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()
