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)
