Build: #135 failed
Job: Test ManyLinux 2.28 Python 3.12 failed
2023 1 00228 s uid a002 x1199f9e x7c24 procedure hifa calimage diffgain regression: Test case result
The below summarizes the result of the test " 2023 1 00228 s uid a002 x1199f9e x7c24 procedure hifa calimage diffgain regression" in build 135 of PIPESPECS - Test Pipeline main with Casa master - Test ManyLinux 2.28 Python 3.12.
- Description
- 2023 1 00228 s uid a002 x1199f9e x7c24 procedure hifa calimage diffgain regression
- Test class
- tests.regression.fast.alma_if_fast_test
- Method
- test_2023_1_00228_S__uid___A002_X1199f9e_X7c24__procedure_hifa_calimage_diffgain__regression
- Duration
- 609 mins
- Status
- Failed (New Failure)
Error Log
@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
@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