def test_outlier_step_image_weak_CR_nodither(exptype, tsovisit):
"""Test whole step with an outlier for TSO & coronagraphic modes"""
bkg = 1.5
sig = 0.02
container = datamodels.ModelContainer(
we_many_sci(background=bkg,
sigma=sig,
signal=7.0,
exptype=exptype,
tsovisit=tsovisit))
# Drop a weak CR on the science array
# no noise so it should always be above the default threshold of 5
container[0].data[12, 12] = bkg + sig * 10
result = OutlierDetectionStep.call(container)
# Make sure nothing changed in SCI array
for image, corrected in zip(container, result):
np.testing.assert_allclose(image.data, corrected.data)
# Verify source is not flagged
for r in result:
assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"]
# Verify CR is flagged
assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE
def test_outlier_step_no_outliers(we_three_sci):
"""Test whole step, no outliers"""
container = datamodels.ModelContainer(list(we_three_sci))
pristine = container.copy()
result = OutlierDetectionStep.call(container)
# Make sure nothing changed in SCI and DQ arrays
for image, uncorrected in zip(pristine, container):
np.testing.assert_allclose(image.data, uncorrected.data)
np.testing.assert_allclose(image.dq, uncorrected.dq)
# Make sure nothing changed in SCI and DQ arrays
for image, corrected in zip(container, result):
np.testing.assert_allclose(image.data, corrected.data)
np.testing.assert_allclose(image.dq, corrected.dq)
def test_outlier_step(we_three_sci):
"""Test whole step with an outlier"""
container = datamodels.ModelContainer(list(we_three_sci))
# Drop a CR on the science array
container[0].data[12, 12] += 1e3
result = OutlierDetectionStep.call(container)
# Make sure nothing changed in SCI array
for image, corrected in zip(container, result):
np.testing.assert_allclose(image.data, corrected.data)
# Verify source is not flagged
for r in result:
assert r.dq[7, 7] == datamodels.dqflags.pixel["GOOD"]
# Verify CR is flagged
assert result[0].dq[12, 12] == OUTLIER_DO_NOT_USE
def test_outlier_step_square_source_no_outliers(we_three_sci):
"""Test whole step with square source with sharp edges, no outliers"""
container = datamodels.ModelContainer(list(we_three_sci))
# put a square source in all three exposures
for ccont in container:
ccont.data[5:15, 5:15] += 1e3
pristine = container.copy()
result = OutlierDetectionStep.call(container)
# Make sure nothing changed in SCI and DQ arrays
for image, uncorrected in zip(pristine, container):
np.testing.assert_allclose(image.data, uncorrected.data)
np.testing.assert_allclose(image.dq, uncorrected.dq)
# Make sure nothing changed in SCI and DQ arrays
for image, corrected in zip(container, result):
np.testing.assert_allclose(image.data, corrected.data)
np.testing.assert_allclose(image.dq, corrected.dq)