filenames = glob.glob(os.path.join(args.stacked_bias, "d*.fits")) if len(filenames) > 100: np.random.shuffle(filenames) filenames = filenames[0:100] sb = StackedBias(filenames, hdu_num) sb.save() elif args.stacked_image: filenames = glob.glob(os.path.join(args.stacked_image, "d*.fits")) np.random.shuffle(filenames) si = StackedFlat(filenames[0:100], "/scratch2/scratchdirs/nugent/ryan_counts/mar_biases/stackedbias.%02d.fits" % hdu_num, "/scratch2/scratchdirs/nugent/ryan_counts/mar_biases/stackedbias.err.%02d.fits" % hdu_num, hdu_num) si.save() # At some point will want to rename the below data products to use %02d. elif args.processed_image: filenames = sorted(glob.glob("/scratch2/scratchdirs/nugent/ryan_counts/20150313/d*.fits")) sb = "/scratch2/scratchdirs/nugent/ryan_counts/mar_biases/stackedbias.%02d.fits" % hdu_num sb_err = "/scratch2/scratchdirs/nugent/ryan_counts/mar_biases/stackedbias.err.%02d.fits" % hdu_num sf = "/scratch2/scratchdirs/nugent/ryan_counts/mar_1000/stackedflat.%02d.fits" % hdu_num sf_err = "/scratch2/scratchdirs/nugent/ryan_counts/mar_1000/stackedflat.err.%02d.fits" % hdu_num sf_mask = "/scratch2/scratchdirs/nugent/ryan_counts/mar_1000/stackedflat.mask.%02d.fits" % hdu_num for filename in filenames: if ".p." in filename: continue ri = RawImage(filename, hdu_num) ri.subtract_overscan() ri.subtract_stackedbias(sb, sb_err) ri.divide_stackedflat(sf, sf_err) ri.update_mask(sf_mask) ri.save()