def test__centre_mass_profile_on_grid_coordinate__peak_density_is_correct_index(
):
image_grid = grids.RegularGrid.from_shape_and_pixel_scale(shape=(5, 5),
pixel_scale=1.0)
sis = mp.SphericalIsothermal(centre=(2.0, -2.0))
density_1d = sis.surface_density_from_grid(grid=image_grid)
density_2d = image_grid.array_2d_from_array_1d(array_1d=density_1d)
assert density_1d.argmax() == 0
assert np.unravel_index(density_2d.argmax(), density_2d.shape) == (0, 0)
sis = mp.SphericalIsothermal(centre=(2.0, 2.0))
density_1d = sis.surface_density_from_grid(grid=image_grid)
density_2d = image_grid.array_2d_from_array_1d(array_1d=density_1d)
assert density_1d.argmax() == 4
assert np.unravel_index(density_2d.argmax(), density_2d.shape) == (0, 4)
sis = mp.SphericalIsothermal(centre=(-2.0, -2.0))
density_1d = sis.surface_density_from_grid(grid=image_grid)
density_2d = image_grid.array_2d_from_array_1d(array_1d=density_1d)
assert density_1d.argmax() == 20
assert np.unravel_index(density_2d.argmax(), density_2d.shape) == (4, 0)
sis = mp.SphericalIsothermal(centre=(-2.0, 2.0))
density_1d = sis.surface_density_from_grid(grid=image_grid)
density_2d = image_grid.array_2d_from_array_1d(array_1d=density_1d)
assert density_1d.argmax() == 24
assert np.unravel_index(density_2d.argmax(), density_2d.shape) == (4, 4)
def pipeline():
lens_mass = mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.6)
lens_subhalo = mp.SphericalIsothermal(centre=(1.0, 1.0),
einstein_radius=0.0)
source_light = lp.SphericalSersic(centre=(0.0, 0.0),
intensity=1.0,
effective_radius=0.5,
sersic_index=1.0)
lens_galaxy = galaxy.Galaxy(mass=lens_mass, subhalo=lens_subhalo)
source_galaxy = galaxy.Galaxy(light=source_light)
tools.reset_paths(test_name=test_name, output_path=output_path)
tools.simulate_integration_image(test_name=test_name,
pixel_scale=0.1,
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
target_signal_to_noise=30.0)
ccd_data = ccd.load_ccd_data_from_fits(
image_path=path + '/data/' + test_name + '/image.fits',
psf_path=path + '/data/' + test_name + '/psf.fits',
noise_map_path=path + '/data/' + test_name + '/noise_map.fits',
pixel_scale=0.1)
pipeline = make_pipeline(test_name=test_name)
result = pipeline.run(data=ccd_data)
print(dir(result))
def phase():
pixel_scale = 0.1
image_shape = (150, 150)
tools.reset_paths(test_name=test_name, output_path=output_path)
grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(shape=image_shape, pixel_scale=pixel_scale,
sub_grid_size=4)
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0))
deflections = galaxy_util.deflections_of_galaxies_from_grid(galaxies=[galaxy], grid=grid_stack.sub)
deflections_y = grid_stack.regular.scaled_array_from_array_1d(array_1d=deflections[:,0])
deflections_x = grid_stack.regular.scaled_array_from_array_1d(array_1d=deflections[:,1])
noise_map = scaled_array.ScaledSquarePixelArray(array=np.ones(deflections_y.shape), pixel_scale=pixel_scale)
data_y = gd.GalaxyData(image=deflections_y, noise_map=noise_map, pixel_scale=pixel_scale)
data_x = gd.GalaxyData(image=deflections_x, noise_map=noise_map, pixel_scale=pixel_scale)
phase = ph.GalaxyFitPhase(galaxies=dict(gal=gm.GalaxyModel(light=mp.SphericalIsothermal)), use_deflections=True,
sub_grid_size=4,
optimizer_class=nl.MultiNest, phase_name=test_name+'/')
phase.run(galaxy_data=[data_y, data_x])
def simulate():
from autolens.data.array import grids
from autolens.model.galaxy import galaxy as g
from autolens.lens import ray_tracing
psf = ccd.PSF.simulate_as_gaussian(shape=(11, 11),
sigma=0.1,
pixel_scale=0.1)
image_plane_grid_stack = grids.GridStack.grid_stack_for_simulation(
shape=(130, 130), pixel_scale=0.1, psf_shape=(11, 11))
lens_galaxy = g.Galaxy(
mass=mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.6))
source_galaxy = g.Galaxy(light=lp.SphericalExponential(
centre=(0.0, 0.0), intensity=0.2, effective_radius=0.2))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=image_plane_grid_stack)
ccd_simulated = ccd.CCDData.simulate(
array=tracer.image_plane_image_for_simulation,
pixel_scale=0.1,
exposure_time=300.0,
psf=psf,
background_sky_level=0.1,
add_noise=True)
return ccd_simulated
def test__tracer_and_tracer_sensitive_are_identical__added__likelihood_is_noise_term(
self, lens_data_blur):
g0 = g.Galaxy(mass_profile=mp.SphericalIsothermal(einstein_radius=1.0))
g1 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=2.0))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[g0],
source_galaxies=[g1],
image_plane_grid_stack=lens_data_blur.grid_stack)
fit = sensitivity_fit.SensitivityProfileFit(lens_data=lens_data_blur,
tracer_normal=tracer,
tracer_sensitive=tracer)
assert (fit.fit_normal.image == lens_data_blur.image).all()
assert (fit.fit_normal.noise_map == lens_data_blur.noise_map).all()
model_image_1d = util.blurred_image_1d_from_1d_unblurred_and_blurring_images(
unblurred_image_1d=tracer.image_plane_image_1d,
blurring_image_1d=tracer.image_plane_blurring_image_1d,
convolver=lens_data_blur.convolver_image)
model_image = lens_data_blur.map_to_scaled_array(
array_1d=model_image_1d)
assert (fit.fit_normal.model_image == model_image).all()
residual_map = fit_util.residual_map_from_data_mask_and_model_data(
data=lens_data_blur.image,
mask=lens_data_blur.mask,
model_data=model_image)
assert (fit.fit_normal.residual_map == residual_map).all()
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 == chi_squared_map).all()
assert (fit.fit_sensitive.image == lens_data_blur.image).all()
assert (fit.fit_sensitive.noise_map == lens_data_blur.noise_map).all()
assert (fit.fit_sensitive.model_image == model_image).all()
assert (fit.fit_sensitive.residual_map == residual_map).all()
assert (fit.fit_sensitive.chi_squared_map == chi_squared_map).all()
chi_squared = fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=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 +
noise_normalization)
assert fit.fit_sensitive.likelihood == -0.5 * (chi_squared +
noise_normalization)
assert fit.figure_of_merit == 0.0
def make_fit(lens_data):
lens_galaxy = g.Galaxy(mass=mp.SphericalIsothermal(einstein_radius=1.0),
redshift=1.0)
lens_subhalo = g.Galaxy(mass=mp.SphericalIsothermal(einstein_radius=0.1),
redshift=1.0)
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(intensity=1.0),
redshift=2.0)
tracer_normal = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
cosmology=cosmo.Planck15)
tracer_sensitivity = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy, lens_subhalo],
source_galaxies=[source_galaxy],
image_plane_grid_stack=lens_data.grid_stack,
cosmology=cosmo.Planck15)
return sensitivity_fit.SensitivityProfileFit(
lens_data=lens_data,
tracer_normal=tracer_normal,
tracer_sensitive=tracer_sensitivity)
def test__galaxy_data_deflections_x(self, image, mask):
galaxy_data = gd.GalaxyData(image=image,
noise_map=2.0 * np.ones((4, 4)),
pixel_scale=3.0)
galaxy_fit_data = gd.GalaxyFitData(galaxy_data=galaxy_data,
mask=mask,
sub_grid_size=2,
use_deflections_x=True)
assert galaxy_fit_data.pixel_scale == 3.0
assert (galaxy_fit_data.image == np.ones((4, 4))).all()
assert (galaxy_fit_data.noise_map == 2.0 * np.ones((4, 4))).all()
assert (galaxy_fit_data.mask == np.array([[True, True, True, True],
[True, False, False, True],
[True, False, False, True],
[True, True, True,
True]])).all()
assert (galaxy_fit_data.image_1d == np.ones(4)).all()
assert (galaxy_fit_data.noise_map_1d == 2.0 * np.ones(4)).all()
assert (galaxy_fit_data.mask_1d == np.array(
[False, False, False, False])).all()
galaxy = MockGalaxy(value=1, shape=4)
deflections_x = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (deflections_x == np.array([1.0, 1.0, 1.0, 1.0])).all()
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(einstein_radius=1.0))
deflections_gal = galaxy.deflections_from_grid(
grid=galaxy_fit_data.grid_stack.sub)
deflections_gal = np.asarray([
galaxy_fit_data.grid_stack.sub.sub_data_to_regular_data(
deflections_gal[:, 0]),
galaxy_fit_data.grid_stack.sub.sub_data_to_regular_data(
deflections_gal[:, 1])
]).T
deflections_gd = galaxy_fit_data.profile_quantity_from_galaxy_and_sub_grid(
galaxies=[galaxy], sub_grid=galaxy_fit_data.grid_stack.sub)
assert (deflections_gal[:, 1] == deflections_gd).all()
def simulate_image_with_offset_centre_psf():
from autolens.data.array import grids
from autolens.model.galaxy import galaxy as g
from autolens.lens import ray_tracing
psf = im.PSF.simulate_as_gaussian(shape=(21, 21),
sigma=0.05,
pixel_scale=0.1,
centre=(0.1, 0.1))
image_plane_grids = grids.GridStack.grid_stack_for_simulation(
shape=(100, 100), pixel_scale=0.1, psf_shape=(21, 21))
lens_galaxy = g.Galaxy(light=lp.SphericalSersic(centre=(0.0, 0.0),
intensity=0.3,
effective_radius=1.0,
sersic_index=2.0),
mass=mp.SphericalIsothermal(centre=(0.0, 0.0),
einstein_radius=1.2))
source_galaxy = g.Galaxy(light=lp.SphericalSersic(centre=(0.0, 0.0),
intensity=0.2,
effective_radius=1.0,
sersic_index=1.5))
tracer = ray_tracing.TracerImageSourcePlanes(
lens_galaxies=[lens_galaxy],
source_galaxies=[source_galaxy],
image_plane_grid_stack=[image_plane_grids])
return im.CCDData.simulate(array=tracer.image_plane_image_for_simulation,
pixel_scale=0.1,
exposure_time=300.0,
psf=psf,
background_sky_level=0.1,
add_noise=True)
def test___same_as_above__image_and_source_plane(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))
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])
manual_blurred_image_i00 = tracer.image_plane.image_plane_images_1d_of_galaxies[
0][0]
manual_blurred_image_i00 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_i00)
manual_blurred_image_i00 = psf_0.convolve(
array=manual_blurred_image_i00)
manual_blurred_image_i01 = tracer.image_plane.image_plane_images_1d_of_galaxies[
0][1]
manual_blurred_image_i01 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_i01)
manual_blurred_image_i01 = psf_0.convolve(
array=manual_blurred_image_i01)
manual_blurred_image_i10 = tracer.image_plane.image_plane_images_1d_of_galaxies[
1][0]
manual_blurred_image_i10 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_i10)
manual_blurred_image_i10 = psf_1.convolve(
array=manual_blurred_image_i10)
manual_blurred_image_i11 = tracer.image_plane.image_plane_images_1d_of_galaxies[
1][1]
manual_blurred_image_i11 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_i11)
manual_blurred_image_i11 = psf_1.convolve(
array=manual_blurred_image_i11)
manual_blurred_image_s00 = tracer.source_plane.image_plane_images_1d_of_galaxies[
0][0]
manual_blurred_image_s00 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_s00)
manual_blurred_image_s00 = psf_0.convolve(
array=manual_blurred_image_s00)
manual_blurred_image_s01 = tracer.source_plane.image_plane_images_1d_of_galaxies[
0][1]
manual_blurred_image_s01 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_s01)
manual_blurred_image_s01 = psf_0.convolve(
array=manual_blurred_image_s01)
manual_blurred_image_s10 = tracer.source_plane.image_plane_images_1d_of_galaxies[
1][0]
manual_blurred_image_s10 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_s10)
manual_blurred_image_s10 = psf_1.convolve(
array=manual_blurred_image_s10)
manual_blurred_image_s11 = tracer.source_plane.image_plane_images_1d_of_galaxies[
1][1]
manual_blurred_image_s11 = padded_grid_stack.regular.map_to_2d_keep_padded(
padded_array_1d=manual_blurred_image_s11)
manual_blurred_image_s11 = psf_1.convolve(
array=manual_blurred_image_s11)
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] ==
manual_blurred_image_i00[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[0][0][1] ==
manual_blurred_image_i01[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[1][0][0] ==
manual_blurred_image_i10[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[1][0][1] ==
manual_blurred_image_i11[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[0][1][0] ==
manual_blurred_image_s00[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[0][1][1] ==
manual_blurred_image_s01[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[1][1][0] ==
manual_blurred_image_s10[1:4, 1:4]).all()
assert (unmasked_blurred_image_of_datas_planes_and_galaxies[1][1][1] ==
manual_blurred_image_s11[1:4, 1:4]).all()
source_galaxy = g.Galaxy(light=lp.EllipticalSersic(centre=(0.1, 0.1),
axis_ratio=0.8,
phi=60.0,
intensity=1.0,
effective_radius=1.0,
sersic_index=2.5),
redshift=1.0)
# Setup our line-of-sight (los) galaxies using Spherical Sersic profiles for their light and Singular
# Isothermal Sphere (SIS) profiles. We'll use 3 galaxies, but you can add more if desired.
los_0 = g.Galaxy(light=lp.SphericalSersic(centre=(4.0, 4.0),
intensity=0.30,
effective_radius=0.3,
sersic_index=2.0),
mass=mp.SphericalIsothermal(centre=(4.0, 4.0),
einstein_radius=0.02),
redshift=0.25)
los_1 = g.Galaxy(light=lp.SphericalSersic(centre=(3.6, -5.3),
intensity=0.20,
effective_radius=0.6,
sersic_index=1.5),
mass=mp.SphericalIsothermal(centre=(3.6, -5.3),
einstein_radius=0.04),
redshift=0.75)
los_2 = g.Galaxy(light=lp.SphericalSersic(centre=(-3.1, -2.4),
intensity=0.35,
effective_radius=0.4,
sersic_index=2.5),
mass=mp.SphericalIsothermal(centre=(-3.1, -2.4),
einstein_radius=0.03),
redshift=1.25)
def test_2d_symmetry(self):
isothermal_1 = mp.SphericalIsothermal(einstein_radius=1.0)
isothermal_2 = mp.SphericalIsothermal(centre=(100, 0),
einstein_radius=1.0)
isothermal_3 = mp.SphericalIsothermal(centre=(0, 100),
einstein_radius=1.0)
isothermal_4 = mp.SphericalIsothermal(centre=(100, 100),
einstein_radius=1.0)
gal_isothermal = g.Galaxy(redshift=0.5,
mass_profile_1=isothermal_1,
mass_profile_2=isothermal_2,
mass_profile_3=isothermal_3,
mass_profile_4=isothermal_4)
assert gal_isothermal.surface_density_from_grid(
np.array([[49.0, 0.0]])) == pytest.approx(
gal_isothermal.surface_density_from_grid(
np.array([[51.0, 0.0]])), 1e-5)
assert gal_isothermal.surface_density_from_grid(
np.array([[0.0, 49.0]])) == pytest.approx(
gal_isothermal.surface_density_from_grid(
np.array([[0.0, 51.0]])), 1e-5)
assert gal_isothermal.surface_density_from_grid(
np.array([[100.0, 49.0]])) == pytest.approx(
gal_isothermal.surface_density_from_grid(
np.array([[100.0, 51.0]])), 1e-5)
assert gal_isothermal.surface_density_from_grid(
np.array([[49.0, 49.0]])) == pytest.approx(
gal_isothermal.surface_density_from_grid(
np.array([[51.0, 51.0]])), 1e-5)
assert gal_isothermal.potential_from_grid(np.array(
[[49.0, 0.0]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[51.0,
0.0]])),
1e-5)
assert gal_isothermal.potential_from_grid(np.array(
[[0.0, 49.0]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[0.0,
51.0]])),
1e-5)
assert gal_isothermal.potential_from_grid(np.array([[
100.0, 49.0
]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[100.0, 51.0]])),
1e-5)
assert gal_isothermal.potential_from_grid(np.array(
[[49.0, 49.0]])) == pytest.approx(
gal_isothermal.potential_from_grid(np.array([[51.0,
51.0]])),
1e-5)
assert -1.0 * gal_isothermal.deflections_from_grid(
np.array([[49.0, 0.0]]))[0, 0] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[51.0, 0.0]]))[0, 0], 1e-5)
assert 1.0 * gal_isothermal.deflections_from_grid(
np.array([[0.0, 49.0]]))[0, 0] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[0.0, 51.0]]))[0, 0], 1e-5)
assert 1.0 * gal_isothermal.deflections_from_grid(
np.array([[100.0, 49.0]]))[0, 0] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[100.0, 51.0]]))[0, 0], 1e-5)
assert -1.0 * gal_isothermal.deflections_from_grid(
np.array([[49.0, 49.0]]))[0, 0] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[51.0, 51.0]]))[0, 0], 1e-5)
assert 1.0 * gal_isothermal.deflections_from_grid(
np.array([[49.0, 0.0]]))[0, 1] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[51.0, 0.0]]))[0, 1], 1e-5)
assert -1.0 * gal_isothermal.deflections_from_grid(
np.array([[0.0, 49.0]]))[0, 1] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[0.0, 51.0]]))[0, 1], 1e-5)
assert -1.0 * gal_isothermal.deflections_from_grid(
np.array([[100.0, 49.0]]))[0, 1] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[100.0, 51.0]]))[0, 1], 1e-5)
assert -1.0 * gal_isothermal.deflections_from_grid(
np.array([[49.0, 49.0]]))[0, 1] == pytest.approx(
gal_isothermal.deflections_from_grid(
np.array([[51.0, 51.0]]))[0, 1], 1e-5)
def make_galaxy_mass_x2():
return g.Galaxy(sis_0=mp.SphericalIsothermal(einstein_radius=1.0),
sis_1=mp.SphericalIsothermal(einstein_radius=1.0))
def make_galaxy():
return g.Galaxy(light=lp.SphericalSersic(intensity=1.0),
mass=mp.SphericalIsothermal(einstein_radius=1.0))
def test___manual_image_and_psf(self, lens_data_stack_manual):
g0 = g.Galaxy(light_profile=lp.EllipticalSersic(intensity=1.0))
g1 = g.Galaxy(mass_profile=mp.SphericalIsothermal(
einstein_radius=1.0))
tracer = ray_tracing_stack.TracerImageSourcePlanesStack(
lens_galaxies=[g0, g1],
source_galaxies=[g0],
image_plane_grid_stacks=lens_data_stack_manual.grid_stacks)
padded_tracer = ray_tracing_stack.TracerImageSourcePlanesStack(
lens_galaxies=[g0, g1],
source_galaxies=[g0],
image_plane_grid_stacks=lens_data_stack_manual.
padded_grid_stacks)
fit = lens_fit_stack.fit_lens_image_stack_with_tracer(
lens_data_stack=lens_data_stack_manual,
tracer=tracer,
padded_tracer=padded_tracer)
assert lens_data_stack_manual.noise_maps[0] == pytest.approx(
fit.noise_maps[0], 1e-4)
assert lens_data_stack_manual.noise_maps[1] == pytest.approx(
fit.noise_maps[1], 1e-4)
model_image_1d_0 = lens_fit_util.blurred_image_1d_from_1d_unblurred_and_blurring_images(
unblurred_image_1d=tracer.image_plane_images_1d[0],
blurring_image_1d=tracer.image_plane_blurring_images_1d[0],
convolver=lens_data_stack_manual.convolvers_image[0])
model_image_1d_1 = lens_fit_util.blurred_image_1d_from_1d_unblurred_and_blurring_images(
unblurred_image_1d=tracer.image_plane_images_1d[1],
blurring_image_1d=tracer.image_plane_blurring_images_1d[1],
convolver=lens_data_stack_manual.convolvers_image[1])
model_image_0 = lens_data_stack_manual.map_to_scaled_arrays[0](
array_1d=model_image_1d_0)
model_image_1 = lens_data_stack_manual.map_to_scaled_arrays[1](
array_1d=model_image_1d_1)
assert model_image_0 == pytest.approx(fit.model_images[0], 1e-4)
assert model_image_1 == pytest.approx(fit.model_images[1], 1e-4)
residual_map_0 = fit_util.residual_map_from_data_mask_and_model_data(
data=lens_data_stack_manual.images[0],
mask=lens_data_stack_manual.masks[0],
model_data=model_image_0)
residual_map_1 = fit_util.residual_map_from_data_mask_and_model_data(
data=lens_data_stack_manual.images[1],
mask=lens_data_stack_manual.masks[1],
model_data=model_image_1)
assert residual_map_0 == pytest.approx(fit.residual_maps[0], 1e-4)
assert residual_map_1 == pytest.approx(fit.residual_maps[1], 1e-4)
chi_squared_map_0 = fit_util.chi_squared_map_from_residual_map_noise_map_and_mask(
residual_map=residual_map_0,
mask=lens_data_stack_manual.masks[0],
noise_map=lens_data_stack_manual.noise_maps[0])
chi_squared_map_1 = fit_util.chi_squared_map_from_residual_map_noise_map_and_mask(
residual_map=residual_map_1,
mask=lens_data_stack_manual.masks[1],
noise_map=lens_data_stack_manual.noise_maps[1])
assert chi_squared_map_0 == pytest.approx(fit.chi_squared_maps[0],
1e-4)
assert chi_squared_map_1 == pytest.approx(fit.chi_squared_maps[1],
1e-4)
chi_squared_0 = fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=chi_squared_map_0,
mask=lens_data_stack_manual.masks[0])
noise_normalization_0 = fit_util.noise_normalization_from_noise_map_and_mask(
noise_map=lens_data_stack_manual.noise_maps[0],
mask=lens_data_stack_manual.masks[0])
likelihood_0 = fit_util.likelihood_from_chi_squared_and_noise_normalization(
chi_squared=chi_squared_0,
noise_normalization=noise_normalization_0)
chi_squared_1 = fit_util.chi_squared_from_chi_squared_map_and_mask(
chi_squared_map=chi_squared_map_1,
mask=lens_data_stack_manual.masks[1])
noise_normalization_1 = fit_util.noise_normalization_from_noise_map_and_mask(
noise_map=lens_data_stack_manual.noise_maps[1],
mask=lens_data_stack_manual.masks[1])
likelihood_1 = fit_util.likelihood_from_chi_squared_and_noise_normalization(
chi_squared=chi_squared_1,
noise_normalization=noise_normalization_1)
assert fit.chi_squareds == [chi_squared_0, chi_squared_1]
assert fit.noise_normalizations == [
noise_normalization_0, noise_normalization_1
]
assert fit.likelihoods == [likelihood_0, likelihood_1]
assert likelihood_0 + likelihood_1 == pytest.approx(
fit.likelihood, 1e-4)
assert likelihood_0 + likelihood_1 == fit.figure_of_merit
blurred_image_of_planes_0 = lens_fit_util.blurred_image_of_planes_from_1d_images_and_convolver(
total_planes=tracer.total_planes,
image_plane_image_1d_of_planes=tracer.
image_plane_images_1d_of_planes[0],
image_plane_blurring_image_1d_of_planes=tracer.
image_plane_blurring_images_1d_of_planes[0],
convolver=lens_data_stack_manual.convolvers_image[0],
map_to_scaled_array=lens_data_stack_manual.
map_to_scaled_arrays[0])
blurred_image_of_planes_1 = lens_fit_util.blurred_image_of_planes_from_1d_images_and_convolver(
total_planes=tracer.total_planes,
image_plane_image_1d_of_planes=tracer.
image_plane_images_1d_of_planes[1],
image_plane_blurring_image_1d_of_planes=tracer.
image_plane_blurring_images_1d_of_planes[1],
convolver=lens_data_stack_manual.convolvers_image[1],
map_to_scaled_array=lens_data_stack_manual.
map_to_scaled_arrays[1])
assert (blurred_image_of_planes_0[0] ==
fit.model_images_of_planes[0][0]).all()
assert (blurred_image_of_planes_0[1] ==
fit.model_images_of_planes[0][1]).all()
assert (blurred_image_of_planes_1[0] ==
fit.model_images_of_planes[1][0]).all()
assert (blurred_image_of_planes_1[1] ==
fit.model_images_of_planes[1][1]).all()
unmasked_blurred_image_0 = \
lens_fit_util.unmasked_blurred_image_from_padded_grid_stack_psf_and_unmasked_image(
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[0], psf=lens_data_stack_manual.psfs[0],
unmasked_image_1d=padded_tracer.image_plane_images_1d[0])
unmasked_blurred_image_1 = \
lens_fit_util.unmasked_blurred_image_from_padded_grid_stack_psf_and_unmasked_image(
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[1], psf=lens_data_stack_manual.psfs[1],
unmasked_image_1d=padded_tracer.image_plane_images_1d[1])
assert (unmasked_blurred_image_0 == fit.unmasked_model_images[0]
).all()
assert (unmasked_blurred_image_1 == fit.unmasked_model_images[1]
).all()
unmasked_blurred_image_of_galaxies_i0 = \
lens_fit_util.unmasked_blurred_image_of_galaxies_from_psf_and_unmasked_1d_galaxy_images(
galaxies=padded_tracer.image_plane.galaxies,
image_plane_image_1d_of_galaxies=padded_tracer.image_plane.image_plane_images_1d_of_galaxies[0],
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[0], psf=lens_data_stack_manual.psfs[0])
unmasked_blurred_image_of_galaxies_s0 = \
lens_fit_util.unmasked_blurred_image_of_galaxies_from_psf_and_unmasked_1d_galaxy_images(
galaxies=padded_tracer.source_plane.galaxies,
image_plane_image_1d_of_galaxies=padded_tracer.source_plane.image_plane_images_1d_of_galaxies[0],
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[0], psf=lens_data_stack_manual.psfs[0])
unmasked_blurred_image_of_galaxies_i1 = \
lens_fit_util.unmasked_blurred_image_of_galaxies_from_psf_and_unmasked_1d_galaxy_images(
galaxies=padded_tracer.image_plane.galaxies,
image_plane_image_1d_of_galaxies=padded_tracer.image_plane.image_plane_images_1d_of_galaxies[0],
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[1], psf=lens_data_stack_manual.psfs[1])
unmasked_blurred_image_of_galaxies_s1 = \
lens_fit_util.unmasked_blurred_image_of_galaxies_from_psf_and_unmasked_1d_galaxy_images(
galaxies=padded_tracer.source_plane.galaxies,
image_plane_image_1d_of_galaxies=padded_tracer.source_plane.image_plane_images_1d_of_galaxies[0],
padded_grid_stack=lens_data_stack_manual.padded_grid_stacks[1], psf=lens_data_stack_manual.psfs[1])
assert (
unmasked_blurred_image_of_galaxies_i0[0] == fit.
unmasked_model_images_of_planes_and_galaxies[0][0][0]).all()
assert (
unmasked_blurred_image_of_galaxies_s0[0] == fit.
unmasked_model_images_of_planes_and_galaxies[0][1][0]).all()
assert (
unmasked_blurred_image_of_galaxies_i1[0] == fit.
unmasked_model_images_of_planes_and_galaxies[1][0][0]).all()
assert (
unmasked_blurred_image_of_galaxies_s1[0] == fit.
unmasked_model_images_of_planes_and_galaxies[1][1][0]).all()
# Get the relative path to the config files and output folder in our workspace.
path = '{}/../../'.format(os.path.dirname(os.path.realpath(__file__)))
# Use this path to explicitly set the config path and output path.
conf.instance = conf.Config(config_path=path + 'config',
output_path=path + 'output')
# First, we'll setup the grid stack we use to simulate a deflection profile.
pixel_scale = 0.05
image_shape = (250, 250)
grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(
shape=image_shape, pixel_scale=pixel_scale, sub_grid_size=4)
# Now lets create two galaxies, using singular isothermal spheres. We'll put the two galaxies at different redshifts,
# and the second galaxy will be much lower mass as if it is a 'perturber' of the main lens galaxy.
lens_galaxy = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.0, 0.0),
einstein_radius=1.0),
redshift=0.5)
perturber = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.5, 0.5),
einstein_radius=0.1),
redshift=0.2)
# We only need the source galaxy to have a redshift - given we're not fitting an image it doens't need a light profile.
source_galaxy = g.Galaxy(redshift=1.0)
# We'll use a tracer to compute our multi-plane deflection angles.
tracer = ray_tracing.TracerMultiPlanes(
galaxies=[lens_galaxy, perturber, source_galaxy],
image_plane_grid_stack=grid_stack)
# We'll now extract the deflection angles from the tracer - we will extract the two deflection angle maps (y and x)
# separately.
def test__deflection_angles():
image_grid = grids.RegularGrid.from_shape_and_pixel_scale(shape=(5, 5),
pixel_scale=1.0)
sis = mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
deflections_1d = sis.deflections_from_grid(grid=image_grid)
deflections_x_2d = image_grid.array_2d_from_array_1d(
array_1d=deflections_1d[:, 1])
assert deflections_x_2d[0,
0] == pytest.approx(-1.0 * deflections_x_2d[0, 4],
1e-2)
assert deflections_x_2d[1,
1] == pytest.approx(-1.0 * deflections_x_2d[1, 3],
1e-2)
assert deflections_x_2d[3,
1] == pytest.approx(-1.0 * deflections_x_2d[3, 3],
1e-2)
assert deflections_x_2d[4,
0] == pytest.approx(-1.0 * deflections_x_2d[4, 4],
1e-2)
assert deflections_x_2d[0, 2] == pytest.approx(deflections_x_2d[4, 2],
1e-2)
sis = mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
deflections_1d = sis.deflections_from_grid(grid=image_grid)
deflections_y_2d = image_grid.array_2d_from_array_1d(
array_1d=deflections_1d[:, 0])
assert deflections_y_2d[0,
0] == pytest.approx(-1.0 * deflections_y_2d[4, 0],
1e-2)
assert deflections_y_2d[1,
1] == pytest.approx(-1.0 * deflections_y_2d[3, 1],
1e-2)
assert deflections_y_2d[1,
3] == pytest.approx(-1.0 * deflections_y_2d[3, 3],
1e-2)
assert deflections_y_2d[0,
4] == pytest.approx(-1.0 * deflections_y_2d[4, 4],
1e-2)
assert deflections_y_2d[2, 0] == pytest.approx(deflections_y_2d[2, 4],
1e-2)
# def test__move_source_galaxy_around_source_plane__peak_follows_source_direction():
#
# image_grid = masks.RegularGrid.from_shape_and_pixel_scale(shape=(5, 5), pixel_scales=1.0)
# sis = mp.SphericalIsothermal(origin=(0.0, 0.0), einstein_radius=1.0)
# sersic = lp.SphericalSersic(origin=(1.0, 0.0))
#
# deflections = sis.deflections_from_grid(grid=image_grid)
# source_grid = np.subtract(image_grid, deflections)
# print(image_grid[22])
# print(deflections[22])
# print(source_grid[22])
# stop
# source_image = sersic.intensities_from_grid(grid=source_grid)
# print(source_image.argmax())
#
# data_grids = masks.GridStack.from_shape_and_pixel_scale(shape=(5, 5), pixel_scales=1.0)
# lens_galaxy = g.Galaxy(mass=sis)
# source_galaxy = g.Galaxy(light=sersic)
# tracer = ray_tracing.TracerImageSourcePlanes(lens_galaxies=[lens_galaxy], source_galaxies=[source_galaxy],
# image_plane_grids=data_grids)
#
# print(source_grid)
# print(tracer.source_plane.grid_stacks.regular)
# print(np.subtract(source_grid, tracer.source_plane.grid_stacks.regular))
# print(tracer.regular_plane_image)
light_profile_2 = light_profiles.SphericalSersic(centre=(1.0, 1.0), intensity=1.0, effective_radius=2.0, sersic_index=3.0) light_profile_3 = light_profiles.SphericalSersic(centre=(1.0, -1.0), intensity=1.0, effective_radius=2.0, sersic_index=2.0) galaxy_with_3_light_profiles = galaxy.Galaxy(light_1=light_profile_1, light_2=light_profile_2, light_3=light_profile_3) # We can print the galaxy to confirm it possesses the Sersic light-profiles above. print(galaxy_with_3_light_profiles) # If we plot the galaxy, we see 3 blobs of light! galaxy_plotters.plot_intensities(galaxy=galaxy_with_3_light_profiles, grid=grid_stack.regular) # We can also plot each individual light profile using the 'subplot' galaxy plotter. galaxy_plotters.plot_intensities_subplot(galaxy=galaxy_with_3_light_profiles, grid=grid_stack.regular) # Mass profiles interact with Galaxy objects in the exact same way as light profiles. # Lets create a galaxy with three SIS mass profiles. mass_profile_1 = mass_profiles.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0) mass_profile_2 = mass_profiles.SphericalIsothermal(centre=(1.0, 1.0), einstein_radius=1.0) mass_profile_3 = mass_profiles.SphericalIsothermal(centre=(1.0, -1.0), einstein_radius=1.0) galaxy_with_3_mass_profiles = galaxy.Galaxy(mass_1=mass_profile_1, mass_2=mass_profile_2, mass_3=mass_profile_3) # We can print a galaxy to confirm it possesses the sis mass-profiles above. print(galaxy_with_3_mass_profiles) # We can use a galaxy plotter to plot these deflection angles. # (Deflection angles of mass-profiles add together just like the light-profile image's above) galaxy_plotters.plot_deflections_y(galaxy=galaxy_with_3_mass_profiles, grid=grid_stack.regular) galaxy_plotters.plot_deflections_x(galaxy=galaxy_with_3_mass_profiles, grid=grid_stack.regular) # I wonder what 3 summed surface density maps or potential's look like ;) galaxy_plotters.plot_surface_density(galaxy=galaxy_with_3_mass_profiles, grid=grid_stack.regular)
def make_galaxy():
return g.Galaxy(redshift=3,
sersic=light_profiles.EllipticalSersic(),
exponential=light_profiles.EllipticalExponential(),
spherical=mass_profiles.SphericalIsothermal())
# that arise from a lens model fit (e.g due to the source reconstruction or quality of data).
# Get the relative path to the config files and output folder in our workspace.
path = '{}/../../'.format(os.path.dirname(os.path.realpath(__file__)))
# Use this path to explicitly set the config path and output path.
conf.instance = conf.Config(config_path=path+'config', output_path=path+'output')
# First, we'll setup the grid stack we use to simulate a surface density profile.
pixel_scale = 0.05
image_shape = (250, 250)
grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(shape=image_shape, pixel_scale=pixel_scale,
sub_grid_size=4)
# Now lets create a galaxy, using a simple singular isothermal sphere.
galaxy = g.Galaxy(mass=mp.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.0))
# Next, we'll generate its surface density profile. Note that, because we're using the galaxy_util surface density
# function, the sub-grid of the grid-stack that is passed to this function is used to over-sample the surface density.
# The surface density averages over this sub-grid, such that it is the shape of the image (250, 250).
surface_density = galaxy_util.surface_density_of_galaxies_from_grid(galaxies=[galaxy], grid=grid_stack.sub)
surface_density = grid_stack.regular.scaled_array_from_array_1d(array_1d=surface_density)
# Now, we'll set this surface density up as our 'galaxy-data', meaning that it is what we'll fit via a non-linear
# search phase. To perform a fit we need a noise-map to help define our chi-squared. Given we are fitting a direct
# lensing quantity the actual values of this noise-map arn't particularly important, so we'll just use a noise-map of
# all 0.1's
noise_map = scaled_array.ScaledSquarePixelArray(array=0.1*np.ones(surface_density.shape), pixel_scale=pixel_scale)
data = gd.GalaxyData(image=surface_density, noise_map=noise_map, pixel_scale=pixel_scale)
# The fit will use a mask, which we setup like any other fit. Lets use a circular mask of 2.0"
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
# - If two or more source galaxies are at the same redshift in the source-plane, their light is ray-traced in the
# same way. Therefore, when determining their lensed images, we can sum the lensed images of each galaxy's light-profiles.
# So, lets do it - lets use the 'plane' module in AutoLens to create a strong lensing system like the one pictured
# above. For simplicity, we'll assume 1 lens galaxy and 1 source galaxy.
# As always, we need grids, where our grids are the coordinates we'll 'trace' from the image-plane to the source-plane
# in the lensing configuration above. Our grid-stack is therefore no longer just a 'grid-stack', but the grid-stack
# representing our image-plane coordinates. Thus, lets name as such.
image_plane_grid_stack = grids.GridStack.from_shape_pixel_scale_and_sub_grid_size(shape=(100, 100), pixel_scale=0.05,
sub_grid_size=2)
# Whereas before we called our galaxy's things like 'galaxy_with_light_profile', lets now refer to them by their role
# in lensing, e.g. 'lens_galaxy' and 'source_galaxy'.
mass_profile = mass_profiles.SphericalIsothermal(centre=(0.0, 0.0), einstein_radius=1.6)
lens_galaxy = galaxy.Galaxy(mass=mass_profile)
light_profile = light_profiles.SphericalSersic(centre=(0.0, 0.0), intensity=1.0, effective_radius=1.0, sersic_index=1.0)
source_galaxy = galaxy.Galaxy(light=light_profile)
# Lets setup our image-plane. This plane takes the lens galaxy we made above and the grid-stack of
# image-plane coordinates.
image_plane = plane.Plane(galaxies=[lens_galaxy], grid_stack=image_plane_grid_stack)
# Up to now, we've kept our galaxies and grids separate, and passed the grid to a galaxy object to compute
# its quantities (e.g. to compute light-profile intensities, we'd write galaxy.intensities_from_grid(grid=grid)).
#
# Plane's combine the galaxies and grids into one object, thus once we've setup a plane there is no longer any need
# have to pass it a grid to compute its quantities. Furthermore, once these quantities are in a plane, they are
# automatically mapped back to their original 2D grid-arrays.
print('deflection-angles of planes regular-grid pixel 1:')
def make_mass_profile():
return mp.SphericalIsothermal(einstein_radius=1.0)
def make_galaxy_mass():
return g.Galaxy(mass=mp.SphericalIsothermal(einstein_radius=1.0), redshift=1.0)
def test__no_light_profile__returns_none(self):
gal = g.Galaxy(redshift=0.5, mass=mp.SphericalIsothermal())
assert gal.luminosity_within_circle(radius=1.0) == None
assert gal.luminosity_within_ellipse(major_axis=1.0) == None
# We'll also give the lens galaxy some attributes we didn't in the last tutorial:
# 1) A light-profile, meaning its light will appear in the image-plane image.
# 2) An external shear, which accounts for the deflection of light due to line-of-sight structures.
# 3) A redshift, which the tracer will use to convert arc second coordinates to kpc.
lens_galaxy = g.Galaxy(light=lp.SphericalSersic(centre=(0.0, 0.0), intensity=2.0, effective_radius=0.5,
sersic_index=2.5),
mass=mp.EllipticalIsothermal(centre=(0.0, 0.0), axis_ratio=0.8, phi=90.0, einstein_radius=1.6),
shear=mp.ExternalShear(magnitude=0.05, phi=45.0),
redshift=0.5)
print(lens_galaxy)
# Lets also create a small satellite galaxy nearby the lens galaxy and at the same redshift.
lens_satellite = g.Galaxy(light=lp.SphericalDevVaucouleurs(centre=(1.0, 0.0), intensity=2.0, effective_radius=0.2),
mass=mp.SphericalIsothermal(centre=(1.0, 0.0), einstein_radius=0.4),
redshift=0.5)
print(lens_satellite)
# Lets have a quick look at the appearance of our lens galaxy and its satellite
galaxy_plotters.plot_intensities(galaxy=lens_galaxy, grid=image_plane_grid_stack.regular, title='Lens Galaxy')
galaxy_plotters.plot_intensities(galaxy=lens_satellite, grid=image_plane_grid_stack.regular, title='Lens Satellite')
# And their deflection angles - note that the satellite doesn't contribute as much to the deflections
galaxy_plotters.plot_deflections_y(galaxy=lens_galaxy, grid=image_plane_grid_stack.regular, title='Lens Galaxy Deflections (y)')
galaxy_plotters.plot_deflections_y(galaxy=lens_satellite, grid=image_plane_grid_stack.regular, title='Lens Satellite Deflections (y)')
galaxy_plotters.plot_deflections_x(galaxy=lens_galaxy, grid=image_plane_grid_stack.regular, title='Lens Galalxy Deflections (x)')
galaxy_plotters.plot_deflections_x(galaxy=lens_satellite, grid=image_plane_grid_stack.regular, title='Lens Satellite Deflections (x)')
# Now, lets make two source galaxies at redshift 1.0. Lets not use the terms 'light' and 'mass' to setup the light and
# mass profiles. Instead, lets use more descriptive names of what we think each component represents ( e.g. a 'bulge' and 'disk').
def make_galaxy_mass():
return g.Galaxy(mass_profile=mp.SphericalIsothermal(einstein_radius=1.0))
