def perform_cosmic_removal(im_name, output=None):
""" Remove cosmics from fits image
:param im_name: fits image to be cleaned from cosmic rays
:return: name of the cleaned image
"""
if output is None:
output = im_name
arguments = [im_name, "--output", output, "--maxiter", "2"]
im = astroim.Astroim(im_name)
needed_keys = [("--gain", im.primary_header.gaink), ("--readnoise", im.primary_header.ccdronk)]
for arg, kk in needed_keys:
if kk is None:
continue
arguments += [arg, kk]
new_name = remove_cosmics.main(arguments=arguments)
return new_name
for im in list_images["filename"]:
image = fits.open(im, mode="update")
image[0].data[mask==2] = 0
image.flush()
image.close()
print "Estimate sky for images of CIG(s), standard(s) and cluster(s) "
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig","standards","clusters"]:
find_sky.main(arguments=[list_images["filename"][index]])
print "Removing cosmic rays from images"
for index, im in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig", "standards", "clusters"]:
newname = remove_cosmics.main(arguments=["--suffix", " -c",
"--gain", str(fits.getval(im, gaink)),
"--readnoise", str(fits.getval(im, read_noisek)),
"--sigclip", "5", "--maxiter", "3", im])
list_images["filename"][index] = newname
#print "Calculate smoother version of images"
#for index, im in enumerate(list_images["filename"]):
# if list_images["type"][index] in ["cig", "standards", "clusters"]:
# imdata = fits.getdata(im)
# #im_median = np.median(imdata)
# #im_MAD = np.median(np.abs(imdata - im_median))
# #imdata[imdata < im_median + 6. * im_MAD] = im_median
# imfiltered = filters.gaussian_filter(imdata, 2)
# filtered_name = utilities.add_suffix_prefix(im, prefix="filtered-")
# fits.writeto(filtered_name, imfiltered)
print "Include WCS"
# "--quad-size-min", "0.05", "--ra", str(RA), "--dec",
# str(DEC), "--radius", str(FoV), "--depth", "100,250",
# "--solved", "solved.txt", np.str(image)])
### Estimation of the sky level for all the images (except for bias)
print "> Estimate sky for images of CIG(s), standard(s)"
for index, image in enumerate(list_images["filename"]):
if list_images["type"][index] in ["cig","standards","unknown"]:
print image
find_sky.main(arguments=[list_images["filename"][index]])
# Cosmic rays removal. Dependent on the telescope (campaign keyword: TELESCOP)
print "> Removing cosmic rays from images"
telescope = 'INT'
cosmic_dict = cosmic_removal_param(telescope) # Read the parameters (gain,readout noise)
for index, im in enumerate(list_images["filename"]):
if list_images["type"][index] not in ["bias", "flats"]:
newname = remove_cosmics.main(arguments=["--suffix", " -c", "--gain",\
cosmic_dict["gain"], "--readnoise", cosmic_dict["readnoise"], \
"--sigclip", cosmic_dict["sigclip"], "--maxiter", "3", im])
list_images["filename"][index] = newname
<<<<<<< HEAD
sys.exit("* * * * * * Reduction finished * * * * * *")
=======
sys.exit("* * * * * * Reduction finished * * * * * *")
>>>>>>> javi/master