def test__setup_pixelization__galaxy_has_pixelization__returns_grids_with_pix_grid(
self):
ma = mask.Mask(np.array([[False, False, False], [False, False, False],
[False, True, False]]),
pixel_scale=1.0)
grid_stack = grids.GridStack.grid_stack_from_mask_sub_grid_size_and_psf_shape(
mask=ma, sub_grid_size=1, psf_shape=(1, 1))
galaxy = g.Galaxy(
pixelization=pixelizations.AdaptiveMagnification(shape=(3, 3)),
regularization=regularization.Constant())
image_plane_pix_grids = \
pixelizations.setup_image_plane_pixelization_grid_from_galaxies_and_grid_stack(galaxies=[galaxy, galaxy],
grid_stack=grid_stack)
assert (image_plane_pix_grids.regular == grid_stack.regular).all()
assert (image_plane_pix_grids.sub == grid_stack.sub).all()
assert (image_plane_pix_grids.blurring == grid_stack.blurring).all()
assert image_plane_pix_grids.pix == pytest.approx(
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, 1.0]]),
1.0e-4)
def test__3x3_simple_grid__include_mask__create_using_regular_grid(self):
regular_grid = np.array([[1.0, 0.0], [0.0, -1.0], [0.0, 0.0],
[0.0, 1.0], [-1.0, 0.0]])
mask = msk.Mask(array=np.array([[True, False, True],
[False, False, False],
[True, False, True]]),
pixel_scale=1.0)
sub_grid = np.array([[1.0, 0.0], [0.0, -1.0], [0.0, 0.0], [0.0, 1.0],
[-1.0, 0.0]])
sub_to_regular = np.array([0, 1, 2, 3, 4])
regular_grid = grids.RegularGrid(arr=regular_grid, mask=mask)
sub_grid = MockSubGrid(sub_grid, sub_to_regular, sub_grid_size=1)
pix = pixelizations.AdaptiveMagnification(shape=(3, 3))
image_plane_pix = pix.image_plane_pix_grid_from_regular_grid(
regular_grid=regular_grid)
grid_stack = MockGridStack(
regular=regular_grid,
sub=sub_grid,
pix=image_plane_pix.sparse_grid,
regular_to_nearest_pix=image_plane_pix.regular_to_sparse)
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=None)
assert mapper.is_image_plane_pixelization == True
assert mapper.geometry.shape_arc_seconds == pytest.approx((2.0, 2.0),
1.0e-4)
assert (mapper.geometry.pixel_centres == image_plane_pix.sparse_grid
).all()
assert mapper.geometry.origin == pytest.approx((0.0, 0.0), 1.0e-4)
assert isinstance(mapper, pm.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 = regularization.Constant(coefficients=(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__5_simple_grid__include_sub_grid__sets_up_correct_mapper(self):
regular_grid = np.array([[1.0, 1.0], [-1.0, 1.0], [0.0, 0.0],
[1.0, -1.0], [-1.0, -1.0]])
sub_grid = np.array([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0], [0.0, 0.0],
[-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],
[1.0, -1.0], [1.0, -1.0], [1.0, -1.0], [0.0, 0.0],
[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0],
[0.0, 0.0]])
sub_to_regular = np.array(
[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4])
pixel_centers = regular_grid
regular_to_sparse = np.array([0, 1, 2, 3, 4])
grid_stack = MockGridStack(regular=regular_grid,
sub=MockSubGrid(sub_grid,
sub_to_regular,
sub_grid_size=2),
pix=pixel_centers,
regular_to_nearest_pix=regular_to_sparse)
pix = pixelizations.AdaptiveMagnification(shape=(5, 1))
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=None)
assert mapper.is_image_plane_pixelization == True
assert mapper.geometry.shape_arc_seconds == pytest.approx((2.0, 2.0),
1.0e-4)
assert (mapper.geometry.pixel_centres == pixel_centers).all()
assert mapper.geometry.origin == (0.0, 0.0)
assert isinstance(mapper, pm.VoronoiMapper)
assert (mapper.mapping_matrix == np.array([[0.75, 0.0, 0.25, 0.0, 0.0],
[0.0, 0.75, 0.25, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.25, 0.75, 0.0],
[0.0, 0.0, 0.25, 0.0,
0.75]])).all()
reg = regularization.Constant(coefficients=(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__5_simple_grid__include_sub_grid(self):
# Source-plane comprises 5 grid, so 5 masked_image pixels traced to the pix-plane.
regular_grid = np.array([[1.0, -1.0], [1.0, 1.0], [0.0, 0.0],
[-1.0, -1.0], [-1.0, 1.0]])
# 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.
sub_grid = np.array([[1.0, -1.0], [1.0, -1.0], [1.0, -1.0], [0.0, 0.0],
[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],
[-1.0, -1.0], [-1.0, -1.0], [-1.0, -1.0],
[0.0, 0.0], [-1.0, 1.0], [-1.0, 1.0], [-1.0, 1.0],
[0.0, 0.0]])
sub_to_regular = np.array(
[0, 0, 0, 2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 2, 4, 4, 4, 2])
grid_stack = MockGridStack(regular=regular_grid,
sub=MockSubGrid(sub_grid,
sub_to_regular,
sub_grid_size=2))
pix = pixelizations.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=None)
assert mapper.is_image_plane_pixelization == False
assert mapper.geometry.shape_arc_seconds == 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.75, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.75, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.75]])).all()
assert mapper.shape == (3, 3)
reg = regularization.Constant(coefficients=(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_fixed_regularization(self):
galaxy_prior = gp.GalaxyModel(variable_redshift=True,
pixelization=pixelizations.Voronoi(),
regularization=regularization.Constant())
arguments = {galaxy_prior.redshift.redshift: 2.0}
galaxy = galaxy_prior.instance_for_arguments(arguments)
assert galaxy.regularization.coefficients == (1., )
def test__same_as_above_but_grid_requires_border_relocation(self):
regular_grid = np.array([[1.0, -1.0], [1.0, 1.0], [0.0, 0.0],
[-1.0, -1.0], [-1.0, 1.0]])
sub_grid = np.array([[2.0, 2.0], [2.0, 2.0], [2.0, 2.0], [2.0, 2.0],
[-2.0, -2.0]])
# These will all be relocated to the regular grid edge.
pix_grid = np.array([[1.1, -1.1], [1.1, 1.1], [0.0, 0.0], [-1.1, -1.1],
[-1.1, 1.1]])
border = grids.RegularGridBorder(arr=np.array([0, 1, 3, 4]))
sub_to_regular = np.array([0, 1, 2, 3, 4])
regular_to_sparse = np.array([0, 1, 2, 3, 4])
grid_stack = MockGridStack(regular=regular_grid,
sub=MockSubGrid(sub_grid,
sub_to_regular,
sub_grid_size=1),
pix=pix_grid,
regular_to_nearest_pix=regular_to_sparse)
pix = pixelizations.AdaptiveMagnification(shape=(5, 1))
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=border)
assert mapper.is_image_plane_pixelization == True
assert mapper.geometry.shape_arc_seconds == pytest.approx((2.0, 2.0),
1.0e-4)
assert mapper.geometry.pixel_centres == pytest.approx(
regular_grid, 1e-4)
assert mapper.geometry.origin == (0.0, 0.0)
assert isinstance(mapper, pm.VoronoiMapper)
assert (mapper.mapping_matrix == np.array([[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0,
0.0]])).all()
reg = regularization.Constant(coefficients=(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_fixed_pixelization(self):
galaxy_prior = gp.GalaxyModel(variable_redshift=True,
pixelization=pixelizations.Rectangular(),
regularization=regularization.Constant())
arguments = {galaxy_prior.redshift.redshift: 2.0}
galaxy = galaxy_prior.instance_for_arguments(arguments)
assert galaxy.pixelization.shape[0] == 3
assert galaxy.pixelization.shape[1] == 3
def test__5_simple_grid__no_sub_grid(self):
# Source-plane comprises 5 grid, so 5 masked_image pixels traced to the pix-plane.
regular_grid = np.array([[1.0, -1.0], [1.0, 1.0], [0.0, 0.0],
[-1.0, -1.0], [-1.0, 1.0]])
sub_grid = np.array([[1.0, -1.0], [1.0, 1.0], [0.0, 0.0], [-1.0, -1.0],
[-1.0, 1.0]])
sub_to_regular = np.array([0, 1, 2, 3, 4])
grid_stack = MockGridStack(regular=regular_grid,
sub=MockSubGrid(
sub_grid=sub_grid,
sub_to_regular=sub_to_regular,
sub_grid_size=1))
# There is no sub-grid, so our sub_grid are just the masked_image grid (note the NumPy weighted_data structure
# ensures this has no sub-gridding)
pix = pixelizations.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_stack_and_border(grid_stack=grid_stack,
border=None)
assert mapper.is_image_plane_pixelization == False
assert mapper.geometry.shape_arc_seconds == 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(
[[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, 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, 0.0, 0.0, 1.0]])).all()
assert mapper.shape == (3, 3)
reg = regularization.Constant(coefficients=(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__grid__requires_border_relocation(self):
regular_grid = np.array([[1.0, -1.0], [1.0, 1.0], [0.0, 0.0],
[-1.0, -1.0], [-1.0, 1.0]])
sub_grid = np.array([[2.0, 2.0], [2.0, 2.0], [2.0, 2.0], [2.0, 2.0],
[-2.0, -2.0]])
border = grids.RegularGridBorder(arr=np.array([0, 1, 3, 4]))
sub_to_regular = np.array([0, 1, 2, 3, 4])
grid_stack = MockGridStack(regular=regular_grid,
sub=MockSubGrid(sub_grid,
sub_to_regular,
sub_grid_size=1))
pix = pixelizations.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_stack_and_border(grid_stack, border)
assert mapper.is_image_plane_pixelization == False
assert mapper.geometry.shape_arc_seconds == 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 = regularization.Constant(coefficients=(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__setup_pixelization__galaxy_has_pixelization__but_grid_is_padded_grid__returns_normal_grids(
self):
ma = mask.Mask(np.array([[False, False, False], [False, False, False],
[False, True, False]]),
pixel_scale=1.0)
grid_stack = grids.GridStack.padded_grid_stack_from_mask_sub_grid_size_and_psf_shape(
mask=ma, sub_grid_size=1, psf_shape=(1, 1))
galaxy = g.Galaxy(
pixelization=pixelizations.AdaptiveMagnification(shape=(3, 3)),
regularization=regularization.Constant())
image_plane_pix_grids = \
pixelizations.setup_image_plane_pixelization_grid_from_galaxies_and_grid_stack(galaxies=[galaxy, galaxy],
grid_stack=grid_stack)
assert image_plane_pix_grids == grid_stack
def test__regularization_matrix__compare_to_regularization_util(self):
pixel_neighbors = np.array([[1, 3, 7, 2], [4, 2, 0, -1], [1, 5, 3, -1],
[4, 6, 0, -1], [7, 1, 5, 3], [4, 2, 8, -1],
[7, 3, 0, -1], [4, 8, 6, -1],
[7, 5, -1, -1]])
pixel_neighbors_size = np.array([4, 3, 3, 3, 4, 3, 3, 3, 2])
reg = regularization.Constant(coefficients=(1.0, ))
regularization_matrix = reg.regularization_matrix_from_pixel_neighbors(
pixel_neighbors, pixel_neighbors_size)
regularization_matrix_util = reg_util.constant_regularization_matrix_from_pixel_neighbors(
coefficients=(1.0, ),
pixel_neighbors=pixel_neighbors,
pixel_neighbors_size=pixel_neighbors_size)
assert (regularization_matrix == regularization_matrix_util).all()
def test__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, ccd_data):
lens_galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=0.1))
source_galaxy = g.Galaxy(
pixelization=pix.Rectangular(shape=(4, 4)),
regularization=reg.Constant(coefficients=(1.0, )))
phase = ph.LensPlanePhase(lens_galaxies=[lens_galaxy],
mask_function=ph.default_mask_function,
cosmology=cosmo.FLRW,
phase_name='test_phase')
analysis = phase.make_analysis(data=ccd_data)
instance = phase.constant
fit_figure_of_merit = analysis.fit(instance=instance)
mask = phase.mask_function(image=ccd_data.image)
lens_data = li.LensData(ccd_data=ccd_data, mask=mask)
tracer = analysis.tracer_for_instance(instance=instance)
fit = lens_fit.LensProfileFit(lens_data=lens_data, tracer=tracer)
assert fit.likelihood == fit_figure_of_merit
phase = ph.LensSourcePlanePhase(lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
mask_function=ph.default_mask_function,
cosmology=cosmo.FLRW,
phase_name='test_phase')
analysis = phase.make_analysis(data=ccd_data)
instance = phase.constant
fit_figure_of_merit = analysis.fit(instance=instance)
mask = phase.mask_function(image=ccd_data.image)
lens_data = li.LensData(ccd_data=ccd_data, mask=mask)
tracer = analysis.tracer_for_instance(instance=instance)
fit = lens_fit.LensProfileInversionFit(lens_data=lens_data,
tracer=tracer)
assert fit.evidence == fit_figure_of_merit
def test___if_galaxy_has_pixelization__unmasked_image_is_none(self):
mask = msk.Mask(array=np.array([[True, True,
True], [True, False, True],
[True, True, True]]),
pixel_scale=1.0)
padded_grid_stack = grids.GridStack.padded_grid_stack_from_mask_sub_grid_size_and_psf_shape(
mask=mask, sub_grid_size=1, psf_shape=(3, 3))
psf_0 = im.PSF(array=(np.array([[0.0, 0.0, 0.0], [0.0, 1.0, 0.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
psf_1 = im.PSF(array=(np.array([[0.0, 3.0, 0.0], [0.0, 1.0, 2.0],
[0.0, 0.0, 0.0]])),
pixel_scale=1.0)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=0.1),
pixelization=pix.Rectangular(),
regularization=reg.Constant())
g1 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=0.2))
g2 = g.Galaxy(mass_profile=mp.SphericalIsothermal(einstein_radius=1.0))
tracer = ray_tracing_stack.TracerImageSourcePlanesStack(
lens_galaxies=[g0, g1, g2],
source_galaxies=[g0, g1],
image_plane_grid_stacks=[padded_grid_stack, padded_grid_stack])
unmasked_blurred_image_of_datas_planes_and_galaxies = \
stack_util.unmasked_blurred_image_of_datas_planes_and_galaxies_from_padded_grid_stacks_and_psf(
planes=tracer.planes, padded_grid_stacks=[padded_grid_stack, padded_grid_stack], psfs=[psf_0, psf_1])
assert unmasked_blurred_image_of_datas_planes_and_galaxies[0][0][
0] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[0][0]
[1]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[1][0][
0] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[1][0]
[1]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[0][1][
0] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[0][1]
[1]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[1][1][
0] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[1][1]
[1]) == scaled_array.ScaledSquarePixelArray
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=0.1))
g1 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=0.2),
pixelization=pix.Rectangular(),
regularization=reg.Constant())
g2 = g.Galaxy(mass_profile=mp.SphericalIsothermal(einstein_radius=1.0))
tracer = ray_tracing_stack.TracerImageSourcePlanesStack(
lens_galaxies=[g0, g1, g2],
source_galaxies=[g0, g1],
image_plane_grid_stacks=[padded_grid_stack, padded_grid_stack])
unmasked_blurred_image_of_datas_planes_and_galaxies = \
stack_util.unmasked_blurred_image_of_datas_planes_and_galaxies_from_padded_grid_stacks_and_psf(
planes=tracer.planes, padded_grid_stacks=[padded_grid_stack, padded_grid_stack], psfs=[psf_0, psf_1])
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[0][0]
[0]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[0][0][
1] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[1][0]
[0]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[1][0][
1] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[0][1]
[0]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[0][1][
1] == None
assert type(unmasked_blurred_image_of_datas_planes_and_galaxies[1][1]
[0]) == scaled_array.ScaledSquarePixelArray
assert unmasked_blurred_image_of_datas_planes_and_galaxies[1][1][
1] == None
def test__tracers_are_different__likelihood_is_non_zero(
self, lens_data_blur):
pixelization = pix.Rectangular(shape=(3, 3))
regularization = reg.Constant(coefficients=(1.0, ))
g0 = g.Galaxy(mass_profile=mp.SphericalIsothermal(einstein_radius=1.0))
g0_subhalo = g.Galaxy(subhalo=mp.SphericalIsothermal(
einstein_radius=0.1))
g1 = g.Galaxy(pixelization=pixelization, regularization=regularization)
tracer_normal = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[g0],
source_galaxies=[g1],
image_plane_grid_stack=lens_data_blur.grid_stack)
tracer_sensitive = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[g0, g0_subhalo],
source_galaxies=[g1],
image_plane_grid_stack=lens_data_blur.grid_stack)
fit = sensitivity_fit.SensitivityInversionFit(
lens_data=lens_data_blur,
tracer_normal=tracer_normal,
tracer_sensitive=tracer_sensitive)
assert (fit.fit_normal.image == lens_data_blur.image).all()
assert (fit.fit_normal.noise_map == lens_data_blur.noise_map).all()
mapper = pixelization.mapper_from_grid_stack_and_border(
grid_stack=tracer_normal.source_plane.grid_stack, border=None)
inversion = inv.inversion_from_image_mapper_and_regularization(
mapper=mapper,
regularization=regularization,
image_1d=lens_data_blur.image_1d,
noise_map_1d=lens_data_blur.noise_map_1d,
convolver=lens_data_blur.convolver_mapping_matrix)
assert fit.fit_normal.model_image == pytest.approx(
inversion.reconstructed_data, 1.0e-4)
residual_map = fit_util.residual_map_from_data_mask_and_model_data(
data=lens_data_blur.image,
mask=lens_data_blur.mask,
model_data=inversion.reconstructed_data)
assert fit.fit_normal.residual_map == pytest.approx(
residual_map, 1.0e-4)
chi_squared_map = fit_util.chi_squared_map_from_residual_map_noise_map_and_mask(
residual_map=residual_map,
mask=lens_data_blur.mask,
noise_map=lens_data_blur.noise_map)
assert fit.fit_normal.chi_squared_map == pytest.approx(
chi_squared_map, 1.0e-4)
assert (fit.fit_sensitive.image == lens_data_blur.image).all()
assert (fit.fit_sensitive.noise_map == lens_data_blur.noise_map).all()
mapper = pixelization.mapper_from_grid_stack_and_border(
grid_stack=tracer_sensitive.source_plane.grid_stack, border=None)
inversion = inv.inversion_from_image_mapper_and_regularization(
mapper=mapper,
regularization=regularization,
image_1d=lens_data_blur.image_1d,
noise_map_1d=lens_data_blur.noise_map_1d,
convolver=lens_data_blur.convolver_mapping_matrix)
assert fit.fit_sensitive.model_image == pytest.approx(
inversion.reconstructed_data, 1.0e-4)
residual_map = fit_util.residual_map_from_data_mask_and_model_data(
data=lens_data_blur.image,
mask=lens_data_blur.mask,
model_data=inversion.reconstructed_data)
assert fit.fit_sensitive.residual_map == pytest.approx(
residual_map, 1.0e-4)
chi_squared_map = fit_util.chi_squared_map_from_residual_map_noise_map_and_mask(
residual_map=residual_map,
mask=lens_data_blur.mask,
noise_map=lens_data_blur.noise_map)
assert fit.fit_sensitive.chi_squared_map == pytest.approx(
chi_squared_map, 1.0e-4)
chi_squared_normal = fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=fit.fit_normal.chi_squared_map,
mask=lens_data_blur.mask)
chi_squared_sensitive = fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=fit.fit_sensitive.chi_squared_map,
mask=lens_data_blur.mask)
noise_normalization = fit_util.noise_normalization_from_noise_map_and_mask(
mask=lens_data_blur.mask, noise_map=lens_data_blur.noise_map)
assert fit.fit_normal.likelihood == -0.5 * (chi_squared_normal +
noise_normalization)
assert fit.fit_sensitive.likelihood == -0.5 * (chi_squared_sensitive +
noise_normalization)
assert fit.figure_of_merit == fit.fit_sensitive.likelihood - fit.fit_normal.likelihood
fit_from_factory = sensitivity_fit.fit_lens_data_with_sensitivity_tracers(
lens_data=lens_data_blur,
tracer_normal=tracer_normal,
tracer_sensitive=tracer_sensitive)
assert fit.figure_of_merit == fit_from_factory.figure_of_merit
results = pd.concat(list_)
image_plane_grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_size(shape_2d=(301, 301), pixel_scales=0.03,
sub_size=2)
for i in range(len(results)):
lens_galaxy = al.Galaxy(mass=al.mp.EllipticalIsothermal(centre=(results['lens_mass_centre_0'][i], results['lens_mass_centre_1'][i]),
axis_ratio=results['lens_mass_axis_ratio'][i], phi=results['lens_mass_phi'][i],
einstein_radius=results['lens_mass_einstein_radius'][i]),
shear=al.mp.ExternalShear(magnitude=results['lens_shear_magnitude'][i], phi=results['lens_shear_phi'][i]),
redshift = slacs['z_lens'][i])
source_galaxy = al.Galaxy(pixelization=pix.AdaptiveMagnification(shape_2d=(results['source_pixelization_shape_0'][i],
results['source_pixelization_shape_1'][i])),
regularization=reg.Constant(results['source_regularization_coefficients_0'][i]),
redshift=slacs['z_source'][i])
tracer = ray_tracing.TracerImageSourcePlanes(lens_galaxies=[lens_galaxy], source_galaxies=[source_galaxy],
image_plane_grid_stack=image_plane_grid_stack, cosmology=cosmology.Planck15)
einstein_mass = tracer.einstein_masses_of_planes[0]
M_Ein.append(einstein_mass)
fig, ax = plt.subplots()
cm = plt.get_cmap('gist_rainbow')
ax.set_color_cycle([cm(1.*i/len(slacs)) for i in range(len(slacs))])
marker = ['o', '+','v','<','s','p','*','D','h','x','8','1','2','3','4',]
for i in range(len(results)):
# results = pd.concat(list_)
image_plane_grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_size(
shape_2d=ccd_data.shape, pixel_scales=ccd_data.pixel_scales, sub_size=2)
#print(data)
#print(data.iloc[0,16])
lens_galaxy = al.Galaxy(mass=al.mp.EllipticalIsothermal(
centre=(data.iloc[0, 5], data.iloc[0, 6]),
axis_ratio=data.iloc[0, 7],
phi=data.iloc[0, 8],
einstein_radius=data.iloc[0, 9]),
redshift=0.285)
source_galaxy = al.Galaxy(
pixelization=pix.AdaptiveMagnification(shape_2d=(data.iloc[0, 14],
data.iloc[0, 15])),
regularization=reg.Constant(data.iloc[0, 16]),
redshift=0.575)
#image_plane_grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_size(shape_2d=(301, 301), pixel_scales=0.03,
# sub_size=2)
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
cosmology=cosmology.Planck15)
print(np.log10(tracer.einstein_masses_of_planes[0]))
radius_arcsec = 3.0
psf_shape = (21, 21)
pixelization_shape = (30, 30)
print('sub grid size = ' + str(sub_grid_size))
print('circular mask radius = ' + str(radius_arcsec) + '\n')
print('psf shape = ' + str(psf_shape) + '\n')
print('pixelization shape = ' + str(pixelization_shape) + '\n')
lens_galaxy = g.Galaxy(mass=mp.EllipticalIsothermal(
centre=(0.0, 0.0), einstein_radius=1.6, axis_ratio=0.7, phi=45.0))
pixelization = pix.AdaptiveMagnification(shape=pixelization_shape)
source_galaxy = g.Galaxy(pixelization=pixelization,
regularization=reg.Constant(coefficients=(1.0, )))
for image_type in ['LSST', 'Euclid', 'HST', 'HST_Up', 'AO']:
ccd_data = tools.load_profiling_ccd_data(image_type=image_type,
lens_name='no_lens_source_smooth',
psf_shape=psf_shape)
mask = msk.Mask.circular(shape=ccd_data.shape,
pixel_scale=ccd_data.pixel_scale,
radius_arcsec=radius_arcsec)
lens_data = ld.LensData(ccd_data=ccd_data,
mask=mask,
sub_grid_size=sub_grid_size)
print('AdaptiveMagnification Inversion fit run times for image type ' +
image_type + '\n')
centre=(results.loc[lens[i]]['param']['lens_mass_centre_0'],
results.loc[lens[i]]['param']['lens_mass_centre_1']),
axis_ratio=results.loc[lens[i]]['+error']['lens_mass_axis_ratio'],
phi=results.loc[lens[i]]['param']['lens_mass_phi'],
einstein_radius=results.loc[
lens[i]]['-error']['lens_mass_einstein_radius']),
shear=al.mp.ExternalShear(
magnitude=results.loc[lens[i]]['param']['lens_shear_magnitude'],
phi=results.loc[lens[i]]['param']['lens_shear_phi']),
redshift=slacs['z_lens'][i])
source_galaxy = al.Galaxy(
pixelization=pix.AdaptiveMagnification(shape_2d=(
results.loc[lens[i]]['param']['source_pixelization_shape_0'],
results.loc[lens[i]]['param']['source_pixelization_shape_1'])),
regularization=reg.Constant(results.loc[lens[i]]['param']
['source_regularization_coefficients_0']),
redshift=slacs['z_source'][i])
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=image_plane_grid_stack,
cosmology=cosmology.Planck15)
tracer_error_hi = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy_hi],
source_galaxies=[source_galaxy],
image_plane_grid_stack=image_plane_grid_stack,
cosmology=cosmology.Planck15)
tracer_error_low = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy_low],
source_galaxies=[source_galaxy],
