# 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", "BIAS SUBTRACTED", "--mask_key", "mask"] + list(list_images["filename"]) + [ "-", superbias["AllFilters"]]) list_images["filename"][:] = newname # Combine skyflats using blocks to distinguish between sunset and sunrise flats. print "Combining sky flats" skyflat_indices = np.where(list_images["type"] == "skyflats") times = list_images["time"][skyflat_indices] # times of the skyflat images block_limits = utilities.group_images_in_blocks(times, limit=20)
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"]): if list_images["type"][ii] not in ["bias", "unknown"]: args = ["--suffix", " -b", "--message", "BIAS SUBTRACTED", "--mask_key", "mask", im, "-", superbias["AllFilters"]] if type_of_bias_subtraction: args = [type_of_bias_subtraction] + args newname = arith.main(arguments=args) list_images["filename"][ii] = newname print "Combine flats" output_flats = os.path.join(directory, "masterskyflat.fits") flat_indices = np.where(list_images["type"] == "skyflats")