def test_opening_mask_ref(tmp_path):
# First make test reference file
file_path = tmp_path / 'testmask.asdf'
utils.mk_mask(filepath=file_path)
mask = datamodels.open(file_path)
assert mask.meta.instrument.optical_element == 'F158'
assert isinstance(mask, datamodels.MaskRefModel)
def test_err():
"""Check that a 4-D ERR array is initialized and all values are zero."""
# size of integration
instrument = 'WFI'
ngroups = 5
xsize = 1032
ysize = 1024
csize = (ngroups, ysize, xsize)
# create raw input data for step
dm_ramp = testutil.mk_ramp((ngroups, ysize, xsize))
dm_ramp.meta.instrument.name = instrument
# create a MaskModel elements for the dq input mask
ref_data = testutil.mk_mask(csize[1:])
ref_data['meta']['instrument']['name'] = instrument
# Filter out validation warnings from ref_data
warnings.filterwarnings("ignore", category=ValidationWarning)
# run correction step
outfile = do_dqinit(dm_ramp, ref_data)
# check that ERR array was created and initialized to zero
errarr = outfile.err
assert(errarr.ndim == 2) # check that output err array is 2-D
assert(np.all(errarr == 0)) # check that values are 0
def test_groupdq():
"""Check that GROUPDQ extension is added to the data and all values are initialized to zero."""
# size of integration
instrument = 'WFI'
ngroups = 5
xsize = 1032
ysize = 1024
csize = (ngroups, ysize, xsize)
# create raw input data for step
dm_ramp = testutil.mk_ramp(csize)
dm_ramp.meta.instrument.name = instrument
# create a MaskModel elements for the dq input mask
ref_data = testutil.mk_mask(csize[1:])
ref_data['meta']['instrument']['name'] = instrument
# run the correction step
outfile = do_dqinit(dm_ramp, ref_data)
# check that GROUPDQ was created and initialized to zero
groupdq = outfile.groupdq
np.testing.assert_array_equal(np.full((ngroups, ysize, xsize), 0, dtype=int),
groupdq, err_msg='groupdq not initialized to zero')
def test_make_mask():
mask = utils.mk_mask(shape=(20, 20))
assert mask.meta.reftype == 'MASK'
assert mask.dq.dtype == np.uint32
# Test validation
mask_model = datamodels.MaskRefModel(mask)
assert mask_model.validate() is None
def test_dq_im(xstart, ystart, xsize, ysize, ngroups, instrument, exp_type):
""" Check that PIXELDQ is initialized with the information from the reference file.
test that a flagged value in the reference file flags the PIXELDQ array"""
csize = (ngroups, ysize, xsize)
# create raw input data for step
dm_ramp = testutil.mk_ramp(csize)
dm_ramp.meta.instrument.name = instrument
# create a MaskModel elements for the dq input mask
dq = np.zeros(csize[1:], dtype=np.uint32)
# edit reference file with known bad pixel values
dq[100, 100] = 2 # Saturated pixel
dq[200, 100] = 4 # Jump detected pixel
dq[300, 100] = 8 # Dropout
dq[400, 100] = 32 # Persistence
dq[500, 100] = 1 # Do_not_use
dq[100, 200] = 3 # Saturated pixel + do not use
dq[200, 200] = 5 # Jump detected pixel + do not use
dq[300, 200] = 9 # Dropout + do not use
dq[400, 200] = 33 # Persistence + do not use
# write mask model
ref_data = testutil.mk_mask(csize[1:])
# Copy in maskmodel elemnts
ref_data['dq'] = dq
ref_data['meta']['instrument']['name'] = instrument
# run do_dqinit
outfile = do_dqinit(dm_ramp, ref_data)
dqdata = outfile['pixeldq']
# assert that the pixels read back in match the mapping from ref data to science data
assert (dqdata[100, 100] == dqflags.pixel['SATURATED'])
assert (dqdata[200, 100] == dqflags.pixel['JUMP_DET'])
assert (dqdata[300, 100] == dqflags.pixel['DROPOUT'])
assert (dqdata[400, 100] == dqflags.pixel['PERSISTENCE'])
assert (dqdata[500, 100] == dqflags.pixel['DO_NOT_USE'])
assert (dqdata[100, 200] == dqflags.pixel['SATURATED'] +
dqflags.pixel['DO_NOT_USE'])
assert (dqdata[200, 200] == dqflags.pixel['JUMP_DET'] +
dqflags.pixel['DO_NOT_USE'])
assert (dqdata[300, 200] == dqflags.pixel['DROPOUT'] +
dqflags.pixel['DO_NOT_USE'])
assert (dqdata[400, 200] == dqflags.pixel['PERSISTENCE'] +
dqflags.pixel['DO_NOT_USE'])
def test_dq_add1_groupdq():
"""
Test if the dq_init code set the groupdq flag on the first
group to 'do_not_use' by adding 1 to the flag, not overwriting to 1
Also test whether two flags on the same pixel are added together.
"""
# size of integration
instrument = 'WFI'
ngroups = 5
xsize = 1032
ysize = 1024
csize = (ngroups, ysize, xsize)
# create raw input data for step
dm_ramp = testutil.mk_ramp((ngroups, ysize, xsize))
dm_ramp.meta.instrument.name = instrument
# create a MaskModel elements for the dq input mask
dq = np.zeros(csize[1:], dtype=np.uint32)
# write reference file with known bad pixel values
dq[505, 505] = 1 # Do_not_use
dq[400, 500] = 3 # do_not_use and saturated pixel
# write mask model
ref_data = testutil.mk_mask(csize[1:])
ref_data['meta']['instrument']['name'] = instrument
# Copy in maskmodel elemnts
ref_data['dq'] = dq
# set a flag in the pixel dq
dm_ramp.pixeldq[505, 505] = 4 # Jump detected pixel
# run correction step
outfile = do_dqinit(dm_ramp, ref_data)
# test if pixels in pixeldq were incremented in value by 1
# check that previous dq flag is added to mask value
assert (outfile.pixeldq[505, 505] == dqflags.pixel['JUMP_DET'] +
dqflags.pixel['DO_NOT_USE'])
# check two flags propagate correctly
assert (outfile.pixeldq[400, 500] == dqflags.pixel['SATURATED'] +
dqflags.pixel['DO_NOT_USE'])
