@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 0x7f9c291ac4a0> 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 6 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: 1.9390070410307714 E new: 1.9390077167919766 E diff: -6.757612052155082e-07 E percent_diff: -3.4850889703643116e-05% E s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.field_1.spw_14.I E values differ by > a relative difference of 1e-07 E expected: 0.5017564867171079 E new: 0.5017594399826114 E diff: -2.9532655034802957e-06 E percent_diff: -0.0005885854157667038% 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: 0.16244485548598875 E new: 0.1624421145945484 E diff: 2.740891440339288e-06 E percent_diff: 0.0016872750030398449% E s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.qa.metric.score_derived_fluxes_snr E values differ by > a relative difference of 1e-07 E expected: 184.08359129153104 E new: 184.08400077411397 E diff: -0.0004094825829383808 E percent_diff: -0.00022244382569106227% 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: -95.10462209180606 E new: -95.10436168021985 E diff: -0.00026041158621126215 E percent_diff: 0.00027381590976712205% E s21.hif_applycal.uid___A002_Xd0a588_X2239.spw_14.qa.metric.phase_vs_freqintercept E values differ by > a relative difference of 1e-07 E expected: -60.75311758869651 E new: -60.753007921244205 E diff: -0.00010966745230689412 E percent_diff: 0.00018051329159657548% E Worst absolute diff, s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.qa.metric.score_derived_fluxes_snr: -0.0004094825829383808 E Worst percentage diff, s17.hifa_gfluxscale.uid___A002_Xd0a588_X2239.field_2.spw_14.I: 0.0016872750030398449% tests/testing_utils.py:440: Failed@pytest.mark.twelve def test_uid___A002_Xee1eb6_Xc58d__procedure_hifa_calsurvey__regression(): """Run ALMA cal+survey regression on a calibration survey test dataset. Recipe name: procedure_hifa_calsurvey Dataset: uid___A002_Xee1eb6_Xc58d """ ref_directory = 'pl-regressiontest/uid___A002_Xee1eb6_Xc58d_calsurvey' pt = PipelineTester( visname=['uid___A002_Xee1eb6_Xc58d'], recipe='procedure_hifa_calsurvey.xml', input_dir=ref_directory, expectedoutput_dir=ref_directory, ) > pt.run() tests/regression/fast/alma_if_fast_test.py:200: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:381: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7fcbd69b0380> new_file = 'uid___A002_Xee1eb6_Xc58d.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 24 result values within tolerances : E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_0.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 6.43684975993458 E new: 6.436848875389339 E diff: 8.845452406092136e-07 E percent_diff: 1.3741896635757482e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_0.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9953313961364421 E new: 0.9953312593590304 E diff: 1.3677741161899348e-07 E percent_diff: 1.374189663361566e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_11.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.3163406759943754 E new: 1.3163409971063522 E diff: -3.211119767865256e-07 E percent_diff: -2.439429113165972e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_11.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9990967728871843 E new: 0.9990970166097599 E diff: -2.43722575565819e-07 E percent_diff: -2.4394291141739037e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_12.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 2.0904035735200677 E new: 2.090402854949724 E diff: 7.185703436718427e-07 E percent_diff: 3.4374718488537085e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_12.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9980266603871862 E new: 0.9980263173183314 E diff: 3.4306885488000205e-07 E percent_diff: 3.437471848165939e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_13.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 3.8784434661073814 E new: 3.878444610019205 E diff: -1.1439118234868317e-06 E percent_diff: -2.9494095594873386e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_13.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9938201645088008 E new: 0.9938204576270702 E diff: -2.931182693233225e-07 E percent_diff: -2.9494095591046623e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_16.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.2906203396900693 E new: 1.2906201504155397 E diff: 1.8927452960504354e-07 E percent_diff: 1.4665391810770324e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_16.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.98873804093041 E new: 0.9887378959281024 E diff: 1.450023076055018e-07 E percent_diff: 1.4665391802772507e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_17.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 3.1207121225170846 E new: 3.1207129025083584 E diff: -7.799912737915804e-07 E percent_diff: -2.4994015569832822e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_17.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0086589464965487 E new: 1.008659198600923 E diff: -2.521043742653717e-07 E percent_diff: -2.4994015582871183e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_18.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 0.7527119295410829 E new: 0.7527121046559739 E diff: -1.7511489103227973e-07 E percent_diff: -2.3264529783531483e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_18.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9828239359200758 E new: 0.9828241645694431 E diff: -2.2864936732869268e-07 E percent_diff: -2.3264529787284978e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_2.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.618410391590432 E new: 1.61841002510508 E diff: 3.664853520657374e-07 E percent_diff: 2.264477254780771e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_2.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9892074575478698 E new: 0.989207233544091 E diff: 2.240037787482052e-07 E percent_diff: 2.2644772543818514e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_3.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.021946498634394 E new: 1.0219468079803513 E diff: -3.0934595729448233e-07 E percent_diff: -3.0270269305472935e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_3.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9884073036939544 E new: 0.988407602887507 E diff: -2.9919355259977465e-07 E percent_diff: -3.0270269299063728e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_4.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.9982802740350474 E new: 1.998279869420348 E diff: 4.046146993008648e-07 E percent_diff: 2.0248145595904948e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_4.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9913095430688047 E new: 0.9913093423470051 E diff: 2.0072179962582481e-07 E percent_diff: 2.0248145599854588e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_8.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.2841153440085296 E new: 1.2841155200899643 E diff: -1.7608143476621763e-07 E percent_diff: -1.3712275582390987e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_8.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9993316695720197 E new: 0.9993318066031321 E diff: -1.3703111245710886e-07 E percent_diff: -1.3712275576715656e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_9.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 1.9139242142944206 E new: 1.9139240026602145 E diff: 2.1163420615977202e-07 E percent_diff: 1.1057606386875262e-05% E s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_9.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9913370742983115 E new: 0.9913369646801599 E diff: 1.0961815166687927e-07 E percent_diff: 1.1057606389276748e-05% E Worst absolute diff, s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_13.spw_16.I: -1.1439118234868317e-06 E Worst percentage diff, s16.hifa_gfluxscale.uid___A002_Xee1eb6_Xc58d.field_12.spw_16.I: 3.4374718488537085e-05% tests/testing_utils.py:440: Failed@pytest.mark.seven @pytest.mark.mpi def test_2023_1_00228_S__uid___A002_X1199f9e_X7c24__procedure_hifa_calimage_diffgain__regression(): """Run ALMA cal+image regression on a 7m B2B dataset with differential gain calibration. Recipe name: procedure_hifa_calimage_diffgain Dataset: 2023.1.00228.S: uid___A002_X1199f9e_X7c24 """ ref_directory = 'pl-regressiontest/2023.1.00228.S' pt = PipelineTester( visname=['uid___A002_X1199f9e_X7c24'], recipe='procedure_hifa_calimage_diffgain.xml', input_dir=ref_directory, expectedoutput_dir=ref_directory, ) > pt.run() tests/regression/fast/alma_if_fast_test.py:142: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:381: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7f98cec24c50> new_file = 'uid___A002_X1199f9e_X7c24.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 13 result values within tolerances : E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_29.I E values differ by > a relative difference of 1e-07 E expected: 0.8378082712078273 E new: 0.8378077335399046 E diff: 5.376679227397929e-07 E percent_diff: 6.417553290141947e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_29.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.8081204912780358 E new: 0.808119972662404 E diff: 5.186156317726898e-07 E percent_diff: 6.417553290258777e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_33.I E values differ by > a relative difference of 1e-07 E expected: 0.8460116243974625 E new: 0.8460103046383503 E diff: 1.319759112217156e-06 E percent_diff: 0.00015599775158610862% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_33.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.8173528334400738 E new: 0.8173515583880312 E diff: 1.2750520426152434e-06 E percent_diff: 0.0001559977515767341% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_41.I E values differ by > a relative difference of 1e-07 E expected: 0.7885692224368216 E new: 0.7885703778722577 E diff: -1.1554354361065222e-06 E percent_diff: -0.00014652301956903893% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_41.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.7642712175824183 E new: 0.764272337415684 E diff: -1.119833265672554e-06 E percent_diff: -0.00014652301956560233% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_29.I E values differ by > a relative difference of 1e-07 E expected: 0.12352226017389038 E new: 0.12352412354044164 E diff: -1.86336655125241e-06 E percent_diff: -0.0015085269235109741% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_29.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.5679953042438604 E new: 0.5680038726059492 E diff: -8.568362088845483e-06 E percent_diff: -0.0015085269235195619% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_33.I E values differ by > a relative difference of 1e-07 E expected: 0.21527453621962267 E new: 0.2152731154481138 E diff: 1.4207715088587758e-06 E percent_diff: 0.0006599812192415119% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_33.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9918525112637544 E new: 0.9918459652234575 E diff: 6.5460402969286235e-06 E percent_diff: 0.0006599812192427765% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_41.I E values differ by > a relative difference of 1e-07 E expected: 0.20283735907845116 E new: 0.20284323676879906 E diff: -5.877690347905196e-06 E percent_diff: -0.0028977355920079243% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_41.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9381597108479458 E new: 0.9381868962357969 E diff: -2.718538785106972e-05 E percent_diff: -0.0028977355920026125% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.qa.score.score_derived_fluxes_snr E values differ by > a relative difference of 1e-07 E expected: 0.8782559648560236 E new: 0.8782558045461518 E diff: 1.6030987182169554e-07 E percent_diff: 1.825320615362696e-05% E Worst absolute diff, s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_41.qa.metric.score_gfluxscale_k_spw: -2.718538785106972e-05 E Worst percentage diff, s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_4.spw_41.I: -0.0028977355920079243% tests/testing_utils.py:440: Failed