def match(args):
""" Match the PSF of a group of images with the PSF of a reference image.
"""
sort_by_seeing(args)
# Get seeing from header and calculate psf of reference image, the first one since we sorted the input
ref_seeing = utils.get_from_header(args.input[0], args.FWHM_key)
ref_psf = psf.main(arguments=[args.input[0], "--stars", args.input_stars[0],
"--sigma_key", args.sigma,
"--gain_key", args.gain_key,
"--ron_key", args.ron_key,
"--expt_key", args.expt_key,
"--airm_key", args.airm_key,
"--FWHM_key", args.FWHM_key])
output_list = []
for image, stars in zip(args.input, args.input_stars):
output = utils.add_suffix_prefix(image, suffix=args.suffix)
# Too small differences of seeing are not worh doing any matching
current_seeing = utils.get_from_header(image, args.FWHM_key)
if abs(ref_seeing - current_seeing) < args.limit: # Not worth equating PSFs for small differences
shutil.copy(image, output) # copy old file into new one
else:
# Calculate psf of the other images
psf_object = psf.main(arguments=[image,
"--stars", stars,
"--sigma_key", args.sigma,
"--gain_key", args.gain_key,
"--ron_key", args.ron_key,
"--expt_key", args.expt_key,
"--airm_key", args.airm_key,
"--FWHM_key", args.FWHM_key])
utils.if_exists_remove("kernel.fits", output)
iraf.psfmatch(image, ref_psf, psf_object, "kernel.fits",
convolution="psf",
filter="cosbell")
iraf.psfmatch(image, ref_psf, psf_object,
convolution="kernel",
kernel="kernel.fits",
output=output,
verbose="no")
utils.if_exists_remove("kernel.fits")
mssg = "Before equating PSFs: " + str(current_seeing)
utils.header_update_keyword(output, args.FWHM_key, ref_seeing, comment=mssg)
output_list.append(output)
return output_list
def convolve(lopsf, hipsf, outker, threshold, highresimg, outname, comp_kernel):
"""Match the higher-res psf to the lower-res psf and
### note for optical and UV, a threshold of 0.14 was found to be ideal.
### for two IR ones, a threshold of 0.03 seemed to work better.
"""
if comp_kernel is True:
# compute the psf matching function and save it to outker
psfmatch(hipsf, lopsf, hipsf, outker, convolution="psf", background="none", threshold=threshold)
# read in kernel
k = fits.getdata(outker)
# read in high res image to be convolved
im, hdr = fits.getdata(highresimg, header=True)
# convolve highresimg with the psf matching function in outker
# to produce output image
convimg = convolve_fft(im, k)
fits.writeto(outname, convimg, header=hdr, clobber=True)
os.system('ls -1 ' + img_G + ' ' + img_R + ' > inp' + ip.patchstr_sex[i] +
'.lis')
f = open('ker' + ip.patchstr_sex[i] + '.lis', 'w')
f.write('ker_' + img_G + '\n')
f.write('ker_' + img_R + '\n')
f.close()
os.system('rm -rfv ' + 'Comb' + ip.patchstr_sex[i] + '.fits')
os.system('rm -rfv ' + 'm' + img_G + ' ' + 'm' + img_R)
os.system('rm -rfv ' + 'ker_' + img_G + ' ' + 'ker_' + img_R)
try:
iraf.psfmatch(input='@inp' + ip.patchstr_sex[i] + '.lis',
referenc=img_I,
psfdata='psf_I' + ip.patchstr_sex[i] + '.reg',
kernel='@ker' + ip.patchstr_sex[i] + '.lis',
output='m//@inp' + ip.patchstr_sex[i] + '.lis',
convolu='image',
dnx=31,
dny=31,
pnx=15,
pny=15)
dat_G = fits.getdata('m' + img_G)
dat_R = fits.getdata('m' + img_R)
dat_I = fits.getdata(img_I)
fits.writeto('Comb' + ip.patchstr_sex[i] + '.fits',
(dat_G + dat_R + dat_I) / 3.0,
overwrite=True)
except:
dat_G = fits.getdata(img_G)
f.close()
# ----- Making PSF-matched & combining them ----- #
os.system('rm -rfv ' + 'comb.fits')
for i in np.arange(np.sum(~ref)):
os.system('rm -rfv ' + 'm' + np.array(ic.nfits)[~ref][i])
for i in np.arange(np.sum(~ref)):
os.system('rm -rfv ' + 'ker_' + np.array(ic.nfits)[~ref][i])
try:
iraf.psfmatch(input='@inp.lis',
referenc=np.array(ic.nfits)[ref][0],
psfdata=np.array(ic.nfits)[ref][0].split('.')[0] +
'_psf.reg',
kernel='@ker.lis',
output='m//@inp.lis',
convolu='image',
dnx=31,
dny=31,
pnx=15,
pny=15)
for i in np.arange(np.sum(~ref)):
if (i == 0):
dat_noref = fits.getdata('m' + np.array(ic.nfits)[~ref][i])
else:
dat_noref += fits.getdata('m' + np.array(ic.nfits)[~ref][i])
dat_ref = fits.getdata(np.array(ic.nfits)[ref][0])
fits.writeto('comb.fits', (dat_noref + dat_ref) / 3.0, overwrite=True)
def psf_matching(i, fil, input, psf165):
ker = "ker%s_gal%s_ref165.fits" % (fil, i + 1)
iraf.psfmatch(input, psf165, input, ker, convolution='psf')
# cl> psfmatch inimage refpsf inpsf kernel convolution=psf
return ker