def get_l2norm(data, layout, pixel_rad=9): radius = data.shape[0] / layout[0] / 2 centres = image.image_centres(data.shape, layout) cube = image_file_io.gen_data_cube(data, centres, pixel_rad) cube = transform.cube2map(np.array([np_adjust.pad2d(x, radius - pixel_rad) for x in cube]), layout) diff = data - cube l2norm = np.linalg.norm(diff) / np.sum(diff > 0) * np.prod(data.shape) print ' - L2 Norm of noise from data:', l2norm return l2norm
def gen_wave_cube(fits_data, pixel_rad, n_obj=None): return gen_data_cube(wave_data, fits_data, pixel_rad, n_obj=n_obj)