +- ...ata.ShadowMode.time_shadow_addantenna.json | 2 +- ...lagdata.TfcropMode.time_tfcrop_abs_rr.json | 2 +- ...ta.TfcropMode.time_tfcrop_extendflags.json | 2 +- ...ench_fringefit.Corrcomb.time_comb_all.json | 2 +- ...efit.Dispersive.time_manual_phase_cal.json | 2 +- ...fringefit.FreqMetaTests.time_metadata.json | 2 +- .../bench_fringefit.Mbd.time_mbd.json | 2 +- ...it.RefantBookkeeping.time_bookkeeping.json | 2 +- .../bench_fringefit.Sbd.time_sbd.json | 2 +- .../bench_fringefit.Sbd.time_uvrange.json | 2 +- .../bench_fringefit.Single.time_param.json | 2 +- .../bench_fringefit.Single.time_single.json | 2 +- ...incal.CalModes.time_gaincal_calmode_A.json | 2 +- ...incal.CalModes.time_gaincal_calmode_P.json | 2 +- ...ncal.FullGainCal.time_gaincal_fullcal.json | 2 +- ...al.GainTypes.time_gaincal_gain_type_K.json | 2 +- ...inTypes.time_gaincal_gain_type_spline.json | 2 +- ...Selections.time_gaincal_antennaSelect.json | 2 +- ...alSelections.time_gaincal_fieldSelect.json | 2 +- ...lSelections.time_gaincal_refantSelect.json | 2 +- ...calSelections.time_gaincal_scanSelect.json | 2 +- ...ncalSelections.time_gaincal_spwSelect.json | 2 +- ...Selections.time_gaincal_uvRangeSelect.json | 2 +- ...aincal.MinBlPerAnt.time_gaincal_minbl.json | 2 +- ...aincal.MinSNRFlag.time_gaincal_minsnr.json | 2 +- ...ApplyTables.time_gaincal_apply_tables.json | 2 +- ...l.RefantSelection.time_gaincal_refant.json | 2 +- ...al.UseSpwMap.time_gaincal_use_spw_map.json | 2 +- .../bench_importasdm.Aca.time_default.json | 2 +- ...ench_importasdm.Aca.time_default_lazy.json | 2 +- ...ch_importasdm.Alma_12m.time_auto_only.json | 2 +- ...portasdm.Alma_12m.time_auto_only_lazy.json | 2 +- ...mportasdm.Alma_12m.time_bdfflags_true.json | 2 +- ...asdm.Alma_12m.time_bdfflags_true_lazy.json | 2 +- ...ench_importasdm.Alma_12m.time_default.json | 2 +- ...importasdm.Alma_12m.time_default_lazy.json | 2 +- ...sdm.AutocorrASDM.time_applyflags_true.json | 2 +- ...AutocorrASDM.time_process_flags_false.json | 2 +- ...tasdm.AutocorrASDM.time_savecmds_true.json | 2 +- ...ch_importasdm.Basic.time_all_defaults.json | 2 +- ...ch_importasdm.Basic.time_lazy_pc_true.json | 2 +- .../bench_importasdm.Basic.time_pc_true.json | 2 +- .../bench_importasdm.Evla.time_all_scans.json | 2 +- ..._importasdm.Evla.time_applyflags_true.json | 2 +- .../bench_importasdm.Evla.time_pc_true.json | 2 +- ...time_process_applyflags_savecmds_true.json | 2 +- ...Evla.time_process_flags_savecmds_true.json | 2 +- ...e_process_flags_true_applyflags_false.json | 2 +- ...h_importasdm.Evla.time_scan_selection.json | 2 +- ....Evla_ephemeris.time_polynomial_ephem.json | 2 +- ..._importasdm.Singledish.time_auto_only.json | 2 +- ...rtasdm.Singledish.time_auto_only_lazy.json | 2 +- ....test_sdatmcor.time_custom_atm_params.json | 2 +- ...atmcor.time_sdatmcor_explicit_atmtype.json | 2 +- ...datmcor.time_sdatmcor_gainfactor_dict.json | 2 +- ...or.test_sdatmcor.time_sdatmcor_normal.json | 2 +- ...ne.Basic.time_blparam_apply_no_remove.json | 2 +- ...baseline.Basic.time_blparam_overwrite.json | 2 +- ...e.Basic.time_chebyshev_blfunc_no_mask.json | 2 +- ...ine.Basic.time_cspline_blfunc_no_mask.json | 2 +- .../bench_sdbaseline.Basic.time_default.json | 2 +- ...seline.Basic.time_poly_blfunc_no_mask.json | 2 +- ...e.BlFuncVariable.time_variable_orders.json | 2 +- ...ine_blfunc_masked_range_spectrum_edge.json | 2 +- ...k.time_masked_ranges_no_edge_spectrum.json | 2 +- ...Table.time_fit_variable_masked_masked.json | 2 +- ...ble.time_fit_variable_masked_unselect.json | 2 +- ...OutBlTable.time_fit_variable_r2p1cout.json | 2 +- ...OutBlTable.time_fit_variable_r2p1less.json | 2 +- ...ble.time_fit_variable_unselect_masked.json | 2 +- ...eWeight.time_blmode_apply_spw_to_flag.json | 2 +- ...e.UpdateWeight.time_remove_weight_col.json | 2 +- .../bench_sdcal.Apply.time_spwmap_dict.json | 2 +- ...l.Apply.time_spwmap_dict_correct_data.json | 2 +- ...pply.time_spwmap_dict_weight_spectrum.json | 2 +- .../bench_sdcal.Apply.time_spwmap_list.json | 2 +- .../bench_sdcal.Apply.time_test05.json | 2 +- ..._sdcal.Apply_Interp.time_interp_empty.json | 2 +- .../bench_sdcal.Apply_Interp.time_linear.json | 2 +- ...l.Apply_Interp.time_linear_linearflag.json | 2 +- ....Apply_Interp.time_linear_nearestflag.json | 2 +- ...pply_Interp.time_linear_spw_selection.json | 2 +- ...bench_sdcal.Apply_Interp.time_nearest.json | 2 +- ...al.Apply_Modified_Tsys.time_apply_sky.json | 2 +- ...y_Modified_Tsys.time_apply_sky_normal.json | 2 +- ...ply_Modified_Tsys.time_apply_sky_tsys.json | 2 +- ...pply_Modified_Tsys.time_preapply_tsys.json | 2 +- .../bench_sdcal.OTF.time_otf01.json | 2 +- .../bench_sdcal.OTF.time_otf02.json | 2 +- .../bench_sdcal.OTF.time_otf03.json | 2 +- .../bench_sdcal.OTF.time_otf04.json | 2 +- .../bench_sdcal.OTF.time_otf05.json | 2 +- .../bench_sdcal.OTF.time_otf06.json | 2 +- .../bench_sdcal.OTF.time_otf07.json | 2 +- ...ch_sdcal.OTFRaster.time_autodetection.json | 2 +- .../bench_sdcal.OTFRaster.time_default.json | 2 +- ...sdcal.OTFRaster.time_fraction_numeric.json | 2 +- ...TFRaster.time_fraction_string_numeric.json | 2 +- ...aster.time_fraction_string_percentage.json | 2 +- ...ench_sdcal.OTFRaster.time_noff_custom.json | 2 +- ...ster.time_noff_priority_over_fraction.json | 2 +- ...ch_sdcal.OTF_Ephemeris.time_otf_ephem.json | 2 +- ...ch_sdcal.PositionSwitch.time_ps_basic.json | 2 +- ...dcal.PositionSwitch.time_ps_selection.json | 2 +- ...le_polarization.time_single_pol_apply.json | 2 +- ...ation.time_single_pol_apply_composite.json | 2 +- ...ingle_polarization.time_single_pol_ps.json | 2 +- ...ic.time_gridfunction_BOX_imsize_40x35.json | 2 +- ...n_BOX_imsize_40x35_phasecenter_detect.json | 2 +- ...etect_cell_default_phasecenter_detect.json | 2 +- ...Basic.time_gridfunction_BOX_start_400.json | 2 +- ...nction_BOX_start_400_phasecenter_null.json | 2 +- ...sic.time_gridfunction_GAUSS_start_400.json | 2 +- ...sic.time_gridfunction_GJINC_start_400.json | 2 +- ...function_GJINC_start_400_minweight_70.json | 2 +- ..._sdimaging.Basic.time_gridfunction_PB.json | 2 +- ...c.time_gridfunction_PB_mode_frequency.json | 2 +- ...gridfunction_PB_mode_velocity_nchan_1.json | 2 +- ...idfunction_PB_mode_velocity_nchan_100.json | 2 +- ...Basic.time_gridfunction_PB_nchan_1024.json | 2 +- ....Basic.time_gridfunction_PB_start_400.json | 2 +- ...gridfunction_PB_start_400_stokes_XXYY.json | 2 +- ...Basic.time_gridfunction_PB_width_1024.json | 2 +- ....Basic.time_gridfunction_SF_start_400.json | 2 +- ...iField.peakmem_multifield_cube_hogbom.json | 2 +- ...tiField.peakmem_multifield_mfs_hogbom.json | 2 +- ...ltiField.peakmem_multifield_mfs_mtmfs.json | 2 +- ...Weighting.peakmem_tclean_setweighting.json | 2 +- ...le_descriptors_cubemode_mosaic_briggs.json | 2 +- ...eld.peakmem_cube_standard_fullsummary.json | 2 +- ..._cube_mosaic_cbFalse_mwFalse_twofield.json | 2 +- ...osaic_cbFalse_mwFalse_twofield_upTrue.json | 2 +- ...m_cube_mosaic_cbFalse_mwTrue_twofield.json | 2 +- ...ieldAWP.peakmem_cube_awproject_hogbom.json | 2 +- ...FieldAWP.peakmem_mfs_awproject_hogbom.json | 2 +- ...eFieldAWP.peakmem_mfs_awproject_mtmfs.json | 2 +- ...anCube.time_cube_standard_channel_gap.json | 2 +- ...anCube.time_cube_standard_fullsummary.json | 2 +- ....TcleanCube.time_cube_standard_hogbom.json | 2 +- ...time_eph_multifield_mosaic_cubesource.json | 2 +- ...emeris.time_eph_multifield_mosaic_mfs.json | 2 +- ...e_eph_singlefield_standard_cubesource.json | 2 +- ...ris.time_eph_singlefield_standard_mfs.json | 2 +- ...ldelVis.time_mfs_automask_modelcolumn.json | 2 +- ....TcleanMoldelVis.time_mfs_modelcolumn.json | 2 +- ...oldelVis.time_mfs_virtual_modelcolumn.json | 2 +- ...ultiField.time_multifield_cube_hogbom.json | 2 +- ...MultiField.time_multifield_mfs_hogbom.json | 2 +- ...nMultiField.time_multifield_mfs_mtmfs.json | 2 +- ...nSingleField.time_cube_standard_pcwdT.json | 2 +- ...anSingleField.time_mfs_standard_clark.json | 2 +- ...nSingleField.time_mfs_standard_hogbom.json | 2 +- ...eld.time_mfs_standard_hogbom_automask.json | 2 +- ...anSingleField.time_mfs_standard_mtmfs.json | 2 +- ...time.TcleanStokes.time_cube_stokes_IV.json | 2 +- ..._time.TcleanStokes.time_mfs_stokes_IV.json | 2 +- ...e.TcleanStokes.time_mtmfs_stokes_IQUV.json | 2 +- ...deFieldAWP.time_cube_awproject_hogbom.json | 2 +- ...ideFieldAWP.time_mfs_awproject_hogbom.json | 2 +- ...WideFieldAWP.time_mfs_awproject_mtmfs.json | 2 +- ..._cube_mosaic_cbFalse_mwFalse_twofield.json | 2 +- ...osaic_cbFalse_mwFalse_twofield_upTrue.json | 2 +- ...e_cube_mosaic_cbFalse_mwTrue_twofield.json | 2 +- ...ic.time_gridfunction_BOX_imsize_40x35.json | 2 +- ...n_BOX_imsize_40x35_phasecenter_detect.json | 2 +- ...etect_cell_default_phasecenter_detect.json | 2 +- ...Basic.time_gridfunction_BOX_start_400.json | 2 +- ...nction_BOX_start_400_phasecenter_null.json | 2 +- ...sic.time_gridfunction_GAUSS_start_400.json | 2 +- ...sic.time_gridfunction_GJINC_start_400.json | 2 +- ...function_GJINC_start_400_minweight_70.json | 2 +- ...tsdimaging.Basic.time_gridfunction_PB.json | 2 +- ...c.time_gridfunction_PB_mode_frequency.json | 2 +- ...gridfunction_PB_mode_velocity_nchan_1.json | 2 +- ...idfunction_PB_mode_velocity_nchan_100.json | 2 +- ...Basic.time_gridfunction_PB_nchan_1024.json | 2 +- ....Basic.time_gridfunction_PB_start_400.json | 2 +- ...gridfunction_PB_start_400_stokes_XXYY.json | 2 +- ...Basic.time_gridfunction_PB_width_1024.json | 2 +- ....Basic.time_gridfunction_SF_start_400.json | 2 +- ...g.Ephemeris.time_ephemeris_cubesource.json | 2 +- ...aging.Ephemeris.time_ephemeris_notset.json | 2 +- ...dimaging.Ephemeris.time_ephemeris_sun.json | 2 +- ...maging.Ephemeris.time_ephemeris_table.json | 2 +- ...aging.Ephemeris.time_ephemeris_trackf.json | 2 +- ...bench_uvcontsub.Basic.time_datacolumn.json | 2 +- ...contsub.Basic.time_fitmethod_casacore.json | 2 +- ...ch_uvcontsub.Basic.time_fitmethod_gsl.json | 2 +- .../bench_uvcontsub.Basic.time_fitorder1.json | 2 +- .../bench_uvcontsub.Basic.time_fitorder2.json | 2 +- ...ub.Basic.time_fitspec3_spws_fitorder0.json | 2 +- ...uvcontsub.Basic.time_fitspec_channels.json | 2 +- ...sub.Basic.time_fitspec_dict_fitorder1.json | 2 +- ...sub.Basic.time_fitspec_dict_fitorder2.json | 2 +- ...ic.time_fitspec_dict_fitspec_one_chan.json | 2 +- ..._fitspec_dict_fitspec_one_chan_order1.json | 2 +- ...e_fitspec_dict_select_spw_mismatching.json | 2 +- ...ch_uvcontsub.Basic.time_fitspec_empty.json | 2 +- ...contsub.Basic.time_fitspec_multifield.json | 2 +- ....Basic.time_fitspec_multifield_blocks.json | 2 +- ...ic.time_fitspec_multifield_fields_sel.json | 2 +- ...pec_multifield_multispw_diff_fitorder.json | 2 +- ...ub.Basic.time_fitspec_separate_fields.json | 2 +- ...ntsub.Basic.time_fitspec_spw_one_chan.json | 2 +- ...nch_uvcontsub.Basic.time_fitspec_spws.json | 2 +- ...uvcontsub.Basic.time_fitspec_spws_sel.json | 2 +- ...asic.time_fitspec_spwschans_fitorder3.json | 2 +- ...ntsub.Basic.time_makes_output_ms_data.json | 2 +- ...bench_uvcontsub.Basic.time_mixed_pols.json | 2 +- ...nch_uvcontsub.Basic.time_select_array.json | 2 +- ...nch_uvcontsub.Basic.time_select_field.json | 2 +- ...ch_uvcontsub.Basic.time_select_intent.json | 2 +- ...contsub.Basic.time_select_observation.json | 2 +- ...ench_uvcontsub.Basic.time_select_scan.json | 2 +- ...bench_uvcontsub.Basic.time_select_spw.json | 2 +- ...c.time_select_spw_mismatching_fitspec.json | 2 +- ...bench_uvcontsub.Basic.time_writemodel.json | 2 +- ....Basic.time_writemodel_from_corrected.json | 2 +- ...writemodel_from_corrected_all_flagged.json | 2 +- ...merical.time_sim_specline_noise_pol_0.json | 2 +- ...merical.time_sim_specline_noise_pol_1.json | 2 +- ...rical.time_sim_specline_nonoise_pol_0.json | 2 +- ...rical.time_sim_specline_nonoise_pol_1.json | 2 +- .../python-3.8/ram-263549216/summary.json | 2 +- ...ic.time_mosaic_cube_eph_pcwdT_restart.json | 2 +- ...pplyModes.time_applycal_cal_only_mode.json | 2 +- ...plyModes.time_applycal_flag_only_mode.json | 2 +- ...s.time_applycal_flag_only_strict_mode.json | 2 +- ....ApplyModes.time_applycal_strict_mode.json | 2 +- ...pplycal.CalFlag.time_applycal_calflag.json | 2 +- ...l.CalLibrary.time_applycal_callibrary.json | 2 +- ...cal.CalWeight.time_applycal_calweight.json | 2 +- ....FlagBackup.time_applycal_flag_backup.json | 2 +- ...cal.GainField.time_applycal_gainfield.json | 2 +- ..._applycal.Interp.time_applycal_interp.json | 2 +- ..._applycal.Parang.time_applycal_parang.json | 2 +- ...ionTypes.time_applycal_select_antenna.json | 2 +- ...ctionTypes.time_applycal_select_field.json | 2 +- ...tionTypes.time_applycal_select_intent.json | 2 +- ...ectionTypes.time_applycal_select_scan.json | 2 +- ...lectionTypes.time_applycal_select_spw.json | 2 +- ...nTypes.time_applycal_select_timerange.json | 2 +- ...ionTypes.time_applycal_select_uvrange.json | 2 +- ..._applycal.SpwMap.time_applycal_spwmap.json | 2 +- ...inTable.time_applycal_basic_gaintable.json | 2 +- ...pplycal.TrialMode.time_applycal_trial.json | 2 +- ...intMode.time_antint_low_threshold_spw.json | 2 +- ...Bandpass.time_bandpass_clip_timerange.json | 2 +- ...CalibrationTsys.time_tsys_clip_fparam.json | 2 +- ...ata.ClipMode.time_clip_corrected_data.json | 2 +- ...gdata.ClipMode.time_clip_with_timeavg.json | 2 +- ...ElevationMode.time_elevation_interval.json | 2 +- ...ta.ListFileMode.time_list_from_3files.json | 2 +- ...gdata.ListMode.time_list_bandpassflag.json | 2 +- ...h_flagdata.ListMode.time_list_summary.json | 2 +- ...nch_flagdata.ListMode.time_list_tbuff.json | 2 +- ...data.RflagMode.time_rflag_numpy_types.json | 2 +- ..._flagdata.Selections.time_sel_abs_wvr.json | 2 +- ...data.Selections.time_sel_autocorr_wvr.json | 2 +- ...hadowMode.time_shadow_absent_antennas.json | 2 +- ...ata.ShadowMode.time_shadow_addantenna.json | 2 +- ...lagdata.TfcropMode.time_tfcrop_abs_rr.json | 2 +- ...ta.TfcropMode.time_tfcrop_extendflags.json | 2 +- ...ench_fringefit.Corrcomb.time_comb_all.json | 2 +- ...efit.Dispersive.time_manual_phase_cal.json | 2 +- ...fringefit.FreqMetaTests.time_metadata.json | 2 +- .../summary/bench_fringefit.Mbd.time_mbd.json | 2 +- ...it.RefantBookkeeping.time_bookkeeping.json | 2 +- .../summary/bench_fringefit.Sbd.time_sbd.json | 2 +- .../bench_fringefit.Sbd.time_uvrange.json | 2 +- .../bench_fringefit.Single.time_param.json | 2 +- .../bench_fringefit.Single.time_single.json | 2 +- ...incal.CalModes.time_gaincal_calmode_A.json | 2 +- ...incal.CalModes.time_gaincal_calmode_P.json | 2 +- ...ncal.FullGainCal.time_gaincal_fullcal.json | 2 +- ...al.GainTypes.time_gaincal_gain_type_K.json | 2 +- ...inTypes.time_gaincal_gain_type_spline.json | 2 +- ...Selections.time_gaincal_antennaSelect.json | 2 +- ...alSelections.time_gaincal_fieldSelect.json | 2 +- ...lSelections.time_gaincal_refantSelect.json | 2 +- ...calSelections.time_gaincal_scanSelect.json | 2 +- ...ncalSelections.time_gaincal_spwSelect.json | 2 +- ...Selections.time_gaincal_uvRangeSelect.json | 2 +- ...aincal.MinBlPerAnt.time_gaincal_minbl.json | 2 +- ...aincal.MinSNRFlag.time_gaincal_minsnr.json | 2 +- ...ApplyTables.time_gaincal_apply_tables.json | 2 +- ...l.RefantSelection.time_gaincal_refant.json | 2 +- ...al.UseSpwMap.time_gaincal_use_spw_map.json | 2 +- .../bench_importasdm.Aca.time_default.json | 2 +- ...ench_importasdm.Aca.time_default_lazy.json | 2 +- ...ch_importasdm.Alma_12m.time_auto_only.json | 2 +- ...portasdm.Alma_12m.time_auto_only_lazy.json | 2 +- ...mportasdm.Alma_12m.time_bdfflags_true.json | 2 +- ...asdm.Alma_12m.time_bdfflags_true_lazy.json | 2 +- ...ench_importasdm.Alma_12m.time_default.json | 2 +- ...importasdm.Alma_12m.time_default_lazy.json | 2 +- ...sdm.AutocorrASDM.time_applyflags_true.json | 2 +- ...AutocorrASDM.time_process_flags_false.json | 2 +- ...tasdm.AutocorrASDM.time_savecmds_true.json | 2 +- ...ch_importasdm.Basic.time_all_defaults.json | 2 +- ...ch_importasdm.Basic.time_lazy_pc_true.json | 2 +- .../bench_importasdm.Basic.time_pc_true.json | 2 +- .../bench_importasdm.Evla.time_all_scans.json | 2 +- ..._importasdm.Evla.time_applyflags_true.json | 2 +- .../bench_importasdm.Evla.time_pc_true.json | 2 +- ...time_process_applyflags_savecmds_true.json | 2 +- ...Evla.time_process_flags_savecmds_true.json | 2 +- ...e_process_flags_true_applyflags_false.json | 2 +- ...h_importasdm.Evla.time_scan_selection.json | 2 +- ....Evla_ephemeris.time_polynomial_ephem.json | 2 +- ..._importasdm.Singledish.time_auto_only.json | 2 +- ...rtasdm.Singledish.time_auto_only_lazy.json | 2 +- ....test_sdatmcor.time_custom_atm_params.json | 2 +- ...atmcor.time_sdatmcor_explicit_atmtype.json | 2 +- ...datmcor.time_sdatmcor_gainfactor_dict.json | 2 +- ...or.test_sdatmcor.time_sdatmcor_normal.json | 2 +- ...ne.Basic.time_blparam_apply_no_remove.json | 2 +- ...baseline.Basic.time_blparam_overwrite.json | 2 +- ...e.Basic.time_chebyshev_blfunc_no_mask.json | 2 +- ...ine.Basic.time_cspline_blfunc_no_mask.json | 2 +- .../bench_sdbaseline.Basic.time_default.json | 2 +- ...seline.Basic.time_poly_blfunc_no_mask.json | 2 +- ...e.BlFuncVariable.time_variable_orders.json | 2 +- ...ine_blfunc_masked_range_spectrum_edge.json | 2 +- ...k.time_masked_ranges_no_edge_spectrum.json | 2 +- ...Table.time_fit_variable_masked_masked.json | 2 +- ...ble.time_fit_variable_masked_unselect.json | 2 +- ...OutBlTable.time_fit_variable_r2p1cout.json | 2 +- ...OutBlTable.time_fit_variable_r2p1less.json | 2 +- ...ble.time_fit_variable_unselect_masked.json | 2 +- ...eWeight.time_blmode_apply_spw_to_flag.json | 2 +- ...e.UpdateWeight.time_remove_weight_col.json | 2 +- .../bench_sdcal.Apply.time_spwmap_dict.json | 2 +- ...l.Apply.time_spwmap_dict_correct_data.json | 2 +- ...pply.time_spwmap_dict_weight_spectrum.json | 2 +- .../bench_sdcal.Apply.time_spwmap_list.json | 2 +- .../bench_sdcal.Apply.time_test05.json | 2 +- ..._sdcal.Apply_Interp.time_interp_empty.json | 2 +- .../bench_sdcal.Apply_Interp.time_linear.json | 2 +- ...l.Apply_Interp.time_linear_linearflag.json | 2 +- ....Apply_Interp.time_linear_nearestflag.json | 2 +- ...pply_Interp.time_linear_spw_selection.json | 2 +- ...bench_sdcal.Apply_Interp.time_nearest.json | 2 +- ...al.Apply_Modified_Tsys.time_apply_sky.json | 2 +- ...y_Modified_Tsys.time_apply_sky_normal.json | 2 +- ...ply_Modified_Tsys.time_apply_sky_tsys.json | 2 +- ...pply_Modified_Tsys.time_preapply_tsys.json | 2 +- .../summary/bench_sdcal.OTF.time_otf01.json | 2 +- .../summary/bench_sdcal.OTF.time_otf02.json | 2 +- .../summary/bench_sdcal.OTF.time_otf03.json | 2 +- .../summary/bench_sdcal.OTF.time_otf04.json | 2 +- .../summary/bench_sdcal.OTF.time_otf05.json | 2 +- .../summary/bench_sdcal.OTF.time_otf06.json | 2 +- .../summary/bench_sdcal.OTF.time_otf07.json | 2 +- ...ch_sdcal.OTFRaster.time_autodetection.json | 2 +- .../bench_sdcal.OTFRaster.time_default.json | 2 +- ...sdcal.OTFRaster.time_fraction_numeric.json | 2 +- ...TFRaster.time_fraction_string_numeric.json | 2 +- ...aster.time_fraction_string_percentage.json | 2 +- ...ench_sdcal.OTFRaster.time_noff_custom.json | 2 +- ...ster.time_noff_priority_over_fraction.json | 2 +- ...ch_sdcal.OTF_Ephemeris.time_otf_ephem.json | 2 +- ...ch_sdcal.PositionSwitch.time_ps_basic.json | 2 +- ...dcal.PositionSwitch.time_ps_selection.json | 2 +- ...le_polarization.time_single_pol_apply.json | 2 +- ...ation.time_single_pol_apply_composite.json | 2 +- ...ingle_polarization.time_single_pol_ps.json | 2 +- ...ic.time_gridfunction_BOX_imsize_40x35.json | 2 +- ...n_BOX_imsize_40x35_phasecenter_detect.json | 2 +- ...etect_cell_default_phasecenter_detect.json | 2 +- ...Basic.time_gridfunction_BOX_start_400.json | 2 +- ...nction_BOX_start_400_phasecenter_null.json | 2 +- ...sic.time_gridfunction_GAUSS_start_400.json | 2 +- ...sic.time_gridfunction_GJINC_start_400.json | 2 +- ...function_GJINC_start_400_minweight_70.json | 2 +- ..._sdimaging.Basic.time_gridfunction_PB.json | 2 +- ...c.time_gridfunction_PB_mode_frequency.json | 2 +- ...gridfunction_PB_mode_velocity_nchan_1.json | 2 +- ...idfunction_PB_mode_velocity_nchan_100.json | 2 +- ...Basic.time_gridfunction_PB_nchan_1024.json | 2 +- ....Basic.time_gridfunction_PB_start_400.json | 2 +- ...gridfunction_PB_start_400_stokes_XXYY.json | 2 +- ...Basic.time_gridfunction_PB_width_1024.json | 2 +- ....Basic.time_gridfunction_SF_start_400.json | 2 +- ...iField.peakmem_multifield_cube_hogbom.json | 2 +- ...tiField.peakmem_multifield_mfs_hogbom.json | 2 +- ...ltiField.peakmem_multifield_mfs_mtmfs.json | 2 +- ...Weighting.peakmem_tclean_setweighting.json | 2 +- ...le_descriptors_cubemode_mosaic_briggs.json | 2 +- ...eld.peakmem_cube_standard_fullsummary.json | 2 +- ..._cube_mosaic_cbFalse_mwFalse_twofield.json | 2 +- ...osaic_cbFalse_mwFalse_twofield_upTrue.json | 2 +- ...m_cube_mosaic_cbFalse_mwTrue_twofield.json | 2 +- ...ieldAWP.peakmem_cube_awproject_hogbom.json | 2 +- ...FieldAWP.peakmem_mfs_awproject_hogbom.json | 2 +- ...eFieldAWP.peakmem_mfs_awproject_mtmfs.json | 2 +- ...anCube.time_cube_standard_channel_gap.json | 2 +- ...anCube.time_cube_standard_fullsummary.json | 2 +- ....TcleanCube.time_cube_standard_hogbom.json | 2 +- ...time_eph_multifield_mosaic_cubesource.json | 2 +- ...emeris.time_eph_multifield_mosaic_mfs.json | 2 +- ...e_eph_singlefield_standard_cubesource.json | 2 +- ...ris.time_eph_singlefield_standard_mfs.json | 2 +- ...ldelVis.time_mfs_automask_modelcolumn.json | 2 +- ....TcleanMoldelVis.time_mfs_modelcolumn.json | 2 +- ...oldelVis.time_mfs_virtual_modelcolumn.json | 2 +- ...ultiField.time_multifield_cube_hogbom.json | 2 +- ...MultiField.time_multifield_mfs_hogbom.json | 2 +- ...nMultiField.time_multifield_mfs_mtmfs.json | 2 +- ...nSingleField.time_cube_standard_pcwdT.json | 2 +- ...anSingleField.time_mfs_standard_clark.json | 2 +- ...nSingleField.time_mfs_standard_hogbom.json | 2 +- ...eld.time_mfs_standard_hogbom_automask.json | 2 +- ...anSingleField.time_mfs_standard_mtmfs.json | 2 +- ...time.TcleanStokes.time_cube_stokes_IV.json | 2 +- ..._time.TcleanStokes.time_mfs_stokes_IV.json | 2 +- ...e.TcleanStokes.time_mtmfs_stokes_IQUV.json | 2 +- ...deFieldAWP.time_cube_awproject_hogbom.json | 2 +- ...ideFieldAWP.time_mfs_awproject_hogbom.json | 2 +- ...WideFieldAWP.time_mfs_awproject_mtmfs.json | 2 +- ..._cube_mosaic_cbFalse_mwFalse_twofield.json | 2 +- ...osaic_cbFalse_mwFalse_twofield_upTrue.json | 2 +- ...e_cube_mosaic_cbFalse_mwTrue_twofield.json | 2 +- ...ic.time_gridfunction_BOX_imsize_40x35.json | 2 +- ...n_BOX_imsize_40x35_phasecenter_detect.json | 2 +- ...etect_cell_default_phasecenter_detect.json | 2 +- ...Basic.time_gridfunction_BOX_start_400.json | 2 +- ...nction_BOX_start_400_phasecenter_null.json | 2 +- ...sic.time_gridfunction_GAUSS_start_400.json | 2 +- ...sic.time_gridfunction_GJINC_start_400.json | 2 +- ...function_GJINC_start_400_minweight_70.json | 2 +- ...tsdimaging.Basic.time_gridfunction_PB.json | 2 +- ...c.time_gridfunction_PB_mode_frequency.json | 2 +- ...gridfunction_PB_mode_velocity_nchan_1.json | 2 +- ...idfunction_PB_mode_velocity_nchan_100.json | 2 +- ...Basic.time_gridfunction_PB_nchan_1024.json | 2 +- ....Basic.time_gridfunction_PB_start_400.json | 2 +- ...gridfunction_PB_start_400_stokes_XXYY.json | 2 +- ...Basic.time_gridfunction_PB_width_1024.json | 2 +- ....Basic.time_gridfunction_SF_start_400.json | 2 +- ...g.Ephemeris.time_ephemeris_cubesource.json | 2 +- ...aging.Ephemeris.time_ephemeris_notset.json | 2 +- ...dimaging.Ephemeris.time_ephemeris_sun.json | 2 +- ...maging.Ephemeris.time_ephemeris_table.json | 2 +- ...aging.Ephemeris.time_ephemeris_trackf.json | 2 +- ...bench_uvcontsub.Basic.time_datacolumn.json | 2 +- ...contsub.Basic.time_fitmethod_casacore.json | 2 +- ...ch_uvcontsub.Basic.time_fitmethod_gsl.json | 2 +- .../bench_uvcontsub.Basic.time_fitorder1.json | 2 +- .../bench_uvcontsub.Basic.time_fitorder2.json | 2 +- ...ub.Basic.time_fitspec3_spws_fitorder0.json | 2 +- ...uvcontsub.Basic.time_fitspec_channels.json | 2 +- ...sub.Basic.time_fitspec_dict_fitorder1.json | 2 +- ...sub.Basic.time_fitspec_dict_fitorder2.json | 2 +- ...ic.time_fitspec_dict_fitspec_one_chan.json | 2 +- ..._fitspec_dict_fitspec_one_chan_order1.json | 2 +- ...e_fitspec_dict_select_spw_mismatching.json | 2 +- ...ch_uvcontsub.Basic.time_fitspec_empty.json | 2 +- ...contsub.Basic.time_fitspec_multifield.json | 2 +- ....Basic.time_fitspec_multifield_blocks.json | 2 +- ...ic.time_fitspec_multifield_fields_sel.json | 2 +- ...pec_multifield_multispw_diff_fitorder.json | 2 +- ...ub.Basic.time_fitspec_separate_fields.json | 2 +- ...ntsub.Basic.time_fitspec_spw_one_chan.json | 2 +- ...nch_uvcontsub.Basic.time_fitspec_spws.json | 2 +- ...uvcontsub.Basic.time_fitspec_spws_sel.json | 2 +- ...asic.time_fitspec_spwschans_fitorder3.json | 2 +- ...ntsub.Basic.time_makes_output_ms_data.json | 2 +- ...bench_uvcontsub.Basic.time_mixed_pols.json | 2 +- ...nch_uvcontsub.Basic.time_select_array.json | 2 +- ...nch_uvcontsub.Basic.time_select_field.json | 2 +- ...ch_uvcontsub.Basic.time_select_intent.json | 2 +- ...contsub.Basic.time_select_observation.json | 2 +- ...ench_uvcontsub.Basic.time_select_scan.json | 2 +- ...bench_uvcontsub.Basic.time_select_spw.json | 2 +- ...c.time_select_spw_mismatching_fitspec.json | 2 +- ...bench_uvcontsub.Basic.time_writemodel.json | 2 +- ....Basic.time_writemodel_from_corrected.json | 2 +- ...writemodel_from_corrected_all_flagged.json | 2 +- ...merical.time_sim_specline_noise_pol_0.json | 2 +- ...merical.time_sim_specline_noise_pol_1.json | 2 +- ...rical.time_sim_specline_nonoise_pol_0.json | 2 +- ...rical.time_sim_specline_nonoise_pol_1.json | 2 +- index.json | 2 +- info.json | 2 +- regressions.json | 2 +- regressions.xml | 28 +++++++++---------- 523 files changed, 536 insertions(+), 536 deletions(-) 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 0180ec61a..c64ea3f02 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 @@ -[[27894, 3604.928358528763], [27924, 3586.559706948232], [27939, 3549.8567470521666], [27965, 3597.1737629221752], [27966, 3575.1558883991092], [28013, 3547.8353902902454], [28051, 3432.334278382361], [28055, 3427.4291054764763], [28094, 3394.7851578714326], [28120, 3370.9739350583404], [28216, 3394.90765146818], [28237, 3588.808441876434], [28256, 3373.7033070819452], [28261, 3367.7887555854395], [28301, 3453.8145619053394], [28307, 3390.51126215799], [28341, 3418.4867323770013], [28361, 3426.2354968898], [28379, 3375.5980877930997], [28425, 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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.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 @@ -[[27906, 7.6052802429767326], [27943, 6.722829051082954], [27960, 7.142801976995543], [28051, 6.612799868918955], [28055, 7.925020662136376], [28094, 6.453071428462863], [28120, 7.319281288422644], [28216, 6.966307133436203], [28237, 7.004389754496515], [28256, 7.288860173895955], [28261, 7.150962912477553], [28301, 7.192464338615537], [28307, 6.631827593984781], [28341, 6.715642978000687], [28361, 7.013514026999474], [28379, 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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.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 @@ -1 +1 @@ -[[27906, 5.13267719297437], [27943, 5.362528800033033], [27960, 5.478025837102905], [28051, 5.427871845662594], [28055, 5.5669910199940205], [28094, 6.0896967044100165], [28120, 5.201820801943541], [28216, 5.723020190373063], [28237, 5.9348662327975035], [28256, 5.668583294376731], [28261, 5.512416770681739], [28301, 5.325583394616842], [28307, 4.897600386990234], [28341, 5.75131576199783], [28361, 5.860316971316934], [28379, 5.576911211013794], [28425, 5.31144828198012], [28438, 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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.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 index 9b09ff6a1..9467937df 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 @ 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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.Selections.time_sel_autocorr_wvr.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.Selections.time_sel_autocorr_wvr.json index 53cd6fd1e..9219f1112 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.Selections.time_sel_autocorr_wvr.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_flagdata.ShadowMode.time_shadow_absent_antennas.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_absent_antennas.json index b8ca50f7d..d6a4c00fd 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.ShadowMode.time_shadow_absent_antennas.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_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 index ca71f573f..d8c6150d2 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.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 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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.TfcropMode.time_tfcrop_abs_rr.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.TfcropMode.time_tfcrop_abs_rr.json index 07ed30b76..3205d9e4a 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.TfcropMode.time_tfcrop_abs_rr.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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index fede899a6..2ebf7b916 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 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 @@ -1 +1 @@ -[[28051, 1.2746906974352896], [28055, 1.324884403962642], [28094, 1.2819784628227353], [28120, 1.1301471474580467], [28216, 1.2889507873915136], [28237, 1.305263471789658], [28256, 1.2992295934818685], [28261, 1.268150474410504], [28301, 1.5007270853966475], [28307, 1.5254357919911854], [28341, 1.3223736439977074], [28361, 1.282900326885283], [28379, 1.4118873989209533], [28425, 1.2684882800094783], [28438, 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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_fringefit.Single.time_single.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_single.json index 3fc8087ea..defdd5784 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_single.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_gaincal.CalModes.time_gaincal_calmode_P.json index 2dfd57d3c..6035741b5 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_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 @@ -[[27906, 6.910216509975726], [27943, 7.523461129050702], [27960, 7.082423851941712], [28051, 6.847544370684773], [28055, 7.277987256646156], 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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.FullGainCal.time_gaincal_fullcal.json index 46803fb8f..d956a4f21 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 @@ -[[27906, 2.976410492497962], [27943, 3.193511824007146], [27960, 2.7490227018715814], [28051, 3.2283961875364184], [28055, 3.086002783384174], [28094, 3.7181921950541437], [28120, 2.9571074983105063], [28216, 2.8054939876310527], [28237, 2.878788178320974], 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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.GainTypes.time_gaincal_gain_type_K.json index 534b8a2c0..63e5ac082 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 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 @@ -1 +1 @@ -[[27906, 5.314333220478147], [27943, 5.3541744344402105], [27960, 5.520761043066159], [28051, 5.677942433860153], [28055, 5.103530549444258], [28094, 5.208625710569322], [28120, 5.213243863545358], [28216, 5.323607471305877], [28237, 5.523003386799246], 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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.GainTypes.time_gaincal_gain_type_spline.json index 648a6db1d..9cebcb742 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_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 @@ -[[27906, 5.602567631489364], [27943, 5.364081067498773], [27960, 5.614960596081801], [28051, 5.9164715628139675], [28055, 6.0763391251675785], [28094, 5.599353791214526], [28120, 5.6574574378319085], [28216, 5.982438230421394], [28237, 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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_antennaSelect.json index d28c96e55..7de69c76e 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 @@ -[[27906, 4.6027543139935005], [27943, 4.525269336998463], [27960, 4.63430169550702], [28051, 4.567946085706353], [28055, 4.3278851099312305], [28094, 4.192690389230847], [28120, 4.69067691732198], [28216, 4.903207466937602], 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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_fieldSelect.json index d58bb5016..0d9a0b555 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 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/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 @@ -1 +1 @@ -[[27906, 7.434977069002343], [27943, 8.143130853539333], [27960, 7.568323752959259], [28051, 7.045591380912811], [28055, 6.845952985808253], [28094, 6.8461440983228385], [28120, 7.563924300484359], [28216, 6.744097414426506], [28237, 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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_refantSelect.json index 7bfe230c1..37d1caa98 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_refantSelect.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_gaincal.GaincalSelections.time_gaincal_refantSelect.json @@ -1 +1 @@ -[[27906, 22.44359128200449], [27943, 21.441697746980935], [27960, 21.560813470976427], [28051, 19.887262648902833], [28055, 21.834414797835052], [28094, 20.53910975344479], [28120, 20.35110928118229], [28216, 21.231500008143485], 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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_scanSelect.json index 46df34a7a..cd87007da 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_scanSelect.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_scanSelect.json @@ -1 +1 @@ -[[27906, 1.0533901599992532], [27943, 1.0135097583988681], [27960, 1.0092622155789286], [28051, 1.0908571919426322], [28055, 1.1350657567381859], [28094, 1.1883967379108071], [28120, 0.9626958924345672], [28216, 1.0686451136134565], 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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_spwSelect.json index 8f3bf5455..f7e4ba068 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_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 @@ -[[27906, 5.363551557995379], [27943, 5.5383641350781545], [27960, 5.724788605002686], [28051, 5.507886020932347], [28055, 5.526622447185218], [28094, 5.397512685973197], [28120, 5.512151596136391], [28216, 5.4410916194319725], [28237, 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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 index 556e75ad7..76d6261ee 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 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 @@ -1 +1 @@ -[[27906, 4.0257462990120985], [27943, 4.0166478655301034], [27960, 3.888125817058608], [28051, 4.5815708371810615], [28055, 4.07150005036965], [28094, 3.6440444490872324], [28120, 3.990920492913574], [28216, 3.8734740023501217], 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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.MinBlPerAnt.time_gaincal_minbl.json index 8c23c2129..31bd947d4 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.MinBlPerAnt.time_gaincal_minbl.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.MinBlPerAnt.time_gaincal_minbl.json @@ -1 +1 @@ -[[27906, 30.161643536994234], [27943, 30.72082659904845], [27960, 30.45119557599537], [28051, 28.4896941781044], [28055, 30.503453565761447], [28094, 29.27222977206111], [28120, 30.156934292055666], [28216, 30.69992817286402], [28237, 30.353914365172386], [28256, 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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.RefantSelection.time_gaincal_refant.json index 1db37a6f4..ea683b3df 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.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 @@ -[[27906, 7.25783205247717], [27943, 7.627597860642709], [27960, 7.430140133015811], [28051, 6.685916284099221], [28055, 7.416100731585175], [28094, 6.968379866797477], [28120, 6.253816423472017], [28216, 6.558457172475755], [28237, 6.824061899911612], 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f28c7fa82..7b0fab93e 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.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.json @@ -1 +1 @@ -[[28425, 5.222105306980666], [28438, 5.507205124478787], [28443, 5.611093825427815], [28484, 5.716813219944015], [28504, 5.43777892246726], [28523, 5.687947440485004], [28633, 5.794261788483709], [28650, 5.76108099729754], [28680, 5.679005928803235], [28711, 5.338903373572975], [28718, 6.059083724860102], [28737, 5.459432535804808], [28773, 5.685210324823856]] \ No newline at end of file +[[28425, 5.222105306980666], [28438, 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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_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 @@ -[[28425, 4.846777895989362], [28438, 4.542825019918382], [28443, 4.5474629434756935], [28484, 4.971523945219815], [28504, 4.884632955509005], [28523, 4.780173498031218], [28633, 4.754401747370139], [28650, 4.31277562957257], [28680, 4.849477632902563], [28711, 4.87556322151795], [28718, 4.602866488974541], [28737, 4.583202380686998], [28773, 4.919638714753091]] \ No newline at end of file +[[28425, 4.846777895989362], [28438, 4.542825019918382], [28443, 4.5474629434756935], [28484, 4.971523945219815], 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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_importasdm.Alma_12m.time_auto_only.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_auto_only.json @@ -1 +1 @@ -[[28425, 8.513722569507081], [28438, 8.476516444934532], [28443, 8.572946338099428], [28484, 8.688321489607915], [28504, 8.698464411514578], [28523, 8.616174888971727], [28633, 9.079633627552539], [28650, 8.675749046029523], [28680, 8.631901731714606], [28711, 8.477247387170792], [28718, 8.549354466609657], [28737, 8.51955857174471], [28773, 8.833099557086825]] \ No newline at end of file +[[28425, 8.513722569507081], [28438, 8.476516444934532], [28443, 8.572946338099428], [28484, 8.688321489607915], [28504, 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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_importasdm.Alma_12m.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.Alma_12m.time_auto_only_lazy.json @@ -1 +1 @@ -[[28425, 8.840934919542633], [28438, 9.178076263982803], [28443, 8.818364388076589], [28484, 9.162866040598601], [28504, 8.778039577504387], [28523, 9.138613303017337], [28633, 9.121450608363375], [28650, 8.779740773607045], [28680, 8.964775971136987], [28711, 9.146879388950765], [28718, 8.940781956538558], [28737, 9.081556545570493], [28773, 8.928897086996585]] \ No newline at end of file +[[28425, 8.840934919542633], [28438, 9.178076263982803], [28443, 8.818364388076589], [28484, 9.162866040598601], [28504, 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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_importasdm.Alma_12m.time_bdfflags_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.Alma_12m.time_bdfflags_true.json @@ -1 +1 @@ -[[28425, 31.1455175220035], [28438, 32.720456612994894], [28443, 30.934902989072725], [28484, 34.57114614918828], [28504, 30.839831303979736], [28523, 32.17741764802486], [28633, 31.99257951695472], [28650, 33.88517223810777], [28680, 34.418884044513106], [28711, 30.964214278385043], [28718, 31.168116251938045], [28737, 32.55632108543068], [28773, 34.586456150747836]] \ No newline at end of file +[[28425, 31.1455175220035], [28438, 32.720456612994894], [28443, 30.934902989072725], [28484, 34.57114614918828], [28504, 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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_importasdm.Alma_12m.time_bdfflags_true_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_bdfflags_true_lazy.json @@ -1 +1 @@ -[[28425, 27.48625819105655], [28438, 26.97236758004874], [28443, 27.29493461898528], [28484, 26.8873543147929], [28504, 29.42241733300034], [28523, 28.02642400295008], [28633, 27.011975813191384], [28650, 27.578046514187008], [28680, 27.771511684171855], [28711, 26.966635335236788], [28718, 27.206090722233057], [28737, 28.055676208809018], [28773, 28.940822061151266]] \ No newline at end of file +[[28425, 27.48625819105655], [28438, 26.97236758004874], [28443, 27.29493461898528], [28484, 26.8873543147929], 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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_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 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 @@ -1 +1 @@ -[[28425, 19.199997165007517], [28438, 19.563682469073683], [28443, 18.664512774907053], [28484, 18.667894800193608], [28504, 18.32146368501708], [28523, 18.73754944303073], [28633, 18.2666924293153], [28650, 18.778304859995842], [28680, 18.816873614676297], [28711, 18.452782795764506], [28718, 19.173879933543503], [28737, 19.998934249393642], [28773, 18.22668696474284]] \ No newline at end of file +[[28425, 19.199997165007517], [28438, 19.563682469073683], [28443, 18.664512774907053], [28484, 18.667894800193608], [28504, 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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_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 @@ -[[28425, 3.3779656579717994], [28438, 3.207887508557178], [28443, 3.5828178625088185], [28484, 3.428337085293606], [28504, 3.4396810739999637], [28523, 3.635158480028622], [28633, 3.791903601260856], [28650, 3.606577510247007], [28680, 3.654713960364461], [28711, 3.281759037170559], [28718, 3.5695716165937483], [28737, 3.54636325314641], [28773, 3.1779914032667875]] \ No newline at end of file +[[28425, 3.3779656579717994], [28438, 3.207887508557178], [28443, 3.5828178625088185], [28484, 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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_importasdm.AutocorrASDM.time_process_flags_false.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_process_flags_false.json @@ -1 +1 @@ -[[28425, 1.4350936349947006], [28438, 1.489700639504008], [28443, 1.3407776219537482], [28484, 1.421175990253687], [28504, 1.2110623150074389], [28523, 1.6102989665232599], [28633, 1.616577924694866], [28650, 1.4779173501301557], [28680, 1.488351995125413], [28711, 1.189014873933047], [28718, 1.6406563185155392], [28737, 1.464057288132608], [28773, 1.2123160269111395]] \ No newline at end of file +[[28425, 1.4350936349947006], [28438, 1.489700639504008], [28443, 1.3407776219537482], [28484, 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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_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 @@ -[[28425, 2.116066374059301], [28438, 2.2713614675449207], [28443, 2.3399929981678724], [28484, 2.5002526622265577], [28504, 2.1946707845199853], [28523, 2.2487681814818643], [28633, 2.2868496938608587], [28650, 2.08787301951088], [28680, 2.0705742868594825], [28711, 1.8578980951569974], [28718, 1.9896567817777395], [28737, 2.0848631355911493], [28773, 2.049298307392746]] \ No newline at end of file +[[28425, 2.116066374059301], [28438, 2.2713614675449207], [28443, 2.3399929981678724], [28484, 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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_importasdm.Basic.time_all_defaults.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Basic.time_all_defaults.json @@ -1 +1 @@ -[[28425, 3.2034601009218022], [28438, 3.1919333471450955], [28443, 3.294537617592141], [28484, 3.252530517987907], [28504, 3.549930362525629], [28523, 3.20061168045504], [28633, 3.5908270440995693], [28650, 3.204300711862743], [28680, 3.2050429577939212], [28711, 3.4006994790397584], [28718, 3.193903401028365], [28737, 3.2233465895988047], [28773, 3.393823229242116]] \ No newline at end of file +[[28425, 3.2034601009218022], [28438, 3.1919333471450955], [28443, 3.294537617592141], [28484, 3.252530517987907], [28504, 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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_importasdm.Evla.time_process_flags_true_applyflags_false.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_true_applyflags_false.json @@ -1 +1 @@ -[[28425, 1.506590838485863], [28438, 1.4998785320203751], [28443, 1.3407982169883326], [28484, 1.748443342745304], [28504, 1.7859229225141462], [28523, 1.7867515734978952], [28633, 1.957658724160865], [28650, 1.2772870110347867], [28680, 1.4702534405514598], [28711, 1.2766527351923287], [28718, 1.6301977769471705], [28737, 1.2760598072782159], [28773, 1.4120770888403058]] \ No newline at end of file +[[28425, 1.506590838485863], [28438, 1.4998785320203751], [28443, 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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_importasdm.Evla.time_scan_selection.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_importasdm.Evla.time_scan_selection.json @@ -1 +1 @@ -[[28425, 1.336264553538058], [28438, 1.5440266884397715], [28443, 1.154044616036117], [28484, 1.59667790052481], [28504, 1.2460235464968719], [28523, 1.6574526060721837], [28633, 1.3820715614128858], [28650, 1.194998720427975], [28680, 1.2366119213402271], [28711, 1.0079313917085528], [28718, 1.0014510718174279], [28737, 1.5308229895308614], [28773, 1.1671158117242157]] \ No newline at end of file +[[28425, 1.336264553538058], [28438, 1.5440266884397715], [28443, 1.154044616036117], [28484, 1.59667790052481], 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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_importasdm.Evla_ephemeris.time_polynomial_ephem.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_ephemeris.time_polynomial_ephem.json @@ -1 +1 @@ -[[28425, 89.61868696496822], [28438, 90.03748416295275], [28443, 93.47402642806992], [28484, 90.79966796701774], [28504, 93.45298510300927], [28523, 88.99037564103492], [28633, 94.58002998260781], [28650, 93.719492347911], [28680, 89.47301436401904], [28711, 89.72279364056885], [28718, 91.17172635439783], [28737, 88.07540922984481], [28773, 87.4753200719133]] \ No newline at end of file +[[28425, 89.61868696496822], [28438, 90.03748416295275], [28443, 93.47402642806992], [28484, 90.79966796701774], 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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_importasdm.Singledish.time_auto_only.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.json @@ -1 +1 @@ -[[28425, 7.085141843010206], [28438, 6.855094158090651], [28443, 7.330301459529437], [28484, 7.138523605186492], [28504, 7.335160538990749], [28523, 6.980286215490196], [28633, 6.837526498129591], [28650, 7.255584380123764], [28680, 6.69498003879562], [28711, 7.410019719041884], [28718, 7.092307318933308], [28737, 7.5656360406428576], [28773, 6.681839904282242]] \ No newline at end of file +[[28425, 7.085141843010206], [28438, 6.855094158090651], [28443, 7.330301459529437], [28484, 7.138523605186492], [28504, 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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_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 @@ -1 +1 @@ -[[28425, 6.113591573492158], [28438, 5.9951887999195606], [28443, 5.819601767114364], [28484, 5.895009434549138], [28504, 6.103349803481251], [28523, 6.282492502010427], [28633, 5.907288302434608], [28650, 5.956198948668316], [28680, 6.333816485479474], [28711, 5.77942257001996], [28718, 6.338783158920705], [28737, 6.778834817465395], [28773, 6.185836211312562]] \ No newline at end of file +[[28425, 6.113591573492158], [28438, 5.9951887999195606], [28443, 5.819601767114364], [28484, 5.895009434549138], [28504, 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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_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 @@ -[[27906, 5.132007137493929], [27943, 5.350821972941048], [27960, 5.758532105479389], [28051, 5.02882080944255], [28055, 5.375686984974891], [28094, 5.166952714789659], [28120, 5.189660323783755], [28216, 5.055982254911214], [28237, 5.3469580677337945], [28256, 5.162476576399058], [28261, 4.99615538911894], [28301, 5.104724197648466], [28307, 5.111607826009276], [28341, 5.373224082504748], [28361, 5.208225944079459], [28379, 5.272661036986392], [28425, 5.198443184432108], [28438, 5.141537650139071], 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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_Interp.time_linear.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.json index 2e30ecb3e..f426990d5 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.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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No newline at end of file +[[27906, 1.0306346469733398], [27943, 0.9697724370053038], [27960, 0.8687413381412625], [28051, 1.011883795261383], [28055, 1.0409088488668203], [28094, 0.9924471932463348], [28120, 1.0102867763489485], [28216, 1.0452486365102232], [28237, 1.0098594785667956], [28256, 1.0333960871212184], [28261, 1.002002571709454], [28301, 1.049978593364358], [28307, 0.9352058950025821], [28341, 1.0264209254964953], [28361, 1.0152689339593053], [28379, 0.9882211300428025], [28425, 1.0078764600330032], [28438, 1.0127011584118009], [28443, 1.020019325078465], [28484, 1.004188756691292], [28504, 0.9934638325066771], [28523, 0.9959170789807104], [28633, 0.9934152199421078], [28650, 1.0076491795480251], [28680, 1.0780092314817011], [28711, 1.018463350366801], [28718, 1.0225638020783663], [28737, 0.9874649662524462], [28773, 1.0183744868263602], [28806, 1.0029787289677188]] \ No newline at end of file diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 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newline at end of file +[[27906, 1.7514548395120073], [27943, 1.5934119845042005], [27960, 1.6392359230667353], [28051, 1.7339118584059179], [28055, 1.7780529353767633], [28094, 1.7439224170520902], [28120, 1.607525766827166], [28216, 1.7886060262098908], [28237, 1.7401033625937998], [28256, 1.7094117710366845], [28261, 1.4855595142580569], [28301, 1.6938738701865077], [28307, 1.5968029910000041], [28341, 1.7712148205027916], [28361, 1.7739869207143784], [28379, 1.681655501422938], [28425, 1.7241976994555444], [28438, 1.7423504639882594], [28443, 1.5340591059066355], [28484, 1.6271258115302771], [28504, 1.7721627735008951], [28523, 1.63332447654102], [28633, 1.559553226456046], [28650, 1.5348385774996132], [28680, 1.7438808879815042], [28711, 1.5793450423516333], [28718, 1.6908553852699697], [28737, 1.7391207097098231], [28773, 1.643468338996172], [28806, 1.7078497644979507]] \ No newline at end of file diff --git a/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_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys.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_tsys.json index 7caaa5669..dcc31f928 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_tsys.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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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_sdcal.OTFRaster.time_autodetection.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_autodetection.json index 3bbbfb1f5..0fbf2fcb0 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_autodetection.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.OTFRaster.time_noff_custom.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.json index c87280f26..bfc244cd1 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_priority_over_fraction.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_priority_over_fraction.json index 1c3ca2869..ed5e0478f 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTFRaster.time_noff_priority_over_fraction.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.OTF_Ephemeris.time_otf_ephem.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_Ephemeris.time_otf_ephem.json index e8f03640b..f4b839b75 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.OTF_Ephemeris.time_otf_ephem.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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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.sdcal_test_single_polarization.time_single_pol_apply_composite.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.sdcal_test_single_polarization.time_single_pol_apply_composite.json index 0f2d43d57..bc98db98f 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.sdcal_test_single_polarization.time_single_pol_apply_composite.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU 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end of file diff --git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps.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.sdcal_test_single_polarization.time_single_pol_ps.json index cd3fbd5e0..e9b511950 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.sdcal_test_single_polarization.time_single_pol_ps.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 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--git a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_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_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect.json index 1e39c44f8..b2e1e4513 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_BOX_imsize_40x35_phasecenter_detect.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU 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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_sdimaging.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_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect.json index eb98eb042..8b64320be 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_BOX_imsize_detect_cell_default_phasecenter_detect.json +++ 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[28773, 2.8849391606636345], [28806, 2.884013456525281]] \ 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_sdimaging.Basic.time_gridfunction_BOX_start_400.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_sdimaging.Basic.time_gridfunction_BOX_start_400.json index 12a625752..e347c56f4 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_BOX_start_400.json +++ 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json index a56ae9c33..b7a3767a4 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 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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.TcleanEphemeris.time_eph_multifield_mosaic_cubesource.json index 102d26126..8ee120baf 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_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 @@ -[[27906, 8.02702758050873], [27943, 7.897791309980676], [27960, 8.091482014977373], [28051, 7.769961839076132], [28055, 7.973258891608566], [28094, 7.813489354681224], [28120, 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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.TcleanEphemeris.time_eph_singlefield_standard_cubesource.json index c6c0a9e6c..715463647 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_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 @@ -[[27906, 2.29901861702092], [27943, 2.43269583908841], [27960, 2.5060208265203983], [28051, 2.464588134549558], [28055, 2.445632105693221], [28094, 2.571654474362731], [28120, 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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.TcleanEphemeris.time_eph_singlefield_standard_mfs.json index abf732ee0..080cbc572 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs.json @@ -1 +1 @@ -[[27906, 2.294040261011105], [27943, 2.291468285024166], [27960, 2.2706877890741453], [28051, 2.266323435585946], [28055, 2.2761732134968042], [28094, 2.299154395237565], [28120, 2.2435731515288353], 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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.TcleanMoldelVis.time_mfs_automask_modelcolumn.json index 4c4f4a36c..26814c4c7 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.TcleanMoldelVis.time_mfs_automask_modelcolumn.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json @@ -1 +1 @@ -[[27906, 18.335549775976688], [27943, 16.91023803781718], [27960, 18.929513417882845], [28051, 18.451672458089888], [28055, 17.335283741354942], [28094, 18.706730869598687], [28120, 17.316141437739134], [28216, 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4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_cube_stokes_IV.json index 9f8b088bf..6580579df 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_cube_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_cube_stokes_IV.json @@ -1 +1 @@ -[[27906, 4.252901372499764], [27943, 4.182558058993891], [27960, 4.256545206997544], [28051, 4.285792702343315], [28055, 4.1317960866726935], [28094, 4.07145254407078], [28120, 4.010877655353397], [28216, 4.080013801343739], [28237, 4.092326642945409], 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4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json index 297dde054..8cdb9535e 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TcleanStokes.time_mfs_stokes_IV.json @@ -1 +1 @@ -[[27906, 2.556216853496153], [27943, 2.58254324097652], [27960, 2.592361458344385], [28051, 2.5016577495262027], [28055, 2.493289525154978], [28094, 2.5159713686443865], [28120, 2.513727271463722], [28216, 2.5433038691990077], [28237, 2.5099102347157896], 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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_cube_awproject_hogbom.json index 9d8157fd7..f82e1222c 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 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/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 @@ -1 +1 @@ -[[27906, 106.31996337004239], [27943, 108.2976877070032], [27960, 106.68930526007898], [28051, 105.07574407942593], [28055, 104.14265106711537], [28094, 104.79520863573998], [28120, 104.21878521703184], [28216, 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2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json index 480e98be6..146f076ea 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 @@ -[[27906, 15.49031763599487], [27943, 15.712867880938575], [27960, 16.948914821026847], [28051, 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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_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35.json index b3e6fe9f9..a68d67b45 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 @@ -[[27906, 2.851944528025342], [27943, 3.099896725965664], [27960, 3.045705058495514], [28051, 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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.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect.json index bdfb64336..3f3bc5a2b 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 @@ -[[27906, 3.1193774375133216], [27943, 3.044706016080454], [27960, 3.1420693745603785], [28051, 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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_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect.json index 4ce5f3f3d..19f4a06b8 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 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 @@ -1 +1 @@ -[[27906, 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No newline at end of file +[[27906, 2.8183441954897717], [27943, 3.0871158404042944], [27960, 2.7606033235788345], [28051, 3.3969534654170275], [28055, 3.3699177862145007], [28094, 3.2833739360794425], [28120, 3.6643023719079792], [28216, 2.8824854069389403], [28237, 2.9678157288581133], [28256, 2.831259234342724], [28261, 2.8376109753735363], [28301, 3.05680622253567], [28307, 3.334816819013213], [28341, 3.016321462004271], [28361, 3.3463694658130407], [28379, 2.9607751024886966], [28425, 2.6454129309859127], [28438, 3.1282786504598334], [28443, 3.2219171681208536], [28484, 3.0776035061571747], [28504, 2.8964827964955475], [28523, 2.689312373055145], [28633, 3.29463667050004], [28650, 2.783344021299854], [28680, 3.0059429863467813], [28711, 2.8970834030769765], [28718, 3.2255854005925357], [28737, 3.324468078557402], [28773, 2.8968001808971167], [28806, 3.161763329466339]] \ No newline at end of file diff --git a/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_uvcontsub.Basic.time_fitspec_dict_fitorder1.json index 062db3e61..218499b8b 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_fitorder1.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitorder1.json @@ -1 +1 @@ -[[27906, 0.5684763670433313], [27943, 0.5809467090293765], [27960, 0.5843614500481635], [28051, 0.5672403061762452], [28055, 0.5656701158732176], [28094, 0.5671842824667692], [28120, 0.565867037512362], [28216, 0.5516621926799417], [28237, 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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_dict_fitorder2.json index 4963c3836..c60d29a06 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_fitorder2.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_fitorder2.json @@ -1 +1 @@ -[[27906, 0.5787938460125588], [27943, 0.5892694019712508], [27960, 0.5921284609939903], [28051, 0.5535553609952331], [28055, 0.5705243637785316], [28094, 0.5721684033051133], [28120, 0.5672942018136382], [28216, 0.5742250364273787], [28237, 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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_dict_fitspec_one_chan.json index 39d4fdd59..54c70bd4e 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 @@ -[[27906, 0.8709858280490153], [27943, 0.8509628300089389], [27960, 0.8852425350341946], [28051, 0.8662368515506387], [28055, 0.8258304968476295], [28094, 0.8646509172394872], [28120, 0.8616190180182457], [28216, 0.8648228971287608], 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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_dict_fitspec_one_chan_order1.json index fbf2c0e68..1b2e5aeb4 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1.json @@ -1 +1 @@ -[[27906, 0.8865680110175163], [27943, 0.8901407290250063], [27960, 0.8722101910971105], [28051, 0.8784776972606778], [28055, 0.8566343719139695], [28094, 0.8651474248617887], [28120, 0.8809947986155748], [28216, 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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_dict_select_spw_mismatching.json index 18e0d1fbf..181d3ff17 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_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 @@ -[[27906, 0.7033926969743334], [27943, 0.703066413057968], [27960, 0.712057103170082], [28051, 0.6928306883201003], [28055, 0.6892064604908228], [28094, 0.6911881435662508], [28120, 0.6898458721116185], [28216, 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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_multifield_multispw_diff_fitorder.json index 756e91c11..fe8e2568a 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 @@ -[[27906, 1.0691314220312051], [27943, 1.0774269690737128], [27960, 1.3471956609282643], [28051, 1.0787403592839837], [28055, 1.1014186441898346], [28094, 1.3295294241979718], [28120, 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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 index 8923bb6dd..3fadcce3a 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 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 @@ -1 +1 @@ -[[27906, 0.8884765199618414], [27943, 0.8892636159434915], [27960, 0.8964433229994029], [28051, 0.9151656720787287], [28055, 0.8826935160905123], [28094, 0.9061304768547416], [28120, 0.8810142166912556], [28216, 0.8942517666146159], [28237, 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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_spw_one_chan.json index f89bbd836..de77d5b59 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spw_one_chan.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_fitspec_spw_one_chan.json @@ -1 +1 @@ -[[27906, 0.8625555509934202], [27943, 0.8437357430811971], [27960, 0.8909037651028484], [28051, 0.8715018490329385], [28055, 0.8585082991048694], [28094, 0.8542300863191485], [28120, 0.8607502859085798], [28216, 0.8465485107153654], [28237, 0.859794014133513], 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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_writemodel_from_corrected.json index 59535e8ef..7433e8ff7 100644 --- a/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel_from_corrected.json +++ b/graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.8/ram-263549216/bench_uvcontsub.Basic.time_writemodel_from_corrected.json @@ -1 +1 @@ -[[27906, 0.9009758749743924], [27943, 0.8272229840513319], [27960, 0.9191502088215202], [28051, 0.948769835755229], [28055, 0.785601963289082], [28094, 0.8712858064100146], [28120, 0.8356452509760857], [28216, 0.752408410422504], [28237, 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[28806, 8.614804162061773]] \ No newline at end of file diff --git a/graphs/summary/bench_applycal.TrialMode.time_applycal_trial.json b/graphs/summary/bench_applycal.TrialMode.time_applycal_trial.json index 60a32d72e..8ab9b0cc9 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], [24188, 3.1601654030382633], [24958, 3.1592727433890104], [25613, 3.1778319384902716], [25614, 3.3074067579582334], [25651, 3.361821711063385], [25733, 3.193049244582653], [25778, 3.235854998230934], [25803, 3.2897350937128067], [25823, 3.4437340293079615], [25833, 3.1917222095653415], [25854, 3.248907440342009], [25889, 3.5208402844145894], [25934, 3.247139417566359], [25978, 3.2299302825704217], [25993, 3.1480995174497366], [26027, 3.1414712741971016], [26050, 3.0699864691123366], [26109, 3.015514305792749], [26166, 3.161252769641578], [26243, 3.2360861860215664], [26258, 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a/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json b/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json index 1b4fb281d..9296d0221 100644 --- a/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json +++ b/graphs/summary/bench_flagdata.Selections.time_sel_autocorr_wvr.json @@ -1 +1 @@ -[[22825, 0.23469004314392805], [23735, 0.2510405918583274], [24188, 0.22881285659968853], [24958, 0.22517723497003317], [25613, 0.20489393081516027], [25614, 0.21214973041787744], [25651, 0.2125429678708315], [25733, 0.20723612699657679], [25778, 0.22303252015262842], [25803, 0.21464633895084262], [25823, 0.21603205054998398], [25833, 0.2372902324423194], [25854, 0.2275311453267932], [25889, 0.22019712487235665], [25934, 0.2266061082482338], [25978, 0.24517985014244914], [25993, 0.22695804433897138], [26027, 0.24016954889521003], [26050, 0.2129596071317792], [26109, 0.2550920844078064], [26166, 0.22816911805421114], [26243, 0.22789178881794214], [26258, 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a/graphs/summary/bench_importasdm.AutocorrASDM.time_savecmds_true.json b/graphs/summary/bench_importasdm.AutocorrASDM.time_savecmds_true.json index 0cef7c96c..8abdf9bc4 100644 --- a/graphs/summary/bench_importasdm.AutocorrASDM.time_savecmds_true.json +++ b/graphs/summary/bench_importasdm.AutocorrASDM.time_savecmds_true.json @@ -1 +1 @@ -[[28425, 2.116066374059301], [28438, 2.2713614675449207], [28443, 2.3399929981678724], [28484, 2.5002526622265577], [28504, 2.1946707845199853], [28523, 2.2487681814818643], [28633, 2.2868496938608587], [28650, 2.08787301951088], [28680, 2.0705742868594825], [28711, 1.9605448546914952], [28718, 1.9580444131731234], [28737, 2.110403398856191], [28773, 2.1240100178317234]] \ No newline at end of file +[[28425, 2.116066374059301], [28438, 2.2713614675449207], [28443, 2.3399929981678724], [28484, 2.5002526622265577], [28504, 2.1946707845199853], [28523, 2.2487681814818643], [28633, 2.2868496938608587], [28650, 2.08787301951088], [28680, 2.0705742868594825], 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a/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_applyflags_true.json @@ -1 +1 @@ -[[28425, 2.607173200522084], [28438, 3.027629328542389], [28443, 2.6429194866213948], [28484, 2.5531254750676453], [28504, 2.608256037987303], [28523, 3.115663048927672], [28633, 3.10673357360065], [28650, 2.5229614065028727], [28680, 2.0963773960247636], [28711, 3.2321433249728275], [28718, 2.7384525135898103], [28737, 2.8066101504883405], [28773, 2.937592981989124]] \ No newline at end of file +[[28425, 2.607173200522084], [28438, 3.027629328542389], [28443, 2.6429194866213948], [28484, 2.5531254750676453], [28504, 2.608256037987303], [28523, 3.115663048927672], [28633, 3.10673357360065], [28650, 2.5229614065028727], [28680, 2.0963773960247636], [28711, 3.2321433249728275], [28718, 2.7384525135898103], [28737, 2.8066101504883405], [28773, 2.937592981989124], [28806, 2.941313734569121]] \ 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 73c4a35c8..8e8f33214 100644 --- a/graphs/summary/bench_importasdm.Evla.time_pc_true.json +++ b/graphs/summary/bench_importasdm.Evla.time_pc_true.json @@ -1 +1 @@ -[[28425, 1.230967463052366], [28438, 1.3190023446222767], [28443, 1.0434273974969983], [28484, 1.4856787950266153], [28504, 1.6929641745227855], [28523, 1.744153085979633], [28633, 1.4707171313930303], [28650, 1.318523341557011], [28680, 1.2838857201859355], [28711, 1.286468117896491], [28718, 1.3502153555219754], [28737, 1.412860366628266], [28773, 1.4691981175932454]] \ No newline at end of file +[[28425, 1.230967463052366], [28438, 1.3190023446222767], [28443, 1.0434273974969983], [28484, 1.4856787950266153], [28504, 1.6929641745227855], [28523, 1.744153085979633], [28633, 1.4707171313930303], [28650, 1.318523341557011], [28680, 1.2838857201859355], [28711, 1.286468117896491], [28718, 1.3502153555219754], 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a/graphs/summary/bench_importasdm.Evla.time_scan_selection.json +++ b/graphs/summary/bench_importasdm.Evla.time_scan_selection.json @@ -1 +1 @@ -[[28425, 1.336264553538058], [28438, 1.5440266884397715], [28443, 1.154044616036117], [28484, 1.59667790052481], [28504, 1.2460235464968719], [28523, 1.6574526060721837], [28633, 1.3820715614128858], [28650, 1.194998720427975], [28680, 1.2366119213402271], [28711, 1.2454674326734332], [28718, 1.1603632588501092], [28737, 1.549053279168442], [28773, 1.1871053767501367]] \ No newline at end of file +[[28425, 1.336264553538058], [28438, 1.5440266884397715], [28443, 1.154044616036117], [28484, 1.59667790052481], [28504, 1.2460235464968719], [28523, 1.6574526060721837], [28633, 1.3820715614128858], [28650, 1.194998720427975], [28680, 1.2366119213402271], [28711, 1.2454674326734332], [28718, 1.1603632588501092], [28737, 1.549053279168442], [28773, 1.1871053767501367], [28806, 1.2274596425122581]] \ 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 1697e7972..4f5c05be9 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 @@ -[[28425, 89.61868696496822], [28438, 90.03748416295275], [28443, 93.47402642806992], [28484, 90.79966796701774], [28504, 93.45298510300927], [28523, 88.99037564103492], [28633, 94.58002998260781], [28650, 93.719492347911], [28680, 89.47301436401904], [28711, 90.99629694309951], [28718, 91.16766627266192], [28737, 87.97344713209534], [28773, 88.21568397499914]] \ No newline at end of file +[[28425, 89.61868696496822], [28438, 90.03748416295275], [28443, 93.47402642806992], [28484, 90.79966796701774], [28504, 93.45298510300927], [28523, 88.99037564103492], [28633, 94.58002998260781], [28650, 93.719492347911], [28680, 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a/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype.json b/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype.json index 8d029847b..445ccdbb3 100644 --- a/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype.json +++ b/graphs/summary/bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype.json @@ -1 +1 @@ -[[27690, 10.706039398908615], [27718, 10.787137787789106], [27732, 10.623237820342183], [27772, 10.569246159866452], [27804, 10.696607263758779], [27879, 10.755196628975682], [27906, 9.909672490000958], [27943, 9.987530676997267], [27960, 10.381897604558617], [28051, 9.734243066050112], [28055, 9.553830203600228], [28094, 9.673985371366143], [28120, 9.694705255329609], [28216, 9.784976005088538], [28237, 9.825888631399721], [28256, 9.863152144942433], [28261, 9.877729576081038], [28301, 10.006003741174936], [28307, 9.978169702502782], [28341, 9.44963899099821], [28361, 9.805345213972032], [28379, 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0a0d2379f..5359f131f 100644 --- a/graphs/summary/bench_sdbaseline.Basic.time_poly_blfunc_no_mask.json +++ b/graphs/summary/bench_sdbaseline.Basic.time_poly_blfunc_no_mask.json @@ -1 +1 @@ -[[27504, 0.6836124025285244], [27541, 0.7913316618651152], [27562, 0.714917266741395], [27568, 0.7830544356256723], [27607, 0.6154204923659563], [27616, 0.7868510838598013], [27621, 0.7250863034278154], [27690, 0.707851966843009], [27718, 0.679331449791789], [27732, 0.7525983303785324], [27772, 0.7536362651735544], [27804, 0.6836103796958923], [27879, 0.5911818069871515], [27906, 0.6675940879795235], [27943, 0.6921495100250468], [27960, 0.6522369200829417], [28051, 0.6617114413529634], [28055, 0.6580763282254338], [28094, 0.6652980563230813], [28120, 0.6685951137915254], [28216, 0.6835431777872145], [28237, 0.6774604916572571], [28256, 0.643984158989042], [28261, 0.6268751653842628], [28301, 0.6719993529841304], [28307, 0.6595282149937702], [28341, 0.6355322985036764], [28361, 0.6686295820400119], 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newline at end of file diff --git a/graphs/summary/bench_sdcal.Apply.time_spwmap_dict.json b/graphs/summary/bench_sdcal.Apply.time_spwmap_dict.json index f5f470021..73b3bf92a 100644 --- a/graphs/summary/bench_sdcal.Apply.time_spwmap_dict.json +++ b/graphs/summary/bench_sdcal.Apply.time_spwmap_dict.json @@ -1 +1 @@ -[[27135, 0.3151933755725622], [27141, 0.40622691810131073], [27163, 0.305503879673779], [27209, 0.41559594217687845], [27250, 0.41817739699035883], [27295, 0.44273365195840597], [27311, 0.3970691170543432], [27329, 0.42377336882054806], [27339, 0.2840112503618002], [27364, 0.332962081039512], [27412, 0.2857319936156273], [27428, 0.30508769303560257], [27447, 0.28263717517256737], [27482, 0.28590321727097034], [27495, 0.379421254619956], [27541, 0.3169946875423193], [27562, 0.42811231687664986], [27568, 0.39318365044891834], [27607, 0.2878364399075508], [27616, 0.3095467835664749], [27621, 0.35600475035607815], [27690, 0.43070305325090885], [27718, 0.36163850128650665], 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a/graphs/summary/bench_sdcal.OTFRaster.time_fraction_string_percentage.json b/graphs/summary/bench_sdcal.OTFRaster.time_fraction_string_percentage.json index fb3af97ef..c29974c05 100644 --- a/graphs/summary/bench_sdcal.OTFRaster.time_fraction_string_percentage.json +++ b/graphs/summary/bench_sdcal.OTFRaster.time_fraction_string_percentage.json @@ -1 +1 @@ -[[27135, 0.308272173628211], [27141, 0.40065628103911877], [27163, 0.3181675001978874], [27209, 0.3972437893971801], [27250, 0.3843481969088316], [27295, 0.3866808982565999], [27311, 0.35868007969111204], [27329, 0.38877023197710514], [27339, 0.3116152547299862], [27364, 0.33098771752839745], [27412, 0.3218418285250664], [27428, 0.30566454119980335], [27447, 0.31419174186885357], [27482, 0.2745984401553869], [27495, 0.3940826877951622], [27541, 0.38762084022164345], [27562, 0.31722095236182213], [27568, 0.3792231120169163], [27607, 0.27722305804491043], [27616, 0.32712665759027004], [27621, 0.31493003480136395], [27690, 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a/graphs/summary/bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.json b/graphs/summary/bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.json index cd375a2ae..b1d8494f3 100644 --- a/graphs/summary/bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.json +++ b/graphs/summary/bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.json @@ -1 +1 @@ -[[23735, 4.490541702136397], [24188, 4.428813189268112], [24958, 3.923556580208242], [25613, 3.8708053920418024], [25614, 3.9274445180781186], [25651, 3.9002426783554256], [25733, 4.516700103413314], [25778, 4.488195668440312], [25803, 3.9084543511271477], [25823, 3.9965918348170817], [25833, 4.4627896547317505], [25854, 3.9284690218046308], [25889, 4.396816499065608], [25934, 4.510093504097313], [25978, 4.482459411025047], [25993, 4.478347682394087], [26027, 4.4898308124393225], [26050, 3.8819435867480934], [26109, 4.451524523086846], [26166, 4.483266890048981], [26243, 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--git a/graphs/summary/bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs.json b/graphs/summary/bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs.json index 9435f948c..8f3b3fd57 100644 --- a/graphs/summary/bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs.json +++ b/graphs/summary/bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs.json @@ -1 +1 @@ -[[27339, 187060224.0], [27346, 197980160.0], [27364, null], [27412, 201359360.0], [27428, 209678336.0], [27447, 184696832.0], [27482, 199299072.0], [27495, 207151104.0], [27541, 193880064.0], [27562, 193331200.0], [27568, 199651328.0], [27607, 193867776.0], [27616, 199868416.0], [27621, 187084800.0], [27690, 195616768.0], [27718, 204398592.0], [27732, 194076672.0], [27772, 200400896.0], [27804, 202489856.0], [27879, 196149248.0], [27906, 239316992.0], [27943, 233218048.0], [27960, 228925440.0], [28051, 230780928.0], [28055, 229494784.0], [28094, 235331584.0], [28120, 238096384.0], 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b/graphs/summary/bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs.json @@ -1 +1 @@ -[[27339, 128.0], [27346, 128.00000000000003], [27364, 128.00000000000003], [27412, 128.0], [27428, 128.0], [27447, 128.0], [27482, 128.0], [27495, 128.0], [27541, 128.0], [27562, 128.0], [27568, 128.0], [27607, 128.0], [27616, 128.0], [27621, 128.0], [27690, 128.0], [27718, 128.0], [27732, 128.0], [27772, 128.0], [27804, 128.0], [27879, 128.0], [27906, 128.0], [27943, 128.0], [27960, 128.0], [28051, 128.0], [28055, 128.0], [28094, 128.0], [28120, 128.0], [28216, 128.0], [28237, 128.0], [28256, 128.0], [28261, 128.0], [28301, 128.0], [28307, 128.0], [28341, 128.0], [28361, 128.0], [28379, 128.0], [28425, 128.0], [28438, 128.0], [28443, 128.0], [28484, 128.0], [28504, 128.0], [28523, 128.0], [28633, 128.0], [28650, 128.0], [28680, 128.0], [28711, 128.00000000000003], [28718, 128.00000000000003], [28737, 128.00000000000003], [28773, 128.00000000000003]] \ No newline at 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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 c9944458a..313e415d8 100644 --- a/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json +++ b/graphs/summary/bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary.json @@ -1 +1 @@ -[[27339, 212549632.0], [27346, 208474112.0], [27412, 209498112.0], [27428, 214245376.0], [27447, 201183232.0], [27482, 209510400.0], [27495, 214941696.0], [27541, 200077312.0], [27562, 205291520.0], [27568, 207736832.0], [27607, 203460608.0], [27616, 198656000.0], [27621, 205701120.0], [27690, 205930496.0], [27718, 200130560.0], [27732, 206184448.0], [27772, 197545984.0], [27804, 212000768.0], [27879, 206213120.0], [27906, 240062464.0], [27943, 232976384.0], [27960, 232771584.0], [28051, 229650432.0], [28055, 227774464.0], [28094, 231616512.0], [28120, 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a/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield.json +++ b/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield.json @@ -1 +1 @@ -[[27339, 378445824.0], [27346, 364288780.2770899], [27364, 370679381.3047998], [27412, 388337664.0], [27428, 385966080.0], [27447, 380100608.0], [27482, 379850752.0], [27495, 385703936.0], [27541, 378494976.0], [27562, 380411904.0], [27568, 378261504.0], [27607, 380583936.0], [27616, 448049152.0], [27621, 448077824.0], [27690, 447791104.0], [27718, 453165056.0], [27732, 452157440.0], [27772, 449953792.0], [27804, 452177920.0], [27879, 454262784.0], [27906, 486305792.0], [27943, 482541568.0], [27960, 483524608.0], [28051, 483016704.0], [28055, 481792000.0], [28094, 486027264.0], [28120, 487481344.0], [28216, 485683200.0], [28237, 488132608.0], [28256, 487473152.0], [28261, 485552128.0], [28301, 490586112.0], [28307, 487608320.0], [28341, 498769920.0], [28361, 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b/graphs/summary/bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue.json @@ -1 +1 @@ -[[27339, 378523648.0], [27346, 364440507.0028004], [27364, 370471503.45980847], [27412, 388329472.0], [27428, 385847296.0], [27447, 378003456.0], [27482, 382169088.0], [27495, 385925120.0], [27541, 378351616.0], [27562, 382504960.0], [27568, 380366848.0], [27607, 380399616.0], [27616, 447672320.0], [27621, 447684608.0], [27690, 447561728.0], [27718, 454320128.0], [27732, 452395008.0], [27772, 450994176.0], [27804, 449011712.0], [27879, 454205440.0], [27906, 491024384.0], [27943, 483741696.0], [27960, 484945920.0], [28051, 485920768.0], [28055, 485855232.0], [28094, 489152512.0], [28120, 485920768.0], [28216, 487645184.0], [28237, 484503552.0], [28256, 486998016.0], [28261, 486240256.0], [28301, 489492480.0], [28307, 487796736.0], [28341, 483106816.0], [28361, 491032576.0], [28379, 486064128.0], [28425, 485539840.0], [28438, 486121472.0], [28443, 485560320.0], 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[28773, 7.558525935465966], [28806, 7.266690640477464]] \ No newline at end of file diff --git a/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource.json b/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource.json index 51a87401c..a77500221 100644 --- a/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource.json +++ b/graphs/summary/bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource.json @@ -1 +1 @@ -[[23735, 2.2722940174862742], [24188, 2.1544458251446486], [24958, 2.252831463702023], [25613, 2.3381150402128696], [25614, 2.4025253863073885], [25651, 2.263109114486724], [25733, 2.4232864594087005], [25778, 2.1432635746896267], [25803, 2.323051836807281], [25823, 2.2705193953588605], [25833, 2.204420846886933], [25854, 2.3218582435511053], [25889, 2.3952002697624266], [25934, 2.3042466724291444], [25978, 2.2153804902918637], [25993, 2.3121126401238143], 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a/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json b/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json index a9f3e0829..c1cc227d1 100644 --- a/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json +++ b/graphs/summary/bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn.json @@ -1 +1 @@ -[[27412, 11.467524880543351], [27428, 10.99891834706068], [27447, 11.1771110445261], [27482, 11.341389724984765], [27495, 10.881591621786356], [27541, 11.41452157869935], [27562, 11.403219312429428], [27568, 10.918525751680136], [27607, 11.322992965579033], [27616, 11.210927858948708], [27621, 11.069966178387403], [27690, 11.222440108656883], [27718, 10.377211909741163], [27732, 10.25312838703394], [27772, 10.529176950454712], [27804, 10.65598227828741], [27879, 10.231275300029665], [27906, 18.335549775976688], [27943, 16.91023803781718], [27960, 18.929513417882845], [28051, 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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 index 7a4225fa9..a84248e0b 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 @@ -[[26614, 0.8704034257680178], [26623, 0.8084709271788597], [26637, 0.7858002353459597], [26664, 0.7761032525449991], [26674, 0.8325192322954535], [26681, 0.8338851146399975], [26749, 0.842907240614295], [26761, 0.7008094908669591], [26817, 0.8371426546946168], [26827, 0.6911372486501932], [26878, 0.8429291266947985], [26905, 0.6854693032801151], [26949, 0.8301213160157204], [26966, 0.8422046266496181], [27023, 0.8513278216123581], [27025, 0.839911138638854], [27053, 0.8401727564632893], [27066, 0.7084604948759079], [27074, 0.8418415654450655], [27095, 0.8489056266844273], [27135, 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a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching.json b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching.json index a957940c7..c688ffff9 100644 --- a/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching.json +++ b/graphs/summary/bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching.json @@ -1 +1 @@ -[[26614, 0.6352838790044188], [26623, 0.7452762247994542], [26637, 0.6045083915814757], [26664, 0.6395971849560738], [26674, 0.6509583685547113], [26681, 0.755252436734736], [26749, 0.6278396220877767], [26761, 0.6176093043759465], [26817, 0.7745668133720756], [26827, 0.6354401288554072], [26878, 0.5971736200153828], [26905, 0.6019237618893385], [26949, 0.7669925726950169], [26966, 0.7883875779807568], [27023, 0.7558828722685575], [27025, 0.7629606779664755], [27053, 0.6367681510746479], [27066, 0.6271755993366241], [27074, 0.6052038706839085], [27095, 0.8023967687040567], [27135, 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"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, "5.0.0-mas-34": 8975, "5.0.0-mas-35": 8982, "5.0.0-mas-36": 8990, "5.0.0-mas-37": 8992, "5.0.0-mas-38": 8998, "5.0.0-mas-39": 9023, "5.0.0-mas-40": 9032, "5.0.0-mas-41": 9035, "5.0.0-mas-42": 9039, "5.0.0-mas-43": 9049, "5.0.0-mas-44": 9056, "5.0.0-mas-45": 9075, "5.0.0-mas-46": 9090, "5.0.0-mas-47": 9095, "5.0.0-mas-48": 9099, "5.0.0-mas-49": 9104, "5.0.0-mas-50": 9106, "5.0.0-mas-51": 9108, "5.0.0-mas-52": 9109, "5.0.0-mas-53": 9126, "5.0.0-mas-54": 9130, "5.0.0-mas-55": 9143, "5.0.0-mas-56": 9164, "5.0.0-mas-57": 9165, "5.0.0-mas-58": 9180, "5.0.0-mas-59": 9183, "5.0.0-mas-60": 9185, "5.0.0-mas-61": 9192, "5.0.0-mas-62": 9195, "5.0.0-mas-63": 9201, "5.0.0-mas-64": 9205, "5.0.0-mas-65": 9212, "5.0.0-mas-66": 9219, "5.0.0-mas-67": 9223, "5.0.0-mas-68": 9226, "5.0.0-mas-69": 9227, "5.0.0-mas-70": 9236, "5.0.0-mas-71": 9245, "5.0.0-mas-72": 9255, "5.0.0-mas-73": 9257, "5.0.0-mas-74": 9258, "5.0.0-mas-75": 9266, "5.0.0-mas-76": 9272, "5.0.0-mas-77": 9275, "5.0.0-mas-78": 9276, "5.0.0-mas-79": 9280, "5.0.0-mas-80": 9286, "5.0.0-mas-81": 9293, "5.0.0-mas-82": 9298, "5.0.0-mas-83": 9303, "5.0.0-mas-84": 9306, "5.0.0-mas-85": 9321, "5.0.0-mas-86": 9323, "5.0.0-mas-87": 9325, "5.0.0-mas-88": 9331, "5.0.0-mas-89": 9333, "5.0.0-mas-9": 8792, "5.0.0-mas-90": 9334, "5.0.0-mas-91": 9337, "5.0.0-mas-92": 9340, "5.0.0-mas-93": 9342, "5.0.0-mas-94": 9344, "5.0.0-mas-95": 9349, "5.0.0-mas-96": 9352, "5.0.0-mas-97": 9367, "5.0.0-mas-98": 9371, "5.0.0-mas-99": 9372, "5.0.0-rel-197": 10186, "5.0.0-rel-198": 10202, "5.0.0-rel-199": 10204, "5.0.0-rel-200": 10206, "5.0.0-rel-201": 10220, "5.0.0-rel-202": 10237, "5.0.0-rel-203": 10248, "5.0.0-rel-204": 10265, "5.0.0-rel-205": 10280, "5.0.0-rel-206": 10317, "5.0.0-rel-207": 10359, "5.0.0-rel-208": 10407, "5.0.0-rel-209": 10418, "5.0.0-rel-210": 10428, "5.0.0-rel-211": 10438, "5.0.0-rel-212": 10453, "5.0.0-rel-213": 10483, "5.0.0-rel-214": 10519, "5.0.0-rel-215": 10546, "5.0.0-rel-216": 10558, "5.0.0-rel-217": 10590, 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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 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "python": "3.10", "pip+casatasks": "trunk", "branch": "master", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "40f5717bec049678804b23a5fac6b83ef62e8ad7", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "4f5295c7309e931c62f928a03eeadbbb4984cf00", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "85a2546777121b18bd5c0e9137b9b8e55f08cb7c", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "a73a44ba2df3c3b824e866ed60bf0d538e532f76", "pip+casatools": null}, {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 3.10.0-1160.71.1.el7.x86_64", "ram": "263925792", "python": "3.6", "pip+casatasks": "trunk", "branch": "ee69dc1e48962bc3300198d59345eca3624057dd", "pip+casatools": null}], "benchmarks": {"bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart": {"code": "class BaseMosaic:\n def time_mosaic_cube_eph_pcwdT_restart(self):\n \"\"\"Mosaic ephemeris cube imaging with briggsbwtaper - field Venus, spw 45\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'], scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=False, restoringbeam='common', pbcor=False, weighting='briggs', robust=0.5, npixels=0, niter=0, threshold='0.0mJy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, savemodel='none', parallel=self.parallel, verbose=True)\n # restart the tclean call to actually perform deconvolution\n ret = tclean(vis=self.msfile, imagename=self.img+\"0\", field='Venus', spw=['1'], antenna=['0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46'],scan=['7,11'], intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='corrected', imsize=[480, 420], cell=['0.14arcsec'], phasecenter='TRACKFIELD', stokes='I', specmode='cubesource', nchan=948, start='261.7643758544GHz', width='0.2441755MHz', perchanweightdensity=True, gridder='mosaic', mosweight=True, usepointing=False, pblimit=0.2, deconvolver='hogbom', restoration=True, restoringbeam='common', pbcor=True, weighting='briggs', robust=0.5, npixels=0, niter=700000, threshold='0.0106Jy', nsigma=0.0, usemask='auto-multithresh', sidelobethreshold=2.0, noisethreshold=4.25, lownoisethreshold=1.5, negativethreshold=15.0, minbeamfrac=0.3, growiterations=50, dogrowprune=True, minpercentchange=1.0, fastnoise=False, restart=True, savemodel='none', calcres=False, calcpsf=False, parallel=self.parallel, verbose=True)\n\n def setup(self):\n self.prepData(\"2018.1.00879.S_tclean.ms\")", "min_run_count": 1, "name": "bench_alma.BaseMosaic.time_mosaic_cube_eph_pcwdT_restart", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 7200, "type": "time", "unit": "seconds", "version": "CAS-14086", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_cal_only_mode": {"code": "class ApplyModes:\n def time_applycal_cal_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_cal_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e26cd2e6d77235fa8e7c3c5c65b22d4fcdca56bf1c8eb4ac430fd60fa546866e", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonly')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a6421f6893712c5910c61b340b2f45a1dd0f5244adb96eaf54cec02b276ccbae", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode": {"code": "class ApplyModes:\n def time_applycal_flag_only_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='flagonlystrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_flag_only_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5d6b2cd5b9cf017fefacf66e0929df5ec228a015989465593e0233cefa48cda", "warmup_time": -1}, "bench_applycal.ApplyModes.time_applycal_strict_mode": {"code": "class ApplyModes:\n def time_applycal_strict_mode(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflagstrict')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.ApplyModes.time_applycal_strict_mode", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "52b7c478c85666778a8b11363dcd53c5c766dadf886b901f04bb8a078224839f", "warmup_time": -1}, "bench_applycal.CalFlag.time_applycal_calflag": {"code": "class CalFlag:\n def time_applycal_calflag(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='calflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalFlag.time_applycal_calflag", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a1e4de3456a21dcc48909b513d5efa4d90bddd213862d4390fdb67c2df14c381", "warmup_time": -1}, "bench_applycal.CalLibrary.time_applycal_callibrary": {"code": "class CalLibrary:\n def time_applycal_callibrary(self):\n applycal(vis=self.vis, docallib=True, callib=self.callibfile)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalLibrary.time_applycal_callibrary", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "835d4e5c3926e3dc5a23e04e71aa9fb433f77e02f65db1846c88677d01ba2a71", "warmup_time": -1}, "bench_applycal.CalWeight.time_applycal_calweight": {"code": "class CalWeight:\n def time_applycal_calweight(self):\n applycal(vis=self.vis, gaintable=[self.gCal], calwt=[False])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.CalWeight.time_applycal_calweight", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f05a2af9251ce10ee5dc59cca8788b998e9da9fded8cce0ee1906db023bc8d01", "warmup_time": -1}, "bench_applycal.FlagBackup.time_applycal_flag_backup": {"code": "class FlagBackup:\n def time_applycal_flag_backup(self):\n applycal(vis=self.vis, gaintable=[self.gCal], flagbackup=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.FlagBackup.time_applycal_flag_backup", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "872b962508540cc6ef4bcbc9753aba34c9da8b7d02cc15ee7765f06901e65e37", "warmup_time": -1}, "bench_applycal.GainField.time_applycal_gainfield": {"code": "class GainField:\n def time_applycal_gainfield(self):\n applycal(vis=self.vis, gaintable=[self.tCal], gainfield='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.GainField.time_applycal_gainfield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "556dfc5fca635bc393e0d68f23e8a9f9e9fef7a4f0c446faec837b3b23356816", "warmup_time": -1}, "bench_applycal.Interp.time_applycal_interp": {"code": "class Interp:\n def time_applycal_interp(self):\n applycal(vis=self.vis, gaintable=[self.tCal], interp='cubicflag')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Interp.time_applycal_interp", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e36d7f0ce102f831a8b90da4499019cebba32ffad3fb79f8e95e30ecfa2ab8a", "warmup_time": -1}, "bench_applycal.Parang.time_applycal_parang": {"code": "class Parang:\n def time_applycal_parang(self):\n applycal(vis=self.vis, gaintable=[self.gCal], parang=True)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.Parang.time_applycal_parang", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "fbef01f79ab1b6afd2bd79669d2271a7a86954b6f26b2a523667e0b8f0ebdce0", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_antenna": {"code": "class SelectionTypes:\n def time_applycal_select_antenna(self):\n applycal(vis=self.vis, gaintable=[self.gCal], antenna='0~5&')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_antenna", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5ca6f069a4262d19625195a53f6dc88cc9ccca31907e944ec501075f5884cd60", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_field": {"code": "class SelectionTypes:\n def time_applycal_select_field(self):\n applycal(vis=self.vis, gaintable=[self.gCal], field='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5d8951157ddc5452e498ffe5fd8d725dfaf0e9df7de5b79747f4993a20ddbc5", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_intent": {"code": "class SelectionTypes:\n def time_applycal_select_intent(self):\n applycal(vis=self.vis, gaintable=[self.gCal], intent='*AMPLI*')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "48c31d0fb8eefd5323fa3319aa8b3695a8ce667466bd2a1303e71107b1e00613", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_scan": {"code": "class SelectionTypes:\n def time_applycal_select_scan(self):\n applycal(vis=self.vis, gaintable=[self.gCal], scan='10')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "96819d5d86bd7855a0112c03f92c63a64f0afa905e961887df14a01b025852f1", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_spw": {"code": "class SelectionTypes:\n def time_applycal_select_spw(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spw='1')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d95a817124907f58102de398fe8a2dedddd11e80b30d4758105288fc5a1149a", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_timerange": {"code": "class SelectionTypes:\n def time_applycal_select_timerange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], timerange='04:33:23~04:38:23')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_timerange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b264ae69c0eada9a00ed6c42d7f361571dab35399621ee8cb2785741c2aae68", "warmup_time": -1}, "bench_applycal.SelectionTypes.time_applycal_select_uvrange": {"code": "class SelectionTypes:\n def time_applycal_select_uvrange(self):\n applycal(vis=self.vis, gaintable=[self.gCal], uvrange='>100klambda')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SelectionTypes.time_applycal_select_uvrange", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f897b2028d0940d47aca1c23d61aa1185690be2fd94bf1c4c94c188f922f9f91", "warmup_time": -1}, "bench_applycal.SpwMap.time_applycal_spwmap": {"code": "class SpwMap:\n def time_applycal_spwmap(self):\n applycal(vis=self.vis, gaintable=[self.gCal], spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.SpwMap.time_applycal_spwmap", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "79550810428ff38b9e649d8bd6b72540535fe7e4b5efe5cefc4aafa24afc8296", "warmup_time": -1}, "bench_applycal.StandardGainTable.time_applycal_basic_gaintable": {"code": "class StandardGainTable:\n def time_applycal_basic_gaintable(self):\n applycal(vis=self.vis, gaintable=[self.gCal, self.tCal])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.StandardGainTable.time_applycal_basic_gaintable", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a70f32c3ac8dfd7542bb10c89868d4bc640a02842253b6f45f105bf41df75780", "warmup_time": -1}, "bench_applycal.TrialMode.time_applycal_trial": {"code": "class TrialMode:\n def time_applycal_trial(self):\n applycal(vis=self.vis, gaintable=[self.gCal], applymode='trial')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 5, "name": "bench_applycal.TrialMode.time_applycal_trial", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "24ae87f7af1553ca58c7bb7ec1ab0a5a78e22b6f1c6fdc4af9ace18ad930eefb", "warmup_time": -1}, "bench_flagdata.AntintMode.time_antint_low_threshold_spw": {"code": "class AntintMode:\n def time_antint_low_threshold_spw(self):\n \"\"\"flagdata: mode antint, with low threshold; (original test_antint_spw3_low_threshold)\"\"\"\n flagdata(vis=self.vis, mode='antint', spw='3', antint_ref_antenna='ea01', minchanfrac=-.1)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.AntintMode.time_antint_low_threshold_spw", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98a303508391da50600d29c7f8a74f95501abb178d78c35154642c8f7ec6f83c", "warmup_time": -1}, "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange": {"code": "class CalibrationBandpass:\n def time_bandpass_clip_timerange(self):\n \"\"\"Flagdata: clip a timerange from field 3C286_A; (original test_cal_time1)\"\"\"\n flagdata(vis=self.vis, mode='clip', timerange='<14:12:52',clipzeros=True,\n clipminmax=[0.,0.35], datacolumn='CPARAM',flagbackup=False)\n\n def setup(self):\n self.setUp_bpass()", "min_run_count": 3, "name": "bench_flagdata.CalibrationBandpass.time_bandpass_clip_timerange", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4595858d10a7650bb7d5b763b20cf03f4921f5bcd8feb0a09a3ce0a9c3cdca38", "warmup_time": -1}, "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam": {"code": "class CalibrationTsys:\n def time_tsys_clip_fparam(self):\n \"\"\"Flagdata: flag default data column FPARAM; (original test_default_fparam)\"\"\"\n flagdata(vis=self.vis, mode='clip', clipminmax=[0,500], flagbackup=False,\n datacolumn='FPARAM')\n\n def setup(self):\n self.setUp_tsys()", "min_run_count": 3, "name": "bench_flagdata.CalibrationTsys.time_tsys_clip_fparam", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7ee644928bb3e8de0bcf4711c3f266bb859ae288d2e7db42a0ea0a66d62550ad", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_corrected_data": {"code": "class ClipMode:\n def time_clip_corrected_data(self):\n \"\"\"flagdata: clip CORRECTED data column; (original test_datacol_corrected)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='CORRECTED',\n clipminmax=[0.,10.])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_corrected_data", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "07c4f1e0d3121fa540b2cd0e228a42009625c87cf37a05e16e62f3d95b57246b", "warmup_time": -1}, "bench_flagdata.ClipMode.time_clip_with_timeavg": {"code": "class ClipMode:\n def time_clip_with_timeavg(self):\n \"\"\"flagdata: clip with time averaging in spw 9; (original test_timeavg_spw9_2scans)\"\"\"\n flagdata(vis=self.vis, flagbackup=False, mode='clip', datacolumn='DATA', spw='9',\n timeavg=True, timebin='2s', clipminmax=[0.0, 0.08])\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.ClipMode.time_clip_with_timeavg", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2be25377fea9922f68fb74d47702f7354f593a21a0c3ef79ea683f411e59cb03", "warmup_time": -1}, "bench_flagdata.ElevationMode.time_elevation_interval": {"code": "class ElevationMode:\n def time_elevation_interval(self):\n \"\"\"Flagdata: flag an interval between upper and lower elevations; (original test_interval)\"\"\"\n flagdata(vis = self.vis,mode = 'elevation',lowerlimit = 55,upperlimit = 60,\n savepars=False,flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()", "min_run_count": 3, "name": "bench_flagdata.ElevationMode.time_elevation_interval", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "cbefafa13742c0d5fe0d9179be380f7801ee669bbc6fc2cbe3ba48c02dcf1b1c", "warmup_time": -1}, "bench_flagdata.ListFileMode.time_list_from_3files": {"code": "class ListFileMode:\n def time_list_from_3files(self):\n \"\"\"Flagdata list mode reading commands from three files; (original test_file_CAS4819)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=[self.inpfile1, self.inpfile2, self.inpfile3],\n flagbackup=False)\n\n def setup(self):\n self.setUp_ngc5921()\n\n def setup_cache(self):\n # Create 3 flagcmd files and save to disk\n myinput = \"scan='1'\\n\" \\\n \"scan='2'\\n\" \\\n \"# a comment line\\n\" \\\n \"scan='3'\"\n create_input(myinput, self.inpfile1)\n \n myinput = \"scan='5'\\n\" \\\n \" \\n\" \\\n \"scan='6'\\n\" \\\n \"scan='7'\"\n create_input(myinput, self.inpfile2)\n \n myinput = \"scan='4' mode='clip' clipminmax=[0,4]\"\n \n create_input(myinput, self.inpfile3)", "min_run_count": 3, "name": "bench_flagdata.ListFileMode.time_list_from_3files", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:217", "timeout": 3600, "type": "time", "unit": "seconds", "version": "dd8b2ea5edb3e265335cac2bf592eef280e5239f17d022f1fca37ab9738a0531", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_bandpassflag": {"code": "class ListMode:\n def time_list_bandpassflag(self):\n \"\"\"Flagdata list mode from pipeline hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_bandpassflag", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "2971c3002f4dafe2840b191e5570935d4b7f7ff2873189a4c55df4ae808dc738", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_summary": {"code": "class ListMode:\n def time_list_summary(self):\n \"\"\"Flagdata list mode from pipeline hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_summary", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3e3764d7dceb23f8ac0736e3a450613d2d10e11dc14ea682bbb0a78e1737111", "warmup_time": -1}, "bench_flagdata.ListMode.time_list_tbuff": {"code": "class ListMode:\n def time_list_tbuff(self):\n \"\"\"Flagdata list mode from pipeline hifa_flagdata\"\"\"\n flagdata(\n vis=self.vis,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n self.setUp_WRay_perf()\n\n def setup_cache(self):\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.datapath, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd)\n )", "min_run_count": 3, "name": "bench_flagdata.ListMode.time_list_tbuff", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_flagdata:149", "timeout": 3600, "type": "time", "unit": "seconds", "version": "53f2aaec8f44b81cb94f73fa21e5aac89218769ace8126661888f9c54374ff75", "warmup_time": -1}, "bench_flagdata.RflagMode.time_rflag_numpy_types": {"code": "class RflagMode:\n def time_rflag_numpy_types(self):\n \"\"\"flagdata: rflag, thresholds using numpy types; (original test_rflag_partial_thresholds)\"\"\"\n flagdata(vis=self.vis, mode='rflag', spw='9,10', timedev=[self.t1, self.t2], freqdev=0.5,\n flagbackup=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()\n self.t1 = [numpy.int32(1), 10, numpy.float32(0.1)]\n self.t2 = [1, numpy.int16(11), numpy.float64(0.07)]", "min_run_count": 3, "name": "bench_flagdata.RflagMode.time_rflag_numpy_types", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3f582e64491988dab48d1b83c4500bde3d09f14006d290ba96e3fa9ded4fa62b", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_abs_wvr": {"code": "class Selections:\n def time_sel_abs_wvr(self):\n \"\"\"flagdata: clip ABS_WVR; (original test_abs_wvr)\"\"\"\n flagdata(vis=self.vis, mode='clip',clipminmax=[0,50], correlation='ABS_WVR', savepars=False,\n flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_abs_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ce5229049856ba918b3464ddc7f5aeee51402d91ce7a7d0cc3fb9d260d9fff87", "warmup_time": -1}, "bench_flagdata.Selections.time_sel_autocorr_wvr": {"code": "class Selections:\n def time_sel_autocorr_wvr(self):\n \"\"\"flagdata: CAS-5286, do not flag auto-correlations in WVR data; (original test_autocorr_wvr)\"\"\"\n flagdata(vis=self.vis, autocorr=True, flagbackup=False)\n\n def setup(self):\n self.setUp_alma_ms()", "min_run_count": 3, "name": "bench_flagdata.Selections.time_sel_autocorr_wvr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "128022df8e55b6c9054c3ef806420b1f80f11fcb4ef8ce073e99894f83617c9c", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_absent_antennas": {"code": "class ShadowMode:\n def time_shadow_absent_antennas(self):\n \"\"\"flagdata: shadow by antennas not present in MS; (original test_CAS2399)\"\"\"\n flagdata(vis=self.vis, mode='shadow', tolerance=0.0, addantenna=self.inpfile, flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_absent_antennas", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "3b94aac813ae30b385cb446c642e9b3115a43bc492770d13667d2fd1bce00339", "warmup_time": -1}, "bench_flagdata.ShadowMode.time_shadow_addantenna": {"code": "class ShadowMode:\n def time_shadow_addantenna(self):\n \"\"\"flagdata: use antenna file in list mode; (original test_addantenna)\"\"\"\n flagdata(vis=self.vis, mode='list', inpfile=self.inpfile, savepars=True, outfile='withdict.txt',\n flagbackup=False)\n\n def setup(self):\n self.setUp_shadowdata()\n \n # Create antennafile in disk\n myinput = \"name=VLA01\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601144.96146691, -5041998.01971858, 3554864.76811967]\\n\" \\\n \"name=VLA02\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601105.7664601889, -5042022.3917835914, 3554847.245159178]\\n\" \\\n \"name=VLA09\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601197.2182404203, -5041974.3604805721, 3554875.1995636248]\\n\" \\\n \"name=VLA10\\n\" \\\n \"diameter=25.0\\n\" \\\n \"position=[-1601227.3367843349,-5041975.7011900628,3554859.1642644769]\\n\"\n \n create_input(myinput, self.antfile)\n \n # Create flagcmd file\n myinput = \"mode='shadow' tolerance=0.0 addantenna='antfile.txt'\"\n create_input(myinput, self.inpfile)", "min_run_count": 3, "name": "bench_flagdata.ShadowMode.time_shadow_addantenna", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "21fb65a9cd413ce64af88b6c00ccfc1ee439a7d0413b88f9b383f4e30252b087", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_abs_rr": {"code": "class TfcropMode:\n def time_tfcrop_abs_rr(self):\n \"\"\"flagdata:: flag with tfcrop mode an ABS_RR correlation; (original test_tfcrop1)\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', correlation='ABS_RR',ntime=51.0,spw='9',\n savepars=False, extendflags=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_abs_rr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bc8ecea57bcf9178074a7728ad87a326ec8796d9a804a085da78d1247047edba", "warmup_time": -1}, "bench_flagdata.TfcropMode.time_tfcrop_extendflags": {"code": "class TfcropMode:\n def time_tfcrop_extendflags(self):\n \"\"\"flagdata: flag with tfcrop mode and extend flags automatically\"\"\"\n flagdata(vis=self.vis, mode='tfcrop', spw='0', extendflags=True, flagbackup=False)\n\n def setup(self):\n self.setUp_data4tfcrop()", "min_run_count": 3, "name": "bench_flagdata.TfcropMode.time_tfcrop_extendflags", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "686884535fe06d88a4737064c91e8fd3cc52616fc17d460c57cba3da7d04f97c", "warmup_time": -1}, "bench_fringefit.Corrcomb.time_comb_all": {"code": "class Corrcomb:\n def time_comb_all(self):\n \"\"\"test_comb --- last part of the corrcomb test, check that results are coombined\"\"\"\n fringefit(vis=self.polcombtestms, caltable=self.testout, refant='0', spw='2~3', corrcomb='all')\n\n def setUp(self):\n if os.path.exists(self.polcombtestms):\n shutil.rmtree(self.polcombtestms)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.polcombtestms)", "min_run_count": 5, "name": "bench_fringefit.Corrcomb.time_comb_all", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Dispersive.time_manual_phase_cal": {"code": "class Dispersive:\n def time_manual_phase_cal(self):\n \"\"\"test_manual_phase_cal --- test on manually flagged data\"\"\"\n fringefit(vis=self.prefix + '.ms',\n caltable='n14p1.disp', refant=\"WB\",\n scan='1', solint='60', spw='0,1,2',\n paramactive=[True, True, True],\n minsnr=50,\n niter=100,\n gaintable=['n14p1.mpc'],\n parang=True)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n \n fringefit(vis=\"n14p1.ms\", caltable=\"n14p1.mpc\",\n scan=\"1\", solint=\"300\", refant=\"WB\",\n minsnr=50, zerorates=True,\n globalsolve=True, niter=100, gaintable=[],\n parang=True)", "min_run_count": 5, "name": "bench_fringefit.Dispersive.time_manual_phase_cal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.FreqMetaTests.time_metadata": {"code": "class FreqMetaTests:\n def time_metadata(self):\n \"\"\"test_metadata --- check output table metadata\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, spw=\"0,1,2,3\", field='J0916+3854', timerange=\"17:10:00~17:11:00\",\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n if os.path.exists(self.msfile):\n shutil.rmtree(self.msfile)\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.FreqMetaTests.time_metadata", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Mbd.time_mbd": {"code": "class Mbd:\n def time_mbd(self):\n \"\"\"test_mbd --- test with mbd cal table\"\"\"\n sbdcal = self.prefix + '-zerorates.sbdcal'\n mbdcal = self.prefix + '.mbdcal'\n \n fringefit(vis=self.msfile, caltable=mbdcal, field='J0916+3854',\n combine='spw', gaintable=[sbdcal], refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n \n sbdcal = self.prefix + '-zerorates.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, field='4C39.25',\n refant='EF', zerorates=True)", "min_run_count": 5, "name": "bench_fringefit.Mbd.time_mbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.RefantBookkeeping.time_bookkeeping": {"code": "class RefantBookkeeping:\n def time_bookkeeping(self):\n \"\"\"test_bookkeeping --- check effects of using refant WB\"\"\"\n fringefit(vis=self.msfile, caltable=self.sbdcal, refant='WB')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n flagdata(self.prefix + '.ms', mode='manual', spw='*:0~2;29~31')\n flagdata(self.prefix + '.ms', mode='manual', antenna='EF')", "min_run_count": 5, "name": "bench_fringefit.RefantBookkeeping.time_bookkeeping", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_sbd": {"code": "class Sbd:\n def time_sbd(self):\n \"\"\"test_sbd --- test with sbd caltable\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_sbd", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Sbd.time_uvrange": {"code": "class Sbd:\n def time_uvrange(self):\n \"\"\"test_uvraange --- Check that the uvrnage parameter excludes antennas \"\"\"\n # create a caltable with uvrange selection\n fringefit(vis=self.uvfile, caltable='uvrange_with.cal', spw='2', refant='0', uvrange='<1160')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)\n shutil.copytree(os.path.join(datapath, 'gaincaltest2.ms'), self.uvfile)", "min_run_count": 5, "name": "bench_fringefit.Sbd.time_uvrange", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_param": {"code": "class Single:\n def time_param(self):\n \"\"\"test_param --- test different iterations of paramactive\"\"\"\n sbdcal = self.prefix + '-2.sbdcal'\n # We make a triple cartesian product of booleans\n # to test all possible paramactive values\n refant = 0\n refant_s = str(refant)\n for pactive in itertools.product(*3*[[False, True]]):\n fringefit(vis=self.msfile, paramactive=list(pactive), caltable=sbdcal, refant=refant_s)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_param", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_fringefit.Single.time_single": {"code": "class Single:\n def time_single(self):\n \"\"\"test_single --- check that the right params are flagged with a single refant\"\"\"\n sbdcal = self.prefix + '.sbdcal'\n fringefit(vis=self.msfile, caltable=sbdcal, refant='EF')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.msfile), self.msfile)", "min_run_count": 5, "name": "bench_fringefit.Single.time_single", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14113", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_A": {"code": "class CalModes:\n def time_gaincal_calmode_A(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='a')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_A", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "f7eb35dd966d27af8cf12cc750375153a8b0910af44d846f38c327d0bf37f4c2", "warmup_time": -1}, "bench_gaincal.CalModes.time_gaincal_calmode_P": {"code": "class CalModes:\n def time_gaincal_calmode_P(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n calmode='p')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.CalModes.time_gaincal_calmode_P", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1b0b933cc4c5f58b1b99775ecdf726fd4b7397322314ae15faf4e2d13c102cbc", "warmup_time": -1}, "bench_gaincal.FullGainCal.time_gaincal_fullcal": {"code": "class FullGainCal:\n def time_gaincal_fullcal(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0', refant='0',\n smodel=[1, 0, 0, 0], scan='0~9')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.FullGainCal.time_gaincal_fullcal", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "321b6da59741ba85c3e914725f481e45ca88f4ef73a637f3ade2bed74a5abaa1", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_K": {"code": "class GainTypes:\n def time_gaincal_gain_type_K(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='KCROSS', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_K", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "616aabddbc63928cc0fdb731743036617d3e88b5249398335973b002b9ea2e05", "warmup_time": -1}, "bench_gaincal.GainTypes.time_gaincal_gain_type_spline": {"code": "class GainTypes:\n def time_gaincal_gain_type_spline(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n gaintype='GSPLINE', refant='0')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GainTypes.time_gaincal_gain_type_spline", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2b9405658f1efab3530c340ed099fb37b1ae5888f52762c3f8cf6df6321126ac", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect": {"code": "class GaincalSelections:\n def time_gaincal_antennaSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', field='0', solint='inf', combine='scan', antenna='0~5&',\n smodel=[1, 0, 0, 0], gaintype='G')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_antennaSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d75f902a1c09757939ff4e77e062c55293a92b7f886d8bbe0cf4e5344918c39f", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect": {"code": "class GaincalSelections:\n def time_gaincal_fieldSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', field='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_fieldSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "10de6efc06ad79e8cf856e46bef9b61f435eb4030f27473d284c71c5bdb75ccf", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_refantSelect": {"code": "class GaincalSelections:\n def time_gaincal_refantSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, combine='scan', solint='inf', refant='0',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_refantSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "34c394342899a19f1647ae25d1f7f7d8f19eee8ff14c26b61f0b1bca06443cd2", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_scanSelect": {"code": "class GaincalSelections:\n def time_gaincal_scanSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], scan='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_scanSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ed56707772521ef042364e25b1b5cfede92141267082dcfb3deffd2eecc05e91", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_spwSelect": {"code": "class GaincalSelections:\n def time_gaincal_spwSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf',\n smodel=[1, 0, 0, 0], spw='2')\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_spwSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3b9a18fd82b436f740ec7e34af58655909f6b8731136f253ce9eb7ec3846e3e", "warmup_time": -1}, "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect": {"code": "class GaincalSelections:\n def time_gaincal_uvRangeSelect(self):\n gaincal(vis=self.vis, caltable=self.cal, spw='2', refant='0', uvrange='<1160', minblperant=1)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.GaincalSelections.time_gaincal_uvRangeSelect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b0342a4bc2c4bc568d98a402b5d1611ef82ab395834afb1c0527b636f559ba9", "warmup_time": -1}, "bench_gaincal.MinBlPerAnt.time_gaincal_minbl": {"code": "class MinBlPerAnt:\n def time_gaincal_minbl(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='int', smodel=[1, 0, 0, 0], gaintype='G',\n combine='scan', antenna='0~5&', minblperant=6)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinBlPerAnt.time_gaincal_minbl", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98757eddbbd36be1306e1d3dff8c7368acf302e8e44127c5aa55c1206f1145e1", "warmup_time": -1}, "bench_gaincal.MinSNRFlag.time_gaincal_minsnr": {"code": "class MinSNRFlag:\n def time_gaincal_minsnr(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='30s', field='0', refant='0',\n smodel=[1, 0, 0, 0], minsnr=1000)\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.MinSNRFlag.time_gaincal_minsnr", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "023921a63e2bcaaec078cc39a20cfa4ed25f3aac1ad8b73aeb6903fde979a439", "warmup_time": -1}, "bench_gaincal.PreApplyTables.time_gaincal_apply_tables": {"code": "class PreApplyTables:\n def time_gaincal_apply_tables(self):\n gaincal(vis=self.vis, caltable=self.cal, refant='0', solint='inf', smodel=[1, 0, 0, 0], gaintype='G', field='0',\n gaintable=[self.tCal, self.compCal], gainfield=['0', '0'], interp=[''])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.PreApplyTables.time_gaincal_apply_tables", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "daccc986c167fbbf02e14e3cfa201572b4ec8a51a1629661b8543142b2cae433", "warmup_time": -1}, "bench_gaincal.RefantSelection.time_gaincal_refant": {"code": "class RefantSelection:\n def time_gaincal_refant(self):\n gaincal(vis=self.vis, caltable=self.cal, solint='inf', field='0', combine='scan', refant='1',\n smodel=[1, 0, 0, 0])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.RefantSelection.time_gaincal_refant", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "be0118dd9b630d5373b3604c19a8afb518dbc25a2e7e7758d644c5e5faf634f0", "warmup_time": -1}, "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map": {"code": "class UseSpwMap:\n def time_gaincal_use_spw_map(self):\n gaincal(vis=self.vis, caltable=self.cal, field='0', smodel=[1, 0, 0, 0], solint='inf', combine='scan',\n refant='0', spwmap=[0, 0, 1, 1])\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_gaincal.UseSpwMap.time_gaincal_use_spw_map", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c524f53702087f79992918ad289b5b863018b412527f507e27589ac99e0574b9", "warmup_time": -1}, "bench_importasdm.Aca.time_default": {"code": "class Aca:\n def time_default(self):\n '''test6_lazy1 - standard fill : Test good ACA ASDM with mixel pol/channelization input with default filler'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Aca.time_default_lazy": {"code": "class Aca:\n def time_default_lazy(self):\n '''test6_lazy1 - lazy fill: Test good ACA ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Aca.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only": {"code": "class Alma_12m:\n def time_auto_only(self):\n '''test7_lazy4 - standard fill: Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_auto_only_lazy": {"code": "class Alma_12m:\n def time_auto_only_lazy(self):\n '''test7_lazy4 - lazy fill, Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true": {"code": "class Alma_12m:\n def time_bdfflags_true(self):\n '''test7_bdflags1 - Test good 12 m ASDM with mixed pol/channelisation input with default filler selecting \"co\" on output and using the BDF flags'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode=\"co\", bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_bdfflags_true_lazy": {"code": "class Alma_12m:\n def time_bdfflags_true_lazy(self):\n '''test7_lazy2 - Test good 12 m ASDM with mixed pol/channelisation input with default filler in lazy mode with reading the BDF flags'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, bdfflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_bdfflags_true_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default": {"code": "class Alma_12m:\n def time_default(self):\n '''test7_lazy1 - standard fill: Test good 12 m ASDM with mixed pol/channelization with default filler'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=False, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Alma_12m.time_default_lazy": {"code": "class Alma_12m:\n def time_default_lazy(self):\n '''test7_lazy1 - lazy fill: Test good 12 m ASDM with mixed pol/channelization input with default filler in lazy mode'''\n importasdm(self.asdm_name, vis=self.ms_name, lazy=True, scans='0:1~4')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Alma_12m.time_default_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_applyflags_true": {"code": "class AutocorrASDM:\n def time_applyflags_true(self):\n '''test_flagautocorr1 - importasdm: test that auto-correlations from online flags are correctly flagged'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_process_flags_false": {"code": "class AutocorrASDM:\n def time_process_flags_false(self):\n '''test_flagautocorr3 - importasdm: do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_process_flags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.AutocorrASDM.time_savecmds_true": {"code": "class AutocorrASDM:\n def time_savecmds_true(self):\n '''test_autocorr - importasdm: auto-correlations should be written to online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='3', savecmds=True, outfile=self.outfile)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.AutocorrASDM.time_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_all_defaults": {"code": "class Basic:\n def time_all_defaults(self):\n '''test_import2 --- importasdm with default arguments'''\n importasdm(self.asdm_name)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_all_defaults", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_lazy_pc_true": {"code": "class Basic:\n def time_lazy_pc_true(self):\n '''test1_lazy1 --- lazy mode, with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=True, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_lazy_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Basic.time_pc_true": {"code": "class Basic:\n def time_pc_true(self):\n '''test1_lazy1 --- standard fill (lazy=False), with pointing_correction=True'''\n importasdm(asdm=self.asdm_name, vis=self.ms_name, lazy=False, with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Basic.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_all_scans": {"code": "class Evla:\n def time_all_scans(self):\n '''test_evla_apply5 - test of importing evla data: all scans, do not process flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, ocorr_mode='co', with_pointing_correction=True,\n process_flags=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_all_scans", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_applyflags_true": {"code": "class Evla:\n def time_applyflags_true(self):\n '''test_evla_apply2 - test of importing evla data and applying the online flags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=False, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_applyflags_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_pc_true": {"code": "class Evla:\n def time_pc_true(self):\n '''test_evlatest1 - test of importing evla data, with_pointing_correction=True is normal for EVLA data'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2',ocorr_mode='co',with_pointing_correction=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_pc_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_applyflags_savecmds_true": {"code": "class Evla:\n def time_process_applyflags_savecmds_true(self):\n '''test_evla_apply1_flagdata - test of importing evla data, apply onlineflags'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=True, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_applyflags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_savecmds_true": {"code": "class Evla:\n def time_process_flags_savecmds_true(self):\n '''test_evla_savepars - test importing evla data: save the flag commands and do not apply'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_savecmds_true", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_process_flags_true_applyflags_false": {"code": "class Evla:\n def time_process_flags_true_applyflags_false(self):\n '''test_evla_savepars_flagdata - test importing evla data: save the flag commands and do not apply; using flagdata'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name,scans='11~13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=True, applyflags=False, savecmds=True, outfile=self.cmdfile, flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_process_flags_true_applyflags_false", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla.time_scan_selection": {"code": "class Evla:\n def time_scan_selection(self):\n '''test_evla_apply3 - test of importing evla data, two difference scans, do not apply online flags'''\n # appears to also be the same importasdm as test_evla_apply3_flagdata\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='2,13', ocorr_mode='co', with_pointing_correction=True,\n process_flags=False,flagbackup=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla.time_scan_selection", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Evla_ephemeris.time_polynomial_ephem": {"code": "class Evla_ephemeris:\n def time_polynomial_ephem(self):\n '''test_evlatest2 - test of importing evla data, test2: Good input asdm with polynomial ephemeris'''\n \n importasdm(asdm=self.asdm_name, vis=self.ms_name, scans='0:5',ocorr_mode='co',with_pointing_correction=True,polyephem_tabtimestep=0.001,convert_ephem2geo=False)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Evla_ephemeris.time_polynomial_ephem", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only": {"code": "class Singledish:\n def time_auto_only(self):\n '''test7_lazy5 - standard fill : Test TP asdm with default filler selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", lazy=False, bdfflags=True, applyflags=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_importasdm.Singledish.time_auto_only_lazy": {"code": "class Singledish:\n def time_auto_only_lazy(self):\n '''test7_lazy5 - lazy fill : Test TP asdm with default filler in lazy mode selecting only AUTO data, writing to FLOAT_DATA'''\n importasdm(self.asdm_name, vis=self.ms_name, ocorr_mode=\"ao\", bdfflags=True, applyflags=True, lazy=True)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath,self.asdm_name), self.asdm_name)", "min_run_count": 1, "name": "bench_importasdm.Singledish.time_auto_only_lazy", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "14114", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_custom_atm_params": {"code": "class test_sdatmcor:\n def time_custom_atm_params(self):\n \"\"\"Test customized ATM parameters.\"\"\"\n sdatmcor(\n infile=self.infile, outfile=self.outfile, datacolumn='data',\n dtem_dh='-5.7K/km', h0='2010m',\n atmdetail=True,\n altitude='5.1km', temperature='290K', pressure='700hPa',\n humidity=30, pwv='0.1cm', dp='10hPa', dpm=1.2,\n layerboundaries='800m,1.5km', layertemperature='250K,200K'\n )\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_custom_atm_params", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype": {"code": "class test_sdatmcor:\n def time_sdatmcor_explicit_atmtype(self):\n \"\"\"Test specifying atmtype explicitly.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data', atmtype=2)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_explicit_atmtype", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict": {"code": "class test_sdatmcor:\n def time_sdatmcor_gainfactor_dict(self):\n \"\"\"Test gainfactor: dict input.\"\"\"\n gainfactor = {'19': 10.0, '23': 45.0}\n sdatmcor(infile=self.infile2, outfile=self.outfile, datacolumn='data', gainfactor=gainfactor)\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_gainfactor_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal": {"code": "class test_sdatmcor:\n def time_sdatmcor_normal(self):\n \"\"\"Test normal usage of sdatmcor.\"\"\"\n sdatmcor(infile=self.infile, outfile=self.outfile, datacolumn='data')\n\n def setUp(self):\n \n smart_remove(self.infile)\n smart_remove(self.infile2)\n smart_remove(self.outfile)\n smart_remove(self.caltable)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile2)\n # Gainfactor test set up\n gainfactor = {'19': 10.0, '23': 45.0}\n apply_gainfactor(self.infile2, 19, gainfactor['19'])\n apply_gainfactor(self.infile2, 23, gainfactor['23'])", "min_run_count": 5, "name": "bench_sdatmcor.test_sdatmcor.time_sdatmcor_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13982", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_apply_no_remove": {"code": "class Basic:\n def time_blparam_apply_no_remove(self):\n \"\"\"sdbaseline: blparam file(s) should not be removed in apply mode - test062\"\"\"\n tid = '062'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n blformat = ['text', 'table']\n blmode = 'apply'\n bltable = self.infile_no_remove + '_blparam.bltable'\n overwrite = True\n \n sdbaseline(infile=self.infile_no_remove, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn, blmode=blmode, bltable=bltable)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_apply_no_remove", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_blparam_overwrite": {"code": "class Basic:\n def time_blparam_overwrite(self):\n \"\"\"sdbaseline: blparam file(s) should be overwritten when overwrite=True in fit mode - test060\"\"\"\n tid = '060'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n overwrite = True\n \n sdbaseline(infile=self.infile_overwrite, outfile=outfile, overwrite=overwrite, datacolumn=datacolumn)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_blparam_overwrite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask": {"code": "class Basic:\n def time_chebyshev_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'chebyshev', maskmode = 'list' and masklist=[] (no mask) - test002\"\"\"\n tid = '002'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'chebyshev'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_chebyshev_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask": {"code": "class Basic:\n def time_cspline_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'cspline', maskmode = 'list' and masklist=[] (no mask) - test003\"\"\"\n print(\"\")\n \n tid = '003'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'cspline'\n overwrite = True\n npiece = 3\n spw='3'\n pol='LL'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n npiece=npiece,spw=spw,\n pol=pol,\n outfile=outfile,overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_cspline_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_default": {"code": "class Basic:\n def time_default(self):\n \"\"\"sdbaseline: default values for all parameters - test000\"\"\"\n tid = '000'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n \n result = sdbaseline(infile=infile, datacolumn=datacolumn, outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Basic.time_poly_blfunc_no_mask": {"code": "class Basic:\n def time_poly_blfunc_no_mask(self):\n \"\"\"sdbaseline: simple successful case: blfunc = 'poly', maskmode = 'list' and masklist=[] (no mask) - test001\"\"\"\n tid = '001'\n infile = self.infile\n outfile = self.outroot+tid+'.ms'\n datacolumn = 'float_data'\n maskmode = 'list'\n blfunc = 'poly'\n spw = '3'\n pol = 'LL'\n overwrite = True\n result = sdbaseline(infile=infile, datacolumn=datacolumn,\n maskmode=maskmode, blfunc=blfunc,\n spw=spw, pol=pol, outfile=outfile,\n overwrite=overwrite)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n\n def setup_cache(self):\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_no_remove)\n if os.path.exists(self.infile_no_remove):\n shutil.rmtree(self.infile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_overwrite)\n \n sdbaseline(infile=self.infile_overwrite, outfile=self.outfile_overwrite, overwrite=False, datacolumn='float_data')\n shutil.rmtree(self.outfile_overwrite)\n \n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_no_remove)\n \n sdbaseline(infile=self.infile_no_remove, outfile=self.outfile_no_remove, overwrite=False, datacolumn='float_data', blmode='fit', blformat=['text', 'table'])\n shutil.rmtree(self.outfile_no_remove)", "min_run_count": 5, "name": "bench_sdbaseline.Basic.time_poly_blfunc_no_mask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:191", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.BlFuncVariable.time_variable_orders": {"code": "class BlFuncVariable:\n def time_variable_orders(self):\n \"\"\"sdbaseline: Test blfunc='variable' with variable baseline functions and orders - testVariable00\"\"\"\n print('why is this failing?')\n infile = 'analytic_variable.ms'\n self.paramfile = 'analytic_variable_blparam.txt'\n self._refetch_files([infile, self.paramfile], self.datapath)\n sdbaseline(infile=infile, blfunc='variable', outfile=self.outfile, blparam=self.paramfile, datacolumn=self.column)\n\n def setUp(self):\n pass", "min_run_count": 5, "name": "bench_sdbaseline.BlFuncVariable.time_variable_orders", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge": {"code": "class Mask:\n def time_cspline_blfunc_masked_range_spectrum_edge(self):\n \"\"\"sdbaseline: with masked ranges at the edges of spectrum. blfunc must be cspline. - test100\"\"\"\n self.tid='100'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.search)))\n pol = 'RR'\n blfunc = 'cspline'\n npiece = 4\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n spw=spw,pol=pol,blfunc=blfunc,npiece=npiece,\n outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_cspline_blfunc_masked_range_spectrum_edge", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum": {"code": "class Mask:\n def time_masked_ranges_no_edge_spectrum(self):\n \"\"\"sdbaaseline: with masked ranges not touching spectrum edge - test101\"\"\"\n self.tid='101'\n infile = self.infile\n outfile = self.outroot+self.tid+'.ms'\n datacolumn='float_data'\n mode = 'list'\n spw = '2:%s'%(';'.join(map(self._get_range_in_string,self.blchan2)))\n pol = 'RR'\n \n result = sdbaseline(infile=infile,datacolumn=datacolumn,maskmode=mode,\n outfile=outfile,spw=spw,pol=pol)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')", "min_run_count": 5, "name": "bench_sdbaseline.Mask.time_masked_ranges_no_edge_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked": {"code": "class OutBlTable:\n def time_fit_variable_masked_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304a'\n infile = self.infile_variable_a\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = ''\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=self.infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect": {"code": "class OutBlTable:\n def time_fit_variable_masked_unselect(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304b'\n infile = self.infile_variable_b\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'RR'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_masked_unselect", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout": {"code": "class OutBlTable:\n def time_fit_variable_r2p1cout(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302b'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1cout'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1cout'+'.ms'\n blparam = self.outroot+self.tid+'r2p1cout'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1cout')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1cout", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less": {"code": "class OutBlTable:\n def time_fit_variable_r2p1less(self):\n \"\"\"sdbaseline: per-spectrum baselining, output bltable - test302\"\"\"\n self.tid='302a'\n infile = self.infile\n datacolumn='float_data'\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n \n bloutput= self.outroot+self.tid+'r2p1less'+'.bltable'\n outfile = self.outroot+self.tid+'r2p1less'+'.ms'\n blparam = self.outroot+self.tid+'r2p1less'+'.blparam'\n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, 'r2p1less')\n result = sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_r2p1less", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked": {"code": "class OutBlTable:\n def time_fit_variable_unselect_masked(self):\n \"\"\"sdbaseline: testing shortening baseline table for blfunc=variable - test304\"\"\"\n self.tid = '304c'\n infile = self.infile_variable_c\n datacolumn='float_data'\n spw=''\n blmode='fit'\n blformat='table'\n blfunc='variable'\n dosubtract=True\n blparam = self.outroot+'.blparam'\n pol = 'LL'\n \n outfile = self.outroot+self.tid+blfunc+'.ms'\n bloutput= self.outroot+self.tid+blfunc+'.bltable'\n sdbaseline(infile=infile,datacolumn=datacolumn,\n blmode=blmode,blformat=blformat,bloutput=bloutput,\n spw=spw,pol=pol,blfunc=blfunc,blparam=blparam,\n dosubtract=dosubtract,outfile=outfile)\n\n def setUp(self):\n if os.path.exists(self.infile):\n shutil.rmtree(self.infile)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile)\n \n if os.path.exists(self.infile+'_blparam.txt'):\n os.remove(self.infile+ '_blparam.txt')\n if os.path.exists(self.infile+'_blparam.csv'):\n os.remove(self.infile+ '_blparam.csv')\n if os.path.exists(self.infile+'_blparam.btable'):\n shutil.rmtree(self.infile+ '_blparam.btable')\n \n blparam = self.outroot+'.blparam'\n \n self._createBlparamFile(blparam, self.blparam_order, self.blparam_dic, '')\n\n def setup_cache(self):\n if os.path.exists(self.infile_variable_a):\n shutil.rmtree(self.infile_variable_a)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_a)\n if os.path.exists(self.infile_variable_b):\n shutil.rmtree(self.infile_variable_b)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_b)\n if os.path.exists(self.infile_variable_c):\n shutil.rmtree(self.infile_variable_c)\n shutil.copytree(os.path.join(self.datapath,self.infile), self.infile_variable_c)\n \n self.modify_table(self.infile_variable_a, [0,1])\n self.modify_table(self.infile_variable_b, [0])\n self.modify_table(self.infile_variable_c, [1])", "min_run_count": 5, "name": "bench_sdbaseline.OutBlTable.time_fit_variable_unselect_masked", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "setup_cache_key": "bench_sdbaseline:439", "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag": {"code": "class UpdateWeight:\n def time_blmode_apply_spw_to_flag(self):\n \"\"\"sdbaseline: blmode='apply', spw to flag channels 4500-6499 - test052\"\"\"\n sdbaseline(infile=self.infile2, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', datacolumn='float_data', spw=self.spw)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_blmode_apply_spw_to_flag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdbaseline.UpdateWeight.time_remove_weight_col": {"code": "class UpdateWeight:\n def time_remove_weight_col(self):\n \"\"\"sdbaseline: confirm if WEIGHT_SPECTRUM column is removed - test00\"\"\"\n sdbaseline(infile=self.infile, outfile=self.outfile, intent='OBSERVE_TARGET#ON_SOURCE', spw='9', datacolumn='data', updateweight=True)\n\n def setUp(self):\n remove_files_dirs(self.infile)\n shutil.copytree(os.path.join(self.datapath, self.infile), self.infile)\n remove_files_dirs(self.infile2)\n shutil.copytree(os.path.join(self.datapath, self.infile2), self.infile2)", "min_run_count": 5, "name": "bench_sdbaseline.UpdateWeight.time_remove_weight_col", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13981", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict": {"code": "class Apply:\n def time_spwmap_dict(self):\n \"\"\"Test01: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) dictionary version.\"\"\"\n # Not sure what name is given to outfile in this run. Check!!\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "eca49a873b8027503c11a240f6e59585740e41ae697515593a11176fb2ab48da", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_correct_data": {"code": "class Apply:\n def time_spwmap_dict_correct_data(self):\n \"\"\"Test03: Validation of CORRECTED_DATA = DATA X FPARAM (spwmap={1:[1], 3:[3], 5:[5], 7:[7]}).\"\"\"\n spwmap = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_correct_data", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5d5dd55ee3170d7722bc51ded3a558d04fc83f79a9db111eacbd805111dcad70", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum": {"code": "class Apply:\n def time_spwmap_dict_weight_spectrum(self):\n \"\"\"Test06: weight_spectrum = 1/(SIGMA**2) X 1/(FPARAMx**2) dictionary version.\"\"\"\n spwmap_dict = {1: [1], 3: [3], 5: [5], 7: [7]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_dict,\n applytable=self.tsystable, interp='nearest', outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_dict_weight_spectrum", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1158df62c77f3f40601226240f4c2b0a6910cf4d18c236a4f4a0181e7e689c1e", "warmup_time": -1}, "bench_sdcal.Apply.time_spwmap_list": {"code": "class Apply:\n def time_spwmap_list(self):\n \"\"\"Test02: weight = 1/(SIGMA**2) X 1/(FPARAM_ave**2) list version.\"\"\"\n # focus on antenna1=0, data_disk_id=1\n spwmap_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap_list,\n applytable=self.tsystable, outfile='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_spwmap_list", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ec21f3f30b77cfea3de4710e9bff48d698654d8024e10d401465c714e2f96a39", "warmup_time": -1}, "bench_sdcal.Apply.time_test05": {"code": "class Apply:\n def time_test05(self):\n \"\"\"Test05: Validation of CORRECTED_DATA = DATA X FPARAM.\n (spwmap={1:[9], 3:[11], 5:[13], 7:[15]})\n antanna1=0, DATA_DISC_ID=9, FPARAM_average\n \"\"\"\n spwmap = {1: [9], 3: [11], 5: [13], 7: [15]}\n sdcal(infile=self.infile, calmode='apply', spwmap=spwmap, applytable=self.tsystable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n # Create TSYS table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n # Run initweights on input MS\n initweights(vis=self.infile, wtmode='nyq', dowtsp=True)", "min_run_count": 5, "name": "bench_sdcal.Apply.time_test05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0c349e3cabc717e2018727f49142a43f1867aacefc7a32b74fea55c09cd43521", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_interp_empty": {"code": "class Apply_Interp:\n def time_interp_empty(self):\n \"\"\"test_apply_sky14 --- apply data (interp='').\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=self.applytable, interp='')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_interp_empty", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d701d56a9753f8ad50dbc76d44d4b658680d8c7c02f8f475620f691fbac607ce", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear": {"code": "class Apply_Interp:\n def time_linear(self):\n \"\"\"test_apply_sky08 --- apply data (linear).\"\"\"\n sdcal(infile=self.infile, calmode='apply',applytable=[self.applytable], interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "078d836c355da5773813afc9b5b3f5b9b5af4c7441926882bed13a33c8eb89f1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_linearflag": {"code": "class Apply_Interp:\n def time_linear_linearflag(self):\n \"\"\"test_apply_sky11 --- apply data (linearflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,linearflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_linearflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "42bdfd6f7ae95d7cf1b6a99a5ea926b0c5ef23326aa25e525ffed5720eb087e3", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_nearestflag": {"code": "class Apply_Interp:\n def time_linear_nearestflag(self):\n \"\"\"test_apply_sky12 --- apply data (nearestflag for frequency interpolation).\"\"\"\n sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], interp='linear,nearestflag')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_nearestflag", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b666aed07e5ff1d2684ae9c5af455f2ed84aaea7a5f31e8814fd23ef31161a1", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_linear_spw_selection": {"code": "class Apply_Interp:\n def time_linear_spw_selection(self):\n \"\"\"test_apply_sky09 --- apply selected data.\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply', applytable=[\n self.applytable], spw='9', interp='linear')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_linear_spw_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "bbc0db0908cc81aff108d4e7635e5be7320ff855384a176dc974409e67acf6dc", "warmup_time": -1}, "bench_sdcal.Apply_Interp.time_nearest": {"code": "class Apply_Interp:\n def time_nearest(self):\n \"\"\"test_apply_sky10 --- apply data (nearest).\"\"\"\n self.result = sdcal(infile=self.infile, calmode='apply',\n applytable=[self.applytable], interp='nearest')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.Apply_Interp.time_nearest", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a3f87e4523727137a12fa96c6ca02b6d24325a73c3cc7b1ed699768a9e8f8b83", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky(self):\n \"\"\"test_apply_composite03 --- on-the-fly application of sky table ('otfraster,apply').\"\"\"\n sdcal(infile=self.infile, calmode='tsys,apply', applytable=self.applytable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a2e30839f58f4d7cae58ebed88e373ca0266cb545998f2cdc9bd9e99c86a3bb4", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal": {"code": "class Apply_Modified_Tsys:\n def time_apply_sky_normal(self):\n \"\"\"test_apply_composite00 --- on-the-fly application of sky table ('ps,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_normal", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9156519f2fd7ea628cdc9bd83ff8300424ede18396aabf47374e2269671763f1", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_apply_sky_tsys(self):\n \"\"\"test_apply_composite02 --- on-the-fly application of sky and tsys tables ('ps,tsys,apply').\"\"\"\n sdcal(infile=self.infile, calmode='ps,tsys,apply',\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_apply_sky_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d0b7e9739bae796f78bb578757177ac7aad7d83dd613adf15dfc3629a1f8e11a", "warmup_time": -1}, "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys": {"code": "class Apply_Modified_Tsys:\n @modify_tsys\n #@normal_case(tsys=100.0)\n def time_preapply_tsys(self):\n \"\"\"test_apply_composite01 --- on-the-fly application of sky table with existing Tsys table.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply', applytable=self.tsystable,\n spwmap={1: [9], 3: [11]})\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)\n shutil.copytree(os.path.join(datapath, self.applytable), self.applytable)\n # generate Tsys table\n self.tsystable = self.infile.rstrip('/') + '.tsys'\n try:\n # generate Tsys table\n sdcal(infile=self.infile, calmode='tsys', outfile=self.tsystable)\n except:\n print(\"Error generating tsys table\")", "min_run_count": 5, "name": "bench_sdcal.Apply_Modified_Tsys.time_preapply_tsys", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "98d92f7d22040c3043807a62e0870768ef9b5093d9e054752f82069890feeb6b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf01": {"code": "class OTF:\n def time_otf01(self):\n \"\"\"test_otf01 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf01", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "73890f5299363c64dc00df3929185044042aecbf7528479fdd2048b07bae4e5a", "warmup_time": -1}, "bench_sdcal.OTF.time_otf02": {"code": "class OTF:\n def time_otf02(self):\n \"\"\"test_otf02 --- Compute calibration table. calmode='otf' ms=squares.dec60_cs.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_squares, calmode='otf', fraction=0.2, noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='', spw='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf02", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d3f6cfeb66f0f58a7c7af4db78f37b6cb55b571f9a99cacd26c26271231c9eb9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf03": {"code": "class OTF:\n def time_otf03(self):\n \"\"\"test_otf03 --- Compute calibration table. calmode='otf' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf03", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "943238908dd59e211bdf634348dd5f42ac03ffc10488f932e3c34bde04cb2ff9", "warmup_time": -1}, "bench_sdcal.OTF.time_otf04": {"code": "class OTF:\n def time_otf04(self):\n \"\"\"test_otf04 --- Compute calibration table. calmode='otf' ms=lissajous.ms edges_fraction=20%.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf', fraction='20%', noff=-1, width=0.5, elongated=False,\n applytable='',interp='', spwmap={}, outfile=self.outfile, overwrite=False, field='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf04", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2a260b2487f65ee23665c8bedd0c4148bc2e3d6f36d6a332f015d755bbf015e1", "warmup_time": -1}, "bench_sdcal.OTF.time_otf05": {"code": "class OTF:\n def time_otf05(self):\n \"\"\"test_otf05 --- Sky calibration. calmode='otf,apply' ms=squares.dec60_cs.ms.\"\"\"\n # In the original test it runs sdcal() twice, first with calmode=apply and then with otf,apply\n sdcal(infile=self.infile_squares, calmode='otf,apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable='', interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf05", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4a19756f66b7ef9544328df9520128cd57da97e46f6c462cbd508c850951500b", "warmup_time": -1}, "bench_sdcal.OTF.time_otf06": {"code": "class OTF:\n def time_otf06(self):\n \"\"\"test_otf06 --- Sky calibration reusing caltable pre-computed with calmode='otf'. calmode='apply' ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='apply', fraction='10%', noff=-1, width=0.5,\n elongated=False, applytable=self.applytable, interp='', spwmap={}, outfile='', overwrite=False)\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf06", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "522a3b5d1d1fdabe2ba018b8273e019b5eddafc14dfdbf7f95daec034fb5f4aa", "warmup_time": -1}, "bench_sdcal.OTF.time_otf07": {"code": "class OTF:\n def time_otf07(self):\n \"\"\"test_otf07 --- Sky calibration + Tsys conversion, composite calmode='otf,tsys,apply'. ms=lissajous.ms.\"\"\"\n sdcal(infile=self.infile_lissajous, calmode='otf,tsys,apply', fraction='10%', noff=-1, width=0.5, elongated=False,\n applytable='', interp='', spwmap={}, outfile='', overwrite=False, field='', spw='', scan='', intent='')\n\n def setup(self):\n for infile in self.infiles:\n shutil.copytree(os.path.join(datapath, infile), infile)\n shutil.copytree(os.path.join(self.ref_datapath, self.applytable), self.applytable)", "min_run_count": 5, "name": "bench_sdcal.OTF.time_otf07", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "748ba7dd3174afcf0e776988ea8b438690c52ce4db2c52cd38f4008560cb2781", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_autodetection": {"code": "class OTFRaster:\n def time_autodetection(self):\n \"\"\"test_otfraster11 --- OTF raster calibration ('otfraster') with auto detection.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile,\n calmode='otfraster', fraction=0, noff=0)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_autodetection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4ab1d39226973c46b69401fb6d1cdebad9f92dcffdb9e3e05a81a21353a46501", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_default": {"code": "class OTFRaster:\n def time_default(self):\n \"\"\"test_otfraster07 --- OTF raster calibration ('otfraster') with default setting.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_default", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b1cfc4dc086f0f79a3eb1aa50bd34c6e2fb2fe269bce47df997cfb8e78c6197", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_numeric": {"code": "class OTFRaster:\n def time_fraction_numeric(self):\n \"\"\"test_otfraster10 --- OTF raster calibration ('otfraster') with numeric fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction=0.3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "9a1b91b89d7822ba87cb6acef313ffa79716c45ed9b6d7a6a307e452e7f2a76c", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_numeric": {"code": "class OTFRaster:\n def time_fraction_string_numeric(self):\n \"\"\"test_otfraster08 --- OTF raster calibration ('otfraster') with string fraction (numeric value).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='0.3')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_numeric", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "8e7b855976e9c451b5b3231e2ab52aeb3ec21c80edde166cc9c6ed70d310e202", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_fraction_string_percentage": {"code": "class OTFRaster:\n def time_fraction_string_percentage(self):\n \"\"\"test_otfraster09 --- OTF raster calibration ('otfraster') with string fraction (percentage).\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='30%')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_fraction_string_percentage", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "99b8030521d84e0d5c0d814a2205f20bd9fd2c1cca59feff9cea414f8790c81b", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_custom": {"code": "class OTFRaster:\n def time_noff_custom(self):\n \"\"\"test_otfraster12 --- OTF raster calibration ('otfraster') with custom noff.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_custom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b0d73553a431b883fcbd52935ecc5b25ca21ab6dcae20bd7943b107a3256a398", "warmup_time": -1}, "bench_sdcal.OTFRaster.time_noff_priority_over_fraction": {"code": "class OTFRaster:\n def time_noff_priority_over_fraction(self):\n \"\"\"test_otfraster13 --- check if noff takes priority over fraction.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otfraster', fraction='90%', noff=3)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTFRaster.time_noff_priority_over_fraction", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "2c5fb1d0dca19405b4dc976ff1992e112b24758c778b4bb23711529cc94a785d", "warmup_time": -1}, "bench_sdcal.OTF_Ephemeris.time_otf_ephem": {"code": "class OTF_Ephemeris:\n def time_otf_ephem(self):\n \"\"\"test_otfephem01: Sky calibration of 'otf' mode for ephemeris object.\"\"\"\n sdcal(infile=self.infile, outfile=self.outfile, calmode='otf')\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.OTF_Ephemeris.time_otf_ephem", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ab9adf8942dfdd994dd3781f749699fa9662bf58575015560e5daa006f34b730", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_basic": {"code": "class PositionSwitch:\n def time_ps_basic(self):\n \"\"\"test_ps05 --- position switch calibration ('ps').\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_basic", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7be7f0ff76e3fe922cbeca2f39ba103e79e44de6d83b4b99be250473cbbbfee", "warmup_time": -1}, "bench_sdcal.PositionSwitch.time_ps_selection": {"code": "class PositionSwitch:\n def time_ps_selection(self):\n \"\"\"test_ps06 --- position switch calibration ('ps') with data selection.\"\"\"\n sdcal(infile=self.infile, calmode='ps', spw='9', outfile=self.applytable)\n\n def setup(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.PositionSwitch.time_ps_selection", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c6f801e6c4a066a039b23af69d5fef7d2ff87044ac6f963b222b34644a63e410", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply(self):\n \"\"\"test_single_pol_apply --- apply caltable to single-polarization data.\"\"\"\n # Generate ouput file for apply case\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n sdcal(infile=self.infile, calmode='apply', applytable=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a191063314c95b694fc29c97d3e0dccb94184c2aa1aa185ac280f091ed025233", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_apply_composite(self):\n \"\"\"test_single_pol_apply_composite --- on-the-fly calibration/application on single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps,apply')\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_apply_composite", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b7552259dc52ed23dd95af462d5d9ef0a35d33f2f79af3ecb6eabd3b97e147ff", "warmup_time": -1}, "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps": {"code": "class sdcal_test_single_polarization:\n def time_single_pol_ps(self):\n \"\"\"test_single_pol_ps --- generate caltable for single-polarization data.\"\"\"\n sdcal(infile=self.infile, calmode='ps', outfile=self.outfile)\n\n def setUp(self):\n shutil.copytree(os.path.join(datapath, self.infile), self.infile)", "min_run_count": 5, "name": "bench_sdcal.sdcal_test_single_polarization.time_single_pol_ps", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "4e55128473bdf00218cb5fd33d6a7c9e00e1779f913ec0f11008a756dbc973be", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_sdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_sdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_cube_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs": {"code": "class MemoryMultiField:\n def peakmem_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "name": "bench_tclean_memory.MemoryMultiField.peakmem_multifield_mfs_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting": {"code": "class MemorySetWeighting:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs": {"code": "class MemorySetWeighting:\n def track_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "bench_tclean_memory.MemorySetWeighting.track_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "bench_tclean_memory:167", "timeout": 10000, "type": "track", "unit": "file descriptors", "version": "CAS-13924"}, "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary": {"code": "class MemorySingleField:\n def peakmem_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData(\"refim_point_withline.ms\")", "name": "bench_tclean_memory.MemorySingleField.peakmem_cube_standard_fullsummary", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "CAS-13924"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "213736aba4861d6ada012c27b036d0eefbe891ce27250b0867d8354eeafb9ecb"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=False,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "8e2ae00738c0ed705cbc3b1df59069743febbe8c160b1dd759a6537097168c0d"}, "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class MemoryWideField:\n def peakmem_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024, phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1', conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',pbcor=True,mosweight=True,parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "name": "bench_tclean_memory.MemoryWideField.peakmem_cube_mosaic_cbFalse_mwTrue_twofield", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "ae1661113bf2b4e2383bbc84b618de00882e9df2d34c706fc6182d5ee88ea29c"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_cube_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "535d7210070d64a24f08ced41f22af7c7ff735b2c653c074297c175a08e8cb0d"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_hogbom", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "b7a2bc60edebed77059280a26d38d60297776207e13bca67e77066c2383a48b1"}, "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs": {"code": "class MemoryWideFieldAWP:\n def peakmem_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "name": "bench_tclean_memory.MemoryWideFieldAWP.peakmem_mfs_awproject_mtmfs", "param_names": [], "params": [], "timeout": 3600, "type": "peakmemory", "unit": "bytes", "version": "d58b12d0e026ea8c921dacf72de9d8f859c6305623fbd66ade64637f8950312d"}, "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap": {"code": "class TcleanCube:\n def time_cube_standard_channel_gap(self):\n \"\"\"tclean\" mosaic cube with gap in channel selection - test_cube_21\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0:4~9;12~14', imagename=self.img + 'Cubetest_st4gap', start=4,\n width='', veltype='radio', outframe='LSRK', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_channel_gap", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "955f38acfd52f97ee9d29fc749cb59a1cc43e4aba8a57c2cdf36b817208f65a1", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary": {"code": "class TcleanCube:\n def time_cube_standard_fullsummary(self):\n \"\"\"tclean: single field cube with fullsummary parameter - test_iterbot_cube_fullsummary_true\"\"\"\n retpar = tclean(vis=self.msfile, imagename=self.img + \"Cubetest_fullsummary\", imsize=100, cell='8.0arcsec',\n specmode='cube', deconvolver='clark', niter=10, threshold='0.75Jy', fullsummary=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_fullsummary", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanCube.time_cube_standard_hogbom": {"code": "class TcleanCube:\n def time_cube_standard_hogbom(self):\n \"\"\"tclean: mosaic cube with Hogbom deconvolver - test_cube_0\"\"\"\n ret = tclean(vis=self.msfile, field='0', imsize=100, cell='8.0arcsec', niter=10,\n specmode='cube', nchan=10, restfreq=['1.25GHz'],\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", deconvolver='hogbom',\n spw='0', imagename=self.img + 'Cubetest_chandefstdefwidth',\n start=0, width=1, veltype='radio', outframe='LSRK', interpolation='linear', parallel=False)\n\n def setup(self):\n self.prepData('refim_point.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanCube.time_cube_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ead0eaac76215fd3ed59ddd926e9fdfb598dec50a29590840e6bf8a9c1139cfa", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_cubesource(self):\n \"\"\"tclean: multifield (mosaic gridder), cubesource mode - test_multifield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs": {"code": "class TcleanEphemeris:\n def time_eph_multifield_mosaic_mfs(self):\n \"\"\"tclean: multifield (mosaic gridder), mfs mode - test_multifield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=[480, 420], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='mosaic', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_multifield_mosaic_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_cubesource(self):\n \"\"\"tclean: single field (standard gridder), cubesource mode - test_onefield_cube_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='cubesource', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs": {"code": "class TcleanEphemeris:\n def time_eph_singlefield_standard_mfs(self):\n \"\"\"single field (standard gridder), mfs mode - test_onefield_mfs_eph\"\"\"\n ret = tclean(vis=self.msfile, field='0', imagename=self.img, imsize=[288, 288], cell=['0.14arcsec'],\n phasecenter='TRACKFIELD', specmode='mfs', gridder='standard', niter=0, parallel=False)\n\n def setup(self):\n self.prepData('venus_ephem_test.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanEphemeris.time_eph_singlefield_standard_mfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_automask_modelcolumn(self):\n \"\"\"tclean: mfs with automask and save model column, internally a separate predit model step - test_modelvis_12\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='modelcolumn',usemask='auto-multithresh', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_automask_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d60a63bc05d020e7f8100acf6044bbeda8cd4ef0f53267b53984b3580a421bc", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_modelcolumn(self):\n \"\"\"tclean: mfs mode saving a model column - test_modelvis_2\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n savemodel='modelcolumn',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "39a0309c515e5dfe440b399768551ee76165e4551a994dcb41e2ed255523940e", "warmup_time": -1}, "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn": {"code": "class TcleanMoldelVis:\n def time_mfs_virtual_modelcolumn(self):\n \"\"\"tclean: mfs saving a virtual model column - test_modelvis_3\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n savemodel='virtual',parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twochan.ms\")\n delmod(self.msfile)\n th.delmodels(self.msfile, modcol='delete')", "min_run_count": 3, "name": "bench_tclean_time.TcleanMoldelVis.time_mfs_virtual_modelcolumn", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "322f52d2bb9054965cfef3b9c72cf5750386b28c13444d0055c32f9854934461", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_cube_hogbom(self):\n \"\"\"tclean: Two fields, both cube - test_multifield_both_cube\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nimagename=' + self.img + '2\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:48.895 +40.55.58.543\\n')\n \n retpar = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', specmode='cube', nchan=2, interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_cube_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom": {"code": "class TcleanMultiField:\n def time_multifield_mfs_hogbom(self):\n \"\"\"tclean : Two fields, both mfs - test_multifield_both_mfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\nnchan=1\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs": {"code": "class TcleanMultiField:\n def time_multifield_mfs_mtmfs(self):\n \"\"\" tclean: Two fields, both mt-mfs - test_multifield_both_mtmfs\"\"\"\n th.write_file(self.img + '.out.txt',\n 'imagename=' + self.img + '1\\n\\nimsize=[80,80]\\ncell=[8.0arcsec,8.0arcsec]\\nphasecenter=J2000 19:58:40.895 +40.55.58.543\\nusemask=user\\nmask=circle[[40pix,40pix],10pix]')\n \n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\", outlierfile=self.img + '.out.txt', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_twopoints_twochan.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanMultiField.time_multifield_mfs_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT": {"code": "class TcleanSingleField:\n def time_cube_standard_pcwdT(self):\n \"\"\"tclean: cube with perchanweightdensity True and briggs weighting - test_onefield_pcwdT_and_pcwdF\"\"\"\n ret = tclean(self.msfile, imagename=self.img + '1', imsize=20, cell='8.0arcsec', niter=0, nchan=1,\n spw='0:1', gridder='standard', perchanweightdensity=True, specmode='cube',\n weighting='briggs', robust=0.5)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_cube_standard_pcwdT", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark": {"code": "class TcleanSingleField:\n def time_mfs_standard_clark(self):\n \"\"\"tclean: mfs with clark minor cycle - test_onefield_clark\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='clark', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_clark", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom(self):\n \"\"\"tclean: mfs with hogbom minor cycle - test_onefield_hogbom\"\"\"\n tricky_imagename = self.img + '_uid___A001_X1234a_X56cb.s19_0.J2253+1608_bp.more-dash.virtspw19.mfs.I.iter0.hoghbom'\n ret = tclean(vis=self.msfile, imagename=tricky_imagename, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='hogbom', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask": {"code": "class TcleanSingleField:\n def time_mfs_standard_hogbom_automask(self):\n \"\"\"tclean: multi-threshold Autobox (minbeamfrac=0.3) - test_mask_autobox_multithresh_with_prune\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=1000,cell='8.0arcsec',niter=10,\n deconvolver='hogbom', usemask='auto-multithresh',\n minbeamfrac=0.3,parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_hogbom_automask", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs": {"code": "class TcleanSingleField:\n def time_mfs_standard_mtmfs(self):\n \"\"\"tclean: mt-mfs with minor cycle iterations - test_onefield_mtmfs\"\"\"\n ret = tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10,\n deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData('refim_twochan.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanSingleField.time_mfs_standard_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_cube_stokes_IV": {"code": "class TcleanStokes:\n def time_cube_stokes_IV(self):\n \"\"\"tclean: cube with stokes V - test_stokes_cube_IV\"\"\"\n ret = tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10,\n stokes='IV', specmode='cube', interpolation='nearest', parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_cube_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV": {"code": "class TcleanStokes:\n def time_mfs_stokes_IV(self):\n \"\"\"tclean: mfs with stokes IV - test_stokes_mfs_IV\"\"\"\n tclean(vis=self.msfile,imagename=self.img,imsize=100,cell='8.0arcsec',niter=10, stokes='IV',\n parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mfs_stokes_IV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "d35d9a07fff354a74fbeb5aecec4abcc4dc94d0e83e10f366cb69e439004ed44", "warmup_time": -1}, "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV": {"code": "class TcleanStokes:\n def time_mtmfs_stokes_IQUV(self):\n \"\"\"tclean: mtmfs with stokes IQUV - test_stokes_mtmfs_IQUV\"\"\"\n tclean(vis=self.msfile, imagename=self.img, imsize=100, cell='8.0arcsec', niter=10, stokes='IQUV',\n deconvolver='mtmfs', nterms=2, parallel=False)\n\n def setup(self):\n self.prepData('refim_point_linRL.ms')", "min_run_count": 3, "name": "bench_tclean_time.TcleanStokes.time_mtmfs_stokes_IQUV", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7d3a14b77c55eebaebca13da5c79f9b48d38bf677f7e4aaf3c5103ca66e21006", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_cube_awproject_hogbom(self):\n \"\"\"tclean: Cube with AW-Projection and rotation off - test_widefield_aproj_cube\"\"\"\n ret = tclean(vis=self.msfile, field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",specmode='cube', niter=1, gain=1.0,\n gridder='awproject', cfcache=self.img + '.cfcache',wbawp=True,conjbeams=False,\n psterm=False, computepastep=360.0, rotatepastep=360.0, deconvolver='hogbom',\n parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_cube_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e5933d46b7d76f6e722504feff6442d7b22d7a07286b441227931d33b9adfae7", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_hogbom(self):\n \"\"\"tclean: MFS with narrowband AWProjection - test_widefield_aproj_mfs\"\"\"\n ret = tclean(vis=self.msfile, spw='1', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=30, gridder='awproject', cfcache='',\n wbawp=False, conjbeams=True, psterm=False,computepastep=360.0,rotatepastep=360.0,\n deconvolver='hogbom', savemodel='modelcolumn', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_hogbom", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b822057988cc0cc532d1b070ee27c6cca7c74940c97f32e4cae5442b0a580aec", "warmup_time": -1}, "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs": {"code": "class TcleanWideFieldAWP:\n def time_mfs_awproject_mtmfs(self):\n \"\"\"tclean: MFS with AWProjection and nt=2 stokes I - test_widefield_aproj_mtmfs \"\"\"\n ret = tclean(vis=self.msfile, spw='*', field='*', imagename=self.img, imsize=512, cell='10.0arcsec',\n phasecenter=\"J2000 19:59:28.500 +40.44.01.50\",niter=0, gridder='awproject',\n cfcache=self.img + '.cfcache', wbawp=True, conjbeams=False, psterm=False,\n computepastep=360.0,rotatepastep=360.0, deconvolver='mtmfs', parallel=False)\n\n def setup(self):\n self.prepData(\"refim_mawproject.ms\")", "min_run_count": 3, "name": "bench_tclean_time.TcleanWideFieldAWP.time_mfs_awproject_mtmfs", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "67a1d66f43c33d52452f3438452c599001c854eae7b69f546049457c746928d6", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwFalse_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "CAS-13924", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue(self):\n \"\"\"tclean: Cube mosaic with conjbeams=F, mosaicweight=F, usepointing=T, of two fields - test_cube_mosaic_cbFalse_mwFalse_twofield_upTrue\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n usepointing = True, conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=False, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwFalse_twofield_upTrue", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "6692e661298a1a5355c1201359883748b54bbd123c50dd2b2ea3ec5b412ec827", "warmup_time": -1}, "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield": {"code": "class TestWideField:\n def time_cube_mosaic_cbFalse_mwTrue_twofield(self):\n \"\"\"tclean: - cube mosaic using conjbeams=False - test_cube_mosaic_cbFalse_mwTrue_twofield\"\"\"\n tclean(vis=self.msfile, imagename=self.img,niter=10,specmode='cube',spw='*',imsize=1024,\n phasecenter='J2000 19h59m28.5 +40d40m01.5',cell='10.0arcsec',gridder='mosaic',field='0,1',\n conjbeams=False, wbawp=True, psterm=False,pblimit=0.1,reffreq='1.5GHz',\n pbcor=True, mosweight=True, parallel=False)\n\n def setup(self):\n self.prepData('refim_oneshiftpoint.mosaic.ms')", "min_run_count": 3, "name": "bench_tclean_time.TestWideField.time_cube_mosaic_cbFalse_mwTrue_twofield", "number": 1, "param_names": [], "params": [], "rounds": 3, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad3861f21889712e38ffbf6ef492929807086c6012d61b230a740229d1d6b7c5", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1cabc32a44329c9516713575172e17491a87d77a616062663d7ef91f65148be3", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_40x35_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[40, 35], cell=['320arcsec', '320arcsec'], phasecenter=\"\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_40x35_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b930349db5cc439111860320dde2e9b691abd6d7f9b0ec6689dd11d93b41f84f", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect": {"code": "class Basic:\n def time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"channel\", nchan=1, width=1024, gridfunction='BOX', imsize=[], minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_imsize_detect_cell_default_phasecenter_detect", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "336acba07a722b00d07d7f1e576db7324acbb0f8b62094628e049205931267c4", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400": {"code": "class Basic:\n def time_gridfunction_BOX_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f75fe21b7e8d74833923c99f159a218e55bf3f8f8f9678410d7d1bdcc7f7b34", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null": {"code": "class Basic:\n def time_gridfunction_BOX_start_400_phasecenter_null(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='BOX', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1248c14417eaf3acae22a2d74d1856434726fdae49f3cf1f99f38e5a3e236f19", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400": {"code": "class Basic:\n def time_gridfunction_GAUSS_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GAUSS', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GAUSS_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "53e251e5821f6f4979c73a56b7aa34456880ac756589b0df55077a9c3127a334", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "5627d3dd41598393b2e3bd58277169bbafe63781c6b1b5b01c99b9d60d84cbfc", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70": {"code": "class Basic:\n def time_gridfunction_GJINC_start_400_minweight_70(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='GJINC', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=70.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_GJINC_start_400_minweight_70", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "7b01ed523cee17466c300376e39e4aacc76b5a67f4ded517d201a5dd7ea56f2b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB": {"code": "class Basic:\n def time_gridfunction_PB(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "c919d306e25f5ec15a0459ba15528f60a1a0dcaae1bf19a352dd255a2dedcf63", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency": {"code": "class Basic:\n def time_gridfunction_PB_mode_frequency(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"frequency\", nchan=100, start=\"1420.200000MHz\", width=\"0.010000MHz\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_frequency", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "11be513d35e8f08e044f3236b2b2ce34adde14a6ff376d9d7d167096237a3316", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_1(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=1, start=\"-50256.681248364m/s\", width=\"527652.833436753m/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', restfreq=\"1420405800.0Hz\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "a5ade0c08d8b30b806d808aa43cd05eee6f36f09bf14ab0a002acb9b0577b2ad", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100": {"code": "class Basic:\n def time_gridfunction_PB_mode_velocity_nchan_100(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', mode=\"velocity\", nchan=100, start=\"-200.000000km/s\", width=\"2.000000km/s\", gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b5092872498a21e2f8184389383bcd9c29b6b0a38b06ad926ac76f436f5894e2", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024": {"code": "class Basic:\n def time_gridfunction_PB_nchan_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1024, width=1, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_nchan_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "385177e17d19f7fb3812a7e94b7ed7b644c5ed42350f2698e63e42c69f980efb", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400": {"code": "class Basic:\n def time_gridfunction_PB_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "b9e23dd0fd8a0537036bc41d832bb013cf550d67faa2807e46e9f2f7361e5e21", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY": {"code": "class Basic:\n def time_gridfunction_PB_start_400_stokes_XXYY(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, outframe='', gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', stokes=\"XXYY\", minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_start_400_stokes_XXYY", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1eadce5c526c138fe91b3fbc8ad1e714e468817da26e6c0434558be40125081e", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024": {"code": "class Basic:\n def time_gridfunction_PB_width_1024(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=1, width=1024, gridfunction='PB', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_PB_width_1024", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "1f31655e48c01e2cc72c95e525fdfe8504693b8ad12dbbedf284ff3b88611c8b", "warmup_time": -1}, "bench_tsdimaging.Basic.time_gridfunction_SF_start_400": {"code": "class Basic:\n def time_gridfunction_SF_start_400(self):\n sdimaging( infiles=[self.vis], outfile=self.out, intent='', nchan=40, start=400, width=10, gridfunction='SF', imsize=[75, 75], cell=['3.0arcmin', '3.0arcmin'], phasecenter='J2000 17:18:29 +59.31.23', minweight=0.0 )\n\n def setup(self):\n self.setup_basic()", "min_run_count": 3, "name": "bench_tsdimaging.Basic.time_gridfunction_SF_start_400", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "ad4195f91c64c959d4a9da8efd6e024b0ac662770633e565c781f603fc6d2ab0", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource": {"code": "class Ephemeris:\n def time_ephemeris_cubesource(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cubesource', outframe='',\n gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec',\n phasecenter='TRACKFIELD', projection='SIN',pointingcolumn='direction', restfreq='', stokes='I',\n minweight=0.1, brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_cubesource", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e1590a631850386242adaf9bd659e0dc1827e29a18273a09188b0b52c3e6f76e", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_notset": {"code": "class Ephemeris:\n def time_ephemeris_notset(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_notset", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "0f8206812c10517da8551f9b08b58784bdb8a969ae3fc745bdd975a628da94de", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_sun": {"code": "class Ephemeris:\n def time_ephemeris_sun(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='SUN', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_sun", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "e8e8766d8a4af7429cd16c665606e2e5f05b124fbe24196c8a90ae14856f5670", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_table": {"code": "class Ephemeris:\n def time_ephemeris_table(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,veltype='radio', specmode='cube',\n outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,gwidth=-1, jwidth=-1,\n imsize=[1000], cell='4arcsec',phasecenter=self.ephtab,\n projection='SIN',pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1,\n brightnessunit='',clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_table", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "abb49342590225dc02b50b7a4fc1558664794ae427a0503721ac0a6ae0d95959", "warmup_time": -1}, "bench_tsdimaging.Ephemeris.time_ephemeris_trackf": {"code": "class Ephemeris:\n def time_ephemeris_trackf(self):\n sdimaging(infiles=[self.vis], outfile=self.out, overwrite=False, field='Sol',\n spw='18', mode='channel', nchan=1, start=0, width=1,\n veltype='radio', specmode='cube', outframe='', gridfunction='BOX', convsupport=-1, truncate=-1,\n gwidth=-1, jwidth=-1, imsize=[1000], cell='4arcsec', phasecenter='TRACKFIELD', projection='SIN',\n pointingcolumn='direction', restfreq='', stokes='I', minweight=0.1, brightnessunit='',\n clipminmax=False)\n\n def setup(self):\n self.setup_ephemeris()", "min_run_count": 3, "name": "bench_tsdimaging.Ephemeris.time_ephemeris_trackf", "number": 1, "param_names": [], "params": [], "rounds": 1, "sample_time": 0.01, "timeout": 3600, "type": "time", "unit": "seconds", "version": "dc585b5f985c2244f266a12e7a01143c4ce9702c0bdbed596e13752673bfc047", "warmup_time": -1}, "bench_uvcontsub.Basic.time_datacolumn": {"code": "class Basic:\n def time_datacolumn(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_datacolumn", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8de4cfdf0dc3bf0bed17e8d3988ba003ec80e4ec5f8c98958fb4cc44bea1f96b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_casacore": {"code": "class Basic:\n def time_fitmethod_casacore(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='casacore')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_casacore", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "dfb214a4421164ab4c380082077ebe9f5d3903a3c5c171837c379819feae42ea", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitmethod_gsl": {"code": "class Basic:\n def time_fitmethod_gsl(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitmethod='gsl')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitmethod_gsl", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0d8fee718ac26b9dcd2c95fe20e8b4254e1125c0ea176bce6dbeeb7b1414efce", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder1": {"code": "class Basic:\n def time_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "8fd68c9b3e632dd7b26ef564729b42105fc47f1779e544fae389d5adc08e98e7", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitorder2": {"code": "class Basic:\n def time_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec='0:2~20')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "3114172327fd2768029d264e578ebde390247d280c059d46d578da04ef3ea9b8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0": {"code": "class Basic:\n def time_fitspec3_spws_fitorder0(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=0,\n fitspec='3')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec3_spws_fitorder0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9d83a87c6ebaa6f3b9bbd80256e790db10fdaad1d7869f1801bda6f548548816", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_channels": {"code": "class Basic:\n def time_fitspec_channels(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0:5~19')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_channels", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "211807ad73464bccf9f2d9954603775df109fd39308249c8f0a8be8468d0d504", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1": {"code": "class Basic:\n def time_fitspec_dict_fitorder1(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=1,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "788bb7c38a92579219296d435a9f8831a5e8600ec97baf7c70505174b4112826", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2": {"code": "class Basic:\n def time_fitspec_dict_fitorder2(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitorder=2,\n fitspec={'0': {'0': {'chan': '2~20',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitorder2", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c2d39c9e5d998c65266f1496a25fb286d7e9394bb9c3764d4a6890f796e54ef3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "68512d1a0bce4206afd18b13bd2704c6ac07ee1ef9a1e0652600336643789aab", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1": {"code": "class Basic:\n def time_fitspec_dict_fitspec_one_chan_order1(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0',\n fitspec={'0':\n {'1': {'chan': '',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_fitspec_one_chan_order1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "69adae3d962899abd6303be60d0a44ddd656a8b72910418f80f587da540cd78c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching": {"code": "class Basic:\n def time_fitspec_dict_select_spw_mismatching(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', spw='1',\n fitspec={'0':\n {'0': {'chan': '2~127',\n 'fitorder': 0}},\n '1':\n {'0': {'chan': '0~40',\n 'fitorder': 1}},\n '3':\n {'0': {'chan': '1~33;56~119',\n 'fitorder': 1}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_dict_select_spw_mismatching", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "faa5b02f1961d7e57a2431ef5fb58c23bade435413dd1890f34460c73f7a6c61", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_empty": {"code": "class Basic:\n def time_fitspec_empty(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_empty", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1162b94dcdcd8c1468b01cce5b33eee0e86e593f92e9f394436369c566e59d03", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield": {"code": "class Basic:\n def time_fitspec_multifield(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0': {'0': {'chan': '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 0}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "2200609258dcdd47db8e8f5ad4d0e55a502812ffa3624f3a48358904d23ad6a0", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_blocks": {"code": "class Basic:\n def time_fitspec_multifield_blocks(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={\n '0, 1': {'0': {'chan': '100~500;600~900;1200~1900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_blocks", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "99f4e00c2743d33e8c1c353c1cadc351fcea655f7878133259e3365e398f5abf", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel": {"code": "class Basic:\n def time_fitspec_multifield_fields_sel(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1,2',\n fitspec={'1': {'0': {'chan': '100~500;600~900',\n 'fitorder': 0}},\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 0}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_fields_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "c5a598b1002088c1224c744c4cc7865f8d7583a29b35f620b379ea9b8c7f6ef5", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder": {"code": "class Basic:\n def time_fitspec_multifield_multispw_diff_fitorder(self):\n uvcontsub(vis=ms_alma, outputvis=self.output,\n fitspec={'0': {'0': {'chan':\n '100~500;600~910;1215~1678;1810~1903',\n 'fitorder': 1},\n '1': {'chan': '0',\n 'fitorder': 2}},\n '1': 'NONE',\n '2': {'0': {'chan': '100~1903',\n 'fitorder': 2}}\n })\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_multifield_multispw_diff_fitorder", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1884692bb3e4d160ac28dfe708bf2b0bf06f9d75589fca4f1688c9abadfc948c", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_separate_fields": {"code": "class Basic:\n def time_fitspec_separate_fields(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='1',\n fitspec='0:100~500;600~910;1215~1678;1810~1903')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_separate_fields", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf05e60ae71d4af643accde4c61a2ae44cf1a783edf0ce36a95fee1cb106b923", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spw_one_chan": {"code": "class Basic:\n def time_fitspec_spw_one_chan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='0', fitspec='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spw_one_chan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "9c251da305a5aa0f63a2a75105ca215862963f9d11cc4a6b695cf8a702dcdc84", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws": {"code": "class Basic:\n def time_fitspec_spws(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ab9cf480d9ce00340dbf39ac9cbc14a74a1bb716c43bfffc6322b5af7020a160", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spws_sel": {"code": "class Basic:\n def time_fitspec_spws_sel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, spw='0', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spws_sel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "33ec54a7a626bd87b33c1f5ed0b20762be14e0e6a17f4bc6076f269384958cf8", "warmup_time": -1}, "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3": {"code": "class Basic:\n def time_fitspec_spwschans_fitorder3(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output, fitorder=3,\n fitspec='4:50~503;600~850;900~1002')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_fitspec_spwschans_fitorder3", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "fa14ea078998aaba67b8acd52f7693a1bbdd7d1f273ce738bd3959de96ce8c71", "warmup_time": -1}, "bench_uvcontsub.Basic.time_makes_output_ms_data": {"code": "class Basic:\n def time_makes_output_ms_data(self):\n uvcontsub(vis=ms_simple, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_makes_output_ms_data", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0f51dc9c6b96c74c4dbd8c11f8388a28712b70fcf187ab98cfd821e516aa90d3", "warmup_time": -1}, "bench_uvcontsub.Basic.time_mixed_pols": {"code": "class Basic:\n def time_mixed_pols(self):\n uvcontsub(vis=ms_mixed_pols, outputvis=self.output)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_mixed_pols", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "90b6d31e3a0ee607357ff1720317395dc6ee5269a5e70d651ef0ef9c727c3878", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_array": {"code": "class Basic:\n def time_select_array(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, array='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_array", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "ea3c135bafff1459cbc837162309e4b6ae3fece4a6be027a4df51bdda97ab7fb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_field": {"code": "class Basic:\n def time_select_field(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, field='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_field", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "1886548da1c74441518ea6cdbc2ed5b6fa259fcb2fcec3d4e5df5c1e0b1c865d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_intent": {"code": "class Basic:\n def time_select_intent(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, intent='*AMPLI*')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_intent", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "b87258a05737dcccf486624392643fc288a6a2c8474c205ece3cb80d87d202eb", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_observation": {"code": "class Basic:\n def time_select_observation(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, observation='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_observation", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a6b18c12fe61a940c0ecbdcfa89944ed0e091b8b4d4198b17c345a9f3c4c4c4b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_scan": {"code": "class Basic:\n def time_select_scan(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, scan='2')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_scan", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "6fe8f7df4455762e4e5bc08a95d1cd7417fd105e9c5aa51272d28f53c7696b9b", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw": {"code": "class Basic:\n def time_select_spw(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "cf736c29415ea590041754714b678dd26b800a40ab3134c319bac8dbe12600dd", "warmup_time": -1}, "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec": {"code": "class Basic:\n def time_select_spw_mismatching_fitspec(self):\n uvcontsub(vis=ms_alma, outputvis=self.output, spw='1', fitspec='0')\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_select_spw_mismatching_fitspec", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "158ef3ee6006d0930dc734a45cceab1d358d189bb5c2da6fba1f21528b7d587a", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel": {"code": "class Basic:\n def time_writemodel(self):\n uvcontsub(vis=ms_simple, outputvis=self.output, writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "84ee71017eadec38583d3c0e1539ed8d9af22f22a326abf0e4730b08fe77142d", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected": {"code": "class Basic:\n def time_writemodel_from_corrected(self):\n uvcontsub(vis=ms_papersky, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "49f06856290be0914b13df703b42f5742c7d8d6c65041f711909ffe6bb54b038", "warmup_time": -1}, "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged": {"code": "class Basic:\n def time_writemodel_from_corrected_all_flagged(self):\n uvcontsub(vis=ms_corr, outputvis=self.output, datacolumn='CORRECTED',\n writemodel=True)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setup_cache is enough\n # Default output name for simple tests\n self.output = 'test_uvcs_output.ms'\n\n def setup_cache(self):\n shutil.copytree(datapath_simple, ms_simple)\n shutil.copytree(datapath_alma, ms_alma)\n shutil.copytree(datapath_corr, ms_corr)\n shutil.copytree(datapath_papersky, ms_papersky)\n shutil.copytree(datapath_mixed_pols, ms_mixed_pols)", "min_run_count": 5, "name": "bench_uvcontsub.Basic.time_writemodel_from_corrected_all_flagged", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:48", "timeout": 1800, "type": "time", "unit": "seconds", "version": "bb9609826a0e5903041743e876e9c1130f97601a78f33da3d9171171b66d44bc", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0": {"code": "class Numerical:\n def time_sim_specline_noise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a3dc88dff98704afaa2065680f87615524f3c67b46d920634e59c82d0f4cf406", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1": {"code": "class Numerical:\n def time_sim_specline_noise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1. Gaussian noise included\"\"\"\n uvcontsub(vis=self.ms_cont_noise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_noise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "4b6fc8f71c9d351593ef5059ab3339340b3e8aab807d18261c20224c22fd0b9c", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_0(self):\n \"\"\" Check fitting of continuum as polynomial order 0\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_0, outputvis=self.output,\n fitorder=0, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_0", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "a06063282ce6f0ecae2282332b977438de648778798aab364359767983f41fac", "warmup_time": -1}, "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1": {"code": "class Numerical:\n def time_sim_specline_nonoise_pol_1(self):\n \"\"\" Check fitting of continuum as polynomial order 1\"\"\"\n uvcontsub(vis=self.ms_cont_nonoise_order_1, outputvis=self.output,\n fitorder=1, fitspec=self.fitspec)\n\n def setup(self):\n # Input MS is always strictly read-only, one copy in setUpClass is enough\n # Default output name for simple tests\n self.output = 'test_numerical_uvcs_output.ms'\n self.fitspec = '0:0~59;86~127'\n\n def setup_cache(self):\n for sim in self.sim_mss:\n datapath_sim = ctsys.resolve(os.path.join(datapath, sim))\n shutil.copytree(datapath_sim, sim)", "min_run_count": 5, "name": "bench_uvcontsub.Numerical.time_sim_specline_nonoise_pol_1", "number": 1, "param_names": [], "params": [], "repeat": 5, "rounds": 1, "sample_time": 0.01, "setup_cache_key": "bench_uvcontsub:237", "timeout": 1800, "type": "time", "unit": "seconds", "version": "0574b1e7222ff924033c48c38b27199dd8addd98dcec9671894b820ef6ec72d2", "warmup_time": -1}, "casademo.calibration_suite.time_applycal_callib": {"code": "class calibration_suite:\n def time_applycal_callib(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Note that this is from on-the-fly application of preliminary phase-up, bandpass, and amplitude caltables, not the later stage hifa_applycal\n Expected to take ~48s, could be sped up by splitting out SPWs (especially the square law detector windows) or applying to only one of them.\n \"\"\"\n applycal(\n vis=self.input_ms,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"*&*\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n docallib=True,\n callib=self.applycal_library,\n gaintable=[],\n gainfield=[],\n interp=[],\n spwmap=[],\n calwt=[True],\n parang=False,\n applymode=\"calflagstrict\",\n flagbackup=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_applycal_callib", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "25f369e7a681d0d6ccb62c48d6a227c1fa45a7e9f79792f60a3fd40a6997e2e2", "warmup_time": -1}, "casademo.calibration_suite.time_bandpass": {"code": "class calibration_suite:\n def time_bandpass(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\"\"\"\n bandpass(\n vis=self.input_ms,\n caltable=self.bandpass_table,\n field=\"J1924-2914\",\n spw=\"22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n antenna=\"0~9\",\n solint=\"inf,15.625000MHz\",\n combine=\"scan\",\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n minblperant=4,\n minsnr=3.0,\n solnorm=True,\n bandtype=\"B\",\n append=True,\n gaintable=[self.tsyscal_table, self.gaincal_table],\n gainfield=[\"J1924-2914\", \"nearest\"],\n interp=[\"linear,linear\", \"linear,linear\"],\n spwmap=[\n [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n [],\n ],\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_bandpass", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3ab2720c5ee4aa0f74882ac18410ea0ab34d6e7f1e940421a993d057380f2fdb", "warmup_time": -1}, "casademo.calibration_suite.time_gaincal": {"code": "class calibration_suite:\n def time_gaincal(self):\n \"\"\"Taken from the hifa_bandpassflag step of ALMA pipeline run 2019.1.01056.S_2021_07_20T07_45_18.149/\n \n Expected to take ~7s\n \"\"\"\n gaincal(\n vis=self.input_ms,\n caltable=self.gaincal_table,\n field=\"J1924-2914\",\n spw=\"16,18,20,22\",\n intent=\"CALIBRATE_BANDPASS#ON_SOURCE\",\n selectdata=True,\n timerange=\"\",\n uvrange=\"\",\n antenna=\"0~9\",\n scan=\"\",\n observation=\"\",\n msselect=\"\",\n solint=\"int\",\n combine=\"\",\n preavg=-1.0,\n refant=\"CM03,CM10,CM02,CM12,CM06,CM05,CM11,CM04,CM07,CM01\",\n refantmode=\"flex\",\n minblperant=4,\n minsnr=3.0,\n solnorm=False,\n normtype=\"mean\",\n gaintype=\"G\",\n smodel=[],\n calmode=\"p\",\n solmode=\"\",\n rmsthresh=[],\n corrdepflags=False,\n append=False,\n splinetime=3600.0,\n npointaver=3,\n phasewrap=180.0,\n docallib=False,\n callib=\"\",\n gaintable=[self.gaincal_table],\n gainfield=[\"J1924-2914\"],\n interp=[\"linear,linear\"],\n spwmap=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 16, 18, 20, 22, 16, 16, 18, 18, 20, 20, 22,],\n parang=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # fresh copy of the test MS to the tmp directory where tests are run\n shutil.copytree(\n os.path.join(self.dataroot, self.input_ms),\n os.path.join(os.getcwd(), self.input_ms),\n )\n \n # Copy the callibrary (and associated tables) into temporary test directory\n shutil.copyfile(\n os.path.join(self.dataroot, self.applycal_library),\n os.path.join(os.getcwd(), self.applycal_library),\n )\n \n for st in self.library_subtables:\n if not os.path.exists(st):\n shutil.copytree(\n os.path.join(self.dataroot, st), os.path.join(os.getcwd(), st)\n )\n # Redundant to copy gaincal_table explicitly unless it changes\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.calibration_suite.time_gaincal", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:287", "timeout": 10000, "type": "time", "unit": "seconds", "version": "a5eafd63e34c53ae63cd3949f23f8acc371d5751a08dcd1681914bf7c0723779", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_bandpassflag": {"code": "class flagdata_suite:\n def time_flagdata_bandpassflag(self):\n \"\"\"hifa_bandpassflag\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='16' antenna='CM05' \\\n timerange='20:09:50~20:09:52' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:09:20~20:09:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='20' antenna='CM05' \\\n timerange='20:10:21~20:10:22' field='J1924-2914' reason='bad antenna timestamp'\",\n \"intent='CALIBRATE_BANDPASS#ON_SOURCE' spw='22' antenna='CM05' \\\n timerange='20:09:30~20:09:32' field='J1924-2914' reason='bad antenna timestamp'\",\n ],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_bandpassflag", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "f55e0ffa6be9d655555c5081a0e7bf2bc00b253273ba7c220de0bd2cfb3baf6c", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list": {"code": "class flagdata_suite:\n def time_flagdata_list(self):\n \"\"\"hifa_flagdata\"\"\"\n flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=self.flags_cmd,\n tbuff=[0.048, 0.0],\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "1a40b66ffb4a43d48af3407bae817ed4f7667fe778880859384f5f2e7c9c5d1f", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_list_summary": {"code": "class flagdata_suite:\n def time_flagdata_list_summary(self):\n \"\"\"hifa_rawflagchans\"\"\"\n summary_dict = flagdata(\n vis=self.datapath,\n mode=\"list\",\n inpfile=[\"mode='summary' name='before'\"],\n reason=\"any\",\n action=\"apply\",\n flagbackup=False,\n savepars=False,\n )\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_list_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "3eb284062687484026a1defb4fceb705e83b244e37405f8794d431940ff44b08", "warmup_time": -1}, "casademo.flagdata_suite.time_flagdata_summary": {"code": "class flagdata_suite:\n def time_flagdata_summary(self):\n \"\"\"hifa_importdata\"\"\"\n summary_dict = flagdata(vis=self.datapath, flagbackup=False, mode=\"summary\")\n\n def setup(self):\n # run for each repeated test\n \n # iterations per sample\n self.number = 2\n \n # assign our test dataset\n self.datapath = os.path.join(self.dataroot, self.input_ms)\n \n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # if not os.path.exists(self.input_ms):\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),\n # os.path.join(os.getcwd(), self.input_ms))\n \n # Copy the flagcmd text file into temporary test directory\n if not os.path.exists(self.flags_cmd):\n shutil.copyfile(\n os.path.join(self.dataroot, self.flags_cmd),\n os.path.join(os.getcwd(), self.flags_cmd),\n )\n\n def setup_cache(self):\n # only run once for repeated tests\n pass", "min_run_count": 2, "name": "casademo.flagdata_suite.time_flagdata_summary", "number": 0, "param_names": [], "params": [], "repeat": 0, "rounds": 2, "sample_time": 0.01, "setup_cache_key": "casademo:182", "timeout": 10000, "type": "time", "unit": "seconds", "version": "9e569fb4e2a2f68e59c510bbf889632173be867104a4e5ba5a98d0eb110e82fb", "warmup_time": -1}, "casademo.tclean_memory_suite.peakmem_tclean_setweighting": {"code": "class tclean_memory_suite:\n def peakmem_tclean_setweighting(self):\n \"\"\"Adapted from CAS-13026\"\"\"\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.peakmem_tclean_setweighting", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "peakmemory", "unit": "bytes", "version": "8f03400a55e077279ae97bfcf867ab2364d450662c2cdd1f58f3293e06630979"}, "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs": {"code": "class tclean_memory_suite:\n def track_tclean_file_descriptors_cubemode_mosaic_briggs(self):\n \"\"\"Adapted from CAS-8755\n \n https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/browse/casatests/performance/test_perf_tclean_mem_setweighting.py?at=refs%2Fheads%2FCAS-13026#166-225\n \n Requires user configuration ~/.casa/rc to contain `synthesis.imager.memprofile.enable: 1`\n Expected output is casa.synthesis.imager.memprofile.PID.HOSTNAME.DATE_TIME_when_test_started.txt\n \"\"\"\n # attribute for tracking metric\n unit = \"file descriptors\"\n \n casalog.setlogfile(self.templogfile)\n tclean(\n vis=self.datapath,\n imagename=self.imagename,\n phasecenter=self.phasecenter,\n scan=[\"17,11,13\"],\n restoration=False,\n datacolumn=\"data\",\n pbcor=False,\n spw=\"0\",\n weighting=\"briggs\",\n intent=\"OBSERVE_TARGET#ON_SOURCE\",\n threshold=\"0mJy\",\n robust=0.5,\n savemodel=\"none\",\n imsize=self.imsize,\n stokes=\"I\",\n nchan=self.nchan,\n deconvolver=\"hogbom\",\n field=\"Pillar_3\",\n npixels=0,\n niter=0,\n pblimit=0.2,\n restoringbeam=[],\n cell=[\"0.94arcsec\"],\n start=\"230.490186515GHz\",\n outframe=\"LSRK\",\n specmode=\"cube\",\n width=\"0.0610478663509MHz\",\n gridder=\"mosaic\",\n interactive=False,\n parallel=False,\n )\n \n with open(self.templogfile) as mylog:\n for line in mylog:\n a_match = re.search(\"casa.synthesis.imager.memprofile\", line)\n if a_match:\n str_match = a_match.string\n break\n \n # Get name of memprofile created by tclean\n (start, middle, end) = str_match.partition(\"casa.synthesis.imager.memprofile\")\n mem_profile = middle + end.rstrip()\n \n # Get the memory values of the second column named MemRSS_(VmRSS)_MB, for each row\n with open(mem_profile, \"r\") as mfile:\n memdict = OrderedDict()\n maxFDSize = 0\n for myrow in mfile:\n linelist = []\n print(myrow.rstrip())\n if myrow.startswith(\"#\"):\n continue\n \n linelist = myrow.split(\",\")\n tclean_step = str(linelist[-1].rstrip())\n memdict[tclean_step.strip(\"[]\")] = int(linelist[1])\n maxFDSize = max(maxFDSize, int(linelist[7]))\n \n return maxFDSize\n\n def setup(self):\n ## fresh copy of the test MS to the tmp directory where tests are run ?\n # shutil.copytree(os.path.join(self.dataroot, self.input_ms),os.path.join(os.getcwd(), self.input_ms))\n pass\n # note: creating objects in setup method confounds memory benchmarks\n # (https://asv.readthedocs.io/en/stable/writing_benchmarks.html#memory)\n\n def setup_cache(self):\n # only run once for repeated tests\n pass\n # TODO: consider paramterizing here instead of setup/test methods for more accurate measurement", "name": "casademo.tclean_memory_suite.track_tclean_file_descriptors_cubemode_mosaic_briggs", "param_names": [], "params": [], "setup_cache_key": "casademo:37", "timeout": 10000, "type": "track", "unit": "unit", "version": "f9c40e4b16d7caf92b2d5a831995e197fae67947f0ec9bdad00b852f28656239"}}, "machines": {"NRAO workstation": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-1660 v4 @ 3.20GHz", "machine": "NRAO workstation", "num_cpu": "16", "os": "Linux 3.10.0-1160.15.2.el7.x86_64", "ram": "32GB", "version": 1}, "casa-perf-test": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz", "machine": "casa-perf-test", "num_cpu": "16", "os": "Linux 4.18.0-477.13.1.el8_8.x86_64", "ram": "263549216", "version": 1}, "cvpost029": {"arch": "x86_64", "cpu": "Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz", "machine": "cvpost029", "num_cpu": "32", "os": "Linux 3.10.0-957.5.1.el7.x86_64", "ram": "264033420", "version": 1}}, "tags": {"1.2.3-rel-1": 12515, "1.2.3-rel-2": 12516, "2019.212": 17631, "2019.213": 17635, "2019.214": 17638, "2019.215": 17646, "2019.216": 17660, "2019.217": 17663, "2019.218": 17683, "2019.219": 17693, "2019.220": 17697, "2019.221": 17699, "2019.222": 17700, "2019.223": 17705, "2019.224": 17726, "2019.225": 17729, "2019.226": 17740, "2019.227": 17755, "2019.228": 17761, "2019.229": 17772, "2019.230": 17796, "2019.231": 17807, "2019.232": 17824, "2019.233": 17836, "2019.234": 17846, "2019.235": 17879, "2019.236": 17894, "2019.237": 17905, "2019.238": 17917, "2019.239": 17976, "4.7-mas-1": 8092, "4.7-mas-10": 8292, "4.7-mas-11": 8298, "4.7-mas-12": 8308, "4.7-mas-13": 8313, "4.7-mas-14": 8325, "4.7-mas-15": 8334, "4.7-mas-16": 8349, "4.7-mas-17": 8354, "4.7-mas-18": 8357, "4.7-mas-19": 8361, "4.7-mas-2": 8101, "4.7-mas-20": 8365, "4.7-mas-21": 8366, "4.7-mas-22": 8367, "4.7-mas-23": 8371, "4.7-mas-24": 8376, "4.7-mas-25": 8382, "4.7-mas-26": 8386, "4.7-mas-27": 8391, "4.7-mas-28": 8400, "4.7-mas-29": 8406, "4.7-mas-3": 8143, "4.7-mas-30": 8415, 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6.907316077500582, [[null, 28773, 6.907316077500582, 7.798299254849553]]], ["bench_sdcal.OTF.time_otf03", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdcal.OTF.time_otf03.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": "3.10", "branch": "master"}, null, 0.5909788277931511, 0.3265495002269745, [[null, 28773, 0.3730332269333303, 0.5909788277931511]]], ["bench_applycal.CalWeight.time_applycal_calweight", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_applycal.CalWeight.time_applycal_calweight.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": "3.10", "branch": "master"}, null, 7.124963979236782, 6.336175509728491, [[null, 28773, 6.505545534193516, 7.124963979236782]]], ["bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100.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": "3.10", "branch": "master"}, null, 4.213335432577878, 3.878064370714128, [[null, 28773, 3.915629353374243, 4.213335432577878]]], ["bench_applycal.SelectionTypes.time_applycal_select_antenna", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_applycal.SelectionTypes.time_applycal_select_antenna.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": "3.10", "branch": "master"}, null, 5.861057904548943, 5.469483111053705, [[null, 28718, 5.469483111053705, 5.60196463111788]]], ["bench_sdbaseline.BlFuncVariable.time_variable_orders", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdbaseline.BlFuncVariable.time_variable_orders.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": "3.10", "branch": "master"}, null, 0.8993045319803059, 0.8535666270181537, [[null, 28718, 0.8535666270181537, 0.8799696993082762]]], ["bench_sdbaseline.Basic.time_cspline_blfunc_no_mask", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdbaseline.Basic.time_cspline_blfunc_no_mask.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": "3.10", "branch": "master"}, null, 0.6717702075839043, 0.6356746279634535, [[null, 28718, 0.6356746279634535, 0.6717702075839043]]], ["bench_flagdata.Selections.time_sel_autocorr_wvr", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_flagdata.Selections.time_sel_autocorr_wvr.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": "3.10", "branch": "master"}, null, 0.35089784441515803, 0.27707670582458377, [[null, 28718, 0.27707670582458377, 0.35089784441515803]]], ["bench_sdcal.OTFRaster.time_noff_custom", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdcal.OTFRaster.time_noff_custom.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": "3.10", "branch": "master"}, null, 0.5059702023863792, 0.36340787494555116, [[28718, 28737, 0.36340787494555116, 0.5059702023863792]]], ["bench_sdcal.OTFRaster.time_noff_priority_over_fraction", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdcal.OTFRaster.time_noff_priority_over_fraction.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": "3.10", "branch": "master"}, null, 0.48261958034709096, 0.2537552290596068, [[28718, 28737, 0.2537552290596068, 0.46345009794458747]]], ["bench_sdatmcor.test_sdatmcor.time_custom_atm_params", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdatmcor.test_sdatmcor.time_custom_atm_params.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": "3.10", "branch": "master"}, null, 5.542859765700996, 5.104331627953798, [[null, 28773, 5.149267985485494, 5.542859765700996]]], ["bench_sdcal.OTFRaster.time_default", "graphs/arch-x86_64/branch-master/cpu-Intel(R) Xeon(R) CPU E5-2670 0 @ 2.60GHz/machine-casa-perf-test/num_cpu-16/os-Linux 4.18.0-477.13.1.el8_8.x86_64/pip+casatasks-trunk/pip+casatools-null/python-3.10/ram-263549216/bench_sdcal.OTFRaster.time_default.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": "3.10", "branch": "master"}, null, 0.47229585191234946, 0.42897768737748265, [[null, 28718, 0.42897768737748265, 0.46615403797477484]]]]} \ No newline at end of file diff --git a/regressions.xml b/regressions.xml index bd44c4b67..93c17a349 100644 --- a/regressions.xml +++ b/regressions.xml @@ -1,23 +1,23 @@ -tag:casa6.asv,1970-01-01:/c2e2f57dbf857b15eeaa2dd6f9ab5099083a1d65ab6509d626fc7f09f077b438Airspeed Velocitycasa6 performance regressions2024-01-26T06:19:45Ztag:casa6.asv,2024-01-24:/f00e41729c5d4f900315c7ecb58cba4283893888d06d6b5d214f62e6bbaacb5b8.08% bench_tsdimaging.Basic.time_gridfunction_PB_start_4002024-01-26T06:19:45Z<a href="index.html#bench_tsdimaging.Basic.time_gridfunction_PB_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=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">8.08% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> +tag:casa6.asv,1970-01-01:/c2e2f57dbf857b15eeaa2dd6f9ab5099083a1d65ab6509d626fc7f09f077b438Airspeed Velocitycasa6 performance regressions2024-01-26T00:54:35Ztag:casa6.asv,2024-01-24:/f00e41729c5d4f900315c7ecb58cba4283893888d06d6b5d214f62e6bbaacb5b8.08% bench_tsdimaging.Basic.time_gridfunction_PB_start_4002024-01-26T00:54:35Z<a href="index.html#bench_tsdimaging.Basic.time_gridfunction_PB_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=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">8.08% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 3.34s, old value: 3.09s.<br> - Latest value: 3.34s (10.43% worse than best value 3.03s).tag:casa6.asv,2024-01-24:/0668e4b06a7d426544b6c5cde63b63989a2e38211cd27a86524ee4385529226c25.85% bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null2024-01-26T06:14:36Z<a href="index.html#bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">25.85% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 3.34s (10.43% worse than best value 3.03s).tag:casa6.asv,2024-01-24:/0668e4b06a7d426544b6c5cde63b63989a2e38211cd27a86524ee4385529226c25.85% bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null2024-01-26T00:49:26Z<a href="index.html#bench_tsdimaging.Basic.time_gridfunction_BOX_start_400_phasecenter_null?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">25.85% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 3.82s, old value: 3.04s.<br> - Latest value: 3.82s (25.85% worse than best value 3.04s).tag:casa6.asv,2024-01-24:/4afe750363f77918bb33014d092256a09aad7a94439cc3f61f96b5443cf41c457.60% bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1002024-01-26T05:22:04Z<a href="index.html#bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">7.60% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 3.82s (25.85% worse than best value 3.04s).tag:casa6.asv,2024-01-24:/4afe750363f77918bb33014d092256a09aad7a94439cc3f61f96b5443cf41c457.60% bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_1002024-01-25T23:57:11Z<a href="index.html#bench_sdimaging.Basic.time_gridfunction_PB_mode_velocity_nchan_100?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">7.60% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 4.21s, old value: 3.92s.<br> - Latest value: 4.21s (8.65% worse than best value 3.88s).tag:casa6.asv,2024-01-24:/8335d5813fe49966d5c672d23e5ee4df4e1221f0768e53b440eebeefd7565ff317.27% bench_sdimaging.Basic.time_gridfunction_GJINC_start_4002024-01-26T05:18:32Z<a href="index.html#bench_sdimaging.Basic.time_gridfunction_GJINC_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=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">17.27% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 4.21s (8.65% worse than best value 3.88s).tag:casa6.asv,2024-01-24:/8335d5813fe49966d5c672d23e5ee4df4e1221f0768e53b440eebeefd7565ff317.27% bench_sdimaging.Basic.time_gridfunction_GJINC_start_4002024-01-25T23:53:54Z<a href="index.html#bench_sdimaging.Basic.time_gridfunction_GJINC_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=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">17.27% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 4.20s, old value: 3.58s.<br> - Latest value: 4.20s (17.27% worse than best value 3.58s).tag:casa6.asv,2024-01-24:/b59b3561821df209f8b5334a8e8f5176db593a120a94e0752c496c12811709d710.26% bench_sdcal.OTF.time_otf052024-01-26T05:15:39Z<a href="index.html#bench_sdcal.OTF.time_otf05?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">10.26% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 4.20s (17.27% worse than best value 3.58s).tag:casa6.asv,2024-01-24:/b59b3561821df209f8b5334a8e8f5176db593a120a94e0752c496c12811709d710.26% bench_sdcal.OTF.time_otf052024-01-25T23:50:53Z<a href="index.html#bench_sdcal.OTF.time_otf05?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">10.26% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 954ms, old value: 866ms.<br> - Latest value: 954ms (108.48% worse than best value 458ms).tag:casa6.asv,2024-01-24:/77b05862efa793a3bfe30de7553f1a2782639dd5a1e9867f84c22ec7e9c853cc58.43% bench_sdcal.OTF.time_otf032024-01-26T05:15:33Z<a href="index.html#bench_sdcal.OTF.time_otf03?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">58.43% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 954ms (108.48% worse than best value 458ms).tag:casa6.asv,2024-01-24:/77b05862efa793a3bfe30de7553f1a2782639dd5a1e9867f84c22ec7e9c853cc58.43% bench_sdcal.OTF.time_otf032024-01-25T23:50:48Z<a href="index.html#bench_sdcal.OTF.time_otf03?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">58.43% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 591ms, old value: 373ms.<br> - Latest value: 591ms (80.98% worse than best value 327ms).tag:casa6.asv,2024-01-24:/6fc5e55e122146f42e642fd90be728958878907fd068649e51436fc2fa6b7c8a7.64% bench_sdatmcor.test_sdatmcor.time_custom_atm_params2024-01-26T05:11:10Z<a href="index.html#bench_sdatmcor.test_sdatmcor.time_custom_atm_params?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">7.64% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 591ms (80.98% worse than best value 327ms).tag:casa6.asv,2024-01-24:/6fc5e55e122146f42e642fd90be728958878907fd068649e51436fc2fa6b7c8a7.64% bench_sdatmcor.test_sdatmcor.time_custom_atm_params2024-01-25T23:46:05Z<a href="index.html#bench_sdatmcor.test_sdatmcor.time_custom_atm_params?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">7.64% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 5.54s, old value: 5.15s.<br> - Latest value: 5.54s (8.59% worse than best value 5.10s).tag:casa6.asv,2024-01-24:/24ea719d8a35f970c5fc194b2e92e8d2a751428859f85bf71d7d4c5080de31c212.69% bench_gaincal.GaincalSelections.time_gaincal_fieldSelect2024-01-26T04:51:33Z<a href="index.html#bench_gaincal.GaincalSelections.time_gaincal_fieldSelect?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">12.69% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 5.54s (8.59% worse than best value 5.10s).tag:casa6.asv,2024-01-24:/24ea719d8a35f970c5fc194b2e92e8d2a751428859f85bf71d7d4c5080de31c212.69% bench_gaincal.GaincalSelections.time_gaincal_fieldSelect2024-01-25T23:24:59Z<a href="index.html#bench_gaincal.GaincalSelections.time_gaincal_fieldSelect?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">12.69% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 7.98s, old value: 7.08s.<br> - Latest value: 7.98s (12.69% worse than best value 7.08s).tag:casa6.asv,2024-01-24:/3ec13e68ae82e4937bee5b2c828be38148564a09af5ffea08b41ced81a1ae1cf9.52% bench_applycal.CalWeight.time_applycal_calweight2024-01-26T04:25:50Z<a href="index.html#bench_applycal.CalWeight.time_applycal_calweight?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">9.52% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 7.98s (12.69% worse than best value 7.08s).tag:casa6.asv,2024-01-24:/3ec13e68ae82e4937bee5b2c828be38148564a09af5ffea08b41ced81a1ae1cf9.52% bench_applycal.CalWeight.time_applycal_calweight2024-01-25T22:58:54Z<a href="index.html#bench_applycal.CalWeight.time_applycal_calweight?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">9.52% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 7.12s, old value: 6.51s.<br> - Latest value: 7.12s (12.45% worse than best value 6.34s).tag:casa6.asv,2024-01-24:/0338f6375e4529aa2ed95fffb0e186824f8aa52d8657e762e50450e0d91e441412.90% bench_applycal.ApplyModes.time_applycal_cal_only_mode2024-01-26T04:21:29Z<a href="index.html#bench_applycal.ApplyModes.time_applycal_cal_only_mode?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">12.90% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> + Latest value: 7.12s (12.45% worse than best value 6.34s).tag:casa6.asv,2024-01-24:/0338f6375e4529aa2ed95fffb0e186824f8aa52d8657e762e50450e0d91e441412.90% bench_applycal.ApplyModes.time_applycal_cal_only_mode2024-01-25T22:54:17Z<a href="index.html#bench_applycal.ApplyModes.time_applycal_cal_only_mode?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=2ad6b58ab24c12d5185c3d880236e6897c2ea62d">12.90% regression</a> on 2024-01-24 09:28:37 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/2ad6b58ab24c12d5185c3d880236e6897c2ea62d">2ad6b58a</a>.<br> New value: 7.80s, old value: 6.91s.<br> Latest value: 7.80s (12.90% worse than best value 6.91s).tag:casa6.asv,2024-01-13:/fd26636cf721287a61193be3084223aa8bc4bf730fd73138e4b6e422c900ec1c82.64% bench_sdcal.OTFRaster.time_noff_priority_over_fraction2024-01-15T07:12:33Z<a href="index.html#bench_sdcal.OTFRaster.time_noff_priority_over_fraction?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=326629aeabf322636dcaf8ba8096b6c16291bef7-6f83e42ed2a6681d32e2dfedc0aacc3b095d0af9">82.64% regression</a> on 2024-01-13 11:49:49 in commits <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/326629aeabf322636dcaf8ba8096b6c16291bef7">326629ae...6f83e42e</a>.<br> New value: 463ms, old value: 254ms.<br> @@ -69,11 +69,11 @@ New value: 5.60s, old value: 5.47s.<br> Latest value: 5.86s (7.16% worse than best value 5.47s).tag:casa6.asv,2024-01-10:/dd68e491e301c1302879fc009ca6549adf5430e98c991286df7028289450e4147.33% bench_applycal.CalFlag.time_applycal_calflag2024-01-11T21:42:16Z<a href="index.html#bench_applycal.CalFlag.time_applycal_calflag?cpu=Intel%28R%29+Xeon%28R%29+CPU+E5-2670+0+%40+2.60GHz&machine=casa-perf-test&os=Linux+4.18.0-477.13.1.el8_8.x86_64&ram=263549216&python=3.10&branch=master&commits=326629aeabf322636dcaf8ba8096b6c16291bef7">7.33% regression</a> on 2024-01-10 13:58:06 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/326629aeabf322636dcaf8ba8096b6c16291bef7">326629ae</a>.<br> New value: 6.72s, old value: 6.26s.<br> - Latest value: 7.03s (12.30% worse than best value 6.26s).tag:casa6.asv,2023-11-16:/d096f04aa22147971c3bd04fbd52ef13e02c9b0e7a9694b0b393781b630e79bb88.56% bench_sdcal.OTFRaster.time_fraction_string_numeric2023-11-18T10:08:52Z<a href="index.html#bench_sdcal.OTFRaster.time_fraction_string_numeric?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=1c8ed67f0f6c1211a5644a03b74afe735f7b6cba">88.56% regression</a> on 2023-11-16 09:48:41 in commit <a href="https://open-bitbucket.nrao.edu/projects/CASA/repos/casa6/commits/1c8ed67f0f6c1211a5644a03b74afe735f7b6cba">1c8ed67f</a>.<br> - New value: 465ms, old value: 246ms.<br> - Latest value: 465ms (88.56% worse than best value 246ms).tag: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> + Latest value: 7.03s (12.30% worse than best value 6.26s).tag: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,2023-03-23:/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: 3.36s (16.30% worse than best value 2.89s).tag:casa6.asv,2023-11-08:/880921511c784775bf2a6668574e5ce17d9420cdcd1ad631b2b4438ab4a9eb9462.25% 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">62.25% 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: 1.69s, old value: 1.04s.<br> + Latest value: 1.69s (62.25% worse than best value 1.04s).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>