def test_3d_combiner_with_scaling(): ccd_data = ccd_data_func() # The factors below are not particularly important; just avoid anything # whose average is 1. ccd_data = CCDData(np.ones((5, 5, 5)), unit=u.adu) ccd_data_lower = CCDData(3 * np.ones((5, 5, 5)), unit=u.adu) ccd_data_higher = CCDData(0.9 * np.ones((5, 5, 5)), unit=u.adu) combiner = Combiner([ccd_data, ccd_data_higher, ccd_data_lower]) # scale each array to the mean of the first image scale_by_mean = lambda x: ccd_data.data.mean() / np.ma.average(x) combiner.scaling = scale_by_mean avg_ccd = combiner.average_combine() # Does the mean of the scaled arrays match the value to which it was # scaled? np.testing.assert_almost_equal(avg_ccd.data.mean(), ccd_data.data.mean()) assert avg_ccd.shape == ccd_data.shape median_ccd = combiner.median_combine() # Does median also scale to the correct value? np.testing.assert_almost_equal(np.median(median_ccd.data), np.median(ccd_data.data)) # Set the scaling manually... combiner.scaling = [scale_by_mean(combiner.data_arr[i]) for i in range(3)] avg_ccd = combiner.average_combine() np.testing.assert_almost_equal(avg_ccd.data.mean(), ccd_data.data.mean()) assert avg_ccd.shape == ccd_data.shape
def test_combine_limitedmem_scale_fitsimages(): fitsfile = get_pkg_data_filename('data/a8280271.fits') ccd = CCDData.read(fitsfile, unit=u.adu) ccd_list = [ccd] * 5 c = Combiner(ccd_list) # scale each array to the mean of the first image scale_by_mean = lambda x: ccd.data.mean() / np.ma.average(x) c.scaling = scale_by_mean ccd_by_combiner = c.average_combine() fitsfilename_list = [fitsfile] * 5 avgccd = combine(fitsfilename_list, output_file=None, method='average', mem_limit=1e6, scale=scale_by_mean, unit=u.adu) np.testing.assert_array_almost_equal( avgccd.data, ccd_by_combiner.data, decimal=4)
def test_combiner_scaling_fails(): ccd_data = ccd_data_func() combiner = Combiner([ccd_data, ccd_data.copy()]) # Should fail unless scaling is set to a function or list-like with pytest.raises(TypeError): combiner.scaling = 5