def make_li_blur_stack():
image_0 = np.array([[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
psf_0 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
ccd_0 = ccd.CCDData(image_0,
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((4, 4)))
mask_0 = np.array([[True, True, True, True], [True, False, False, True],
[True, False, False, True], [True, True, True, True]])
mask_0 = msk.Mask(array=mask_0, pixel_scale=1.0)
image_1 = np.array([[0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0],
[0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
psf_1 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
ccd_1 = ccd.CCDData(image_1,
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((4, 4)))
mask_1 = np.array([[True, True, True, True], [True, False, False, True],
[True, False, False, True], [True, True, True, True]])
mask_1 = msk.Mask(array=mask_1, pixel_scale=1.0)
return lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
def test__same_as_above__but_3x4_image(self):
ma = mask.Mask(array=np.array([[True, False, True, True],
[False, False, False, False],
[True, False, True, True]]),
pixel_scale=1.0)
regular_grid = grids.RegularGrid.from_mask(mask=ma)
adaptive_image_grid = pixelizations.ImagePlanePixelization(shape=(3,
4))
pix_grid = adaptive_image_grid.image_plane_pix_grid_from_regular_grid(
regular_grid=regular_grid)
assert pix_grid.total_sparse_pixels == 6
assert (pix_grid.sparse_to_unmasked_sparse == np.array(
[1, 4, 5, 6, 7, 9])).all()
assert (pix_grid.unmasked_sparse_to_sparse == np.array(
[0, 0, 1, 1, 1, 2, 3, 4, 5, 5, 5, 5])).all()
assert (pix_grid.regular_to_unmasked_sparse == np.array(
[1, 4, 5, 6, 7, 9])).all()
assert (pix_grid.regular_to_sparse == np.array([0, 1, 2, 3, 4,
5])).all()
assert (pix_grid.sparse_grid == np.array([[1.0, -0.5], [0.0, -1.5],
[0.0, -0.5], [0.0, 0.5],
[0.0, 1.5], [-1.0,
-0.5]])).all()
def test__solution_different_values__simple_blurred_mapping_matrix__correct_reconstructed_image(self):
matrix_shape = (3,3)
msk = mask.Mask(array=np.array([[True, True, True],
[False, False, False],
[True, True, True]]), pixel_scale=1.0)
grid_stack = grids.GridStack.grid_stack_from_mask_sub_grid_size_and_psf_shape(mask=msk, sub_grid_size=1,
psf_shape=(1,1))
inv = inversions.Inversion(image_1d=np.ones(9), noise_map_1d=np.ones(9), convolver=MockConvolver(matrix_shape),
mapper=MockMapper(matrix_shape, grid_stack), regularization=MockRegularization(matrix_shape))
inv.solution_vector = 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]])
# # CCD pixel 0 maps to 4 pixs pixxels -> value is 1.0 + 2.0 + 3.0 + 4.0 = 10.0
# # CCD pixel 1 maps to 3 pixs pixxels -> value is 1.0 + 3.0 + 4.0
# # CCD pixel 2 maps to 1 pixs pixxels -> value is 1.0
assert (inv.reconstructed_data_vector == np.array([10.0, 8.0, 1.0])).all()
assert (inv.reconstructed_data == np.array([[0.0, 0.0, 0.0],
[10.0, 8.0, 1.0],
[0.0, 0.0, 0.0]]))
def test__pixelization_regular_grid_from_regular_grid__sets_up_with_correct_shape_and_pixel_scales(
self):
ma = mask.Mask(array=np.array([[False, False, False],
[False, False, False],
[False, False, False]]),
pixel_scale=1.0)
regular_grid = grids.RegularGrid.from_mask(mask=ma)
adaptive_image_grid = pixelizations.ImagePlanePixelization(shape=(3,
3))
pix_grid = adaptive_image_grid.image_plane_pix_grid_from_regular_grid(
regular_grid=regular_grid)
assert pix_grid.shape == (3, 3)
assert pix_grid.pixel_scales == (1.0, 1.0)
assert pix_grid.total_sparse_pixels == 9
assert (pix_grid.sparse_to_unmasked_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.unmasked_sparse_to_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.regular_to_unmasked_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.regular_to_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.sparse_grid == 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]])).all()
assert pix_grid.regular_grid == pytest.approx(regular_grid, 1e-4)
def make_grid_stack():
ma = mask.Mask(np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=6.0)
grid_stack = grids.GridStack.grid_stack_from_mask_sub_grid_size_and_psf_shape(
mask=ma, sub_grid_size=2, psf_shape=(3, 3))
# Manually overwrite a set of cooridnates to make tests of grid_stacks and defledctions straightforward
grid_stack.regular[0] = np.array([1.0, 1.0])
grid_stack.regular[1] = np.array([1.0, 0.0])
grid_stack.sub[0] = np.array([1.0, 1.0])
grid_stack.sub[1] = np.array([1.0, 0.0])
grid_stack.sub[2] = np.array([1.0, 1.0])
grid_stack.sub[3] = np.array([1.0, 0.0])
grid_stack.sub[4] = np.array([-1.0, 2.0])
grid_stack.sub[5] = np.array([-1.0, 4.0])
grid_stack.sub[6] = np.array([1.0, 2.0])
grid_stack.sub[7] = np.array([1.0, 4.0])
grid_stack.blurring[0] = np.array([1.0, 0.0])
grid_stack.blurring[1] = np.array([-6.0, -3.0])
grid_stack.blurring[2] = np.array([-6.0, 3.0])
grid_stack.blurring[3] = np.array([-6.0, 9.0])
grid_stack.blurring[4] = np.array([0.0, -9.0])
grid_stack.blurring[5] = np.array([0.0, 9.0])
grid_stack.blurring[6] = np.array([6.0, -9.0])
grid_stack.blurring[7] = np.array([6.0, -3.0])
grid_stack.blurring[8] = np.array([6.0, 3.0])
grid_stack.blurring[9] = np.array([6.0, 9.0])
return grid_stack
def test___3x3_padded_image__one_psf_blurs__other_asymmetric(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)
unmasked_image_1d_1 = np.zeros(25)
unmasked_image_1d_1[12] = 1.0
unmasked_blurred_images_of_datas = \
stack_util.unmasked_blurred_image_of_datas_from_padded_grid_stacks_psfs_and_unmasked_images(
padded_grid_stacks=[padded_grid_stack, padded_grid_stack], psfs=[psf_0, psf_1],
unmasked_images_1d=[np.ones(25), unmasked_image_1d_1])
assert (unmasked_blurred_images_of_datas[0] == np.ones((3, 3))).all()
assert (unmasked_blurred_images_of_datas[1] == np.array(
[[0.0, 3.0, 0.0], [0.0, 1.0, 2.0], [0.0, 0.0, 0.0]])).all()
def grid_stack_for_simulation(cls,
shape,
pixel_scale,
psf_shape,
sub_grid_size=2):
"""Setup a grid-stack of grid_stack for simulating an image of a strong lens, whereby the grid's use padded-grid_stack \
to ensure that the PSF blurring in the simulation routine (*ccd.PrepatoryImage.simulate*) is not degraded \
due to edge effects.
Parameters
-----------
shape : (int, int)
The 2D shape of the array, where all pixels are used to generate the grid-stack's grid_stack.
pixel_scale : float
The size of each pixel in arc seconds.
psf_shape : (int, int)
The shape of the PSF used in the analysis, which defines how much the grid's must be masked to mitigate \
edge effects.
sub_grid_size : int
The size of a sub-pixel's sub-grid (sub_grid_size x sub_grid_size).
"""
return cls.padded_grid_stack_from_mask_sub_grid_size_and_psf_shape(
mask=msk.Mask(array=np.full(shape, False),
pixel_scale=pixel_scale),
sub_grid_size=sub_grid_size,
psf_shape=psf_shape)
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__same_as_above__but_remove_some_centre_pixels_and_change_order__order_does_not_change_mapping(
self):
ma = mask.Mask(array=np.array([[False, False, False],
[False, False, False],
[False, False, False]]),
pixel_scale=1.0)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [2, 2],
[1, 1], [0, 2], [2, 0],
[0, 2]])
total_masked_pixels = mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres)
sparse_to_unmasked_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__1x2_image__tracing_fits_data_with_chi_sq_5_and_chi_sq_4(
self):
psf_0 = ccd.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)
ccd_0 = ccd.CCDData(5.0 * np.ones((3, 4)),
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((3, 4)))
ccd_0.image[1, 2] = 4.0
mask_0 = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
psf_1 = ccd.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)
ccd_1 = ccd.CCDData(5.0 * np.ones((3, 4)),
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((3, 4)))
mask_1 = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
li_stack = lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
# Setup as a ray trace instance, using a light profile for the lens
g0 = g.Galaxy(light_profile=MockLightProfile(value=1.0, size=2))
tracer = ray_tracing_stack.TracerImagePlaneStack(
lens_galaxies=[g0],
image_plane_grid_stacks=li_stack.grid_stacks)
fit = lens_fit_stack.LensProfileFitStack(lens_data_stack=li_stack,
tracer=tracer)
assert fit.chi_squared == 25.0 + 32.0
assert fit.reduced_chi_squared == (25.0 + 32.0) / 2.0
assert fit.likelihood == -0.5 * (
(25.0 + 2.0 * np.log(2 * np.pi * 1.0)) +
(32.0 + 2.0 * np.log(2 * np.pi * 1.0)))
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___same_as_above__use_galaxies_to_make_plane_images(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))
g1 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=0.2))
tracer = ray_tracing_stack.TracerImagePlaneStack(
lens_galaxies=[g0, g1],
image_plane_grid_stacks=[padded_grid_stack, padded_grid_stack])
manual_blurred_image_00 = tracer.image_plane.image_plane_images_1d_of_galaxies[
0][0]
manual_blurred_image_00 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_00)
manual_blurred_image_00 = psf_0.convolve(array=manual_blurred_image_00)
manual_blurred_image_01 = tracer.image_plane.image_plane_images_1d_of_galaxies[
0][1]
manual_blurred_image_01 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_01)
manual_blurred_image_01 = psf_0.convolve(array=manual_blurred_image_01)
manual_blurred_image_10 = tracer.image_plane.image_plane_images_1d_of_galaxies[
1][0]
manual_blurred_image_10 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_10)
manual_blurred_image_10 = psf_1.convolve(array=manual_blurred_image_10)
manual_blurred_image_11 = tracer.image_plane.image_plane_images_1d_of_galaxies[
1][1]
manual_blurred_image_11 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_11)
manual_blurred_image_11 = psf_1.convolve(array=manual_blurred_image_11)
unmasked_blurred_image_of_datas_and_planes = \
stack_util.unmasked_blurred_image_of_datas_and_planes_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_and_planes[0][0] == \
pytest.approx(manual_blurred_image_00[1:4, 1:4] + manual_blurred_image_01[1:4, 1:4], 1.0e-4)
assert unmasked_blurred_image_of_datas_and_planes[1][0] == \
pytest.approx(manual_blurred_image_10[1:4, 1:4] + manual_blurred_image_11[1:4, 1:4], 1.0e-4)
def test__grid_to_pixel__compare_to_array_utill(self):
mask = np.array([[True, True, True], [True, False, False],
[True, True, False]])
mask = msk.Mask(mask, pixel_scale=7.0)
grid_to_pixel_util = util.masked_grid_1d_index_to_2d_pixel_index_from_mask(
mask)
assert mask.masked_grid_index_to_pixel == pytest.approx(
grid_to_pixel_util, 1e-4)
def test__mask_extract_region__uses_the_limits_of_the_mask(self):
mask = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
assert mask.extraction_region == [1, 3, 1, 3]
mask = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, False],
[True, True, True, True]]),
pixel_scale=1.0)
assert mask.extraction_region == [1, 3, 1, 4]
mask = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, False, True]]),
pixel_scale=1.0)
assert mask.extraction_region == [1, 4, 1, 3]
mask = msk.Mask(array=np.array([[True, True, True, True],
[True, False, False, True],
[False, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
assert mask.extraction_region == [1, 3, 0, 3]
mask = msk.Mask(array=np.array([[True, False, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, True, True]]),
pixel_scale=1.0)
assert mask.extraction_region == [0, 3, 1, 3]
def test__array_finalize__masks_pass_attributes(self):
mask = np.array([[True, True, True, True], [True, False, False, True],
[True, True, True, True]])
mask = msk.Mask(mask, pixel_scale=1)
mask_new = mask + mask
assert mask_new.pixel_scale == 1.0
assert mask_new.origin == (0.0, 0.0)
assert mask_new.centre == (0.0, 0.0)
def make_li_manual_stack():
image_0 = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 2.0, 3.0, 0.0],
[0.0, 4.0, 5.0, 6.0, 0.0], [0.0, 7.0, 8.0, 9.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]])
psf_0 = ccd.PSF(array=(np.array([[1.0, 5.0, 9.0], [2.0, 5.0, 1.0],
[3.0, 4.0, 0.0]])),
pixel_scale=1.0)
ccd_0 = ccd.CCDData(image_0,
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((5, 5)))
mask_0 = msk.Mask(array=np.array([[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, True, True, True, True]]),
pixel_scale=1.0)
image_1 = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 2.0, 3.0, 0.0],
[0.0, 4.0, 6.0, 6.0, 0.0], [0.0, 7.0, 8.0, 9.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0]])
psf_1 = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [2.0, 1.0, 1.0],
[3.0, 1.0, 0.0]])),
pixel_scale=1.0)
ccd_1 = ccd.CCDData(image_1,
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((5, 5)))
mask_1 = msk.Mask(array=np.array([[True, True, True, True, True],
[True, False, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True]]),
pixel_scale=1.0)
return lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
def test__image__tracing_fits_data_perfectly__no_psf_blurring__lh_is_noise_normalization(
self):
psf_0 = ccd.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)
ccd_0 = ccd.CCDData(np.ones((3, 3)),
pixel_scale=1.0,
psf=psf_0,
noise_map=np.ones((3, 3)))
mask_0 = msk.Mask(array=np.array([[True, True, True],
[True, False, True],
[True, True, True]]),
pixel_scale=1.0)
psf_1 = ccd.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)
ccd_1 = ccd.CCDData(np.ones((3, 3)),
pixel_scale=1.0,
psf=psf_1,
noise_map=np.ones((3, 3)))
mask_1 = msk.Mask(array=np.array([[True, True, True],
[True, False, True],
[True, True, True]]),
pixel_scale=1.0)
li_stack = lds.LensDataStack(ccd_datas=[ccd_0, ccd_1],
masks=[mask_0, mask_1],
sub_grid_size=1)
g0 = g.Galaxy(light_profile=MockLightProfile(value=1.0))
tracer = ray_tracing_stack.TracerImagePlaneStack(
lens_galaxies=[g0],
image_plane_grid_stacks=li_stack.grid_stacks)
fit = lens_fit_stack.LensProfileFitStack(lens_data_stack=li_stack,
tracer=tracer)
assert fit.likelihood == 2.0 * -0.5 * np.log(2 * np.pi * 1.0)
def test__edge_image_pixels__compare_to_array_util(self):
mask = np.array([[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, False, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True]])
edge_pixels_util = util.edge_pixels_from_mask(mask)
mask = msk.Mask(mask, pixel_scale=3.0)
assert mask.edge_pixels == pytest.approx(edge_pixels_util, 1e-4)
def test__map_2d_array_to_masked_1d_array__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 = mapping_util.map_2d_array_to_masked_1d_array_from_array_2d_and_mask(
mask, array_2d)
mask = msk.Mask(mask, pixel_scale=3.0)
array_1d = mask.map_2d_array_to_masked_1d_array(array_2d)
assert (array_1d == array_1d_util).all()
def test__constructor(self):
mask = np.array([[True, True, True, True], [True, False, False, True],
[True, True, True, True]])
mask = msk.Mask(mask, pixel_scale=1)
assert (mask == np.array([[True, True, True, True],
[True, False, False, True],
[True, True, True, True]])).all()
assert mask.pixel_scale == 1.0
assert mask.central_pixel_coordinates == (1.0, 1.5)
assert mask.shape == (3, 4)
assert mask.shape_arc_seconds == (3.0, 4.0)
def test__setup_pixelization__galaxies_have_no_pixelization__returns_normal_grids(
self):
ma = mask.Mask(np.array([[False, False, False], [False, False, False],
[False, False, 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()
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__ensure_index_of_plane_image_has_negative_arcseconds_at_start(
self, grid_stack_0):
# The grid coordinates -2.0 -> 2.0 mean a plane of shape (5,5) has arc second coordinates running over
# -1.6, -0.8, 0.0, 0.8, 1.6. The origin -1.6, -1.6 of the model_galaxy means its brighest pixel should be
# index 0 of the 1D grid and (0,0) of the 2d plane datas_.
msk = mask.Mask(array=np.full((5, 5), False), pixel_scale=1.0)
grid_stack_0.regular = grids.RegularGrid(np.array([[-2.0, -2.0],
[2.0, 2.0]]),
mask=msk)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(1.6, -1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert plane_stack.plane_images[0].shape == (5, 5)
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (0,
0)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(1.6, 1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (0,
4)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(
centre=(-1.6, -1.6), intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (4,
0)
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(centre=(-1.6, 1.6),
intensity=1.0))
plane_stack = pl_stack.PlaneStack(galaxies=[g0],
grid_stacks=[grid_stack_0])
assert np.unravel_index(plane_stack.plane_images[0].argmax(),
plane_stack.plane_images[0].shape) == (4,
4)
def test__same_as_above__different_mask_and_centres(self):
ma = mask.Mask(array=np.array([[False, False, True],
[False, False, False],
[True, False, False]]),
pixel_scale=1.0)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[0, 2], [0, 2], [1, 1]])
unmasked_sparse_to_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=4)
assert (unmasked_sparse_to_sparse == np.array([0, 1, 2, 2, 2,
2])).all()
def test__blurring_mask_for_psf_shape__compare_to_array_util(self):
mask = np.array([[True, True, True, True, True, True, True, True],
[True, False, True, True, True, False, True, True],
[True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, True, True],
[True, False, True, True, True, False, True, True],
[True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, True, True]])
blurring_mask_util = util.mask_blurring_from_mask_and_psf_shape(
mask=mask, psf_shape=(3, 3))
mask = msk.Mask(mask, pixel_scale=1.0)
blurring_mask = mask.blurring_mask_for_psf_shape(psf_shape=(3, 3))
assert (blurring_mask == blurring_mask_util).all()
def test__mask_is_cross__some_pix_pixels_are_masked__omitted_from_mapping(
self):
ma = mask.Mask(array=np.array([[True, False, True],
[False, False, False],
[True, False, True]]),
pixel_scale=1.0)
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]])
unmasked_sparse_to_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=5)
assert (unmasked_sparse_to_sparse == np.array(
[0, 0, 1, 1, 2, 3, 4, 4, 4])).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__mask_full_false__image_mask_and_pixel_centres_fully_overlap__each_pix_maps_to_unmaked_pix(
self):
ma = mask.Mask(array=np.array([[False, False, False],
[False, False, False],
[False, False, False]]),
pixel_scale=1.0)
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]])
unmasked_sparse_to_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=9)
assert (unmasked_sparse_to_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
def make_lens_data_blur():
psf = ccd.PSF(array=(np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0]])),
pixel_scale=1.0,
renormalize=False)
im = ccd.CCDData(image=5.0 * np.ones((6, 6)),
pixel_scale=1.0,
psf=psf,
noise_map=np.ones((6, 6)),
exposure_time_map=3.0 * np.ones((6, 6)),
background_sky_map=4.0 * np.ones((6, 6)))
ma = np.array([[True, True, True, True, True, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, False, False, False, False, True],
[True, True, True, True, True, True]])
ma = mask.Mask(array=ma, pixel_scale=1.0)
return ld.LensData(ccd_data=im, mask=ma, sub_grid_size=2)
def test__same_as_above__but_3x4_mask(self):
ma = mask.Mask(array=np.array([[True, True, False, True],
[False, False, False, False],
[True, True, False, True]]),
pixel_scale=1.0)
unmasked_sparse_grid_pixel_centres = np.array([[0, 0], [0, 1], [0, 2],
[0, 2], [0, 2], [1, 1],
[2, 3], [2, 2]])
total_masked_pixels = mask_util.total_sparse_pixels_from_mask(
mask=ma,
unmasked_sparse_grid_pixel_centres=
unmasked_sparse_grid_pixel_centres)
sparse_to_unmasked_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([2, 3, 4, 5, 7])).all()
def test__pixelization_regular_grid_from_regular_grid__offset_mask__origin_shift_corrects(
self):
ma = mask.Mask(array=np.array([[True, True, False, False, False],
[True, True, False, False, False],
[True, True, False, False, False],
[True, True, True, True, True],
[True, True, True, True, True]]),
pixel_scale=1.0,
centre=(1.0, 1.0))
regular_grid = grids.RegularGrid.from_mask(mask=ma)
adaptive_image_grid = pixelizations.ImagePlanePixelization(shape=(3,
3))
pix_grid = adaptive_image_grid.image_plane_pix_grid_from_regular_grid(
regular_grid=regular_grid)
assert pix_grid.shape == (3, 3)
assert pix_grid.pixel_scales == (1.0, 1.0)
assert pix_grid.total_sparse_pixels == 9
assert (pix_grid.sparse_to_unmasked_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.unmasked_sparse_to_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.regular_to_unmasked_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.regular_to_sparse == np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8])).all()
assert (pix_grid.sparse_grid == np.array([[2.0, 0.0], [2.0, 1.0],
[2.0, 2.0], [1.0, 0.0],
[1.0, 1.0], [1.0, 2.0],
[0.0, 0.0], [0.0, 1.0],
[0.0, 2.0]])).all()
assert pix_grid.regular_grid == pytest.approx(regular_grid, 1e-4)
