def test_flatfield_step_interface(instrument, exptype):
"""Test that the basic inferface works for data requiring a FLAT reffile"""
shape = (20, 20)
wfi_image = testutil.mk_level2_image(shape=shape)
wfi_image.meta.instrument.name = instrument
wfi_image.meta.instrument.detector = 'WFI01'
wfi_image.meta.instrument.optical_element = 'F158'
wfi_image.meta.exposure.type = exptype
wfi_image.data = np.ones(shape, dtype=np.float32)
wfi_image.dq = np.zeros(shape, dtype=np.uint32)
wfi_image.err = np.zeros(shape, dtype=np.float32)
wfi_image.var_poisson = np.zeros(shape, dtype=np.float32)
wfi_image.var_rnoise = np.zeros(shape, dtype=np.float32)
wfi_image.var_flat = np.zeros(shape, dtype=np.float32)
wfi_image_model = ImageModel(wfi_image)
flatref = stnode.FlatRef()
meta = {}
testutil.add_ref_common(meta)
meta['instrument']['optical_element'] = 'F158'
meta['instrument']['detector'] = 'WFI01'
meta['reftype'] = 'FLAT'
flatref['meta'] = meta
flatref['data'] = np.ones(shape, dtype=np.float32)
flatref['dq'] = np.zeros(shape, dtype=np.uint16)
flatref['err'] = (np.random.random(shape) * 0.05).astype(np.float32)
flatref_model = FlatRefModel(flatref)
result = FlatFieldStep.call(wfi_image_model, override_flat=flatref_model)
assert (result.data == wfi_image.data).all()
assert result.var_flat.shape == shape
assert result.meta.cal_step.flat_field == 'COMPLETE'
def test_flat_model(tmp_path):
# Set temporary asdf file
file_path = tmp_path / "test.asdf"
meta = {}
utils.add_ref_common(meta)
meta['reftype'] = "FLAT"
flatref = stnode.FlatRef()
flatref['meta'] = meta
flatref.meta.instrument['optical_element'] = 'F062'
shape = (4096, 4096)
flatref['data'] = np.zeros(shape, dtype=np.float32)
flatref['dq'] = np.zeros(shape, dtype=np.uint16)
flatref['dq_def'] = np.zeros(10, dtype=table_definitions.DQ_DEF_DTYPE)
flatref['err'] = np.zeros(shape, dtype=np.float32)
# Testing flat file asdf file
with asdf.AsdfFile(meta) as af:
af.tree = {'roman': flatref}
af.write_to(file_path)
# Test that asdf file opens properly
with datamodels.open(file_path) as model:
with pytest.warns(None):
model.validate()
# Confirm that asdf file is opened as flat file model
assert isinstance(model, datamodels.FlatRefModel)
def generate_wfi_reffiles(tmpdir_factory):
gainfile = str(tmpdir_factory.mktemp("ndata").join("gain.asdf"))
readnoisefile = str(tmpdir_factory.mktemp("ndata").join('readnoise.asdf'))
ingain = 6
xsize = 20
ysize = 20
shape = (ysize, xsize)
# Create temporary gain reference file
gain_ref = rds.GainRef()
meta = {}
testutil.add_ref_common(meta)
meta['instrument']['detector'] = 'WFI01'
meta['instrument']['name'] = 'WF1'
meta['author'] = 'John Doe'
meta['reftype'] = 'GAIN'
meta['pedigree'] = 'DUMMY'
meta['description'] = 'DUMMY'
meta['useafter'] = Time('2022-01-01T11:11:11.111')
gain_ref['meta'] = meta
gain_ref['data'] = np.ones(shape, dtype=np.float32) * ingain
gain_ref['dq'] = np.zeros(shape, dtype=np.uint16)
gain_ref['err'] = (np.random.random(shape) * 0.05).astype(np.float64)
gain_ref_model = GainRefModel(gain_ref)
gain_ref_model.save(gainfile)
# Create temporary readnoise reference file
rn_ref = rds.ReadnoiseRef()
meta = {}
testutil.add_ref_common(meta)
meta['instrument']['detector'] = 'WFI01'
meta['instrument']['name'] = 'WF1'
meta['author'] = 'John Doe'
meta['reftype'] = 'READNOISE'
meta['pedigree'] = 'DUMMY'
meta['description'] = 'DUMMY'
meta['useafter'] = Time('2022-01-01T11:11:11.111')
exposure = {}
exposure['type'] = 'WFI_IMAGE'
exposure['frame_time'] = 666
rn_ref['meta'] = meta
rn_ref['meta']['exposure'] = exposure
rn_ref['data'] = np.ones(shape, dtype=np.float32)
rn_ref_model = ReadnoiseRefModel(rn_ref)
rn_ref_model.save(readnoisefile)
return gainfile, readnoisefile
def test_dqinit_refpix(instrument, exptype):
"""Test that the basic inferface works for data requiring a DQ reffile"""
# Set test size
shape = (2, 20, 20)
# Create test science raw model
wfi_sci_raw = testutil.mk_level1_science_raw(shape)
wfi_sci_raw.meta.instrument.name = instrument
wfi_sci_raw.meta.instrument.detector = 'WFI01'
wfi_sci_raw.meta.instrument.optical_element = 'F158'
wfi_sci_raw.meta.exposure.type = exptype
wfi_sci_raw.data = np.ones(shape, dtype=np.uint16)
wfi_sci_raw_model = ScienceRawModel(wfi_sci_raw)
# Create mask model
maskref = stnode.MaskRef()
meta = {}
testutil.add_ref_common(meta)
meta['instrument']['optical_element'] = 'F158'
meta['instrument']['detector'] = 'WFI01'
meta['reftype'] = 'MASK'
maskref['meta'] = meta
maskref['data'] = np.ones(shape[1:], dtype=np.float32)
maskref['dq'] = np.zeros(shape[1:], dtype=np.uint16)
maskref['err'] = (np.random.random(shape[1:]) * 0.05).astype(np.float32)
maskref_model = MaskRefModel(maskref)
# Perform Data Quality application step
result = DQInitStep.call(wfi_sci_raw_model, override_mask=maskref_model)
# check if reference pixels are correct
assert result.data.shape == (2, 20, 20) # no pixels should be trimmed
assert result.amp33.shape == (2, 4096 ,128)
assert result.border_ref_pix_right.shape == (2, 20, 4)
assert result.border_ref_pix_left.shape == (2, 20, 4)
assert result.border_ref_pix_top.shape == (2, 4, 20)
assert result.border_ref_pix_bottom.shape == (2, 4, 20)
assert result.dq_border_ref_pix_right.shape == (20, 4)
assert result.dq_border_ref_pix_left.shape == (20, 4)
assert result.dq_border_ref_pix_top.shape == (4, 20)
assert result.dq_border_ref_pix_bottom.shape == (4, 20)
def test_dqinit_step_interface(instrument, exptype):
"""Test that the basic inferface works for data requiring a DQ reffile"""
# Set test size
shape = (2, 20, 20)
# Create test science raw model
wfi_sci_raw = testutil.mk_level1_science_raw(shape)
wfi_sci_raw.meta.instrument.name = instrument
wfi_sci_raw.meta.instrument.detector = 'WFI01'
wfi_sci_raw.meta.instrument.optical_element = 'F158'
wfi_sci_raw.meta.exposure.type = exptype
wfi_sci_raw.data = np.ones(shape, dtype=np.uint16)
wfi_sci_raw_model = ScienceRawModel(wfi_sci_raw)
# Create mask model
maskref = stnode.MaskRef()
meta = {}
testutil.add_ref_common(meta)
meta['instrument']['optical_element'] = 'F158'
meta['instrument']['detector'] = 'WFI01'
meta['reftype'] = 'MASK'
maskref['meta'] = meta
maskref['data'] = np.ones(shape[1:], dtype=np.float32)
maskref['dq'] = np.zeros(shape[1:], dtype=np.uint16)
maskref['err'] = (np.random.random(shape[1:]) * 0.05).astype(np.float32)
maskref_model = MaskRefModel(maskref)
# Perform Data Quality application step
result = DQInitStep.call(wfi_sci_raw_model, override_mask=maskref_model)
# Test dq_init results
assert (result.data == wfi_sci_raw.data).all()
assert result.pixeldq.shape == shape[1:]
assert result.meta.cal_step.dq_init == 'COMPLETE'
assert result.data.dtype == np.float32
assert result.err.dtype == np.float32
assert result.pixeldq.dtype == np.uint32
assert result.groupdq.dtype == np.uint8