def run_jump_step(self, infile, threshold, run_steps):
'''Function to run the jump detection step.'''
# output file name
out = infile[:-5] + "_jump_CRthresh" + str(threshold) + ".fits"
# if run_steps, run all steps prior to jump
if run_steps:
m = DQInitStep.call(infile)
m = SaturationStep.call(m)
m = SuperBiasStep.call(m)
m_ref = RefPixStep.call(m, config_file='refpix.cfg')
m_lin = LinearityStep.call(m)
m_dark = DarkCurrentStep.call(m)
# if threshold is given, use that rejection threshold
if threshold is not None:
m = JumpStep.call(m,
output_file=out,
rejection_threshold=threshold)
else:
m = JumpStep.call(m, output_file=out)
# else, run only jump_step
else:
if threshold is not None:
m = JumpStep.call(infile,
output_file=out,
rejection_threshold=threshold)
else:
m = JumpStep.call(infile, output_file=out)
return m
def test_jump_niriss(_bigdata):
"""
Regression test of jump step performed on NIRISS data.
"""
suffix = 'jump'
output_file_base, output_file = add_suffix('jump1_output.fits', suffix)
JumpStep.call(
_bigdata +
'/niriss/test_jump/jw00034001001_01101_00001_NIRISS_linearity.fits',
rejection_threshold=20.0,
output_file=output_file_base,
suffix=suffix)
h = fits.open(output_file)
href = fits.open(
_bigdata +
'/niriss/test_jump/jw00034001001_01101_00001_NIRISS_jump.fits')
newh = fits.HDUList(
[h['primary'], h['sci'], h['err'], h['pixeldq'], h['groupdq']])
newhref = fits.HDUList([
href['primary'], href['sci'], href['err'], href['pixeldq'],
href['groupdq']
])
result = fits.diff.FITSDiff(
newh,
newhref,
ignore_keywords=['DATE', 'CAL_VER', 'CAL_VCS', 'CRDS_VER', 'CRDS_CTX'],
rtol=0.00001)
assert result.identical, result.report()
def test_two_CRs(generate_miri_reffiles, max_cores, setup_inputs):
override_gain, override_readnoise = generate_miri_reffiles
grouptime = 3.0
deltaDN = 5
ingain = 6
inreadnoise = np.float64(7)
ngroups = 100
CR_fraction = 5
xsize = 103
ysize = 102
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
nrows=ysize, ncols=xsize,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
for i in range(ngroups):
model1.data[0, i, :, :] = deltaDN * i
first_CR_group_locs = [x for x in range(1,89) if x % 5 == 0]
CR_locs = [x for x in range(xsize*ysize) if x % CR_fraction == 0]
CR_x_locs = [x % ysize for x in CR_locs]
CR_y_locs = [np.int(x / xsize) for x in CR_locs]
CR_pool = cycle(first_CR_group_locs)
for i in range(len(CR_x_locs)):
CR_group = next(CR_pool)
model1.data[0,CR_group:,CR_y_locs[i], CR_x_locs[i]] = \
model1.data[0,CR_group:, CR_y_locs[i], CR_x_locs[i]] + 500
model1.data[0, CR_group+8:, CR_y_locs[i], CR_x_locs[i]] = \
model1.data[0, CR_group+8:, CR_y_locs[i], CR_x_locs[i]] + 700
out_model = JumpStep.call(model1, override_gain=override_gain,
override_readnoise=override_readnoise, maximum_cores=max_cores)
CR_pool = cycle(first_CR_group_locs)
for i in range(len(CR_x_locs)):
CR_group = next(CR_pool)
assert (4 == np.max(out_model.groupdq[0, CR_group, CR_y_locs[i], CR_x_locs[i]]))
assert (4 == np.max(out_model.groupdq[0, CR_group+8, CR_y_locs[i], CR_x_locs[i]]))
def test_nircam(generate_nircam_reffiles, setup_inputs, max_cores):
override_gain, override_readnoise = generate_nircam_reffiles
grouptime = 3.0
deltaDN = 5
ingain = 6
inreadnoise = np.float64(7)
ngroups = 100
CR_fraction = 5
nrows = 20
ncols = 20
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
nrows=nrows, ncols=ncols, gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
for i in range(ngroups):
model1.data[0, i, :, :] = deltaDN * i
first_CR_group_locs = [x for x in range(1,89) if x % 5 == 0]
CR_locs = [x for x in range(nrows*ncols) if x % CR_fraction == 0]
CR_x_locs = [x % ncols for x in CR_locs]
CR_y_locs = [np.int(x / nrows) for x in CR_locs]
CR_pool = cycle(first_CR_group_locs)
for i in range(len(CR_x_locs)):
CR_group = next(CR_pool)
model1.data[0,CR_group:,CR_y_locs[i], CR_x_locs[i]] = \
model1.data[0,CR_group:, CR_y_locs[i], CR_x_locs[i]] + 500
print("number of CRs {}".format(len(CR_x_locs)))
out_model = JumpStep.call(model1, override_gain=override_gain,
override_readnoise=override_readnoise, maximum_cores=max_cores)
CR_pool = cycle(first_CR_group_locs)
for i in range(len(CR_x_locs)):
CR_group = next(CR_pool)
assert (4 == np.max(out_model.groupdq[0, CR_group, CR_y_locs[i], CR_x_locs[i]]))
def test_two_group_integration(generate_miri_reffiles, max_cores, setup_inputs):
override_gain, override_readnoise = generate_miri_reffiles
grouptime = 3.0
ingain = 6
inreadnoise = np.float64(7)
ngroups = 2
xsize = 103
ysize = 102
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
nrows=ysize, ncols=xsize,
gain=ingain, readnoise=inreadnoise, deltatime=grouptime)
out_model = JumpStep.call(model1, override_gain=override_gain,
override_readnoise=override_readnoise, maximum_cores=max_cores)
assert(out_model.meta.cal_step.jump == 'SKIPPED')
def test_five_group_integration(generate_miri_reffiles, setup_inputs):
override_gain, override_readnoise = generate_miri_reffiles
grouptime = 3.0
ingain = 6
inreadnoise = 7
ngroups = 5
xsize = 103
ysize = 102
model1, gdq, rnModel, pixdq, err, gain = setup_inputs(ngroups=ngroups,
nrows=ysize, ncols=xsize,
gain=ingain, readnoise=inreadnoise,
deltatime=grouptime)
out_model = JumpStep.call(model1, override_gain=override_gain,
override_readnoise=override_readnoise, maximum_cores='none')
assert (out_model.meta.cal_step.jump == 'COMPLETE')
if len(line) > 3:
files.append(line.strip())
#assume they've been run thru bpm and saturation
#so now do superbias subtraction, refpix corr,
#linearity corr, jump step
#hardwire for A1 at the moment:
reffile_dir = '/ifs/jwst/wit/witserv/data7/nrc/reference_files/SSB/CV3/cv3_reffile_conversion/'
sbfile = '/ifs/jwst/wit/witserv/data4/nrc/hilbert/superbias/cv3/A1/A1_superbias_from_list_of_biasfiles.list.fits'
linfile = reffile_dir + 'linearity/NRCA1_17004_LinearityCoeff_ADU0_2016-05-14_ssblinearity_DMSorient.fits'
gainfile = reffile_dir + 'gain/NRCA1_17004_Gain_ISIMCV3_2016-01-23_ssbgain_DMSorient.fits'
ronfile = '/grp/jwst/wit/nircam/reference_files/SSB/CV2/delivery_Dec_2015/Read_Noise/NRCA1_16989_CDSNoise_2014-10-24_ssbreadnoise_DMSorient.fits'
for file in files:
sbout = file[0:-5] + '_superbias.fits'
data = SuperBiasStep.call(file,
override_superbias=sbfile,
output_file=sbout)
refout = sbout[0:-5] + '_refpix.fits'
data = RefPixStep.call(data, output_file=refout)
linout = refout[0:-5] + '_linearity.fits'
data = LinearityStep.call(data,
override_linearity=linfile,
output_file=linout)
jumpout = linout[0:-5] + '_jump.fits'
data = JumpStep.call(data,
override_gain=gainfile,
override_readnoise=ronfile,
output_file=jumpout)
def test_jump_step(fits_input):
"""Make sure the JumpStep runs without error."""
fname = fits_input[0].header['filename'].replace('.fits', '_jumpstep.fits')
JumpStep.call(datamodels.open(fits_input),
output_file=fname,
save_results=True)
def test_jump_step(fits_input):
"""Make sure the JumpStep runs without error."""
JumpStep.call(datamodels.open(fits_input), save_results=True)