list_images = rename.main(arguments=["--copy", "--objectk", objectk,\
"--filterk", filterk, "--datek", datek,\
"--overwrite", "--exptime", exptimek,\
directory])
# Remove images in list remove_images.
print "Removing images as selected by user, if any."
for im in remove_images:
index = [i for i,v in enumerate(list_images["filename"]) if str(im) in v]
for key in list_images.keys(): # remove that item from all the lists
list_images[key] = np.delete(list_images[key], index)
# Create masks for all images. Stars in blanks need to be removed (max_val small).
print "Creating masks"
whr = np.where(list_images["type"] != "blanks")
create_masks.main(arguments=["--max_val", "50000", "--circular"] +
list(list_images["filename"][whr]))
whr = np.where(list_images["type"] == "blanks")
create_masks.main(arguments=["--max_val", "30000", "--min_val", "1000", "--stars",
"--circular"] + list(list_images["filename"][whr]))
# Combine bias images
print "Combining bias images"
whr = np.where(list_images["type"] == "bias")
bias_images = list(list_images["filename"][whr])
superbias = combine_images.main(arguments=["--average", "median",
"--all_together", "--output", "superbias.fits",
"--mask_key", "mask", "--filterk", filterk] + bias_images[:])
# Subtract bias from all images.
print "Subtracting bias"
newname = arith.main(arguments=["--suffix", " -b", "--message",
try:
for im in remove_images:
index = [i for i,v in enumerate(list_images["filename"]) if str(im) in v]
for key in list_images.keys(): # remove that item from all the lists
list_images[key] = np.delete(list_images[key], index)
except NameError: # variable remove_images not defined
pass
print "Create masks for images"
for ii,im in enumerate(list_images["filename"]):
# Arguments to be passed to create_masks
args = ["--max_val", str(max_counts), "--min_val", "0", "--mask_key", "mask", "--outside_val", "2", im]
if circular_FoV: # from the campaign file
args = ["--circular"] + args
create_masks.main(arguments=args )
print "Combine bias"
whr = np.where(list_images["type"] == "bias")
bias_images = list(list_images["filename"][whr])
print "Bias images", bias_images
output_bias = os.path.join(directory, "superbias.fits")
superbias = combine_images.main(arguments=["--average", "median",
"--all_together",
"--output", output_bias,
"--mask_key", "mask",
"--filterk", filterk] +\
bias_images[:])
print "Subtract bias"
for ii, im in enumerate(list_images["filename"]):
