def test_nis_wfss_background(filters, pupils, make_wfss_datamodel):
"""Test background subtraction for NIRISS WFSS modes."""
data = make_wfss_datamodel
data.meta.instrument.filter = filters
data.meta.instrument.pupil = pupils
data.meta.instrument.detector = 'NIS'
data.meta.instrument.name = 'NIRISS'
data.meta.exposure.type = 'NIS_WFSS'
wcs_corrected = AssignWcsStep.call(data)
# Get References
wavelenrange = Step().get_reference_file(wcs_corrected, "wavelengthrange")
bkg_file = Step().get_reference_file(wcs_corrected, 'wfssbkg')
mask = mask_from_source_cat(wcs_corrected, wavelenrange)
with datamodels.open(bkg_file) as bkg_ref:
bkg_ref = no_NaN(bkg_ref)
# calculate backgrounds
pipeline_data_mean = robust_mean(wcs_corrected.data[mask])
test_data_mean, _, _ = sigma_clipped_stats(wcs_corrected.data, sigma=2)
pipeline_reference_mean = robust_mean(bkg_ref.data[mask])
test_reference_mean, _, _ = sigma_clipped_stats(bkg_ref.data, sigma=2)
assert np.isclose([pipeline_data_mean], [test_data_mean], rtol=1e-3)
assert np.isclose([pipeline_reference_mean], [test_reference_mean],
rtol=1e-1)
def test_no_Nan():
"""Make sure that nan values are filled with fill value"""
# Make data model
model = datamodels.ImageModel()
data = np.random.rand(10, 10)
# Randomly insert NaNs
data.ravel()[np.random.choice(data.size, 10, replace=False)] = np.nan
model.data = data
# Randomly select fill value
fill_val = np.random.randint(0, 20)
# Call no_NaN
result = no_NaN(model, fill_value=fill_val)
# Use np.NaN to find NaNs.
test_result = np.isnan(model.data)
# Assign fill values to NaN indices
model.data[test_result] = fill_val
# Make sure arrays are equal.
assert np.array_equal(model.data, result.data)