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__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__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__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()
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(
self,
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()
def test__5_simple_grid__include_sub_grid(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=2.0, sub_size=2)
# Assume a 2x2 sub-grid, so each of our 5 masked_image-pixels are split into 4.
# The grid below is unphysical in that the (0.0, 0.0) terms on the end of each sub-grid probably couldn't
# happen for a real lensing calculation. This is to make a mapping_matrix matrix which explicitly tests the
# sub-grid.
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],
[1.0, 1.0],
[-1.0, -1.0],
[-1.0, -1.0],
[-1.0, -1.0],
[-1.0, 1.0],
[-1.0, 1.0],
[-1.0, 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],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
]
),
mask=mask,
sub_size=2,
)
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((2.0, 2.0), 1.0e-4)
assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4)
assert (
mapper.mapping_matrix
== np.array(
[
[0.75, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.75],
[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],
]
)
).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()
