@pytest.mark.seven @pytest.mark.mpi def test_2022_1_00207_S__uid___A001_X2d20_X373d__PPR__regression(): """Run ALMA polcal+image regression on a multi-EB 7m test dataset with a PPR file. PPR: pl-regressiontest/2022.1.00207.S/PPR.xml Project: 2022.1.00207.S MOUS: uid___A001_X2d20_X373d EBs: uid___A002_X10b6f7c_X41d1 uid___A002_X10b6f7c_X46cc """ ref_directory = 'pl-regressiontest/2022.1.00207.S' pt = PipelineTester( visname=['uid___A002_X10b6f7c_X41d1', 'uid___A002_X10b6f7c_X46cc'], ppr=f"{ref_directory}/PPR.xml", input_dir=ref_directory, expectedoutput_dir=ref_directory, ) > pt.run() tests/regression/fast/alma_if_fast_test.py:123: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:376: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7f4f02ef2150> new_file = 'uid___A002_X10b6f7c_X41d1.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 29 result values within tolerances : E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9998218986723444 E new: 0.9998227851376125 E diff: -8.864652680795615e-07 E percent_diff: -8.866231768444877e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_18.I E values differ by > a relative difference of 1e-07 E expected: 1.3653809717912258 E new: 1.3653796551526987 E diff: 1.3166385270935166e-06 E percent_diff: 9.64301212844819e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_20.I E values differ by > a relative difference of 1e-07 E expected: 1.313188383648403 E new: 1.3131887675624525 E diff: -3.8391404943993734e-07 E percent_diff: -2.9235260852164803e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_20.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0019692397005648 E new: 1.001970498830253 E diff: -1.2591296880692937e-06 E percent_diff: -0.00012566550330882217% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_22.I E values differ by > a relative difference of 1e-07 E expected: 1.3102862582551928 E new: 1.3102863988307627 E diff: -1.4057556985136443e-07 E percent_diff: -1.072861513777593e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_0.spw_22.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0054226609922603 E new: 1.0054237383915183 E diff: -1.0773992580315195e-06 E percent_diff: -0.00010715883974290224% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_16.I E values differ by > a relative difference of 1e-07 E expected: 0.35676380919541717 E new: 0.3567643335721676 E diff: -5.243767504103936e-07 E percent_diff: -0.00014698148660117216% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_16.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0030947541027135 E new: 1.0030954559681338 E diff: -7.018654202273211e-07 E percent_diff: -6.997000207175368e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_18.I E values differ by > a relative difference of 1e-07 E expected: 0.3542420210295326 E new: 0.354242293836381 E diff: -2.7280684838926206e-07 E percent_diff: -7.701143065873559e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_20.I E values differ by > a relative difference of 1e-07 E expected: 0.3438184465624654 E new: 0.34382060584345014 E diff: -2.1592809847370553e-06 E percent_diff: -0.0006280294167825443% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_20.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9972084475163907 E new: 0.997213942310064 E diff: -5.494793673288534e-06 E percent_diff: -0.0005510175617719302% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_22.I E values differ by > a relative difference of 1e-07 E expected: 0.3424346420160027 E new: 0.34243473636467536 E diff: -9.434867265234459e-08 E percent_diff: -2.7552315413209704e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.field_2.spw_22.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 0.9969142110040273 E new: 0.9969137179394586 E diff: 4.93064568729551e-07 E percent_diff: 4.945907714897236e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X41d1.qa.metric.score_derived_fluxes_snr E values differ by > a relative difference of 1e-07 E expected: 260.41168489992793 E new: 260.41175375126136 E diff: -6.885133342393601e-05 E percent_diff: -2.643941782043862e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_10.I E values differ by > a relative difference of 1e-07 E expected: 1.3100103840087651 E new: 1.3100067493136076 E diff: 3.6346951575172426e-06 E percent_diff: 0.0002774554462991893% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_10.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0076214074604941 E new: 1.0076153277561768 E diff: 6.079704317318857e-06 E percent_diff: 0.0006033718887177598% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_4.I E values differ by > a relative difference of 1e-07 E expected: 1.3751936458839125 E new: 1.3751907513694928 E diff: 2.8945144197045636e-06 E percent_diff: 0.00021048049693714953% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_4.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0035071238173334 E new: 1.0035017410336444 E diff: 5.382783689000803e-06 E percent_diff: 0.0005363971576529257% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_6.I E values differ by > a relative difference of 1e-07 E expected: 1.3621147057074792 E new: 1.3621191450900558 E diff: -4.439382576659767e-06 E percent_diff: -0.00032591840893120397% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_8.I E values differ by > a relative difference of 1e-07 E expected: 1.3182131439092004 E new: 1.3182179334732627 E diff: -4.789564062246399e-06 E percent_diff: -0.0003633376047247415% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_1.spw_8.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0082150098813702 E new: 1.0082153871460893 E diff: -3.7726471902033154e-07 E percent_diff: -3.741907384068024e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_10.I E values differ by > a relative difference of 1e-07 E expected: 0.35352838264456693 E new: 0.35353247318978354 E diff: -4.090545216606234e-06 E percent_diff: -0.0011570627472699464% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_10.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0129126775618893 E new: 1.0129262614066323 E diff: -1.3583844743036977e-05 E percent_diff: -0.0013410676995112443% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_4.I E values differ by > a relative difference of 1e-07 E expected: 0.3634560120039281 E new: 0.3634562609375893 E diff: -2.489336612399029e-07 E percent_diff: -6.849072598012563e-05% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_4.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0057282529903182 E new: 1.0057307923905467 E diff: -2.5394002285406003e-06 E percent_diff: -0.0002524936752040361% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_6.I E values differ by > a relative difference of 1e-07 E expected: 0.3599419377377514 E new: 0.3599412754356427 E diff: 6.623021087115433e-07 E percent_diff: 0.0001840024846435336% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_8.I E values differ by > a relative difference of 1e-07 E expected: 0.352711869536587 E new: 0.3527122673387697 E diff: -3.978021826944733e-07 E percent_diff: -0.0001127838944623918% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_8.qa.metric.score_gfluxscale_k_spw E values differ by > a relative difference of 1e-07 E expected: 1.0068029434447139 E new: 1.0068059315042126 E diff: -2.9880594987208298e-06 E percent_diff: -0.00029678692520478426% E s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.qa.metric.score_derived_fluxes_snr E values differ by > a relative difference of 1e-07 E expected: 257.1270701945043 E new: 257.1271593408154 E diff: -8.914631109746551e-05 E percent_diff: -3.4670138398897714e-05% E Worst absolute diff, s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.qa.metric.score_derived_fluxes_snr: -8.914631109746551e-05 E Worst percentage diff, s21.hifa_gfluxscale.uid___A002_X10b6f7c_X46cc.field_3.spw_10.qa.metric.score_gfluxscale_k_spw: -0.0013410676995112443% tests/testing_utils.py:435: 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:143: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ tests/testing_utils.py:376: in run self.__compare_results(new_file, default_relative_tolerance) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <tests.testing_utils.PipelineTester object at 0x7f443b387920> 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}." ^^^^^^^^^^^^^^^^ E AssertionError: Expected key s21.hif_applycal.uid___A002_X1199f9e_X7c24.spw_31.qa.metric.phase_vs_freqslope does not match new key s21.hif_applycal.uid___A002_X1199f9e_X7c24.spw_31.qa.metric.phase_vs_freqintercept. tests/testing_utils.py:409: AssertionError