def test_ramp_fit_step(fits_input):
"""Make sure the RampFitStep runs without error."""
fname = fits_input[0].header['filename'].replace('.fits',
'_rampfitstep.fits')
RampFitStep.call(datamodels.open(fits_input),
output_file=fname,
save_results=True)
def test_ramp_fit_nirspec(self):
"""
Regression test of ramp_fit step performed on NIRSpec data. This is a single
integration dataset.
"""
input_file = self.get_data(self.test_dir,
'jw00023001001_01101_00001_NRS1_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits', name='RampFit'
)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00023001001_01101_00001_NRS1_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00023001001_01101_00001_NRS1_opt.fits',
['primary','slope','sigslope','yint','sigyint',
'pedestal','weights','crmag'])
]
self.compare_outputs(outputs)
def test_ramp_fit_nirspec(self):
"""
Regression test of ramp_fit step performed on NIRSpec data. This is a single
integration dataset.
"""
input_file = self.get_data(self.test_dir,
'jw00023001001_01101_00001_NRS1_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits', name='RampFit'
)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00023001001_01101_00001_NRS1_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00023001001_01101_00001_NRS1_opt.fits',
['primary','slope','sigslope','yint','sigyint',
'pedestal','weights','crmag'])
]
self.compare_outputs(outputs)
def test_fake_pedestals(darkcases, rates, pedestals):
'''Test ramp-fit step with fake data.'''
# open ramp to get data shape and headers
m = RampModel(darkcases)
tgroup = m.meta.exposure.group_time
rates = np.float(rates)
pedestals = np.float(pedestals)
nrows = int(m.meta.subarray.xsize)
ncols = int(m.meta.subarray.ysize)
ngroups = int(m.meta.exposure.ngroups)
nints = int(m.meta.exposure.nints)
# create fake ramps with known slope and pedestal
new_data = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32)
for i in np.arange(0, ngroups):
for j in np.arange(4, 2044):
for k in np.arange(4, 2044):
new_data[0, i, j, k] = pedestals + rates * ((i + 1) * tgroup)
# save it
m.data = new_data
m.err = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32)
fake_data_outname = darkcases[:-5] + "_rate" + str(
rates) + "_pedestal" + str(
pedestals) + "_test_fake_pedestals_uncal.fits"
m.save(fake_data_outname, overwrite=True)
output, outint = RampFitStep.call(
m,
output_file=fake_data_outname[:-5] + "_rate.fits",
save_opt=True,
opt_name=fake_data_outname[:-5] + "_rate_opt.fits")
optoutput = fits.open(fake_data_outname[:-5] + "rate_opt.fits")
# check pedestal
clip = sigma_clip(optoutput['PEDESTAL'].data)
clip.data[clip.mask] = np.nan
meanped = np.nanmean(clip.data)
assert np.allclose(pedestals, meanped, rtol=2, atol=2) == True
optoutput.close()
def test_ramp_fit_miri1(self):
"""
Regression test of ramp_fit step performed on MIRI data.
"""
input_file = self.get_data(self.test_dir, 'jw00001001001_01101_00001_MIRIMAGE_jump.fits')
result = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit1_opt_out.fits')
output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit'))
int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int'))
result[0].close()
result[1].close()
outputs = [(output_file,
'jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits'),
(int_output,
'jw00001001001_01101_00001_MIRIMAGE_int.fits'),
('rampfit1_opt_out_fitopt.fits',
'jw00001001001_01101_00001_MIRIMAGE_opt.fits')
]
self.compare_outputs(outputs)
def test_extensions(darkcases):
'''Test ramp-fit step with fake data.'''
# open ramp to get data shape and headers
m = RampModel(darkcases)
fake_data_outname = darkcases[:-5] + "_test_extensions_uncal.fits"
output, outint = RampFitStep.call(m,
save_opt=True,
opt_name=fake_data_outname[:-5] +
"_rate_opt.fits")
with fits.open(fake_data_outname[:-5] + "_rate_opt.fits") as h:
# check all optional outputs are there
assert h[1] == h['SLOPE']
assert h[2] == h['SIGSLOPE']
assert h[3] == h['YINT']
assert h[4] == h['SIGYINT']
assert h[5] == h['PEDESTAL']
assert h[6] == h['WEIGHTS']
assert h[7] == h['CRMAG']
def test_ramp_fit_niriss(self):
"""
Regression test of ramp_fit step performed on NIRISS data.
"""
input_file = self.get_data(
self.test_dir, 'jw00034001001_01101_00001_NIRISS_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits')
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00034001001_01101_00001_NIRISS_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00034001001_01101_00001_NIRISS_uncal_opt.fits', [
'primary', 'slope', 'sigslope', 'yint', 'sigyint',
'pedestal', 'weights', 'crmag'
])]
self.compare_outputs(outputs)
def test_ramp_fit_miri1(self):
"""
Regression test of ramp_fit step performed on MIRI data.
"""
input_file = self.get_data(self.test_dir, 'jw00001001001_01101_00001_MIRIMAGE_jump.fits')
result = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit1_opt_out.fits')
output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit'))
int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int'))
result[0].close()
result[1].close()
outputs = [(output_file,
'jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits'),
(int_output,
'jw00001001001_01101_00001_MIRIMAGE_int.fits'),
('rampfit1_opt_out_fitopt.fits',
'jw00001001001_01101_00001_MIRIMAGE_opt.fits')
]
self.compare_outputs(outputs)
def test_ramp_fit_niriss(self):
"""
Regression test of ramp_fit step performed on NIRISS data.
"""
input_file = self.get_data(self.test_dir,
'jw00034001001_01101_00001_NIRISS_jump.fits')
result, result_int = RampFitStep.call(input_file,
save_opt=True,
opt_name='rampfit_opt_out.fits'
)
output_file = result.meta.filename
result.save(output_file)
result.close()
outputs = [(output_file,
'jw00034001001_01101_00001_NIRISS_ramp_fit.fits'),
('rampfit_opt_out_fitopt.fits',
'jw00034001001_01101_00001_NIRISS_uncal_opt.fits',
['primary','slope','sigslope','yint','sigyint',
'pedestal','weights','crmag'])
]
self.compare_outputs(outputs)
def test_dq(darkcases):
'''Test ramp-fit step with fake data.'''
# open ramp to get data shape and headers
m = RampModel(darkcases)
# create one saturated pixel
m.data[0, :, 250, 250] = m.data[0, :, 250, 250] + 57000
m.pixeldq[250, 250] = 2.0
# add in jump to one of the pixels
m.data[0, 2:, 500, 500] = m.data[0, 2:, 500, 500] + 5000
m.groupdq[0, 2, 500, 500] = 4.0
output, outint = RampFitStep.call(m)
# check DQ array
before_satdq = m.pixeldq[250, 250]
after_satdq = output.dq[250, 250]
before_CRdq = m.groupdq[0, 2, 500, 500]
after_CRdq = output.dq[500, 500]
assert before_satdq == after_satdq
assert before_CRdq == after_CRdq
def test_CR_handling(darkcases, rates, pedestals):
'''Test ramp-fit step with fake data.'''
# open ramp to get data shape and headers
m = RampModel(darkcases)
tgroup = m.meta.exposure.group_time
rates = np.float(rates)
pedestals = np.float(pedestals)
ngroups = 10
nints = 1
nrows = int(m.meta.subarray.xsize)
ncols = int(m.meta.subarray.ysize)
m.meta.exposure.ngroups = ngroups
m.meta.exposure.ngroup = ngroups
m.meta.exposure.nints = nints
m.err = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32)
m.groupdq = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32)
# create fake ramps with known slope and pedestal
new_data = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32)
for ints in np.arange(0, nints):
for i in np.arange(0, ngroups):
for j in np.arange(4, 2044):
for k in np.arange(4, 2044):
new_data[ints, i, j,
k] = pedestals + rates * ((i + 1) * tgroup)
# add in jump to one of the pixels
new_data[0, 2:, 500, 500] = new_data[0, 2:, 500, 500] + (rates * 5)
m.groupdq[0, 2, 500, 500] = 4.0
# add in two jumps to one of the pixels
new_data[0, 2:, 740, 740] = new_data[0, 2:, 740, 740] + (rates * 5)
new_data[0, 6:, 740, 740] = new_data[0, 6:, 740, 740] + (rates * 6)
m.groupdq[0, 2, 740, 740] = 4.0
m.groupdq[0, 6, 740, 740] = 4.0
# save it
m.data = new_data
fake_data_outname = darkcases[:-5] + "_rate" + str(
rates) + "_pedestal" + str(pedestals) + "_test2_uncal.fits"
# m.save(fake_data_outname,overwrite=True)
output, outint = RampFitStep.call(
m,
output_file=fake_data_outname[:-5] + "rate.fits",
save_opt=True,
opt_name=fake_data_outname[:-5] + "rate_opt.fits")
optoutput = fits.open(fake_data_outname[:-5] + "rate_opt.fits")
# check output rates in regular output
clip = sigma_clip(output.data)
clip.data[clip.mask] = np.nan
clip.data[output.dq != 0] = np.nan
meanrate = np.nanmean(clip.data)
assert np.allclose(meanrate, rates, rtol=8, atol=8) == True
# check output rates in INTS output
if nints > 1:
for i in np.arange(0, nints):
clip = sigma_clip(outint.data[nints, :, :])
clip.data[clip.mask] = np.nan
clip.data[output.dq != 0] = np.nan
meanrate = np.nanmean(clip.data)
assert np.allclose(meanrate, rates, rtol=8, atol=8) == True
# CR rates from rate_opt.fits file
ratebeforeCR1 = optoutput['SLOPE'].data[0, 0, 740, 740]
rateafterCR1 = optoutput['SLOPE'].data[0, 1, 740, 740]
ratebeforeCR2 = optoutput['SLOPE'].data[0, 1, 740, 740]
rateafterCR2 = optoutput['SLOPE'].data[0, 2, 740, 740]
assert np.allclose(ratebeforeCR1, rateafterCR1, rtol=1e-2,
atol=1e-2) == True
assert np.allclose(ratebeforeCR2, rateafterCR2, rtol=1e-2,
atol=1e-2) == True
# # check to make sure slope is weighted average of intervals
# weights = optoutput['WEIGHTS'].data
# interval1 = optoutput['SLOPE'].data[0,0,740,740]*weights[0,0,740,740]
# interval2 = optoutput['SLOPE'].data[0,1,740,740]*weights[0,1,740,740]
# interval3 = optoutput['SLOPE'].data[0,2,740,740]*weights[0,2,740,740]
# calc = (interval1 + interval2 + interval3)/(weights[0,0,740,740] + weights[0,1,740,740] +weights[0,2,740,740])
# print(calc)
# other integrations shouldn't have CR hit
if nints > 1:
int2_noCRbefore = optoutput['SLOPE'].data[1, 0, 740, 740]
int2_noCRafter1 = optoutput['SLOPE'].data[1, 1, 740, 740]
int2_noCRafter2 = optoutput['SLOPE'].data[1, 2, 740, 740]
assert int2_noCRbefore == rates
assert int2_noCRafter1 == 0.0
assert int2_noCRafter2 == 0.0
# CR rates for pix with no CR hit
ratebefore = optoutput['SLOPE'].data[0, 0, 800, 800]
rateafter = optoutput['SLOPE'].data[0, 1, 800, 800]
assert ratebefore == output.data[800, 800]
assert rateafter == 0.0
# Check CR magnitude
# right now this is just calculated as the difference
# between the two group values for the pixel. Is that right?
manualCRmag = new_data[0, 2, 500, 500] - new_data[0, 1, 500, 500]
pipeCRmag = optoutput['CRMAG'].data[0, 0, 500, 500]
assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True
manualCRmag = new_data[0, 2, 740, 740] - new_data[0, 1, 740, 740]
pipeCRmag = optoutput['CRMAG'].data[0, 0, 740, 740]
assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True
manualCRmag = new_data[0, 6, 740, 740] - new_data[0, 5, 740, 740]
pipeCRmag = optoutput['CRMAG'].data[0, 1, 740, 740]
assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True
optoutput.close()
def test_ramp_fit_step(fits_input):
"""Make sure the RampFitStep runs without error."""
RampFitStep.call(datamodels.open(fits_input), save_results=True)