def test__sub_array_to_1d_and_2d_and_back__returns_original_array(self): mask = np.array([[False, False, True], [False, True, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = np.array([ 1.0, 10.0, 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 0.0, 0.0, 4.0, ]) sub_array_2d = mapping.sub_array_2d_from_sub_array_1d( sub_array_1d=sub_array_1d) sub_array_1d_new = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d( sub_array_2d=sub_array_2d) assert (sub_array_1d == sub_array_1d_new).all()
def test__same_as_above__but_remove_some_centre_pixels_and_change_order__order_does_not_change_mapping( self): ma = al.Mask( array=np.array([[False, False, False], [False, False, False], [False, False, False]]), pixel_scale=1.0, sub_size=1, ) unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [2, 2], [1, 1], [0, 2], [2, 0], [0, 2]]) total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask( mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres( total_sparse_pixels=total_masked_pixels, mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) assert (sparse_to_unmasked_sparse == np.array([0, 1, 2, 3, 4, 5, 6])).all()
def test__sub_array_2d_from_sub_array_1d__use_2x3_mask(self): mask = np.array([[False, False, True], [False, True, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = np.array([ 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, ]) sub_array_2d = mapping.sub_array_2d_from_sub_array_1d( sub_array_1d=sub_array_1d) assert (sub_array_2d == np.array([ [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], ])).all()
def test__3x3_grid__extracts_max_min_coordinates__creates_grid_including_half_pixel_offset_from_edge( self): galaxy = al.Galaxy( redshift=0.5, light=al.light_profiles.EllipticalSersic(intensity=1.0)) grid = np.array([[-1.5, -1.5], [1.5, 1.5]]) plane_image = al.lens_util.plane_image_of_galaxies_from_grid( shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0) mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False), pixel_scale=1.0, sub_size=1) grid = al.Grid( arr=np.array([ [-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0], ]), mask=mask, ) plane_image_galaxy = galaxy.profile_image_from_grid(grid) assert (plane_image == plane_image_galaxy).all()
def test__same_as_above__different_mask_and_centres(self): ma = al.Mask( array=np.array([[False, False, True], [False, False, False], [True, False, False]]), pixel_scale=1.0, sub_size=1, ) unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2], [0, 2], [0, 2], [1, 1]]) total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask( mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres( total_sparse_pixels=total_masked_pixels, mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) assert (sparse_to_unmasked_sparse == np.array([0, 1, 5])).all()
def test__3x3_grid__buffer_aligns_two_grids(self): galaxy = al.Galaxy( redshift=0.5, light=al.light_profiles.EllipticalSersic(intensity=1.0)) grid_without_buffer = np.array([[-1.48, -1.48], [1.48, 1.48]]) plane_image = al.lens_util.plane_image_of_galaxies_from_grid( shape=(3, 3), grid=grid_without_buffer, galaxies=[galaxy], buffer=0.02) mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False), pixel_scale=1.0, sub_size=1) grid = al.Grid( arr=np.array([ [-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0], ]), mask=mask, ) plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid) assert (plane_image == plane_image_galaxy).all()
def test__same_as_above__but_4x3_mask(self): ma = al.Mask( array=np.array([ [True, False, True], [True, False, True], [False, False, False], [True, False, True], ]), pixel_scale=1.0, sub_size=1, ) unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2], [0, 2], [0, 2], [1, 1], [2, 2], [3, 1]]) unmasked_sparse_to_sparse = al.sparse_mapping_util.unmasked_sparse_to_sparse_from_mask_and_pixel_centres( mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, total_sparse_pixels=6, ) assert (unmasked_sparse_to_sparse == np.array([0, 0, 1, 1, 1, 1, 2, 3])).all()
def test__mask_full_false__image_mask_and_pixel_centres_fully_overlap__each_sparse_maps_to_unmaked_sparse( self): ma = al.Mask( array=np.array([[False, False, False], [False, False, False], [False, False, False]]), pixel_scale=1.0, sub_size=1, ) unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]]) total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask( mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres( total_sparse_pixels=total_masked_pixels, mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) assert (sparse_to_unmasked_sparse == np.array( [0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
def test__mask_is_cross__some_pix_pixels_are_masked__omitted_from_mapping( self): ma = al.Mask( array=np.array([[True, False, True], [False, False, False], [True, False, True]]), pixel_scale=1.0, sub_size=1, ) unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2], [2, 0], [2, 1], [2, 2]]) total_masked_pixels = al.mask_util.total_sparse_pixels_from_mask( mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) sparse_to_unmasked_sparse = al.sparse_mapping_util.sparse_to_unmasked_sparse_from_mask_and_pixel_centres( total_sparse_pixels=total_masked_pixels, mask=ma, unmasked_sparse_grid_pixel_centres= unmasked_sparse_grid_pixel_centres, ) assert (sparse_to_unmasked_sparse == np.array([1, 3, 4, 5, 7])).all()
def test__scaled_array_2d_from_array_1d__compare_to_util(self): mask = np.array([ [True, True, False, False], [True, False, True, True], [True, True, False, False], ]) array_1d = np.array([1.0, 6.0, 4.0, 5.0, 2.0]) array_2d_util = al.array_mapping_util.sub_array_2d_from_sub_array_1d_mask_and_sub_size( sub_array_1d=array_1d, mask=mask, sub_size=1) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=1) mapping = al.Mapping(mask=mask) array_2d = mapping.array_2d_from_array_1d(array_1d=array_1d) assert (array_2d == array_2d_util).all() scaled_array_2d = mapping.scaled_array_2d_from_array_1d( array_1d=array_1d) assert (scaled_array_2d == array_2d_util).all() assert (scaled_array_2d.xticks == np.array([-6.0, -2.0, 2.0, 6.0])).all() assert (scaled_array_2d.yticks == np.array([-4.5, -1.5, 1.5, 4.5])).all() assert scaled_array_2d.shape_arcsec == (9.0, 12.0) assert scaled_array_2d.pixel_scale == 3.0 assert scaled_array_2d.origin == (0.0, 0.0)
def test__sub_array_1d_from_sub_array_2d__numerical_values(self): sub_array_2d = np.array([ [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], ]) mask = np.array([[False, False, True], [False, True, False]]) mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d( sub_array_2d=sub_array_2d) assert (sub_array_1d == np.array([ 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, ])).all()
def test__sub_grid_2d_with_sub_dimensions_from_sub_grid_1d__use_real_mask_and_grid( self): mask = np.array([[False, True], [False, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_grid_1d = np.array([ [1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 4.0], [9.0, 9.0], [10.0, 10.0], [11.0, 11.0], [12.0, 12.0], [13.0, 13.0], [14.0, 14.0], [15.0, 15.0], [16.0, 16.0], ]) sub_grid_2d = mapping.sub_grid_2d_with_sub_dimensions_from_sub_grid_1d( sub_grid_1d=sub_grid_1d) assert (sub_grid_2d == np.array([ [[1.0, 1.0], [2.0, 2.0], [0.0, 0.0], [0.0, 0.0]], [[3.0, 3.0], [4.0, 4.0], [0.0, 0.0], [0.0, 0.0]], [[9.0, 9.0], [10.0, 10.0], [13.0, 13.0], [14.0, 14.0]], [[11.0, 11.0], [12.0, 12.0], [15.0, 15.0], [16.0, 16.0]], ])).all()
def test__grid_2d_binned_from_sub_grid_1d__use_real_mask_and_grid(self): mask = np.array([[False, True], [False, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_grid_1d = np.array([ [1.0, 1.0], [2.0, 2.0], [3.0, 3.0], [4.0, 6.0], [9.0, 9.0], [10.0, 10.0], [11.0, 11.0], [12.0, 12.0], [13.0, 13.0], [14.0, 14.0], [15.0, 15.0], [16.0, 16.0], ]) grid_2d_binned = mapping.grid_2d_binned_from_sub_grid_1d( sub_grid_1d=sub_grid_1d) assert (grid_2d_binned == np.array([[[2.5, 3.0], [0.0, 0.0]], [[10.5, 10.5], [14.5, 14.5]]])).all()
def test__scaled_array_from_sub_array_1d_by_binning_up(self): mask = np.array([[False, False, True], [False, True, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = np.array([ 1.0, 10.0, 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 0.0, 0.0, 4.0, ]) scaled_array_2d = mapping.scaled_array_2d_binned_from_sub_array_1d( sub_array_1d=sub_array_1d) assert (scaled_array_2d == np.array([[3.5, 2.0, 0.0], [3.0, 0.0, 2.0]])).all() assert scaled_array_2d.pixel_scales == (3.0, 3.0) assert scaled_array_2d.origin == (0.0, 0.0)
def test__2x3_grid__shape_change_correct_and_coordinates_shift(self): galaxy = al.Galaxy( redshift=0.5, light=al.light_profiles.EllipticalSersic(intensity=1.0)) grid = np.array([[-1.5, -1.5], [1.5, 1.5]]) plane_image = al.lens_util.plane_image_of_galaxies_from_grid( shape=(2, 3), grid=grid, galaxies=[galaxy], buffer=0.0) mask = al.Mask(array=np.full(shape=(2, 3), fill_value=False), pixel_scale=1.0, sub_size=1) grid = al.Grid( arr=np.array([ [-0.75, -1.0], [-0.75, 0.0], [-0.75, 1.0], [0.75, -1.0], [0.75, 0.0], [0.75, 1.0], ]), mask=mask, ) plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid) assert (plane_image == plane_image_galaxy).all()
def test__sub_mask_1d_index_to_mask_1d_index__compare_to_util(self): mask = np.array([[True, False, True], [False, False, False], [True, False, False]]) sub_mask_1d_index_to_mask_1d_index_util = al.mask_mapping_util.sub_mask_1d_index_to_mask_1d_index_from_mask( mask=mask, sub_size=2) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) assert (mapping.sub_mask_1d_index_to_mask_1d_index == sub_mask_1d_index_to_mask_1d_index_util).all()
def test__1x1_image__light_profile_fits_data_perfectly__lh_is_noise(self): image = al.ScaledSquarePixelArray(array=np.ones((3, 3)), pixel_scale=1.0) noise_map = al.ScaledSquarePixelArray(array=np.ones((3, 3)), pixel_scale=1.0) galaxy_data = al.GalaxyData(image=image, noise_map=noise_map, pixel_scale=3.0) mask = al.Mask( array=np.array([[True, True, True], [True, False, True], [True, True, True]]), pixel_scale=1.0, sub_size=1, ) g0 = MockGalaxy(value=1.0) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_image=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_convergence=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_potential=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_deflections_y=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_deflections_x=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.likelihood == -0.5 * np.log(2 * np.pi * 1.0)
def test__sub_mask_1d_index_to_submask_index__compare_to_array_util(self): mask = np.array([[True, True, True], [True, False, False], [True, True, False]]) mask = al.Mask(array=mask, pixel_scale=7.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_mask_1d_index_to_submask_index = al.mask_mapping_util.sub_mask_1d_index_to_submask_index_from_mask_and_sub_size( mask=mask, sub_size=2) assert mapping.sub_mask_1d_index_to_submask_index == pytest.approx( sub_mask_1d_index_to_submask_index, 1e-4)
def test__sub_array_1d_from_sub_array_2d__compare_to_util(self): sub_array_2d = np.array([ [1.0, 1.0, 2.0, 2.0, 3.0, 10.0], [1.0, 1.0, 2.0, 2.0, 3.0, 10.0], [3.0, 3.0, 8.0, 1.0, 4.0, 4.0], [3.0, 3.0, 7.0, 2.0, 4.0, 4.0], ]) mask = np.array([[False, False, False], [True, True, False]]) mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = mapping.sub_array_1d_with_sub_dimensions_from_sub_array_2d( sub_array_2d=sub_array_2d) sub_array_1d_util = al.array_mapping_util.sub_array_1d_from_sub_array_2d_mask_and_sub_size( sub_array_2d=sub_array_2d, mask=mask, sub_size=2) assert (sub_array_1d == sub_array_1d_util).all()
def test__solution_different_values__simple_blurred_mapping_matrix__correct_reconstructed_image( self): matrix_shape = (3, 3) mask = al.Mask( array=np.array([[True, True, True], [False, False, False], [True, True, True]]), pixel_scale=1.0, sub_size=1, ) grid = al.Grid.from_mask(mask=mask) inv = al.Inversion.from_data_1d_mapper_and_regularization( image_1d=np.ones(9), noise_map_1d=np.ones(9), convolver=mock_inversion.MockConvolver(matrix_shape), mapper=mock_inversion.MockMapper(matrix_shape=matrix_shape, grid=grid), regularization=mock_inversion.MockRegularization(matrix_shape), ) inv.pixelization_values = np.array([1.0, 2.0, 3.0, 4.0]) inv.blurred_mapping_matrix = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0]]) # # Imaging pixel 0 maps to 4 pixs pixxels -> value is 1.0 + 2.0 + 3.0 + 4.0 = 10.0 # # Imaging pixel 1 maps to 3 pixs pixxels -> value is 1.0 + 3.0 + 4.0 # # Imaging pixel 2 maps to 1 pixs pixxels -> value is 1.0 assert (inv.reconstructed_data_1d == np.array([10.0, 8.0, 1.0])).all() assert inv.reconstructed_data_2d == np.array([[0.0, 0.0, 0.0], [10.0, 8.0, 1.0], [0.0, 0.0, 0.0]]) assert inv.pixelization_errors_with_covariance == pytest.approx( np.array([[0.7, -0.3, -0.3], [-0.3, 0.7, -0.3], [-0.3, -0.3, 0.7]]), 1.0e-4) assert inv.pixelization_errors == pytest.approx( np.array([0.7, 0.7, 0.7]), 1.0e-4)
def test__grid_2d_from_grid_1d__compare_to_util(self): mask = np.array([ [True, True, False, False], [True, False, True, True], [True, True, False, False], ]) grid_1d = np.array([[1.0, 1.0], [6.0, 6.0], [4.0, 4.0], [5.0, 5.0], [2.0, 2.0]]) grid_2d_util = al.grid_mapping_util.sub_grid_2d_from_sub_grid_1d_mask_and_sub_size( sub_grid_1d=grid_1d, mask=mask, sub_size=1) mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=1) mapping = al.Mapping(mask=mask) grid_2d = mapping.grid_2d_from_grid_1d(grid_1d=grid_1d) assert (grid_2d_util == grid_2d).all()
def test__3x3_grid__extracts_max_min_coordinates__ignores_other_coordinates_more_central( self): galaxy = al.Galaxy( redshift=0.5, light=al.light_profiles.EllipticalSersic(intensity=1.0)) grid = np.array([ [-1.5, -1.5], [1.5, 1.5], [0.1, -0.1], [-1.0, 0.6], [1.4, -1.3], [1.5, 1.5], ]) plane_image = al.lens_util.plane_image_of_galaxies_from_grid( shape=(3, 3), grid=grid, galaxies=[galaxy], buffer=0.0) mask = al.Mask(array=np.full(shape=(3, 3), fill_value=False), pixel_scale=1.0, sub_size=1) grid = al.Grid( arr=np.array([ [-1.0, -1.0], [-1.0, 0.0], [-1.0, 1.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0], [1.0, -1.0], [1.0, 0.0], [1.0, 1.0], ]), mask=mask, ) plane_image_galaxy = galaxy.profile_image_from_grid(grid=grid, return_binned=True, return_in_2d=True) assert (plane_image == plane_image_galaxy).all()
def test__array_1d_from_array_2d__compare_to_array_util(self): array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]) mask = np.array([ [True, False, True], [False, False, False], [True, False, True], [True, True, True], ]) array_1d_util = al.array_mapping_util.sub_array_1d_from_sub_array_2d_mask_and_sub_size( mask=mask, sub_array_2d=array_2d, sub_size=1) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=1) mapping = al.Mapping(mask=mask) array_1d = mapping.array_1d_from_array_2d(array_2d=array_2d) assert (array_1d == array_1d_util).all()
def test__grid_1d_from_grid_2d__compare_to_util(self): grid_2d = np.array([ [[1, 1], [2, 2], [3, 3], [4, 4]], [[5, 5], [6, 6], [7, 7], [8, 8]], [[9, 9], [10, 10], [11, 11], [12, 12]], ]) mask = np.array([ [True, False, True, True], [False, False, False, True], [True, False, True, False], ]) grid_1d_util = al.grid_mapping_util.sub_grid_1d_from_sub_grid_2d_mask_and_sub_size( sub_grid_2d=grid_2d, mask=mask, sub_size=1) mask = al.Mask(array=mask, pixel_scale=2.0, sub_size=1) mapping = al.Mapping(mask=mask) grid_1d = mapping.grid_1d_from_grid_2d(grid_2d=grid_2d) assert (grid_1d_util == grid_1d).all()
def test__scaled_sub_array_2d_from_sub_array_1d(self): mask = np.array([[False, False, True], [False, True, False]]) mask = al.Mask(array=mask, pixel_scale=3.0, sub_size=2) mapping = al.Mapping(mask=mask) sub_array_1d = np.array([ 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, ]) scaled_sub_array_2d = mapping.scaled_array_2d_with_sub_dimensions_from_sub_array_1d( sub_array_1d=sub_array_1d) assert (scaled_sub_array_2d == np.array([ [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [1.0, 1.0, 2.0, 2.0, 0.0, 0.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], [3.0, 3.0, 0.0, 0.0, 4.0, 4.0], ])).all() assert scaled_sub_array_2d.pixel_scales == (1.5, 1.5) assert scaled_sub_array_2d.origin == (0.0, 0.0)
import numpy as np import pytest import autofit as af import autolens as al directory = path.dirname(path.realpath(__file__)) @pytest.fixture(scope="session", autouse=True) def do_something(): af.conf.instance = af.conf.Config( config_path="{}/../../test_files/config/radial_min".format(directory)) mask_x1 = al.Mask(array=np.array([[False]]), pixel_scale=1.0, sub_size=1) grid_10 = al.Grid(arr=np.array([[1.0, 0.0]]), mask=mask_x1) grid_01 = al.Grid(arr=np.array([[0.0, 1.0]]), mask=mask_x1) grid_11 = al.Grid(arr=np.array([[1.0, 1.0]]), mask=mask_x1) grid_00 = al.Grid(arr=np.array([[0.0, 0.0]]), mask=mask_x1) grid_minus_11 = al.Grid(arr=np.array([[-1.0, -1.0]]), mask=mask_x1) grid_03 = al.Grid(arr=np.array([[0.0, 3.0]]), mask=mask_x1) grid_34 = al.Grid(arr=np.array([[3.0, 4.0]]), mask=mask_x1) class TestMemoize(object): def test_add_to_cache(self): class MyProfile(object): # noinspection PyMethodMayBeStatic @al.geometry_profiles.cache def my_method(
def test__1x2_image__noise_not_1__alls_correct(self): image = al.ScaledSquarePixelArray(array=5.0 * np.ones((3, 4)), pixel_scale=1.0) image[1, 2] = 4.0 noise_map = al.ScaledSquarePixelArray(array=2.0 * np.ones((3, 4)), pixel_scale=1.0) galaxy_data = al.GalaxyData(image=image, noise_map=noise_map, pixel_scale=3.0) mask = al.Mask( array=np.array([ [True, True, True, True], [True, False, False, True], [True, True, True, True], ]), pixel_scale=1.0, sub_size=1, ) g0 = MockGalaxy(value=1.0, shape=2) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_image=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_convergence=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_potential=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.model_galaxies == [g0] assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_deflections_y=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2)) galaxy_fit_data = al.GalaxyFitData(galaxy_data=galaxy_data, mask=mask, use_deflections_x=True) fit = al.GalaxyFit(galaxy_data=galaxy_fit_data, model_galaxies=[g0]) assert fit.chi_squared == (25.0 / 4.0) assert fit.reduced_chi_squared == (25.0 / 4.0) / 2.0 assert fit.likelihood == -0.5 * ( (25.0 / 4.0) + 2.0 * np.log(2 * np.pi * 2.0**2))
def test__15_grid__no_sub_grid(self): mask = np.array( [ [True, True, True, True, True, True, True], [True, True, True, True, True, True, True], [True, False, False, False, False, False, True], [True, False, False, False, False, False, True], [True, False, False, False, False, False, True], [True, True, True, True, True, True, True], [True, True, True, True, True, True, True], ] ) mask = al.Mask(array=mask, pixel_scale=1.0, sub_size=1) # There is no sub-grid, so our grid are just the masked_image grid (note the NumPy weighted_data structure # ensures this has no sub-gridding) grid = al.Grid( arr=np.array( [ [0.9, -0.9], [1.0, -1.0], [1.1, -1.1], [0.9, 0.9], [1.0, 1.0], [1.1, 1.1], [-0.01, 0.01], [0.0, 0.0], [0.01, 0.01], [-0.9, -0.9], [-1.0, -1.0], [-1.1, -1.1], [-0.9, 0.9], [-1.0, 1.0], [-1.1, 1.1], ] ), mask=mask, sub_size=1, ) pixelization_grid = MockPixelizationGrid(arr=grid) pix = al.pixelizations.Rectangular(shape=(3, 3)) mapper = pix.mapper_from_grid_and_pixelization_grid( grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=False ) assert mapper.is_image_plane_pixelization == False assert mapper.geometry.shape_arcsec == pytest.approx((2.2, 2.2), 1.0e-4) assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4) assert ( mapper.mapping_matrix == np.array( [ [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], ] ) ).all() assert mapper.shape == (3, 3) reg = al.regularization.Constant(coefficient=1.0) regularization_matrix = reg.regularization_matrix_from_pixel_neighbors( mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size ) assert ( regularization_matrix == np.array( [ [2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0], [-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0], [0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0], [0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0], [0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0], [0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001], ] ) ).all()
def test__3x3_simple_grid__include_mask_with_offset_centre(self): mask = al.Mask( array=np.array( [ [True, True, True, False, True], [True, True, False, False, False], [True, True, True, False, True], [True, True, True, True, True], [True, True, True, True, True], ] ), pixel_scale=1.0, sub_size=1, ) grid = np.array([[2.0, 1.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0], [0.0, 1.0]]) grid = al.Grid(arr=grid, mask=mask, sub_size=1) pix = al.pixelizations.VoronoiMagnification(shape=(3, 3)) sparse_to_grid = al.SparseToGrid.from_grid_and_unmasked_2d_grid_shape( unmasked_sparse_shape=pix.shape, grid=grid.unlensed_unsubbed_1d ) pixelization_grid = MockPixelizationGrid( arr=sparse_to_grid.sparse, mask_1d_index_to_nearest_pixelization_1d_index=sparse_to_grid.mask_1d_index_to_sparse_1d_index, ) mapper = pix.mapper_from_grid_and_pixelization_grid( grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=False ) assert mapper.is_image_plane_pixelization == True assert mapper.geometry.shape_arcsec == pytest.approx((2.0, 2.0), 1.0e-4) assert (mapper.geometry.pixel_centres == sparse_to_grid.sparse).all() assert mapper.geometry.origin == pytest.approx((1.0, 1.0), 1.0e-4) assert isinstance(mapper, al.VoronoiMapper) assert ( mapper.mapping_matrix == np.array( [ [1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0], ] ) ).all() reg = al.regularization.Constant(coefficient=1.0) regularization_matrix = reg.regularization_matrix_from_pixel_neighbors( mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size ) assert ( regularization_matrix == np.array( [ [3.00000001, -1.0, -1.0, -1.0, 0.0], [-1.0, 3.00000001, -1.0, 0.0, -1.0], [-1.0, -1.0, 4.00000001, -1.0, -1.0], [-1.0, 0.0, -1.0, 3.00000001, -1.0], [0.0, -1.0, -1.0, -1.0, 3.00000001], ] ) ).all()
def test__grid__requires_border_relocation(self): mask = np.array( [ [True, True, True, True, True, True, True], [True, True, True, True, True, True, True], [True, True, True, False, True, True, True], [True, True, False, False, False, True, True], [True, True, True, False, True, True, True], [True, True, True, True, True, True, True], [True, True, True, True, True, True, True], ] ) mask = al.Mask(array=mask, pixel_scale=1.0, sub_size=1) grid = al.Grid( arr=np.array( [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [-1.0, -1.0]] ), mask=mask, sub_size=1, ) pixelization_grid = MockPixelizationGrid(grid) pix = al.pixelizations.Rectangular(shape=(3, 3)) mapper = pix.mapper_from_grid_and_pixelization_grid( grid=grid, pixelization_grid=pixelization_grid, inversion_uses_border=True ) assert mapper.is_image_plane_pixelization == False assert mapper.geometry.shape_arcsec == pytest.approx((2.0, 2.0), 1.0e-4) assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4) assert ( mapper.mapping_matrix == np.array( [ [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], ] ) ).all() assert mapper.shape == (3, 3) reg = al.regularization.Constant(coefficient=1.0) regularization_matrix = reg.regularization_matrix_from_pixel_neighbors( mapper.geometry.pixel_neighbors, mapper.geometry.pixel_neighbors_size ) assert ( regularization_matrix == np.array( [ [2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0], [0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0], [-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0], [0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0], [0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0], [0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0], [0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0], [0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001], ] ) ).all()