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__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__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_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__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
print()
sub_grid_size = 4
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)
list_.append(data)
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',]
# 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]))
def test__number_of_pixels_and_regularization_set_up_correctly(self):
pix = pixelizations.AdaptiveMagnification(shape=(3, 3))
assert pix.shape == (3, 3)
lens_galaxy_low = al.Galaxy(
mass=al.mp.EllipticalIsothermal(
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)
