@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 0x7fa57d9cb080> 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 24 result values within tolerances : E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_0.spw_17.qa.metric.score_gfluxscale_amp_time_variation E values differ by > a relative difference of 1e-07 E expected: 0.2826429355360464 E new: 0.2826348415450479 E diff: 8.093990998492284e-06 E percent_diff: 0.0028636806305247383% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_0.spw_17.qa.score.score_gfluxscale_amp_time_variation E values differ by > a relative difference of 1e-07 E expected: 0.9926078001475188 E new: 0.9926080118365141 E diff: -2.1168899533297036e-07 E percent_diff: -2.1326549650477224e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_17.I E values differ by > a relative difference of 1e-07 E expected: 2.96334940390477 E new: 2.9633511001495147 E diff: -1.6962447446644546e-06 E percent_diff: -5.7240794569460263e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_19.I E values differ by > a relative difference of 1e-07 E expected: 2.9450689063228372 E new: 2.9450697455899437 E diff: -8.39267106478303e-07 E percent_diff: -2.8497367402048246e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_19.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0015815892465656 E new: 1.0015813013578558 E diff: 2.8788870976015346e-07 E percent_diff: 2.874341070673196e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_21.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9940864959080147 E new: 0.9940859065455091 E diff: 5.893625055763962e-07 E percent_diff: 5.92868435495709e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_23.I E values differ by > a relative difference of 1e-07 E expected: 2.812411183081126 E new: 1.3126185264278747 E diff: 1.4997926566532511 E percent_diff: 53.32764517776377% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_23.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0012707389967634 E new: 0.4673163645401959 E diff: 0.5339543744565676 E percent_diff: 53.32767189337524% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_23.qa.score.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0 E new: 0.5 E diff: 0.5 E percent_diff: 50.0% 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: 0.8081200287037102 E diff: 4.625743255104453e-07 E percent_diff: 5.7240761805072896e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_31.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.7683530533086219 E new: 0.7683526134974809 E diff: 4.3981114106195207e-07 E percent_diff: 5.724076180449491e-05% 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: 0.8173523655810854 E diff: 4.678589884399287e-07 E percent_diff: 5.724076179815812e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_35.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.8043170381858572 E new: 0.8043165777886572 E diff: 4.6039720003054896e-07 E percent_diff: 5.724076181066338e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_37.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.7579807893536823 E new: 0.7579803554797042 E diff: 4.338739780784806e-07 E percent_diff: 5.724076179403409e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_39.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.7465521685997847 E new: 0.7465517412676361 E diff: 4.273321485559478e-07 E percent_diff: 5.724076180201067e-05% 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: 0.7642707801077511 E diff: 4.374746672697327e-07 E percent_diff: 5.7240761814055335e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_43.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.7660652873228057 E new: 0.7660648488211992 E diff: 4.385016064700764e-07 E percent_diff: 5.724076181581375e-05% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_19.I E values differ by > a relative difference of 1e-07 E expected: 0.28519900824130556 E new: 0.2851996656156171 E diff: -6.573743115412256e-07 E percent_diff: -0.0002304967031950631% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_19.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9992592364574632 E new: 0.9992615937343091 E diff: -2.3572768459434457e-06 E percent_diff: -0.00023590243251594812% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_21.I E values differ by > a relative difference of 1e-07 E expected: 0.30002504211639625 E new: 0.30002547559007164 E diff: -4.3347367539858794e-07 E percent_diff: -0.0001444791649193135% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_21.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.081283586696818 E new: 1.081285207377528 E diff: -1.6206807100793696e-06 E percent_diff: -0.00014988488959037476% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_23.I E values differ by > a relative difference of 1e-07 E expected: 0.2946314052186886 E new: 0.13751887847752362 E diff: 0.15711252674116497 E percent_diff: 53.32511197322941% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_23.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0675100275087495 E new: 0.4982591369487334 E diff: 0.569250890560016 E percent_diff: 53.32510945011711% E s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_2.spw_23.qa.score.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0 E new: 0.5 E diff: 0.5 E percent_diff: 50.0% E Worst absolute diff, s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_23.I: 1.4997926566532511 E Worst percentage diff, s16.hifa_gfluxscale.uid___A002_X1199f9e_X7c24.field_1.spw_23.qa.metric.score_gfluxscale_k_spw: 53.32767189337524% tests/testing_utils.py:440: Failed