def test_lin_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
# create a JWST datamodel for MIRI data
im = RampModel((1, ngroups, ysize, xsize))
im.data += 1
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
# Read in reference file
dq = np.zeros((1024, 1032), dtype=int)
numcoeffs = 3
# place dq flags in dq array that would be in subarray
# MASK1550 file has colstart=1, rowstart=467
dq[542, 100:105] = 1
ref_model = LinearityModel((numcoeffs, 1024, 1032))
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.description = "MIRI LINEARITY Correction"
ref_model.meta.reftype = "LINEARITY"
ref_model.meta.author = "Monty Pytest"
ref_model.meta.pedigree = "GROUND"
ref_model.meta.useafter = '2015-08-01T00:00:00'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = 1032
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = 1024
# run through pipeline
outfile = lincorr(im, ref_model)
# read dq array
outpixdq = outfile.pixeldq
# check for dq flag in pixeldq of subarray image
assert (outpixdq[76, 100] == 1)
assert (outpixdq[76, 104] == 1)
def test_lin_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
# create a JWST datamodel for MIRI data
im = MIRIRampModel((1, ngroups, ysize, xsize))
im.data += 1
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
# Read in reference file
dq = np.zeros((1024, 1032), dtype=int)
numcoeffs = 3
# place dq flags in dq array that would be in subarray
# MASK1550 file has colstart=1, rowstart=467
dq[542, 100:105] = 1
ref_model = LinearityModel((numcoeffs, 1024, 1032))
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = 1032
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = 1024
# run through pipeline
outfile = lincorr(im, ref_model)
# read dq array
outpixdq = outfile.pixeldq
# check for dq flag in pixeldq of subarray image
assert(outpixdq[76, 100] == 1)
assert(outpixdq[76, 104] == 1)
def test_pixeldqprop():
"""Check that linearity reference file DQ values are populated into the PIXELDQ array of the data
This does not fully test mapping, as we have not provided a dq_def to define the
mapping of dq flags in the reference file. it's a straightforward mapping (1-1)"""
# size of integration
nints = 1
ngroups = 10
xsize = 1032
ysize = 1024
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# Create reference file
dq = np.zeros((ysize, xsize), dtype=int)
numcoeffs = 5
# set PIXELDQ to 'NO_LIN_CORR'
dq[500, 500] = dqflags.pixel['NO_LIN_CORR']
dq[550, 550] = dqflags.pixel['DO_NOT_USE']
dq[560, 550] = dqflags.pixel['HOT']
dq[550, 560] = dqflags.pixel['DEAD']
dq[500, 300] = np.bitwise_or(dqflags.pixel['HOT'], dqflags.pixel['DO_NOT_USE'])
ref_model = LinearityModel((numcoeffs, ysize, xsize))
ref_model.dq = dq
coeffs = np.asfarray([0.0e+00, 0.85, 4.62e-06, -6.16e-11, 7.23e-16])
# pixels to test using default coeffs.
ref_model.coeffs[:, 550, 550] = coeffs
ref_model.coeffs[:, 560, 550] = coeffs
ref_model.coeffs[:, 550, 560] = coeffs
ref_model.coeffs[:, 500, 300] = coeffs
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = xsize
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = ysize
# run through linearity correction
outfile = lincorr(im, ref_model)
assert(outfile.pixeldq[500, 500] == dqflags.pixel['NO_LIN_CORR'])
assert(outfile.pixeldq[550, 550] == dqflags.pixel['DO_NOT_USE'])
assert(outfile.pixeldq[560, 550] == dqflags.pixel['HOT'])
assert(outfile.pixeldq[550, 560] == dqflags.pixel['DEAD'])
assert(outfile.pixeldq[500, 300] == np.bitwise_or(dqflags.pixel['HOT'], dqflags.pixel['DO_NOT_USE']))
def test_pixeldqprop():
"""Check that linearity reference file DQ values are populated into the PIXELDQ array of the data
This does not fully test mapping, as we have not provided a dq_def to define the
mapping of dq flags in the reference file. it's a straightforward mapping (1-1)"""
# size of integration
nints = 1
ngroups = 10
xsize = 1032
ysize = 1024
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# Create reference file
dq = np.zeros((1024, 1032), dtype=int)
numcoeffs = 3
# set PIXELDQ to 'NO_LIN_CORR'
dq[500, 500] = dqflags.pixel['NO_LIN_CORR']
dq[550, 550] = dqflags.pixel['DO_NOT_USE']
dq[560, 550] = dqflags.pixel['HOT']
dq[550, 560] = dqflags.pixel['DEAD']
dq[500, 300] = np.bitwise_or(dqflags.pixel['HOT'], dqflags.pixel['DO_NOT_USE'])
ref_model = LinearityModel()
ref_model.data = np.zeros(shape=(numcoeffs, 1024, 1032), dtype=np.float32)
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = 1032
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = 1024
# run through linearity correction
outfile = lincorr(im, ref_model)
assert(outfile.pixeldq[500, 500] == dqflags.pixel['NO_LIN_CORR'])
assert(outfile.pixeldq[550, 550] == dqflags.pixel['DO_NOT_USE'])
assert(outfile.pixeldq[560, 550] == dqflags.pixel['HOT'])
assert(outfile.pixeldq[550, 560] == dqflags.pixel['DEAD'])
assert(outfile.pixeldq[500, 300] == np.bitwise_or(dqflags.pixel['HOT'], dqflags.pixel['DO_NOT_USE']))
def test_nolincorr():
"""Check that correction is not applied for pixels flagged NO_LIN_CORR in the DQ array
of the reference file."""
# size of integration
nints = 1
ngroups = 10
xsize = 1032
ysize = 1024
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# set data value
im.data[0, 5, 500, 500] = 35
# Create reference file
dq = np.zeros((ysize, xsize), dtype=int)
numcoeffs = 3
# set reference file DQ to 'NO_LIN_CORR'
dq[500, 500] = dqflags.pixel['NO_LIN_CORR']
ref_model = LinearityModel((numcoeffs, 1024, 1032))
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = xsize
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = ysize
# run through pipeline (saturation and linearity steps)
outfile = lincorr(im, ref_model)
assert (outfile.pixeldq[500, 500] == dqflags.pixel['NO_LIN_CORR'])
assert (outfile.data[0, 5, 500, 500] == 35
) # NO_LIN_CORR, sci value should not change
def test_nolincorr():
"""Check that correction is not applied for pixels flagged NO_LIN_CORR in the DQ array
of the reference file."""
# size of integration
nints = 1
ngroups = 10
xsize = 1032
ysize = 1024
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# set data value
im.data[0, 5, 500, 500] = 35
# Create reference file
dq = np.zeros((1024, 1032), dtype=int)
numcoeffs = 3
# set reference file DQ to 'NO_LIN_CORR'
dq[500, 500] = dqflags.pixel['NO_LIN_CORR']
ref_model = LinearityModel()
ref_model.data = np.zeros(shape=(numcoeffs, 1024, 1032), dtype=np.float32)
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = 1032
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = 1024
# run through pipeline (saturation and linearity steps)
outfile = lincorr(im, ref_model)
assert(outfile.pixeldq[500, 500] == dqflags.pixel['NO_LIN_CORR'])
assert(outfile.data[0, 5, 500, 500] == 35) # NO_LIN_CORR, sci value should not change
def test_coeff_dq():
"""Test linearity algorithm with random data ramp (does algorithm match expected algorithm)
also test a variety of dq flags and expected output """
# size of integration
nints = 1
ngroups = 160
xsize = 103
ysize = 102
# create the data and groupdq arrays
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# Create reference file
numcoeffs = 5
ref_model = LinearityModel((numcoeffs, ysize, xsize))
ref_model.dq = np.zeros((ysize, xsize), dtype=int)
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.xsize = xsize
ref_model.meta.subarray.ysize = ysize
# Set coefficient values in reference file to check the algorithm
# Equation is DNcorr = L0 + L1*DN(i) + L2*DN(i)^2 + L3*DN(i)^3 + L4*DN(i)^4
# DN(i) = signal in pixel, Ln = coefficient from ref file
# L0 = 0 for all pixels for CDP6
L0 = 0
L1 = 0.85
L2 = 4.62E-6
L3 = -6.16E-11
L4 = 7.23E-16
coeffs = np.asfarray([0.0e+00, 0.85, 4.62e-06, -6.16e-11, 7.23e-16])
ref_model.data[:, 30, 50] = coeffs
# check behavior with NaN coefficients: should not alter pixel values
coeffs2 = np.asfarray([L0, np.nan, L2, L3, L4])
ref_model.data[:, 20, 50] = coeffs2
im.data[0, 50, 20, 50] = 500.0
tgroup = 2.775
# set pixel values (DN) for specific pixels up the ramp
im.data[0, :, 30, 50] = np.arange(ngroups) * 100 * tgroup
scival = 40000.0
im.data[0, 45, 30,
50] = scival # to check linearity multiplication is done correctly
im.data[
0, 30, 35,
36] = 35 # pixel to check that dq=2 meant no correction was applied
# check if dq flags in pixeldq are correctly populated in output
im.pixeldq[50, 40] = dqflags.pixel['DO_NOT_USE']
im.pixeldq[50, 41] = dqflags.pixel['SATURATED']
im.pixeldq[50, 42] = dqflags.pixel['DEAD']
im.pixeldq[50, 43] = dqflags.pixel['HOT']
# set dq flags in DQ of reference file
ref_model.dq[35, 35] = dqflags.pixel['DO_NOT_USE']
ref_model.dq[35, 36] = dqflags.pixel['NO_LIN_CORR']
ref_model.dq[30, 50] = dqflags.pixel['GOOD']
# run through Linearity pipeline
outfile = lincorr(im, ref_model)
# check that multiplication of polynomial was done correctly for specified pixel
outval = L0 + (L1 * scival) + (L2 * scival**2) + (L3 * scival**3) + (
L4 * scival**4)
assert (np.isclose(outfile.data[0, 45, 30, 50], outval, rtol=0.00001))
# check that dq value was handled correctly
assert outfile.pixeldq[35, 35] == dqflags.pixel['DO_NOT_USE']
assert outfile.pixeldq[35, 36] == dqflags.pixel['NO_LIN_CORR']
# NO_LIN_CORR, sci value should not change
assert outfile.data[0, 30, 35, 36] == 35
# NaN coefficient should not change data value
assert outfile.data[0, 50, 20, 50] == 500.0
def test_coeff_dq():
"""Test linearity algorithm with random data ramp (does algorithm match expected algorithm)
also test a variety of dq flags and expected output """
# size of integration
nints = 1
ngroups = 160
xsize = 1032
ysize = 1024
# create the data and groupdq arrays
# create a JWST datamodel for MIRI data
im = make_rampmodel(nints, ngroups, ysize, xsize)
im.meta.instrument.detector = 'MIRIMAGE'
# Create reference file
numcoeffs = 5
ref_model = LinearityModel((numcoeffs, ysize, xsize))
ref_model.dq = np.zeros((ysize, xsize), dtype=int)
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.xsize = xsize
ref_model.meta.subarray.ysize = ysize
# Set coefficient values in reference file to check the algorithm
# Equation is DNcorr = L0 + L1*DN(i) + L2*DN(i)^2 + L3*DN(i)^3 + L4*DN(i)^4
# DN(i) = signal in pixel, Ln = coefficient from ref file
# L0 = 0 for all pixels for CDP6
L0 = 0
L1 = 0.85
L2 = 4.62E-6
L3 = -6.16E-11
L4 = 7.23E-16
coeffs = np.asfarray([0.0e+00, 0.85, 4.62e-06, -6.16e-11, 7.23e-16])
ref_model.data[:, 300, 500] = coeffs
# check behavior with NaN coefficients: should not alter pixel values
coeffs2 = np.asfarray([L0, np.nan, L2, L3, L4])
ref_model.data[:, 200, 500] = coeffs2
im.data[0, 50, 200, 500] = 500.0
tgroup = 2.775
# set pixel values (DN) for specific pixels up the ramp
im.data[0, :, 300, 500] = np.arange(ngroups) * 100 * tgroup
scival = 40000.0
im.data[0, 45, 300, 500] = scival # to check linearity multiplication is done correctly
im.data[0, 30, 350, 360] = 35 # pixel to check that dq=2 meant no correction was applied
# check if dq flags in pixeldq are correctly populated in output
im.pixeldq[50, 40] = 1
im.pixeldq[50, 41] = 2
im.pixeldq[50, 42] = 1024
im.pixeldq[50, 43] = 2048
# set dq flags in DQ of reference file
ref_model.dq[350, 350] = dqflags.pixel['DO_NOT_USE']
ref_model.dq[350, 360] = dqflags.pixel['NO_LIN_CORR']
ref_model.dq[300, 500] = dqflags.pixel['GOOD']
# run through Linearity pipeline
outfile = lincorr(im, ref_model)
# check that multiplication of polynomial was done correctly for specified pixel
outval = L0+(L1*scival)+(L2*scival**2)+(L3*scival**3)+(L4*scival**4)
assert(np.isclose(outfile.data[0, 45, 300, 500], outval, rtol=0.00001))
# check that dq value was handled correctly
assert(outfile.pixeldq[350, 350] == 1)
assert(outfile.pixeldq[350, 360] == dqflags.pixel['NO_LIN_CORR']) # NO_LIN_CORR flag
assert(outfile.data[0, 30, 350, 360] == 35) # NO_LIN_CORR, sci value should not change
assert(outfile.data[0, 50, 200, 500] == 500.0) # NaN coefficient should not change data value
def test_lin_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
# 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
# Read in reference file
dq = np.zeros((1024, 1032), dtype=int)
numcoeffs = 3
# place dq flags in dq array that would be in subarray
# MASK1550 file has colstart=1, rowstart=467
dq[542, 100:105] = 1
ref_model = LinearityModel()
ref_model.data = np.zeros(shape=(numcoeffs, 1024, 1032), dtype=np.float32)
ref_model.dq = dq
ref_model.meta.instrument.name = 'MIRI'
ref_model.meta.instrument.detector = 'MIRIMAGE'
ref_model.meta.subarray.xstart = 1
ref_model.meta.subarray.xsize = 1032
ref_model.meta.subarray.ystart = 1
ref_model.meta.subarray.ysize = 1024
# run through pipeline
outfile = lincorr(im, ref_model)
# read dq array
outpixdq = outfile.pixeldq
# check for dq flag in pixeldq of subarray image
assert(outpixdq[76, 100] == 1)
assert(outpixdq[76, 104] == 1)
