def test_raft_image_mosaic(self):
"""
Test of raft-level mosaicking code.
"""
infiles = sorted(
glob.glob(
os.path.join(_root_dir, 'S??', '*_lambda_flat_1000_*.fits')))
infiles = OrderedDict([(filename.split('/')[-2], filename)
for filename in infiles])
test_files = dict()
step = 100
level = step
for slot, infile in list(infiles.items()):
outfile = '%s_test_image_%05i.fits' % (slot, level)
with fits.open(infile) as hdu_list:
for hdu in hdu_list[1:17]:
hdu.data = np.ones(hdu.data.shape,
dtype=np.float32) * level
level += step
fitsWriteto(hdu_list, outfile, overwrite=True)
test_files[slot] = outfile
raft_mosaic = raftTest.RaftMosaic(test_files, bias_subtract=False)
raft_mosaic.plot(title='Test pattern')
plt.savefig(self.outfile)
def make_image(config, slot_names, mean_frame_pattern='_mean_bias_image.fits'):
"""
Make the mosaic image of the entire raft, when illuminated by the CCOB
according to config. Returns:
- the raw image of the raft
- the corrected image where mean bias frame has been removed and gains applied
"""
file_list = sorted(find_files(config))
fits_files_dict = {slot_names[i] : file_list[i] for i in range(len(file_list))}
ccd_dict = {}
ccd_dict_wbias = {}
gains_dict = {}
for slot in slot_names:
mean_bias_file = slot + mean_frame_pattern
ccd_dict[slot] = sensorTest.MaskedCCD(fits_files_dict[slot])
outfile = os.path.join(config['tmp_dir'],'ccd' + slot + '.fits')
image={}
ccd_dict_wbias[slot]=sensorTest.MaskedCCD(fits_files_dict[slot],\
bias_frame=os.path.join(config['tmp_dir'],mean_bias_file))
outfile_wbias = os.path.join(config['tmp_dir'],'ccd' + slot + '_wbias.fits')
image_wbias={}
eotest_results_file = os.path.join(config['eo_data_path'],'{}_eotest_results.fits'.format(ccd_dict[slot].md('LSST_NUM')))
gains_dict[slot] = gains(eotest_results_file)
for amp in ccd_dict[slot]:
image[amp] = ccd_dict[slot].bias_subtracted_image(amp)
image[amp] *= gains_dict[slot][amp]
image_wbias[amp] = ccd_dict_wbias[slot].bias_subtracted_image(amp)
image_wbias[amp] *= gains_dict[slot][amp]
imutils.writeFits({amp: image_wbias[amp].getImage() for amp in ccd_dict_wbias[slot]},
outfile_wbias, fits_files_dict[slot])
imutils.writeFits({amp: image[amp].getImage() for amp in ccd_dict[slot]},
outfile, fits_files_dict[slot])
fits_files_dict_corr={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'.fits') for slot in slot_names}
fits_files_dict_corr_wbias={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'_wbias.fits') for slot in slot_names}
im_corr = raft.RaftMosaic(fits_files_dict_corr, bias_subtract=False)
im_corr_wbias = raft.RaftMosaic(fits_files_dict_corr_wbias, bias_subtract=False)
im_raw = raft.RaftMosaic(fits_files_dict, bias_subtract=False)
return im_raw, im_corr, im_corr_wbias
gains = dict()
for slot, res_file in results_files.items():
results = sensorTest.EOTestResults(res_file)
gains[slot] = dict([(amp, gain) for amp, gain
in zip(results['AMP'], results['GAIN'])])
# Collect super bias files for bias frame subtraction.
bias_frames = slot_dependency_glob('*median_bias.fits', 'dark_defects_raft')
title = '%s, %s' % (raft_id, run_number)
file_prefix = '%s_%s' % (raft_id, run_number)
# Raft-level mosaics of median darks, bias, superflats high and low.
dark_mosaic = raftTest.RaftMosaic(slot_dependency_glob('*median_dark_bp.fits',
'bright_defects_raft'),
gains=gains, bias_frames=bias_frames)
dark_mosaic.plot(title='%s, medianed dark frames' % title,
annotation='e-/pixel, gain-corrected, bias-subtracted',
rotate180=True)
plt.savefig('%s_medianed_dark.png' % file_prefix)
del dark_mosaic
median_bias = raftTest.RaftMosaic(bias_frames, bias_subtract=False)
median_bias.plot(title='%s, median bias frames' % title, annotation='ADU/pixel',
rotate180=True)
plt.savefig('%s_median_bias.png' % file_prefix)
del median_bias
sflat_high = raftTest.RaftMosaic(slot_dependency_glob('*superflat_high.fits',
'cte_raft'),