def build_mask(dqarr, bitvalue):
""" Builds a bit-mask from an input DQ array and a bitvalue flag
"""
bitvalue = interpret_bit_flags(bitvalue)
if bitvalue is None:
return (np.ones(dqarr.shape, dtype=np.uint8))
return np.logical_not(np.bitwise_and(dqarr, ~bitvalue)).astype(np.uint8)
def process(self, target, psf):
# Open the input science target model
with datamodels.open(target) as target_model:
# Get the name of the psf mask reference file to use
self.mask_name = self.get_reference_file(target_model, 'psfmask')
self.log.info('Using PSFMASK reference file %s', self.mask_name)
# Check for a valid reference file
if self.mask_name == 'N/A':
self.log.warning('No PSFMASK reference file found')
self.log.warning('Align_refs step will be skipped')
return None
# Open the psf mask reference file
mask_model = datamodels.ImageModel(self.mask_name)
# Open the input psf images
psf_model = datamodels.open(psf)
# Retrieve the box size for the filter
box_size = self.median_box_length
# Get the bit value of bad pixels. A value of 0 treats all pixels as good.
bad_bitvalue = self.bad_bits
bad_bitvalue = interpret_bit_flags(bad_bitvalue)
if bad_bitvalue is None:
bad_bitvalue = 0
# Replace bad pixels in the psf images
psf_model = median_replace_img(psf_model, box_size, bad_bitvalue)
# Replace bad pixels in the target images
target_model = median_replace_img(target_model, box_size,
bad_bitvalue)
# Call the alignment routine
result = imageregistration.align_models(target_model, psf_model,
mask_model)
result.meta.cal_step.align_psfs = 'COMPLETE'
mask_model.close()
psf_model.close()
return result