def test_dq_im(xstart, ystart, xsize, ysize, nints, 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"""
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart,
xstart, exp_type)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# edit reference file with known bad pixel values
dq[100, 100] = 2 # Dead pixel
dq[200, 100] = 4 # Hot pixel
dq[300, 100] = 8 # Unreliable_slope
dq[400, 100] = 16 # RC
dq[500, 100] = 1 # Do_not_use
dq[100, 200] = 3 # Dead pixel + do not use
dq[200, 200] = 5 # Hot pixel + do not use
dq[300, 200] = 9 # Unreliable slope + do not use
dq[400, 200] = 17 # RC + do not use
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# run do_dqinit
outfile = do_dqinit(dm_ramp, ref_data)
if instrument == "FGS":
dqdata = outfile.dq
else:
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['DEAD'])
assert (dqdata[200, 100] == dqflags.pixel['HOT'])
assert (dqdata[300, 100] == dqflags.pixel['UNRELIABLE_SLOPE'])
assert (dqdata[400, 100] == dqflags.pixel['RC'])
assert (dqdata[500, 100] == dqflags.pixel['DO_NOT_USE'])
assert (dqdata[100, 200] == 1025)
assert (dqdata[200, 200] == 2049)
assert (dqdata[300, 200] == 16777217)
assert (dqdata[400, 200] == 16385)
def test_dq_im(xstart, ystart, xsize, ysize, nints, 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"""
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart, xstart, exp_type)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# edit reference file with known bad pixel values
dq[100, 100] = 2 # Dead pixel
dq[200, 100] = 4 # Hot pixel
dq[300, 100] = 8 # Unreliable_slope
dq[400, 100] = 16 # RC
dq[500, 100] = 1 # Do_not_use
dq[100, 200] = 3 # Dead pixel + do not use
dq[200, 200] = 5 # Hot pixel + do not use
dq[300, 200] = 9 # Unreliable slope + do not use
dq[400, 200] = 17 # RC + do not use
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# run do_dqinit
outfile = do_dqinit(dm_ramp, ref_data)
if instrument == "FGS":
dqdata = outfile.dq
else:
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['DEAD'])
assert(dqdata[200, 100] == dqflags.pixel['HOT'])
assert(dqdata[300, 100] == dqflags.pixel['UNRELIABLE_SLOPE'])
assert(dqdata[400, 100] == dqflags.pixel['RC'])
assert(dqdata[500, 100] == dqflags.pixel['DO_NOT_USE'])
assert(dqdata[100, 200] == 1025)
assert(dqdata[200, 200] == 2049)
assert(dqdata[300, 200] == 16777217)
assert(dqdata[400, 200] == 16385)
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
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
# create raw input data for step
dm_ramp = make_rampmodel(nints, ngroups, ysize, xsize)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write reference file with known bad pixel values
dq[505, 505] = 1 # Do_not_use
dq[400, 500] = 3 # do_not_use and dead pixel
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = 'MIRI'
ref_data.meta.subarray.xstart = 1
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = 1
ref_data.meta.subarray.ysize = ysize
# set a flag in the pixel dq
dm_ramp.pixeldq[505, 505] = 4
# run correction step
outfile = do_dqinit(dm_ramp, ref_data)
# test if pixels in pixeldq were incremented in value by 1
assert (outfile.pixeldq[505, 505] == 5
) # check that previous dq flag is added to mask value
assert (outfile.pixeldq[400, 500] == 1025
) # check two flags propagate correctly
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
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
# create raw input data for step
dm_ramp = make_rampmodel(nints, ngroups, ysize, xsize)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write reference file with known bad pixel values
dq[505, 505] = 1 # Do_not_use
dq[400, 500] = 3 # do_not_use and dead pixel
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = 'MIRI'
ref_data.meta.subarray.xstart = 1
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = 1
ref_data.meta.subarray.ysize = ysize
# set a flag in the pixel dq
dm_ramp.pixeldq[505, 505] = 4
# run correction step
outfile = do_dqinit(dm_ramp, ref_data)
# test if pixels in pixeldq were incremented in value by 1
assert(outfile.pixeldq[505, 505] == 5) # check that previous dq flag is added to mask value
assert(outfile.pixeldq[400, 500] == 1025) # check two flags propagate correctly
def test_groupdq():
"""Check that GROUPDQ extension is added to the data and all values are initialized to zero."""
# size of integration
instrument = 'MIRI'
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
xstart = 1
ystart = 1
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart,
xstart)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# 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((1, ngroups, ysize, xsize),
0,
dtype=int),
groupdq,
err_msg='groupdq not initialized to zero')
def test_err():
"""Check that a 4-D ERR array is initialized and all values are zero."""
# size of integration
instrument = 'MIRI'
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
xstart = 1
ystart = 1
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart,
xstart)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# 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 == 4) # check that output err array is 4-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 = 'MIRI'
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
xstart = 1
ystart = 1
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart, xstart)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# 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((1, ngroups, ysize, xsize),
0,
dtype=int),
groupdq,
err_msg='groupdq not initialized to zero')
def test_err():
"""Check that a 4-D ERR array is initialized and all values are zero."""
# size of integration
instrument = 'MIRI'
nints = 1
ngroups = 5
xsize = 1032
ysize = 1024
xstart = 1
ystart = 1
# create raw input data for step
dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart, xstart)
# create a MaskModel for the dq input mask
dq, dq_def = make_maskmodel(ysize, xsize)
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = instrument
ref_data.meta.subarray.xstart = xstart
ref_data.meta.subarray.xsize = xsize
ref_data.meta.subarray.ystart = ystart
ref_data.meta.subarray.ysize = ysize
# 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 == 4) # check that output err array is 4-D
assert(np.all(errarr == 0)) # check that values are 0
def test_dq_subarray():
"""Test that the pipeline properly extracts the subarray from the reference file."""
# put dq flags in specific pixels and make sure they match in the output subarray file
# create input data
# create model of data with 0 value array
ngroups = 50
ysize = 224
xsize = 288
fullxsize = 1032
fullysize = 1024
# create the data and groupdq arrays
csize = (1, ngroups, ysize, xsize)
data = np.full(csize, 1.0)
pixeldq = np.zeros((ysize, xsize), dtype=int)
groupdq = np.zeros(csize, dtype=int)
# create a JWST datamodel for MIRI data
im = MIRIRampModel(data=data, pixeldq=pixeldq, groupdq=groupdq)
im.meta.instrument.name = 'MIRI'
im.meta.instrument.detector = 'MIRIMAGE'
im.meta.instrument.filter = 'F1500W'
im.meta.instrument.band = 'N/A'
im.meta.observation.date = '2016-06-01'
im.meta.observation.time = '00:00:00'
im.meta.exposure.type = 'MIR_IMAGE'
im.meta.subarray.name = 'MASK1550'
im.meta.subarray.xstart = 1
im.meta.subarray.xsize = xsize
im.meta.subarray.ystart = 467
im.meta.subarray.ysize = ysize
# create full size mask model
dq, dq_def = make_maskmodel(fullysize, fullxsize)
# place dq flags in dq array that would be in subarray
# MASK1550 file has colstart=1, rowstart=467
dq[542, 100] = 2
dq[550, 100] = 1
dq[580, 80] = 4
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = 'MIRI'
ref_data.meta.subarray.xstart = 1
ref_data.meta.subarray.xsize = fullxsize
ref_data.meta.subarray.ystart = 1
ref_data.meta.subarray.ysize = fullysize
# Filter out validation warnings from ref_data
warnings.filterwarnings("ignore", category=ValidationWarning)
# run correction step
outfile = do_dqinit(im, ref_data)
# read dq array
outpixdq = outfile.pixeldq
# check for dq flag in pixeldq of subarray image
assert (outpixdq[76, 100] == 1024)
assert (outpixdq[84, 100] == 1)
assert (outpixdq[114, 80] == 2048
) # check that pixel was flagged 'NO_SAT_CHECK'
def test_dq_subarray():
"""Test that the pipeline properly extracts the subarray from the reference file."""
# put dq flags in specific pixels and make sure they match in the output subarray file
# create input data
# create model of data with 0 value array
ngroups = 50
ysize = 224
xsize = 288
fullxsize = 1032
fullysize = 1024
# create the data and groupdq arrays
csize = (1, ngroups, ysize, xsize)
data = np.full(csize, 1.0)
pixeldq = np.zeros((ysize, xsize), dtype=int)
groupdq = np.zeros(csize, dtype=int)
# create a JWST datamodel for MIRI data
im = MIRIRampModel(data=data, pixeldq=pixeldq, groupdq=groupdq)
im.meta.instrument.name = 'MIRI'
im.meta.instrument.detector = 'MIRIMAGE'
im.meta.instrument.filter = 'F1500W'
im.meta.instrument.band = 'N/A'
im.meta.observation.date = '2016-06-01'
im.meta.observation.time = '00:00:00'
im.meta.exposure.type = 'MIR_IMAGE'
im.meta.subarray.name = 'MASK1550'
im.meta.subarray.xstart = 1
im.meta.subarray.xsize = xsize
im.meta.subarray.ystart = 467
im.meta.subarray.ysize = ysize
# create full size mask model
dq, dq_def = make_maskmodel(fullysize, fullxsize)
# place dq flags in dq array that would be in subarray
# MASK1550 file has colstart=1, rowstart=467
dq[542, 100] = 2
dq[550, 100] = 1
dq[580, 80] = 4
# write mask model
ref_data = MaskModel(dq=dq, dq_def=dq_def)
ref_data.meta.instrument.name = 'MIRI'
ref_data.meta.subarray.xstart = 1
ref_data.meta.subarray.xsize = fullxsize
ref_data.meta.subarray.ystart = 1
ref_data.meta.subarray.ysize = fullysize
# Filter out validation warnings from ref_data
warnings.filterwarnings("ignore", category=ValidationWarning)
# run correction step
outfile = do_dqinit(im, ref_data)
# read dq array
outpixdq = outfile.pixeldq
# check for dq flag in pixeldq of subarray image
assert(outpixdq[76, 100] == 1024)
assert(outpixdq[84, 100] == 1)
assert(outpixdq[114, 80] == 2048) # check that pixel was flagged 'NO_SAT_CHECK'
