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test_lens_fit_stack.py
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/
test_lens_fit_stack.py
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import numpy as np
import pytest
from autofit.tools import fit_util
from autolens.data import ccd
from autolens.data.array import mask as msk
from autolens.model.galaxy import galaxy as g
from autolens.lens.util import lens_fit_util
from autolens.lens.stack import ray_tracing_stack
from autolens.lens.stack import lens_fit_stack
from autolens.lens.stack import lens_data_stack as lds
from autolens.model.profiles import light_profiles as lp
from autolens.model.profiles import mass_profiles as mp
from test.mock.mock_profiles import MockLightProfile
@pytest.fixture(name='lens_data_stack_blur')
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)
@pytest.fixture(name='lens_data_stack_manual')
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)
class TestLensProfileFit:
class TestLikelihood:
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__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)))
class TestCompareToManual:
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()