else:
hdul = fits.open(args.data)
data = pd.DataFrame(hdul[1].data)
if args.signal_table is not None:
signal_pts = pd.read_hdf(args.signal, args.signal_table)
else:
hdul = fits.open(args.signal)
signal_pts = pd.DataFrame(hdul[1].data)
signal_columns = args.signal_columns
if signal_columns is None:
signal_columns = signal_pts.columns
signal_filter = PointFilter(
signal_pts,
filtered_columns=signal_columns,
sigma_vec=np.repeat(0.2, len(signal_columns)))
dsr = data
weights = signal_filter.get_weights(dsr)
dsr['weights'] = weights
dsr.to_hdf('output.h5', 'photometry')
if args.create_image is not None:
out_img = cubeify(
dsr,
n=(int(args.nra), int(args.ndec)),
columns=['RA', 'DEC'],
target='weights')
else:
hdul = fits.open(args.signal)
signal_pts = pd.DataFrame(hdul[1].data)
if args.noise_table is not None:
noise_pts = pd.read_hdf(args.noise, args.noise_table)
else:
hdul = fits.open(args.noise)
noise_pts = pd.DataFrame(hdul[1].data)
signal_columns = args.signal_columns
if signal_columns is None:
signal_columns = signal_pts.columns
signal_filter = PointFilter(
signal_pts,
filtered_columns=signal_columns,
sigma_vec=np.repeat(0.2, len(signal_columns)))
noise_filter = PointFilter(
noise_pts,
filtered_columns=signal_columns,
sigma_vec=np.repeat(0.2, len(signal_columns)))
print('fitering')
dsr = data
print('signal')
signal_weights = signal_filter.get_weights(dsr)
print('noise')
noise_weights = noise_filter.get_weights(dsr)
weights = signal_weights - noise_weights