for image in image_names:
beammajor.append(imhead(imagename=image, mode='get', hdkey='beammajor'))
beamminor.append(imhead(imagename=image, mode='get', hdkey='beamminor'))
beampa.append(imhead(imagename=image, mode='get', hdkey='beampa'))
# determining what size the spectral line images should be smoothed to.
targetbeammajor = max(beammajor)
targetidx = beammajor.index(targetbeammajor)
targetbeamminor = beamminor[targetidx]
targetbeampa = beampa[targetidx]
# Apply PB correction
for image in image_names:
outputimage = image + ".pbcor"
impbcor(imagename=image, pbimage=image.replace(".image", ".flux"),
outfile=outputimage)
image_names = [name + ".pbcor" for name in image_names]
# smoothing the images
for image in image_names:
outputimage = image + '.smooth'
if image != image_names[targetidx]:
imsmooth(imagename=image, outfile=outputimage, kernel='gauss',
major=str(targetbeammajor['value']) + targetbeammajor['unit'],
minor=str(targetbeamminor['value']) + targetbeamminor['unit'],
pa=str(targetbeampa['value']) + targetbeampa['unit'],
targetres=True)
else:
# need to skip the one with the largest resolution or imsmooth fails.
os.system('cp -ir ' + image + ' ' + outputimage)
for suffix in ('image', 'residual', 'model'):
ia.open(lineimagename + "." + suffix)
ia.sethistory(origin='almaimf_line_imaging',
history=[
"{0}: {1}".format(key, val)
for key, val in impars.items()
])
ia.sethistory(origin='almaimf_line_imaging',
history=[
"git_version: {0}".format(git_version),
"git_date: {0}".format(git_date)
])
ia.close()
impbcor(imagename=lineimagename + '.image',
pbimage=lineimagename + '.pb',
outfile=lineimagename + '.image.pbcor',
overwrite=True)
# TODO: Save the desired files, maybe as FITS or maybe not?
# the cont_channel_selection is purely in frequency, so it should
# "just work"
# (there may be several cont.dats - we're just grabbing the first)
# Different data sets are actually found to have different channel selections.
path = os.path.split(vis[0])[0]
contfile = os.path.join(path, '../calibration/cont.dat')
cont_freq_selection = parse_contdotdat(contfile)
logprint("Selected {0} as continuum channels".format(
cont_freq_selection),
robust=0.5,
niter=int(1e7),
cycleniter=100, # Force many major cycles
nsigma=1.,
# usemask='auto-multithresh',
usemask='user', # Use auto-mask from last step.
sidelobethreshold=1.0,
noisethreshold=3.0,
lownoisethreshold=1.5,
negativethreshold=0.0,
minbeamfrac=0.1,
growiterations=75,
dogrowprune=True,
minpercentchange=1.0,
fastnoise=False,
threshold='',
interactive=0,
savemodel='none',
parallel=False,
calcres=False,
calcpsf=False,
smallscalebias=0.6
)
np.save(stage2_summary_name, stage2_tclean_dict)
impbcor(imagename="{}.image".format(cleanimage_name),
pbimage="{}.pb".format(cleanimage_name),
outfile="{}.image.pbcor".format(cleanimage_name),
overwrite=True)