def blot_segmap(filename, files):
"""
Blots the segmap values from the drizzled image onto the individual
files than went into the drizzle, and write these out as new segmaps.
Parameters
----------
filename : str
The drizzled file.
files : list
The files that went into creating the drizzled file.
Outputs
-------
{file}_seg.fits
A corresponding segmap for each file in files.
"""
# Make an image model using the drizzled image WCS and the corresponding
# segmentation maps data.
drizzle_model = ImageModel(filename)
drizzle_segmap = fits.getdata(filename.replace('.fits', '_seg.fits'))
drizzle_segmap[drizzle_segmap != 0] = 1
drizzle_model.data = drizzle_segmap
# Blot the segmap data from the drizzled image onto each individual file,
# and write out the corresponding segmap for each.
for f in files:
outfile = f.replace('.fits', '_seg.fits')
model = ImageModel(f)
blotted_data = gwcs_blot(drizzle_model, model, interp='nearest')
fits.writeto(outfile, blotted_data, overwrite=True)
def test_boundingbox_from_indices():
dm = ImageModel()
dm.data = np.ones((10,10))
bbox = ((1,2), (3,4))
result = boundingbox_to_indices(dm, bbox)
assert (result == (1, 3, 3, 5))
def _im(ysize, xsize):
# create the data arrays
im = ImageModel((ysize, xsize))
im.data = np.random.rand(ysize, xsize)
return im
im.meta.wcsinfo.pa_v3 = header_slp['PA_V3']
print(im.meta.wcsinfo.pa_v3)
im.meta.wcsinfo.ra_ref = header_slp['RA_REF']
im.meta.wcsinfo.dec_ref = header_slp['DEC_REF']
im.meta.wcsinfo.roll_ref = header_slp['ROLL_REF']
references = {
"area": "crds/jwst_nircam_area_0017.fits",
"distortion": "crds/jwst_nircam_distortion_0093.asdf",
"drizpars": "crds/jwst_nircam_drizpars_0001.fits",
"flat": "crds/jwst_nircam_flat_0337.fits",
"photom": "crds/jwst_nircam_photom_0074.fits"
}
load_wcs(im, references)
im.data = hdul_rate['SCI'].data
im.err = hdul_rate['ERR'].data
im.dq = hdul_rate['DQ'].data
im.var_poisson = hdul_rate['VAR_POISSON'].data
im.var_rnoise = hdul_rate['VAR_RNOISE'].data
im.meta.target.proper_motion_epoch = "2000"
im.meta.dither.primary_type = "IMAGING"
ra_ref = header_slp['RA_REF']
dec_ref = header_slp['DEC_REF']
crval1_ra = header_slp['CRVAL1']
crval2_dec = header_slp['CRVAL2']
targ_ra = header_slp['TARG_RA']
targ_dec = header_slp['TARG_DEC']
def test_flag():
wcsinfo = {
'dec_ref': -0.00601415671349804,
'ra_ref': -0.02073605215697509,
'roll_ref': -0.0,
'v2_ref': -453.5134,
'v3_ref': -373.4826,
'v3yangle': 0.0,
'vparity': -1}
instrument = {
'detector': 'NRS1',
'filter': 'F100LP',
'grating': 'G140M',
'name': 'NIRSPEC',
'gwa_tilt': 37.0610,
'gwa_xtilt': 0.0001,
'gwa_ytilt': 0.0001,
'msa_metadata_id':12}
observation = {
'date': '2016-09-05',
'time': '8:59:37'}
exposure = {
'duration': 11.805952,
'end_time': 58119.85416,
'exposure_time': 11.776,
'frame_time': 0.11776,
'group_time': 0.11776,
'groupgap': 0,
'integration_time': 11.776,
'nframes': 1,
'ngroups': 100,
'nints': 1,
'nresets_between_ints': 0,
'nsamples': 1,
'readpatt': 'NRSRAPID',
'sample_time': 10.0,
'start_time': 58119.8333,
'type': 'NRS_MSASPEC',
'zero_frame': False}
im = ImageModel()
im.data = np.random.rand(2048, 2048)
im.error = np.random.rand(2048, 2048)
im.dq = np.zeros((2048, 2048))
im.meta.wcsinfo._instance.update(wcsinfo)
im.meta.instrument._instance.update(instrument)
im.meta.observation._instance.update(observation)
im.meta.exposure._instance.update(exposure)
metafl = get_file_path('msa_configuration.fits')
im.meta.instrument.msa_metadata_file = metafl
im.meta.dither.position_number = 1
step = AssignWcsStep()
msa_oper = step.get_reference_file(im, 'msaoper')
im = step.call(im)
wcs_ref_files = create_reference_filename_dictionary(im)
failed_slitlets = create_slitlets(im, msa_oper)
result = flag(im, failed_slitlets, wcs_ref_files)
msa_open_dq = 536870912
# Get all dqflags that are nonzero
nonzero = np.nonzero(result.dq)
# Make sure that all nonzero dqs are equal to the msa_open_dq
assert (all(x == msa_open_dq for x in result.dq[nonzero]))