def blot(self):
"""MAIN FUNCTION
Resamples the input image onto the WCS associated with the given
instrument/apertures
"""
# Get detector names from the aperture list
if isinstance(self.aperture, str):
self.aperture = [self.aperture]
self.detector = [element.split('_')[0] for element in self.aperture]
# Make sure detector, ra, dec, and roll have same number
# of elements
if ((len(self.center_dec) != len(self.center_ra)) | \
(len(self.center_dec) != len(self.pav3))):
raise ValueError(('WARNING: center_ra, center_dec '
'and pav3 all must have the same number '
'of elements.'))
if type(self.blotfile) == str:
input_mod = datamodels.ImageModel(self.blotfile)
outbase = self.blotfile
elif type(self.blotfile) == datamodels.image.ImageModel:
# Not a great solution at the moment. Save
# imagemodel instance so that you have a fits
# file from which the header can be retrieved
input_mod = copy(self.blotfile)
self.blotfile.save('temp.fits')
self.blotfile = 'temp.fits'
outbase = 'mosaic'
else:
raise ValueError('WARNING: unrecognized type for blotfile')
# Create a GWCS object from the input file's header
input_header = fits.getheader(self.blotfile, ext=1)
transform = gwcs.utils.make_fitswcs_transform(input_header)
input_mod.meta.wcs = gwcs.WCS(forward_transform=transform,
output_frame='world')
# Filter and pupil information
filtername = input_mod.meta.instrument.filter
try:
pupil = input_mod.meta.instrument.pupil
except:
pupil = 'CLEAR'
# Get position angle of input data
input_pav3 = input_mod.meta.wcsinfo.roll_ref
# Name of temporary file output for set_telescope_pointing
# to work on
shellname = 'temp_wcs_container.fits'
blist = []
for (aper, det, ra, dec, roll) in \
zip(self.aperture, self.detector, self.center_ra, self.center_dec, self.pav3):
# Get aperture-specific info
self.siaf = get_instance(self.instrument)[aper]
# Create datamodel with appropriate metadata
bmodel = self.make_model(det, ra, dec, input_pav3, filtername,
pupil)
bmodel.save(shellname, overwrite=True)
# Use set_telescope_pointing to compute local roll
# angle and PC matrix
stp.add_wcs(shellname, roll=roll)
bmodel = datamodels.open(shellname)
# Now we need to run assign_wcs step so that these
# files get a gwcs object attached to them.
if self.distortion_file is not None:
bmodel = AssignWcsStep.call(
bmodel, override_distortion=self.distortion_file)
else:
bmodel = AssignWcsStep.call(bmodel)
# Add to the list of data model instances to blot to
blist.append(bmodel)
# Place the model instances to blot to in a ModelContainer
blot_list = container.ModelContainer(blist)
# Blot the image to each of the WCSs in the blot_list
pars = {'sinscl': 1.0, 'interp': 'poly5'}
reffiles = {}
blotter = outlier_detection.OutlierDetection(blot_list,
reffiles=reffiles,
**pars)
blotter.input_models = blot_list
self.blotted_datamodels = blotter.blot_median(input_mod)
def blot(self):
# Make sure detector, ra, dec, and roll have same number
# of elements
if ((len(self.detector) != len(self.center_ra)) | \
(len(self.detector) != len(self.center_dec)) | \
(len(self.detector) != len(self.pav3))):
print('WARNING: detector, center_ra, center_dec')
print('and pav3 all must have the same number')
print('of elements.')
sys.exit()
if type(self.blotfile) == str:
input_mod = datamodels.ImageModel(self.blotfile)
outbase = self.blotfile
# Create a GWCS object from the input file's header
#input_header = fits.getheader(self.blotfile)
#transform = gwcs.utils.make_fitswcs_transform(header)
#input_mod.meta.wcs = gwcs.WCS(transform)
elif type(self.blotfile) == datamodels.image.ImageModel:
# Not a great solution at the moment. Save
# imagemodel instance so that you have a fits
# file from which the header can be retrieved
input_mod = copy(self.blotfile)
self.blotfile.save('temp.fits')
self.blotfile = 'temp.fits'
outbase = 'mosaic'
else:
print('WARNING: unrecognized type for blotfile')
sys.exit()
# Create a GWCS object from the input file's header
input_header = fits.getheader(self.blotfile, ext=1)
transform = gwcs.utils.make_fitswcs_transform(input_header)
input_mod.meta.wcs = gwcs.WCS(forward_transform=transform,
output_frame='world')
# Filter and pupil information
filter = input_mod.meta.instrument.filter
try:
pupil = input_mod.meta.instrument.pupil
except:
pupil = 'CLEAR'
# get position angle of input data
input_pav3 = input_mod.meta.wcsinfo.roll_ref
# parity is always -1 for nircam
parity = -1
# Name of temporary file output for set_telescope_pointing
# to work on
shellname = 'temp_wcs_container.fits'
blist = []
for (det,ra,dec,roll) in \
zip(self.detector,self.center_ra,\
self.center_dec,self.pav3):
# get detector-specific info
v2ref, v3ref, v3ang = self.get_siaf_info(det)
# create datamodel with appropriate metadata
bmodel = self.make_model(det, ra, dec, v2ref, v3ref, v3ang, parity,
input_pav3, filter, pupil)
#shellname = 'wcs_model_to_blot_to_{}_{}_{}_{}.fits'.format(det,ra,dec,roll)
bmodel.save(shellname, overwrite=True)
#tmpname = 'wcs_model_to_blot_to_BASEMODEL_{}_{}_{}_{}.fits'.format(det,ra,dec,roll)
#bmodel.save(tmpname,overwrite=True)
# use set_telescope_pointing to compute local roll
# angle and PC matrix
stp.add_wcs(shellname, roll=roll)
bmodel = datamodels.open(shellname)
# Now we need to run assign_wcs step so that these
# files get a gwcs object attached to them.
dist_reffile = [s for s in self.distfiles if det in s][0]
bmodel = AssignWcsStep.call(bmodel,
override_distortion=dist_reffile)
#tmpname = 'wcs_model_to_blot_to_ASSIGNWCS_{}_{}_{}_{}.fits'.format(det,ra,dec,roll)
#bmodel.save(tmpname,overwrite=True)
# Add to the list of data model instances to blot to
blist.append(bmodel)
#place the model instances to blot to in a ModelContainer
blot_list = container.ModelContainer(blist)
#blot the image to each of the WCSs in the blot_list
pars = {'sinscl': 1.0, 'interp': 'poly5'}
reffiles = {}
mm = outlier_detection.OutlierDetection(blot_list,
reffiles=reffiles,
**pars)
blotted_datamodels = outlier_detection.blot_median(
input_mod, blot_list, **mm.outlierpars)
for (bltted,det,ra,dec,roll) in \
zip(blotted_datamodels,self.detector,self.center_ra,\
self.center_dec,self.pav3):
if self.outfile is None:
self.outfile = 'blotted_from_{}_to_{}_{}_{}_{}.fits'.format(
outbase, det, ra, dec, roll)
bltted.save(self.outfile)