def test__total_mappers(interferometer_7):
g0 = al.Galaxy(redshift=0.5)
g1 = al.Galaxy(redshift=1.0)
g2 = al.Galaxy(redshift=2.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
assert fit.total_mappers == 0
g2 = al.Galaxy(
redshift=2.0,
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
assert fit.total_mappers == 1
def test__total_inversions(self, interferometer_7):
g0 = al.Galaxy(redshift=0.5)
g1 = al.Galaxy(redshift=1.0)
g2 = al.Galaxy(redshift=2.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(interferometer=interferometer_7,
tracer=tracer)
assert fit.total_inversions == 0
g2 = al.Galaxy(
redshift=2.0,
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(interferometer=interferometer_7,
tracer=tracer)
assert fit.total_inversions == 1
g0 = al.Galaxy(
redshift=0.5,
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
)
g1 = al.Galaxy(
redshift=1.0,
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
)
g2 = al.Galaxy(
redshift=2.0,
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(interferometer=interferometer_7,
tracer=tracer)
assert fit.total_inversions == 3
def fit_interferometer_from_agg_obj(
agg_obj,
settings_masked_interferometer=None,
settings_pixelization=None,
settings_inversion=None,
):
"""Compute a generator of *FitInterferometer* objects from an input aggregator, which generates a list of the
*FitInterferometer* objects for every set of results loaded in the aggregator.
This is performed by mapping the *fit_interferometer_from_agg_obj* with the aggregator, which sets up each fit
using only generators ensuring that manipulating the fits of large sets of results is done in a memory efficient
way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits."""
masked_interferometer = masked_interferometer_from_agg_obj(
agg_obj=agg_obj,
settings_masked_interferometer=settings_masked_interferometer)
tracer = tracer_from_agg_obj(agg_obj=agg_obj)
if settings_pixelization is None:
settings_pixelization = agg_obj.settings.settings_pixelization
if settings_inversion is None:
settings_inversion = agg_obj.settings.settings_inversion
return al.FitInterferometer(
masked_interferometer=masked_interferometer,
tracer=tracer,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
def test___lens_fit_galaxy_model_image_dict__corresponds_to_profile_galaxy_images(
self, interferometer_7_grid):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0,
light_profile=al.lp.EllSersic(intensity=1.0))
g2 = al.Galaxy(redshift=1.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(interferometer=interferometer_7_grid,
tracer=tracer)
traced_grids_of_planes = tracer.traced_grids_of_planes_from_grid(
grid=interferometer_7_grid.grid)
g0_image = g0.image_2d_from_grid(grid=traced_grids_of_planes[0])
g1_image = g1.image_2d_from_grid(grid=traced_grids_of_planes[1])
assert fit.galaxy_model_image_dict[g0].slim == pytest.approx(
g0_image, 1.0e-4)
assert fit.galaxy_model_image_dict[g1].slim == pytest.approx(
g1_image, 1.0e-4)
def test___lens_fit_galaxy_model_visibilities_dict__has_inversion_mapped_reconstructed_visibilities(
self, interferometer_7):
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
g0 = al.Galaxy(redshift=0.5)
g1 = al.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(interferometer=interferometer_7,
tracer=tracer)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=interferometer_7.grid, sparse_grid=None)
inversion = inversions.InversionInterferometerMatrix.from_data_mapper_and_regularization(
mapper=mapper,
regularization=reg,
visibilities=interferometer_7.visibilities,
noise_map=interferometer_7.noise_map,
transformer=interferometer_7.transformer,
)
assert (fit.galaxy_model_visibilities_dict[g0] == (0.0 + 0.0j) *
np.zeros((7, ))).all()
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
inversion.mapped_reconstructed_visibilities.slim, 1.0e-4)
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g1].slim, 1.0e-4)
def test__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, interferometer_7, mask_7x7, visibilities_mask_7x2):
lens_galaxy = al.Galaxy(redshift=0.5,
light=al.lp.EllipticalSersic(intensity=0.1))
phase_interferometer_7 = al.PhaseInterferometer(
real_space_mask=mask_7x7,
galaxies=[lens_galaxy],
cosmology=cosmo.FLRW,
sub_size=2,
phase_name="test_phase",
)
analysis = phase_interferometer_7.make_analysis(
dataset=interferometer_7,
mask=visibilities_mask_7x2,
results=mock_pipeline.MockResults(),
)
instance = phase_interferometer_7.model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.fit(instance=instance)
real_space_mask = phase_interferometer_7.meta_dataset.mask_with_phase_sub_size_from_mask(
mask=mask_7x7)
masked_interferometer = al.MaskedInterferometer(
interferometer=interferometer_7,
visibilities_mask=visibilities_mask_7x2,
real_space_mask=real_space_mask,
)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.FitInterferometer(masked_interferometer=masked_interferometer,
tracer=tracer)
assert fit.likelihood == fit_figure_of_merit
def test__model_visibilities_of_planes_list(interferometer_7):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1_linear = al.Galaxy(redshift=0.75,
light_profile=al.lp_linear.EllSersic())
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
galaxy_pix_0 = al.Galaxy(redshift=1.0,
pixelization=pix,
regularization=reg)
galaxy_pix_1 = al.Galaxy(redshift=1.0,
pixelization=pix,
regularization=reg)
tracer = al.Tracer.from_galaxies(
galaxies=[g0, g1_linear, galaxy_pix_0, galaxy_pix_1])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert fit.model_visibilities_of_planes_list[0] == pytest.approx(
fit.galaxy_model_visibilities_dict[g0], 1.0e-4)
assert fit.model_visibilities_of_planes_list[1] == pytest.approx(
fit.galaxy_model_visibilities_dict[g1_linear], 1.0e-4)
assert fit.model_visibilities_of_planes_list[2] == pytest.approx(
fit.galaxy_model_visibilities_dict[galaxy_pix_0] +
fit.galaxy_model_visibilities_dict[galaxy_pix_1],
1.0e-4,
)
def test__figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, interferometer_7):
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
lens_galaxy = al.Galaxy(redshift=0.5,
light=al.lp.EllSersic(intensity=0.1))
model = af.Collection(
galaxies=af.Collection(lens=lens_galaxy),
hyper_background_noise=hyper_background_noise,
)
analysis = al.AnalysisInterferometer(dataset=interferometer_7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.FitInterferometer(
interferometer=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == analysis_log_likelihood
def fit_interferometer_from_agg_obj(
agg_obj: af.SearchOutput,
settings_interferometer: al.SettingsInterferometer = None,
settings_pixelization: al.SettingsPixelization = None,
settings_inversion: al.SettingsInversion = None,
) -> "al.FitInterferometer":
"""
Returns a generator of `FitInterferometer` objects from an input aggregator, which generates a list of the
`FitInterferometer` objects for every set of results loaded in the aggregator.
This is performed by mapping the `fit_interferometer_from_agg_obj` with the aggregator, which sets up each fit
using only generators ensuring that manipulating the fits of large sets of results is done in a memory efficient
way.
Parameters
----------
aggregator : af.Aggregator
A PyAutoFit aggregator object containing the results of PyAutoLens model-fits.
"""
interferometer = interferometer_from_agg_obj(
agg_obj=agg_obj, settings_interferometer=settings_interferometer)
tracer = tracer_from_agg_obj(agg_obj=agg_obj)
if settings_pixelization is None:
settings_pixelization = agg_obj.settings_pixelization
if settings_inversion is None:
settings_inversion = agg_obj.settings_inversion
return al.FitInterferometer(
interferometer=interferometer,
tracer=tracer,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
def make_masked_interferometer_fit_x2_plane_inversion_7x7(
masked_interferometer_7, tracer_x2_plane_inversion_7x7
):
return al.FitInterferometer(
masked_interferometer=masked_interferometer_7,
tracer=tracer_x2_plane_inversion_7x7,
)
def test___all_lens_fit_quantities__hyper_background_noise(
self, interferometer_7):
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
hyper_noise_map = hyper_background_noise.hyper_noise_map_from_complex_noise_map(
noise_map=interferometer_7.noise_map)
galaxy_light = al.Galaxy(redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0))
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
galaxy_pix = al.Galaxy(redshift=1.0,
pixelization=pix,
regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[galaxy_light, galaxy_pix])
fit = al.FitInterferometer(
interferometer=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert hyper_noise_map.slim == pytest.approx(fit.inversion.noise_map,
1.0e-4)
assert hyper_noise_map.slim == pytest.approx(fit.noise_map.slim)
def test___stochastic_mode__gives_different_log_likelihood_list(
interferometer_7):
pix = al.pix.VoronoiBrightnessImage(pixels=5)
reg = al.reg.Constant(coefficient=1.0)
g0 = al.Galaxy(
redshift=0.5,
pixelization=pix,
regularization=reg,
hyper_model_image=al.Array2D.ones(shape_native=(3, 3),
pixel_scales=1.0),
hyper_galaxy_image=al.Array2D.ones(shape_native=(3, 3),
pixel_scales=1.0),
)
tracer = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5), g0])
fit_0 = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(is_stochastic=False),
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
fit_1 = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(is_stochastic=False),
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
assert fit_0.log_evidence == fit_1.log_evidence
fit_0 = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(is_stochastic=True),
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
fit_1 = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(is_stochastic=True),
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
assert fit_0.log_evidence != fit_1.log_evidence
def test__fit_figure_of_merit(interferometer_7):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0, light_profile=al.lp.EllSersic(intensity=1.0))
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert (fit.noise_map.slim == np.full(fill_value=2.0 + 2.0j,
shape=(7, ))).all()
assert fit.log_likelihood == pytest.approx(-21709493.32, 1e-4)
assert fit.figure_of_merit == pytest.approx(-21709493.32, 1.0e-4)
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=0.01)
g0 = al.Galaxy(redshift=0.5, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5), g0])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert (fit.noise_map.slim == np.full(fill_value=2.0 + 2.0j,
shape=(7, ))).all()
assert fit.log_evidence == pytest.approx(-66.90612, 1e-4)
assert fit.figure_of_merit == pytest.approx(-66.90612, 1.0e-4)
galaxy_light = al.Galaxy(redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0))
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
galaxy_pix = al.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[galaxy_light, galaxy_pix])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert (fit.noise_map.slim == np.full(fill_value=2.0 + 2.0j,
shape=(7, ))).all()
assert fit.log_evidence == pytest.approx(-1570173.14216, 1e-4)
assert fit.figure_of_merit == pytest.approx(-1570173.14216, 1.0e-4)
def test__model_visibilities(interferometer_7):
g0 = al.Galaxy(redshift=0.5,
light_profile=MockLightProfile(image_2d=np.ones(2)))
tracer = al.Tracer.from_galaxies(galaxies=[g0])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert fit.model_visibilities.slim[0] == pytest.approx(
np.array([1.2933 + 0.2829j]), 1.0e-4)
assert fit.log_likelihood == pytest.approx(-27.06284, 1.0e-4)
def print_fit_time_from(interferometer,
transformer_class,
use_w_tilde,
use_linear_operators,
repeats=1):
settings_interferometer = al.SettingsInterferometer(
transformer_class=transformer_class)
interferometer = interferometer.apply_settings(
settings=settings_interferometer)
"""
__Numba Caching__
Call FitImaging once to get all numba functions initialized.
"""
fit = al.FitInterferometer(
dataset=interferometer,
tracer=tracer,
settings_inversion=al.SettingsInversion(
use_w_tilde=use_w_tilde,
use_linear_operators=use_linear_operators),
)
print(fit.figure_of_merit)
"""
__Fit Time__
Time FitImaging by itself, to compare to profiling dict call.
"""
start = time.time()
for i in range(repeats):
fit = al.FitInterferometer(
dataset=interferometer,
tracer=tracer,
settings_inversion=al.SettingsInversion(
use_w_tilde=use_w_tilde,
use_linear_operators=use_linear_operators),
)
fit.figure_of_merit
fit_time = (time.time() - start) / repeats
print(f"Fit Time = {fit_time} \n")
def test___all_lens_fit_quantities__no_hyper_methods(
self, interferometer_7):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0,
light_profile=al.lp.EllSersic(intensity=1.0))
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(interferometer=interferometer_7,
tracer=tracer)
assert interferometer_7.noise_map == pytest.approx(fit.noise_map)
model_visibilities = tracer.profile_visibilities_from_grid_and_transformer(
grid=interferometer_7.grid,
transformer=interferometer_7.transformer)
assert model_visibilities == pytest.approx(fit.model_visibilities,
1e-4)
residual_map = al.util.fit.residual_map_from(
data=interferometer_7.visibilities, model_data=model_visibilities)
assert residual_map == pytest.approx(fit.residual_map, 1e-4)
normalized_residual_map = al.util.fit.normalized_residual_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7.noise_map)
assert normalized_residual_map == pytest.approx(
fit.normalized_residual_map, 1e-4)
chi_squared_map = al.util.fit.chi_squared_map_complex_from(
residual_map=residual_map, noise_map=interferometer_7.noise_map)
assert chi_squared_map == pytest.approx(fit.chi_squared_map, 1e-4)
chi_squared = al.util.fit.chi_squared_complex_from(
chi_squared_map=fit.chi_squared_map)
noise_normalization = al.util.fit.noise_normalization_complex_from(
noise_map=interferometer_7.noise_map)
log_likelihood = al.util.fit.log_likelihood_from(
chi_squared=chi_squared, noise_normalization=noise_normalization)
assert log_likelihood == pytest.approx(fit.log_likelihood, 1e-4)
assert log_likelihood == fit.figure_of_merit
def test___all_lens_fit_quantities__hyper_background_noise(
self, interferometer_7):
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
hyper_noise_map = hyper_background_noise.hyper_noise_map_from_complex_noise_map(
noise_map=interferometer_7.noise_map)
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0,
light_profile=al.lp.EllSersic(intensity=1.0))
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(
interferometer=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert hyper_noise_map.slim == pytest.approx(fit.noise_map.slim)
fit = al.FitInterferometer(
interferometer=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
use_hyper_scaling=False,
)
assert fit.noise_map == pytest.approx(interferometer_7.noise_map,
1.0e-4)
assert fit.noise_map != pytest.approx(hyper_noise_map.slim, 1.0e-4)
def test__noise_map__with_and_without_hyper_background(interferometer_7):
g0 = al.Galaxy(redshift=0.5,
light_profile=MockLightProfile(image_2d=np.ones(2)))
tracer = al.Tracer.from_galaxies(galaxies=[g0])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert (fit.noise_map.slim == np.full(fill_value=2.0 + 2.0j,
shape=(7, ))).all()
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert (fit.noise_map.slim == np.full(fill_value=3.0 + 3.0j,
shape=(7, ))).all()
assert fit.log_likelihood == pytest.approx(-30.24288, 1.0e-4)
def test__1x2_image__1x2_visibilities__simple_fourier_transform(self):
# The image plane image generated by the galaxy is [1.0, 1.0]
# Thus the chi squared is 4.0**2.0 + 3.0**2.0 = 25.0
uv_wavelengths = np.array([[0.0, 0.0]])
real_space_mask = al.Mask2D.manual(
mask=np.array([
[True, True, True, True],
[True, False, False, True],
[True, True, True, True],
]),
pixel_scales=1.0,
)
interferometer = al.Interferometer(
visibilities=al.Visibilities.full(fill_value=5.0,
shape_slim=(1, )),
noise_map=al.Visibilities.ones(shape_slim=(1, )),
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
settings=al.SettingsInterferometer(
sub_size=1, transformer_class=al.TransformerDFT),
)
interferometer.visibilities[0] = 5.0 + 4.0j
# Setup as a ray trace instance, using a light profile for the lens
g0 = al.Galaxy(redshift=0.5,
light_profile=MockLightProfile(value=1.0, size=2))
tracer = al.Tracer.from_galaxies(galaxies=[g0])
fit = al.FitInterferometer(interferometer=interferometer,
tracer=tracer)
assert (fit.visibilities.slim == np.array([5.0 + 4.0j])).all()
assert (fit.noise_map.slim == np.array([1.0 + 1.0j])).all()
assert (fit.model_visibilities.slim == np.array([2.0 + 0.0j])).all()
assert (fit.residual_map.slim == np.array([3.0 + 4.0j])).all()
assert (fit.normalized_residual_map.slim == np.array([3.0 + 4.0j
])).all()
assert (fit.chi_squared_map.slim == np.array([9.0 + 16.0j])).all()
assert fit.chi_squared == 25.0
assert fit.noise_normalization == (2.0 * np.log(2 * np.pi * 1.0**2.0))
assert fit.log_likelihood == -0.5 * (
25.0 + 2.0 * np.log(2 * np.pi * 1.0**2.0))
def test__simulate_interferometer_data_and_fit__known_likelihood():
mask = al.Mask2D.circular(radius=3.0,
shape_native=(31, 31),
pixel_scales=0.2,
sub_size=1)
grid = al.Grid2D.from_mask(mask=mask)
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.lp.EllSersic(centre=(0.1, 0.1), intensity=0.1),
mass=al.mp.EllIsothermal(centre=(0.1, 0.1), einstein_radius=1.8),
)
source_galaxy_0 = al.Galaxy(
redshift=1.0,
pixelization=al.pix.Rectangular(shape=(16, 16)),
regularization=al.reg.Constant(coefficient=(1.0)),
)
source_galaxy_1 = al.Galaxy(
redshift=2.0,
pixelization=al.pix.Rectangular(shape=(16, 16)),
regularization=al.reg.Constant(coefficient=(1.0)),
)
tracer = al.Tracer.from_galaxies(
galaxies=[lens_galaxy, source_galaxy_0, source_galaxy_1])
simulator = al.SimulatorInterferometer(
uv_wavelengths=np.ones(shape=(7, 2)),
transformer_class=al.TransformerDFT,
exposure_time=300.0,
noise_seed=1,
)
interferometer = simulator.via_tracer_from(tracer=tracer, grid=grid)
interferometer = interferometer.apply_settings(
settings=al.SettingsInterferometer(
transformer_class=al.TransformerDFT))
fit = al.FitInterferometer(
dataset=interferometer,
tracer=tracer,
settings_inversion=al.SettingsInversion(use_w_tilde=False),
)
assert fit.figure_of_merit == pytest.approx(-5.433894158056919, 1.0e-2)
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
interferometer_7):
lens_galaxy = al.Galaxy(redshift=0.5, light=al.lp.EllSersic(intensity=0.1))
model = af.Collection(galaxies=af.Collection(lens=lens_galaxy))
analysis = al.AnalysisInterferometer(dataset=interferometer_7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert fit.log_likelihood == analysis_log_likelihood
def test___fit_figure_of_merit__linear_operator(interferometer_7_lop):
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=0.01)
g0 = al.Galaxy(redshift=0.5, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5), g0])
fit = al.FitInterferometer(
dataset=interferometer_7_lop,
tracer=tracer,
settings_inversion=al.SettingsInversion(use_linear_operators=True),
)
assert (fit.noise_map.slim == np.full(fill_value=2.0 + 2.0j,
shape=(7, ))).all()
assert fit.log_evidence == pytest.approx(-71.5177, 1e-4)
assert fit.figure_of_merit == pytest.approx(-71.5177, 1.0e-4)
def test__hyper_background_changes_background_sky__reflected_in_likelihood(
self, ):
uv_wavelengths = np.array([[1.0, 0.0], [1.0, 1.0], [2.0, 2.0]])
real_space_mask = al.Mask2D.manual(
mask=np.array([
[True, True, True, True, True],
[True, False, False, False, True],
[True, True, True, True, True],
]),
pixel_scales=1.0,
)
interferometer = al.Interferometer(
visibilities=al.Visibilities.full(fill_value=5.0,
shape_slim=(3, )),
noise_map=al.Visibilities.full(fill_value=2.0, shape_slim=(3, )),
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
settings=al.SettingsInterferometer(sub_size=1),
)
# Setup as a ray trace instance, using a light profile for the lens
g0 = al.Galaxy(redshift=0.5,
light_profile=MockLightProfile(value=1.0, size=2))
tracer = al.Tracer.from_galaxies(galaxies=[g0])
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
fit = al.FitInterferometer(
interferometer=interferometer,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert (fit.visibilities.slim == (1.0 + 1.0j) *
np.full(fill_value=5.0, shape=(3, ))).all()
assert (fit.noise_map.slim == (1.0 + 1.0j) *
np.full(fill_value=3.0, shape=(3, ))).all()
def test___lens_fit_galaxy_visibilities_dict__corresponds_to_galaxy_visibilities(
self, interferometer_7_grid):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0,
light_profile=al.lp.EllSersic(intensity=1.0))
g2 = al.Galaxy(redshift=1.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(interferometer=interferometer_7_grid,
tracer=tracer)
traced_grids_of_planes = tracer.traced_grids_of_planes_from_grid(
grid=interferometer_7_grid.grid)
g0_profile_visibilities = g0.profile_visibilities_from_grid_and_transformer(
grid=traced_grids_of_planes[0],
transformer=interferometer_7_grid.transformer,
)
g1_profile_visibilities = g1.profile_visibilities_from_grid_and_transformer(
grid=traced_grids_of_planes[1],
transformer=interferometer_7_grid.transformer,
)
assert fit.galaxy_model_visibilities_dict[g0].slim == pytest.approx(
g0_profile_visibilities, 1.0e-4)
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
g1_profile_visibilities, 1.0e-4)
assert (fit.galaxy_model_visibilities_dict[g2].slim == (0.0 + 0.0j) *
np.zeros((7, ))).all()
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g0].slim +
fit.galaxy_model_visibilities_dict[g1].slim,
1.0e-4,
)
def test__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, interferometer_7, mask_7x7, visibilities_mask_7
):
lens_galaxy = al.Galaxy(
redshift=0.5, light=al.lp.EllipticalSersic(intensity=0.1)
)
phase_interferometer_7 = al.PhaseInterferometer(
galaxies=dict(lens=lens_galaxy),
cosmology=cosmo.FLRW,
settings=al.SettingsPhaseInterferometer(
settings_masked_interferometer=al.SettingsMaskedInterferometer(
sub_size=2
)
),
search=mock.MockSearch("test_phase"),
real_space_mask=mask_7x7,
)
analysis = phase_interferometer_7.make_analysis(
dataset=interferometer_7,
mask=visibilities_mask_7,
results=mock.MockResults(),
)
instance = phase_interferometer_7.model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(instance=instance)
masked_interferometer = al.MaskedInterferometer(
interferometer=interferometer_7,
visibilities_mask=visibilities_mask_7,
real_space_mask=mask_7x7,
settings=al.SettingsMaskedInterferometer(sub_size=2),
)
tracer = analysis.tracer_for_instance(instance=instance)
fit = al.FitInterferometer(
masked_interferometer=masked_interferometer, tracer=tracer
)
assert fit.log_likelihood == fit_figure_of_merit
def test__positions__likelihood_overwrite__changes_likelihood(
interferometer_7, mask_2d_7x7):
lens = al.Galaxy(redshift=0.5, mass=al.mp.SphIsothermal())
source = al.Galaxy(redshift=1.0, light=al.lp.SphSersic())
model = af.Collection(galaxies=af.Collection(lens=lens, source=source))
analysis = al.AnalysisInterferometer(dataset=interferometer_7)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
assert fit.log_likelihood == analysis_log_likelihood
assert analysis_log_likelihood == pytest.approx(-127914.36273, 1.0e-4)
positions_likelihood = al.PositionsLHPenalty(positions=al.Grid2DIrregular([
(1.0, 100.0), (200.0, 2.0)
]),
threshold=0.01)
analysis = al.AnalysisInterferometer(
dataset=interferometer_7, positions_likelihood=positions_likelihood)
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
log_likelihood_penalty_base = positions_likelihood.log_likelihood_penalty_base_from(
dataset=interferometer_7)
log_likelihood_penalty = positions_likelihood.log_likelihood_penalty_from(
tracer=tracer)
assert analysis_log_likelihood == pytest.approx(
log_likelihood_penalty_base - log_likelihood_penalty, 1.0e-4)
assert analysis_log_likelihood == pytest.approx(-22048700567.590656,
1.0e-4)
visibilities_mask=np.full(fill_value=False,
shape=interferometer.visibilities.shape),
settings=al.SettingsMaskedInterferometer(
transformer_class=al.TransformerNUFFT),
)
print("Number of points = " + str(masked_interferometer.grid.sub_shape_slim) +
"\n")
print("Number of visibilities = " +
str(masked_interferometer.visibilities.shape_slim) + "\n")
start_overall = time.time()
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
start = time.time()
for i in range(repeats):
fit = al.FitInterferometer(
masked_interferometer=masked_interferometer,
tracer=tracer,
settings_inversion=al.SettingsInversion(use_linear_operators=False),
)
print(fit.log_likelihood)
diff = time.time() - start
print("Time to compute fit = {}".format(diff / repeats))
aplt.FitInterferometer.subplot_fit_real_space(fit=fit)
def test__fit_figure_of_merit__include_hyper_methods(interferometer_7):
hyper_background_noise = al.hyper_data.HyperBackgroundNoise(
noise_scale=1.0)
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0, light_profile=al.lp.EllSersic(intensity=1.0))
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert (fit.noise_map.slim == np.full(fill_value=3.0 + 3.0j,
shape=(7, ))).all()
assert fit.log_likelihood == pytest.approx(-9648681.9168, 1e-4)
assert fit.figure_of_merit == pytest.approx(-9648681.9168, 1.0e-4)
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
use_hyper_scaling=False,
)
assert fit.noise_map == pytest.approx(interferometer_7.noise_map, 1.0e-4)
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=0.01)
g0 = al.Galaxy(redshift=0.5, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5), g0])
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert (fit.noise_map.slim == np.full(fill_value=3.0 + 3.0j,
shape=(7, ))).all()
assert fit.log_evidence == pytest.approx(-68.63380, 1e-4)
assert fit.figure_of_merit == pytest.approx(-68.63380, 1.0e-4)
galaxy_light = al.Galaxy(redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0))
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
galaxy_pix = al.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[galaxy_light, galaxy_pix])
fit = al.FitInterferometer(
dataset=interferometer_7,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
assert (fit.noise_map.slim == np.full(fill_value=3.0 + 3.0j,
shape=(7, ))).all()
assert fit.log_evidence == pytest.approx(-892439.04665, 1e-4)
assert fit.figure_of_merit == pytest.approx(-892439.04665, 1.0e-4)
def test__simulate_interferometer_data_and_fit__chi_squared_is_0__noise_normalization_correct(
):
grid = al.Grid2D.uniform(shape_native=(51, 51),
pixel_scales=0.1,
sub_size=1)
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.lp.EllSersic(centre=(0.1, 0.1), intensity=0.1),
mass=al.mp.EllIsothermal(centre=(0.1, 0.1), einstein_radius=1.0),
)
source_galaxy = al.Galaxy(redshift=1.0,
light=al.lp.EllExponential(centre=(0.1, 0.1),
intensity=0.5))
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
simulator = al.SimulatorInterferometer(
uv_wavelengths=np.ones(shape=(7, 2)),
transformer_class=al.TransformerDFT,
exposure_time=300.0,
noise_if_add_noise_false=1.0,
noise_sigma=None,
)
interferometer = simulator.from_tracer_and_grid(tracer=tracer, grid=grid)
file_path = path.join(
"{}".format(path.dirname(path.realpath(__file__))),
"data_temp",
"simulate_and_fit",
)
try:
shutil.rmtree(file_path)
except FileNotFoundError:
pass
if path.exists(file_path) is False:
os.makedirs(file_path)
interferometer.output_to_fits(
visibilities_path=path.join(file_path, "visibilities.fits"),
noise_map_path=path.join(file_path, "noise_map.fits"),
uv_wavelengths_path=path.join(file_path, "uv_wavelengths.fits"),
)
real_space_mask = al.Mask2D.unmasked(shape_native=(51, 51),
pixel_scales=0.1,
sub_size=2)
interferometer = al.Interferometer.from_fits(
visibilities_path=path.join(file_path, "visibilities.fits"),
noise_map_path=path.join(file_path, "noise_map.fits"),
uv_wavelengths_path=path.join(file_path, "uv_wavelengths.fits"),
real_space_mask=real_space_mask,
)
interferometer = interferometer.apply_settings(
settings=al.SettingsInterferometer(
transformer_class=al.TransformerDFT))
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
fit = al.FitInterferometer(
interferometer=interferometer,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(use_border=False),
)
assert fit.chi_squared == pytest.approx(0.0)
pix = al.pix.Rectangular(shape=(7, 7))
reg = al.reg.Constant(coefficient=0.0001)
lens_galaxy = al.Galaxy(
redshift=0.5,
light=al.lp.EllSersic(centre=(0.1, 0.1), intensity=0.1),
mass=al.mp.EllIsothermal(centre=(0.1, 0.1), einstein_radius=1.0),
)
source_galaxy = al.Galaxy(redshift=1.0,
pixelization=pix,
regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[lens_galaxy, source_galaxy])
fit = al.FitInterferometer(
interferometer=interferometer,
tracer=tracer,
settings_pixelization=al.SettingsPixelization(use_border=False),
)
assert abs(fit.chi_squared) < 1.0e-4
file_path = path.join("{}".format(path.dirname(path.realpath(__file__))),
"data_temp")
if path.exists(file_path) is True:
shutil.rmtree(file_path)
def test___galaxy_model_visibilities_dict(interferometer_7,
interferometer_7_grid):
g0 = al.Galaxy(
redshift=0.5,
light_profile=al.lp.EllSersic(intensity=1.0),
mass_profile=al.mp.SphIsothermal(einstein_radius=1.0),
)
g1 = al.Galaxy(redshift=1.0, light_profile=al.lp.EllSersic(intensity=1.0))
g2 = al.Galaxy(redshift=1.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
fit = al.FitInterferometer(dataset=interferometer_7_grid, tracer=tracer)
traced_grids_of_planes = tracer.traced_grid_list_from(
grid=interferometer_7_grid.grid)
g0_profile_visibilities = g0.visibilities_via_transformer_from(
grid=traced_grids_of_planes[0],
transformer=interferometer_7_grid.transformer)
g1_profile_visibilities = g1.visibilities_via_transformer_from(
grid=traced_grids_of_planes[1],
transformer=interferometer_7_grid.transformer)
assert fit.galaxy_model_visibilities_dict[g0].slim == pytest.approx(
g0_profile_visibilities, 1.0e-4)
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
g1_profile_visibilities, 1.0e-4)
assert (fit.galaxy_model_visibilities_dict[g2].slim == (0.0 + 0.0j) *
np.zeros((7, ))).all()
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g0].slim +
fit.galaxy_model_visibilities_dict[g1].slim,
1.0e-4,
)
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
g0 = al.Galaxy(redshift=0.5)
g1 = al.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer)
mapper = pix.mapper_from(source_grid_slim=interferometer_7.grid,
source_pixelization_grid=None)
inversion = al.Inversion(dataset=interferometer_7,
linear_obj_list=[mapper],
regularization_list=[reg])
assert (fit.galaxy_model_visibilities_dict[g0] == (0.0 + 0.0j) * np.zeros(
(7, ))).all()
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
inversion.mapped_reconstructed_data.slim, 1.0e-4)
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g1].slim, 1.0e-4)
g0 = al.Galaxy(redshift=0.5, light_profile=al.lp.EllSersic(intensity=1.0))
g1 = al.Galaxy(redshift=0.5, light_profile=al.lp.EllSersic(intensity=2.0))
g2 = al.Galaxy(redshift=0.5)
pix = al.pix.Rectangular(shape=(3, 3))
reg = al.reg.Constant(coefficient=1.0)
galaxy_pix = al.Galaxy(redshift=1.0, pixelization=pix, regularization=reg)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2, galaxy_pix])
fit = al.FitInterferometer(dataset=interferometer_7_grid, tracer=tracer)
traced_grids = tracer.traced_grid_list_from(
grid=interferometer_7_grid.grid)
g0_visibilities = g0.visibilities_via_transformer_from(
grid=traced_grids[0], transformer=interferometer_7_grid.transformer)
g1_visibilities = g1.visibilities_via_transformer_from(
grid=traced_grids[1], transformer=interferometer_7_grid.transformer)
profile_visibilities = g0_visibilities + g1_visibilities
profile_subtracted_visibilities = (interferometer_7_grid.visibilities -
profile_visibilities)
mapper = pix.mapper_from(
source_grid_slim=interferometer_7_grid.grid,
settings=al.SettingsPixelization(use_border=False),
)
inversion = al.InversionInterferometer(
visibilities=profile_subtracted_visibilities,
noise_map=interferometer_7_grid.noise_map,
transformer=interferometer_7_grid.transformer,
linear_obj_list=[mapper],
regularization_list=[reg],
settings=al.SettingsInversion(use_w_tilde=False),
)
assert (fit.galaxy_model_visibilities_dict[g2] == (0.0 + 0.0j) * np.zeros(
(7, ))).all()
assert fit.galaxy_model_visibilities_dict[g0].slim == pytest.approx(
g0_visibilities.slim, 1.0e-4)
assert fit.galaxy_model_visibilities_dict[g1].slim == pytest.approx(
g1_visibilities.slim, 1.0e-4)
assert fit.galaxy_model_visibilities_dict[
galaxy_pix].slim == pytest.approx(
inversion.mapped_reconstructed_data.slim, 1.0e-4)
assert fit.model_visibilities.slim == pytest.approx(
fit.galaxy_model_visibilities_dict[g0].slim +
fit.galaxy_model_visibilities_dict[g1].slim +
inversion.mapped_reconstructed_data.slim,
1.0e-4,
)
