Build: #103 failed
Job: Test ManyLinux 2.28 Python 3.12 failed
uid a002 xee1eb6 xc58d procedure hifa calsurvey regression: Test case result
The below summarizes the result of the test " uid a002 xee1eb6 xc58d procedure hifa calsurvey regression" in build 103 of PIPESPECS - Test Pipeline main with Casa 6.7.4 - Test ManyLinux 2.28 Python 3.12.
- Description
- uid a002 xee1eb6 xc58d procedure hifa calsurvey regression
- Test class
- tests.regression.fast.alma_if_fast_test
- Method
- test_uid___A002_Xee1eb6_Xc58d__procedure_hifa_calsurvey__regression
- Duration
- 560 mins
- Status
- Failed (New Failure)
Error Log
@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.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