def test_13A_537__calibration__PPR__regression(): """Run VLA calibration regression with a PPR file. PPR name: PPR_13A-537.xml Dataset: 13A-537/13A-537.sb24066356.eb24324502.56514.05971091435 """ ref_directory = 'pl-regressiontest/13A-537' pt = PipelineTester( visname=['13A-537.sb24066356.eb24324502.56514.05971091435'], ppr=f'{ref_directory}/PPR_13A-537.xml', input_dir=ref_directory, output_dir='13A_537__calibration__PPR__regression', expectedoutput_dir=ref_directory, ) > pt.run(telescope='vla', omp_num_threads=1) tests/regression/fast/vla_fast_test.py:44: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:381: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7f2120b9ec60> new_file = '13A-537.sb24066356.eb24324502.56514.05971091435.NEW.results.txt' relative_tolerance = 1e-07 def __compare_results(self, new_file: str, relative_tolerance: float) -> None: """ Compare results between new one loaded from file and old one. Args: new_file : file path of new results relative_tolerance : relative tolerance of output value """ with open(self.expectedoutput_file) as expected_fd, open(new_file) as new_fd: expected_results = expected_fd.readlines() new_results = new_fd.readlines() errors = [] worst_diff = (0, 0) worst_percent_diff = (0, 0) for old, new in zip(expected_results, new_results): try: oldkey, oldval, tol = self.__sanitize_results_string(old) newkey, newval, _ = self.__sanitize_results_string(new) except ValueError as e: errorstr = "The results: {0} could not be parsed. Error: {1}".format(new, str(e)) errors.append(errorstr) continue assert oldkey == newkey, f"Expected key {oldkey} does not match new key {newkey}." tolerance = tol if tol else relative_tolerance if newval is not None: LOG.info('Comparing %s to %s with a rel. tolerance of %s', oldval, newval, tolerance) if oldval != pytest.approx(newval, rel=tolerance): diff = oldval-newval percent_diff = (oldval-newval)/oldval * 100 if oldval != 0 else 100 if abs(diff) > abs(worst_diff[0]): worst_diff = diff, oldkey if abs(percent_diff) > abs(worst_percent_diff[0]): worst_percent_diff = percent_diff, oldkey errorstr = f"{oldkey}\n\tvalues differ by > a relative difference of {tolerance}\n\texpected: {oldval}\n\tnew: {newval}\n\tdiff: {diff}\n\tpercent_diff: {percent_diff}%" errors.append(errorstr) elif oldval is not None: # If only the new value is None, fail errorstr = f"{oldkey}\n\tvalue is None\n\texpected: {oldval}\n\tnew: {newval}" errors.append(errorstr) else: # If old and new values are both None, this is expected, so pass LOG.info('Comparing %s and %s... both values are None.', oldval, newval) [LOG.warning(x) for x in errors] n_errors = len(errors) if n_errors > 0: summary_str = f"Worst absolute diff, {worst_diff[1]}: {worst_diff[0]}\nWorst percentage diff, {worst_percent_diff[1]}: {worst_percent_diff[0]}%" errors.append(summary_str) > pytest.fail("Failed to match {0} result value{1} within tolerance{1} :\n{2}".format( n_errors, '' if n_errors == 1 else 's', '\n'.join(errors)), pytrace=True) E Failed: Failed to match 22 result values within tolerances : E s10.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s10.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s12.hifv_fluxboot.13A-537.sb24066356.eb24324502.56514.05971091435.flux_densities.spw_0 E values differ by > a relative difference of 1e-07 E expected: 0.7149940856445874 E new: 0.7150110854670872 E diff: -1.6999822499808026e-05 E percent_diff: -0.0023776172196560484% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 48816966 E new: 48816980 E diff: -14 E percent_diff: -2.8678554091214926e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 48585562 E new: 48585576 E diff: -14 E percent_diff: -2.881514471315573e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 29068884 E new: 29069288 E diff: -404 E percent_diff: -0.0013898022366458926% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_5.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 9127166 E new: 9126980 E diff: 186 E percent_diff: 0.002037872434882854% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_6.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7154697 E new: 7154975 E diff: -278 E percent_diff: -0.0038855593744920296% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_7.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7755085 E new: 7755397 E diff: -312 E percent_diff: -0.004023166735116378% E s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.mean E values differ by > a relative difference of 1e-07 E expected: 0.6294256901955095 E new: 0.6294082667176586 E diff: 1.7423477850941893e-05 E percent_diff: 0.0027681548628130962% E s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.variance E values differ by > a relative difference of 1e-07 E expected: 0.06276085915536926 E new: 0.06275781189517324 E diff: 3.047260196023527e-06 E percent_diff: 0.004855351308177288% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 14429416 E new: 14429430 E diff: -14 E percent_diff: -9.702402370269178e-05% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E Worst absolute diff, s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after: -404 E Worst percentage diff, s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.variance: 0.004855351308177288% tests/testing_utils.py:440: Faileddef test_13A_537__procedure_hifv__regression(): """Run VLA calibration regression for standard procedure_hifv.xml recipe. Recipe name: procedure_hifv Dataset: 13A-537/13A-537.sb24066356.eb24324502.56514.05971091435 """ ref_directory = 'pl-regressiontest/13A-537' pt = PipelineTester( visname=['13A-537.sb24066356.eb24324502.56514.05971091435'], recipe='procedure_hifv.xml', input_dir=ref_directory, output_dir='13A_537__procedure_hifv__regression', expectedoutput_dir=ref_directory, ) > pt.run(telescope='vla', omp_num_threads=1) tests/regression/fast/vla_fast_test.py:25: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:381: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7fc1efb80e30> new_file = '13A-537.sb24066356.eb24324502.56514.05971091435.NEW.results.txt' relative_tolerance = 1e-07 def __compare_results(self, new_file: str, relative_tolerance: float) -> None: """ Compare results between new one loaded from file and old one. Args: new_file : file path of new results relative_tolerance : relative tolerance of output value """ with open(self.expectedoutput_file) as expected_fd, open(new_file) as new_fd: expected_results = expected_fd.readlines() new_results = new_fd.readlines() errors = [] worst_diff = (0, 0) worst_percent_diff = (0, 0) for old, new in zip(expected_results, new_results): try: oldkey, oldval, tol = self.__sanitize_results_string(old) newkey, newval, _ = self.__sanitize_results_string(new) except ValueError as e: errorstr = "The results: {0} could not be parsed. Error: {1}".format(new, str(e)) errors.append(errorstr) continue assert oldkey == newkey, f"Expected key {oldkey} does not match new key {newkey}." tolerance = tol if tol else relative_tolerance if newval is not None: LOG.info('Comparing %s to %s with a rel. tolerance of %s', oldval, newval, tolerance) if oldval != pytest.approx(newval, rel=tolerance): diff = oldval-newval percent_diff = (oldval-newval)/oldval * 100 if oldval != 0 else 100 if abs(diff) > abs(worst_diff[0]): worst_diff = diff, oldkey if abs(percent_diff) > abs(worst_percent_diff[0]): worst_percent_diff = percent_diff, oldkey errorstr = f"{oldkey}\n\tvalues differ by > a relative difference of {tolerance}\n\texpected: {oldval}\n\tnew: {newval}\n\tdiff: {diff}\n\tpercent_diff: {percent_diff}%" errors.append(errorstr) elif oldval is not None: # If only the new value is None, fail errorstr = f"{oldkey}\n\tvalue is None\n\texpected: {oldval}\n\tnew: {newval}" errors.append(errorstr) else: # If old and new values are both None, this is expected, so pass LOG.info('Comparing %s and %s... both values are None.', oldval, newval) [LOG.warning(x) for x in errors] n_errors = len(errors) if n_errors > 0: summary_str = f"Worst absolute diff, {worst_diff[1]}: {worst_diff[0]}\nWorst percentage diff, {worst_percent_diff[1]}: {worst_percent_diff[0]}%" errors.append(summary_str) > pytest.fail("Failed to match {0} result value{1} within tolerance{1} :\n{2}".format( n_errors, '' if n_errors == 1 else 's', '\n'.join(errors)), pytrace=True) E Failed: Failed to match 22 result values within tolerances : E s10.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s10.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s12.hifv_fluxboot.13A-537.sb24066356.eb24324502.56514.05971091435.flux_densities.spw_0 E values differ by > a relative difference of 1e-07 E expected: 0.7149940856445874 E new: 0.7150110854670872 E diff: -1.6999822499808026e-05 E percent_diff: -0.0023776172196560484% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 48816966 E new: 48816980 E diff: -14 E percent_diff: -2.8678554091214926e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 48585562 E new: 48585576 E diff: -14 E percent_diff: -2.881514471315573e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_4.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 11498688 E new: 11498686 E diff: 2 E percent_diff: 1.7393288695197225e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s14.hifv_applycals.13A-537.sb24066356.eb24324502.56514.05971091435.scan_8.num_rows_flagged.before E values differ by > a relative difference of 1e-07 E expected: 3678842 E new: 3678844 E diff: -2 E percent_diff: -5.436493331325455e-05% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 29068884 E new: 29069288 E diff: -404 E percent_diff: -0.0013898022366458926% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_5.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 9127166 E new: 9126980 E diff: 186 E percent_diff: 0.002037872434882854% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_6.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7154697 E new: 7154975 E diff: -278 E percent_diff: -0.0038855593744920296% E s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_7.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7755085 E new: 7755397 E diff: -312 E percent_diff: -0.004023166735116378% E s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.mean E values differ by > a relative difference of 1e-07 E expected: 0.6294256901955095 E new: 0.6294082667176586 E diff: 1.7423477850941893e-05 E percent_diff: 0.0027681548628130962% E s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.variance E values differ by > a relative difference of 1e-07 E expected: 0.06276085915536926 E new: 0.06275781189517324 E diff: 3.047260196023527e-06 E percent_diff: 0.004855351308177288% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 14429416 E new: 14429430 E diff: -14 E percent_diff: -9.702402370269178e-05% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_2.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 7526956 E new: 7526960 E diff: -4 E percent_diff: -5.314233270395098e-05% E s8.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.scan_3.num_rows_flagged.after E values differ by > a relative difference of 1e-07 E expected: 6902460 E new: 6902470 E diff: -10 E percent_diff: -0.0001448758848294666% E Worst absolute diff, s15.hifv_checkflag.13A-537.sb24066356.eb24324502.56514.05971091435.num_rows_flagged.after: -404 E Worst percentage diff, s16.hifv_statwt.13A-537.sb24066356.eb24324502.56514.05971091435.variance: 0.004855351308177288% tests/testing_utils.py:440: Failed@pytest.mark.twelve def test_E2E6_1_00010_S__uid___A002_Xd0a588_X2239__procedure_hifa_image__regression(): """Run ALMA cal+image regression on a 12m moderate-size test dataset in ASDM. Recipe name: procedure_hifa_calimage Dataset: E2E6.1.00010.S: uid___A002_Xd0a588_X2239 """ ref_directory = 'pl-regressiontest/E2E6.1.00010.S' pt = PipelineTester( visname=['uid___A002_Xd0a588_X2239'], recipe='procedure_hifa_calimage.xml', input_dir=ref_directory, expectedoutput_dir=ref_directory, ) > pt.run() tests/regression/fast/alma_if_fast_test.py:162: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:381: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7f3c1068ecf0> new_file = 'uid___A002_Xd0a588_X2239.NEW.results.txt' relative_tolerance = 1e-07 def __compare_results(self, new_file: str, relative_tolerance: float) -> None: """ Compare results between new one loaded from file and old one. Args: new_file : file path of new results relative_tolerance : relative tolerance of output value """ with open(self.expectedoutput_file) as expected_fd, open(new_file) as new_fd: expected_results = expected_fd.readlines() new_results = new_fd.readlines() errors = [] worst_diff = (0, 0) worst_percent_diff = (0, 0) for old, new in zip(expected_results, new_results): try: oldkey, oldval, tol = self.__sanitize_results_string(old) newkey, newval, _ = self.__sanitize_results_string(new) except ValueError as e: errorstr = "The results: {0} could not be parsed. Error: {1}".format(new, str(e)) errors.append(errorstr) continue assert oldkey == newkey, f"Expected key {oldkey} does not match new key {newkey}." tolerance = tol if tol else relative_tolerance if newval is not None: LOG.info('Comparing %s to %s with a rel. tolerance of %s', oldval, newval, tolerance) if oldval != pytest.approx(newval, rel=tolerance): diff = oldval-newval percent_diff = (oldval-newval)/oldval * 100 if oldval != 0 else 100 if abs(diff) > abs(worst_diff[0]): worst_diff = diff, oldkey if abs(percent_diff) > abs(worst_percent_diff[0]): worst_percent_diff = percent_diff, oldkey errorstr = f"{oldkey}\n\tvalues differ by > a relative difference of {tolerance}\n\texpected: {oldval}\n\tnew: {newval}\n\tdiff: {diff}\n\tpercent_diff: {percent_diff}%" errors.append(errorstr) elif oldval is not None: # If only the new value is None, fail errorstr = f"{oldkey}\n\tvalue is None\n\texpected: {oldval}\n\tnew: {newval}" errors.append(errorstr) else: # If old and new values are both None, this is expected, so pass LOG.info('Comparing %s and %s... both values are None.', oldval, newval) [LOG.warning(x) for x in errors] n_errors = len(errors) if n_errors > 0: summary_str = f"Worst absolute diff, {worst_diff[1]}: {worst_diff[0]}\nWorst percentage diff, {worst_percent_diff[1]}: {worst_percent_diff[0]}%" errors.append(summary_str) > pytest.fail("Failed to match {0} result value{1} within tolerance{1} :\n{2}".format( n_errors, '' if n_errors == 1 else 's', '\n'.join(errors)), pytrace=True) E Failed: Failed to match 3 result values within tolerances : E s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.field_0.spw_14.qa.metric.score_gfluxscale_amp_time_variation E values differ by > a relative difference of 1e-07 E expected: 1.9390034714729183 E new: 1.9390070410307714 E diff: -3.569557853078109e-06 E percent_diff: -0.00018409239104489992% E s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.field_2.spw_14.I E values differ by > a relative difference of 1e-07 E expected: 0.16244481701133018 E new: 0.16244485548598875 E diff: -3.847465857020893e-08 E percent_diff: -2.3684756016269453e-05% E s21.hif_applycal.uid___A002_Xd0a588_X2239.spw_14.qa.metric.gt90deg_offset_phase_vs_freqintercept E values differ by > a relative difference of 1e-07 E expected: -95.10457414073348 E new: -95.10462209180606 E diff: 4.7951072588148236e-05 E percent_diff: -5.041931265807614e-05% E Worst absolute diff, s21.hif_applycal.uid___A002_Xd0a588_X2239.spw_14.qa.metric.gt90deg_offset_phase_vs_freqintercept: 4.7951072588148236e-05 E Worst percentage diff, s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.field_0.spw_14.qa.metric.score_gfluxscale_amp_time_variation: -0.00018409239104489992% tests/testing_utils.py:440: Failed