def test__regularization_term__solution_all_1s__regularization_matrix_simple(self):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([1.0, 1.0, 1.0])
inversion.regularization_matrix = np.array(
[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]
)
# G_l term, Warren & Dye 2003 / Nightingale /2015 2018
# G_l = s_T * H * s
# Matrix multiplication:
# s_T * H = [1.0, 1.0, 1.0] * [1.0, 1.0, 1.0] = [(1.0*1.0) + (1.0*0.0) + (1.0*0.0)] = [1.0, 1.0, 1.0]
# [1.0, 1.0, 1.0] [(1.0*0.0) + (1.0*1.0) + (1.0*0.0)]
# [1.0, 1.0, 1.0] [(1.0*0.0) + (1.0*0.0) + (1.0*1.0)]
# (s_T * H) * s = [1.0, 1.0, 1.0] * [1.0] = 3.0
# [1.0]
# [1.0]
assert inversion.regularization_term == 3.0
def inversion_imaging_from(
self,
dataset: aa.Imaging,
image: aa.Array2D,
noise_map: aa.Array2D,
w_tilde: aa.WTildeImaging,
settings_pixelization: aa.SettingsPixelization = aa.
SettingsPixelization(),
settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
preloads: Preloads = Preloads(),
):
linear_obj_galaxy_dict = self.linear_obj_galaxy_dict_from(
dataset=dataset,
settings_pixelization=settings_pixelization,
preloads=preloads,
)
linear_obj_list = list(linear_obj_galaxy_dict.keys())
inversion = inversion_imaging_unpacked_from(
image=image,
noise_map=noise_map,
convolver=dataset.convolver,
w_tilde=w_tilde,
linear_obj_list=linear_obj_list,
regularization_list=self.tracer.regularization_list,
settings=settings_inversion,
preloads=preloads,
profiling_dict=self.tracer.profiling_dict,
)
inversion.linear_obj_galaxy_dict = linear_obj_galaxy_dict
return inversion
def inversion_imaging_from(
self,
grid,
image,
noise_map,
convolver,
w_tilde,
settings_pixelization=aa.SettingsPixelization(),
settings_inversion=aa.SettingsInversion(),
preloads=aa.Preloads(),
):
linear_obj_list = self.linear_obj_list_from(
grid=grid,
settings_pixelization=settings_pixelization,
preloads=preloads)
return inversion_imaging_unpacked_from(
image=image,
noise_map=noise_map,
convolver=convolver,
w_tilde=w_tilde,
linear_obj_list=linear_obj_list,
regularization_list=self.regularization_list,
settings=settings_inversion,
profiling_dict=self.profiling_dict,
)
def inversion_interferometer_from(
self,
dataset: aa.Interferometer,
visibilities: aa.Visibilities,
noise_map: aa.VisibilitiesNoiseMap,
w_tilde,
settings_pixelization: aa.SettingsPixelization = aa.
SettingsPixelization(),
settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
preloads: Preloads = Preloads(),
):
linear_obj_galaxy_dict = self.linear_obj_galaxy_dict_from(
dataset=dataset,
settings_pixelization=settings_pixelization,
preloads=preloads,
)
linear_obj_list = list(linear_obj_galaxy_dict.keys())
inversion = inversion_interferometer_unpacked_from(
visibilities=visibilities,
noise_map=noise_map,
transformer=dataset.transformer,
w_tilde=w_tilde,
linear_obj_list=linear_obj_list,
regularization_list=self.tracer.regularization_list,
settings=settings_inversion,
profiling_dict=self.tracer.profiling_dict,
)
inversion.linear_obj_galaxy_dict = linear_obj_galaxy_dict
return inversion
def inversion_interferometer_from(
self,
grid,
visibilities,
noise_map,
transformer,
settings_pixelization=aa.SettingsPixelization(),
settings_inversion=aa.SettingsInversion(),
preloads=aa.Preloads(),
):
linear_obj_list = self.linear_obj_list_from(
grid=grid,
settings_pixelization=settings_pixelization,
preloads=preloads)
return inversion_interferometer_unpacked_from(
visibilities=visibilities,
noise_map=noise_map,
transformer=transformer,
linear_obj_list=linear_obj_list,
regularization_list=self.regularization_list,
settings=settings_inversion,
profiling_dict=self.profiling_dict,
)
def test__interpolated_errors__also_on_image_grid__interpolates_values(self):
conf.instance = conf.Config(
path.join(directory, path.join("files", "inversion_image_grid")),
path.join(directory, "output"),
)
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
inversion.curvature_reg_matrix = np.eye(N=9)
interpolated_errors = inversion.interpolated_errors_from_shape_native()
assert interpolated_errors.shape_native == (5, 5)
assert interpolated_errors.slim == pytest.approx(np.ones(shape=(25,)), 1.0e-4)
assert interpolated_errors.native == pytest.approx(
np.ones(shape=(5, 5)), 1.0e-4
)
assert interpolated_errors.pixel_scales == pytest.approx((1.0, 1.0), 1.0e-4)
def test__reconstruction_different_values__simple_blurred_mapping_matrix__correct_reconstructed_image(
self,
):
matrix_shape = (9, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[[True, True, True], [False, False, False], [True, True, True]]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape, source_grid_slim=grid),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([1.0, 2.0, 3.0, 4.0])
inversion.blurred_mapping_matrix = np.array(
[[1.0, 1.0, 1.0, 1.0], [1.0, 0.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.0]]
)
# # Imaging pixel 0 maps to 4 pixs pixxels -> value is 1.0 + 2.0 + 3.0 + 4.0 = 10.0
# # Imaging pixel 1 maps to 3 pixs pixxels -> value is 1.0 + 3.0 + 4.0
# # Imaging pixel 2 maps to 1 pixs pixxels -> value is 1.0
assert (
inversion.mapped_reconstructed_image == np.array([10.0, 8.0, 1.0])
).all()
assert (
inversion.mapped_reconstructed_image.native
== np.array([[0.0, 0.0, 0.0], [10.0, 8.0, 1.0], [0.0, 0.0, 0.0]])
).all()
assert inversion.errors_with_covariance == pytest.approx(
np.array(
[
[0.6785, -0.3214, -0.3214],
[-0.3214, 0.6785, -0.3214],
[-0.3214, -0.3214, 0.6785],
]
),
1.0e-2,
)
assert inversion.errors == pytest.approx(
np.array([0.6785, 0.6785, 0.6785]), 1.0e-3
)
def test__interferometer(self):
visibilities_mask = np.full(fill_value=False, shape=(7, ))
real_space_mask = aa.Mask2D.unmasked(shape_native=(7, 7),
pixel_scales=0.1,
sub_size=1)
grid = aa.Grid2D.from_mask(mask=real_space_mask)
pix = aa.pix.VoronoiMagnification(shape=(7, 7))
sparse_grid = pix.sparse_grid_from_grid(grid=grid)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=sparse_grid,
settings=aa.SettingsPixelization(use_border=False),
)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.Visibilities.manual_slim(visibilities=[
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
])
noise_map = aa.VisibilitiesNoiseMap.ones(shape_slim=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.Interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
)
inversion = aa.Inversion(
dataset=interferometer,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
assert inversion.mapped_reconstructed_visibilities == pytest.approx(
1.0 + 0.0j * np.ones(shape=(7, )), 1.0e-4)
assert (np.imag(inversion.mapped_reconstructed_visibilities) <
0.0001).all()
assert (np.imag(inversion.mapped_reconstructed_visibilities) >
0.0).all()
def test__interferometer_linear_operator(self):
real_space_mask = aa.Mask2D.unmasked(shape_native=(7, 7),
pixel_scales=0.1,
sub_size=1)
grid = aa.Grid2D.from_mask(mask=real_space_mask)
pix = aa.pix.Rectangular(shape=(7, 7))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=None,
settings=aa.SettingsPixelization(use_border=False),
)
reg = aa.reg.Constant(coefficient=0.0)
visibilities = aa.Visibilities.manual_slim(visibilities=[
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
1.0 + 0.0j,
])
noise_map = aa.VisibilitiesNoiseMap.ones(shape_slim=(7, ))
uv_wavelengths = np.ones(shape=(7, 2))
interferometer = aa.Interferometer(
visibilities=visibilities,
noise_map=noise_map,
uv_wavelengths=uv_wavelengths,
real_space_mask=real_space_mask,
settings=aa.SettingsInterferometer(
transformer_class=aa.TransformerNUFFT),
)
inversion = aa.Inversion(
dataset=interferometer,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(use_linear_operators=True,
check_solution=False),
)
assert inversion.mapped_reconstructed_visibilities == pytest.approx(
1.0 + 0.0j * np.ones(shape=(7, )), 1.0e-4)
assert (np.imag(inversion.mapped_reconstructed_visibilities) <
0.0001).all()
assert (np.imag(inversion.mapped_reconstructed_visibilities) >
0.0).all()
def __init__(
self,
dataset: Union[aa.Imaging, aa.Interferometer],
hyper_dataset_result: ResultDataset = None,
cosmology=cosmo.Planck15,
settings_pixelization: aa.SettingsPixelization = None,
settings_inversion: aa.SettingsInversion = None,
):
"""
Abstract Analysis class for all model-fits which fit galaxies (or objects containing galaxies like a plane)
to a dataset, like imaging or interferometer data.
This class stores the settings used to perform the model-fit for certain components of the model (e.g. a
pixelization or inversion), the Cosmology used for the analysis and hyper datasets used for certain model
classes.
Parameters
----------
dataset
The dataset that is the model is fitted too.
hyper_dataset_result
The hyper-model image and hyper galaxies images of a previous result in a model-fitting pipeline, which are
used by certain classes for adapting the analysis to the properties of the dataset.
cosmology
The Cosmology assumed for this analysis.
settings_pixelization
settings controlling how a pixelization is fitted during the model-fit, for example if a border is used
when creating the pixelization.
settings_inversion
Settings controlling how an inversion is fitted during the model-fit, for example which linear algebra
formalism is used.
"""
super().__init__(cosmology=cosmology)
self.dataset = dataset
self.hyper_dataset_result = hyper_dataset_result
if self.hyper_dataset_result is not None:
if hyper_dataset_result.search is not None:
hyper_dataset_result.search.paths = None
self.set_hyper_dataset(result=self.hyper_dataset_result)
else:
self.hyper_galaxy_image_path_dict = None
self.hyper_model_image = None
self.settings_pixelization = settings_pixelization or aa.SettingsPixelization()
self.settings_inversion = settings_inversion or aa.SettingsInversion()
self.preloads = aa.Preloads()
def test__brightest_reconstruction_pixel_and_centre(self):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape,
source_pixelization_grid=aa.Grid2DVoronoi.manual_slim(
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [5.0, 0.0]]
),
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array([2.0, 3.0, 5.0, 0.0])
assert inversion.brightest_reconstruction_pixel == 2
assert inversion.brightest_reconstruction_pixel_centre.in_list == [(5.0, 6.0)]
def test__preloads(self):
matrix_shape = (9, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[[True, True, True], [False, False, False], [True, True, True]]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
blurred_mapping_matrix = 2.0 * np.ones(matrix_shape)
curvature_matrix = 18.0 * np.ones((matrix_shape[1], matrix_shape[1]))
curvature_matrix_sparse_preload, curvature_matrix_preload_counts = aa.util.inversion.curvature_matrix_sparse_preload_via_mapping_matrix_from(
mapping_matrix=blurred_mapping_matrix
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape=matrix_shape, source_grid_slim=grid),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
preloads=aa.Preloads(
blurred_mapping_matrix=blurred_mapping_matrix,
curvature_matrix_sparse_preload=curvature_matrix_sparse_preload,
curvature_matrix_preload_counts=curvature_matrix_preload_counts,
),
)
assert (inversion.blurred_mapping_matrix == blurred_mapping_matrix).all()
def __init__(
self,
dataset: aa.Imaging,
tracer: Tracer,
hyper_image_sky: Optional[ag.hyper_data.HyperImageSky] = None,
hyper_background_noise: Optional[
ag.hyper_data.HyperBackgroundNoise] = None,
use_hyper_scaling: bool = True,
settings_pixelization: aa.SettingsPixelization = aa.
SettingsPixelization(),
settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
preloads: Preloads = Preloads(),
profiling_dict: Optional[Dict] = None,
):
"""
An lens fitter, which contains the tracer's used to perform the fit and functions to manipulate \
the lens dataset's hyper_galaxies.
Parameters
-----------
tracer : Tracer
The tracer, which describes the ray-tracing and strong lens configuration.
"""
super().__init__(dataset=dataset, profiling_dict=profiling_dict)
super(AbstractFit).__init__()
self.tracer = tracer
self.hyper_image_sky = hyper_image_sky
self.hyper_background_noise = hyper_background_noise
self.use_hyper_scaling = use_hyper_scaling
self.settings_pixelization = settings_pixelization
self.settings_inversion = settings_inversion
self.preloads = preloads
def test__regularization_term__solution_and_regularization_matrix_range_of_values(
self,
):
matrix_shape = (9, 3)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(9),
noise_map=np.ones(9),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(matrix_shape),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
# G_l term, Warren & Dye 2003 / Nightingale /2015 2018
# G_l = s_T * H * s
# Matrix multiplication:
# s_T * H = [2.0, 3.0, 5.0] * [2.0, -1.0, 0.0] = [(2.0* 2.0) + (3.0*-1.0) + (5.0 *0.0)] = [1.0, -1.0, 7.0]
# [-1.0, 2.0, -1.0] [(2.0*-1.0) + (3.0* 2.0) + (5.0*-1.0)]
# [ 0.0, -1.0, 2.0] [(2.0* 0.0) + (3.0*-1.0) + (5.0 *2.0)]
# (s_T * H) * s = [1.0, -1.0, 7.0] * [2.0] = 34.0
# [3.0]
# [5.0]
inversion.reconstruction = np.array([2.0, 3.0, 5.0])
inversion.regularization_matrix = np.array(
[[2.0, -1.0, 0.0], [-1.0, 2.0, -1.0], [0.0, -1.0, 2.0]]
)
assert inversion.regularization_term == 34.0
def make_analysis_imaging_7x7():
return ag.AnalysisImaging(
dataset=make_masked_imaging_7x7(),
settings_inversion=aa.SettingsInversion(use_w_tilde=False),
)
def __init__(
self,
dataset: aa.Imaging,
plane: Plane,
hyper_image_sky=None,
hyper_background_noise=None,
use_hyper_scalings: bool = True,
settings_pixelization: aa.SettingsPixelization = aa.SettingsPixelization(),
settings_inversion: aa.SettingsInversion = aa.SettingsInversion(),
):
""" An lens fitter, which contains the plane's used to perform the fit and functions to manipulate \
the lens dataset's hyper_galaxies.
Parameters
-----------
plane
The plane of galaxies whose model images are used to fit the imaging data.
"""
self.plane = plane
if use_hyper_scalings:
image = hyper_image_from(
image=dataset.image, hyper_image_sky=hyper_image_sky
)
noise_map = hyper_noise_map_from(
noise_map=dataset.noise_map,
plane=plane,
hyper_background_noise=hyper_background_noise,
)
else:
image = dataset.image
noise_map = dataset.noise_map
self.blurred_image = self.plane.blurred_image_2d_via_convolver_from(
grid=dataset.grid,
convolver=dataset.convolver,
blurring_grid=dataset.blurring_grid,
)
self.profile_subtracted_image = image - self.blurred_image
if not plane.has_pixelization:
inversion = None
model_image = self.blurred_image
else:
inversion = plane.inversion_imaging_from(
grid=dataset.grid_inversion,
image=self.profile_subtracted_image,
noise_map=noise_map,
convolver=dataset.convolver,
w_tilde=dataset.w_tilde,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
)
model_image = self.blurred_image + inversion.mapped_reconstructed_image
fit = aa.FitData(
data=image,
noise_map=noise_map,
model_data=model_image,
mask=dataset.mask,
inversion=inversion,
use_mask_in_fit=False,
)
super().__init__(dataset=dataset, fit=fit)
def __init__(
self,
dataset,
tracer,
hyper_background_noise=None,
use_hyper_scaling=True,
settings_pixelization=aa.SettingsPixelization(),
settings_inversion=aa.SettingsInversion(),
preloads=Preloads(),
profiling_dict: Optional[Dict] = None,
):
""" An lens fitter, which contains the tracer's used to perform the fit and functions to manipulate \
the lens dataset's hyper_galaxies.
Parameters
-----------
tracer : Tracer
The tracer, which describes the ray-tracing and strong lens configuration.
"""
self.tracer = tracer
self.hyper_background_noise = hyper_background_noise
self.use_hyper_scaling = use_hyper_scaling
self.settings_pixelization = settings_pixelization
self.settings_inversion = settings_inversion
self.preloads = preloads
self.profiling_dict = profiling_dict
if use_hyper_scaling:
if hyper_background_noise is not None:
noise_map = hyper_background_noise.hyper_noise_map_complex_from(
noise_map=dataset.noise_map)
else:
noise_map = dataset.noise_map
else:
noise_map = dataset.noise_map
self.tracer = tracer
self.profile_visibilities = self.tracer.visibilities_via_transformer_from(
grid=dataset.grid, transformer=dataset.transformer)
self.profile_subtracted_visibilities = (dataset.visibilities -
self.profile_visibilities)
if not tracer.has_pixelization:
inversion = None
model_visibilities = self.profile_visibilities
else:
inversion = tracer.inversion_interferometer_from(
grid=dataset.grid_inversion,
visibilities=self.profile_subtracted_visibilities,
noise_map=noise_map,
transformer=dataset.transformer,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
preloads=preloads,
)
model_visibilities = (self.profile_visibilities +
inversion.mapped_reconstructed_data)
fit = aa.FitDataComplex(
data=dataset.visibilities,
noise_map=noise_map,
model_data=model_visibilities,
inversion=inversion,
use_mask_in_fit=False,
profiling_dict=profiling_dict,
)
super().__init__(dataset=dataset,
fit=fit,
profiling_dict=profiling_dict)
def test__15_grid__no_sub_grid(self):
mask = aa.Mask2D.manual(
mask=[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, False, False, False, False, False, True],
[True, False, False, False, False, False, True],
[True, False, False, False, False, False, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
],
pixel_scales=1.0,
sub_size=1,
)
# There is no sub-grid, so our grid are just the masked_image grid (note the NumPy weighted_data structure
# ensures this has no sub-gridding)
grid = aa.Grid2D.manual_mask(
grid=[
[0.9, -0.9],
[1.0, -1.0],
[1.1, -1.1],
[0.9, 0.9],
[1.0, 1.0],
[1.1, 1.1],
[-0.01, 0.01],
[0.0, 0.0],
[0.01, 0.01],
[-0.9, -0.9],
[-1.0, -1.0],
[-1.1, -1.1],
[-0.9, 0.9],
[-1.0, 1.0],
[-1.1, 1.1],
],
mask=mask,
)
pix = aa.pix.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=None,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.data_pixelization_grid == None
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.2, 2.2), 1.0e-4)
assert mapper.source_pixelization_grid.origin == pytest.approx(
(0.0, 0.0), 1.0e-4)
assert (mapper.mapping_matrix == np.array([
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
])).all()
assert mapper.shape_native == (3, 3)
reg = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
assert (regularization_matrix == np.array([
[2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
[-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0],
[0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0],
[0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0],
[0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001],
])).all()
image = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(15), 1.0e-4)
def test__3x3_simple_grid__include_mask_with_offset_centre(self):
mask = aa.Mask2D.manual(
mask=[
[True, True, True, True, True, True, True],
[True, True, True, True, False, True, True],
[True, True, True, False, False, False, True],
[True, True, True, True, False, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
],
pixel_scales=1.0,
sub_size=1,
)
grid = np.array([[2.0, 1.0], [1.0, 0.0], [1.0, 1.0], [1.0, 2.0],
[0.0, 1.0]])
grid = aa.Grid2D.manual_mask(grid=grid, mask=mask)
pix = aa.pix.VoronoiMagnification(shape=(3, 3))
sparse_grid = aa.Grid2DSparse.from_grid_and_unmasked_2d_grid_shape(
grid=grid, unmasked_sparse_shape=pix.shape)
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=sparse_grid,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert (mapper.source_pixelization_grid == sparse_grid).all()
# assert mapper.pixelization_grid.origin == pytest.approx((1.0, 1.0), 1.0e-4)
assert isinstance(mapper, mappers.MapperVoronoi)
assert (mapper.mapping_matrix == np.array([
[1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0],
])).all()
reg = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
assert (regularization_matrix == np.array([
[3.00000001, -1.0, -1.0, -1.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0],
[-1.0, -1.0, 4.00000001, -1.0, -1.0],
[-1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, -1.0, -1.0, -1.0, 3.00000001],
])).all()
image = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(5), 1.0e-4)
def test__5_simple_grid__include_sub_grid(self):
mask = aa.Mask2D.manual(
mask=[
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, False, True, True, True],
[True, True, False, False, False, True, True],
[True, True, True, False, True, True, True],
[True, True, True, True, True, True, True],
[True, True, True, True, True, True, True],
],
pixel_scales=2.0,
sub_size=2,
)
# Assume a 2x2 sub-grid, so each of our 5 masked_image-pixels are split into 4.
# The grid below is unphysical in that the (0.0, 0.0) terms on the end of each sub-grid probably couldn't
# happen for a real lens calculation. This is to make a mapping_matrix matrix which explicitly tests the
# sub-grid.
grid = aa.Grid2D.manual_mask(
grid=[
[1.0, -1.0],
[1.0, -1.0],
[1.0, -1.0],
[1.0, 1.0],
[1.0, 1.0],
[1.0, 1.0],
[-1.0, -1.0],
[-1.0, -1.0],
[-1.0, -1.0],
[-1.0, 1.0],
[-1.0, 1.0],
[-1.0, 1.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
[0.0, 0.0],
],
mask=mask,
)
pix = aa.pix.Rectangular(shape=(3, 3))
mapper = pix.mapper_from_grid_and_sparse_grid(
grid=grid,
sparse_grid=None,
settings=aa.SettingsPixelization(use_border=False),
)
assert mapper.data_pixelization_grid == None
assert mapper.source_pixelization_grid.shape_native_scaled == pytest.approx(
(2.0, 2.0), 1.0e-4)
assert mapper.source_pixelization_grid.origin == pytest.approx(
(0.0, 0.0), 1.0e-4)
assert (mapper.mapping_matrix == np.array([
[0.75, 0.0, 0.25, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.25, 0.0, 0.75],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
])).all()
assert mapper.shape_native == (3, 3)
reg = aa.reg.Constant(coefficient=1.0)
regularization_matrix = reg.regularization_matrix_from_mapper(
mapper=mapper)
assert (regularization_matrix == np.array([
[2.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[-1.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 2.00000001, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0],
[-1.0, 0.0, 0.0, 3.00000001, -1.0, 0.0, -1.0, 0.0, 0.0],
[0.0, -1.0, 0.0, -1.0, 4.00000001, -1.0, 0.0, -1.0, 0.0],
[0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, 0.0, 0.0, -1.0],
[0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 2.00000001, -1.0, 0.0],
[0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 3.00000001, -1.0],
[0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, -1.0, 2.00000001],
])).all()
image = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
noise_map = aa.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0)
psf = aa.Kernel2D.no_blur(pixel_scales=1.0)
imaging = aa.Imaging(image=image, noise_map=noise_map, psf=psf)
masked_imaging = imaging.apply_mask(mask=mask)
inversion = aa.Inversion(
dataset=masked_imaging,
mapper=mapper,
regularization=reg,
settings=aa.SettingsInversion(check_solution=False),
)
assert (
inversion.blurred_mapping_matrix == mapper.mapping_matrix).all()
assert (inversion.regularization_matrix == regularization_matrix).all()
assert inversion.mapped_reconstructed_image == pytest.approx(
np.ones(5), 1.0e-4)
def __init__(
self,
dataset,
tracer,
hyper_image_sky=None,
hyper_background_noise=None,
use_hyper_scaling=True,
settings_pixelization=aa.SettingsPixelization(),
settings_inversion=aa.SettingsInversion(),
preloads=Preloads(),
profiling_dict: Optional[Dict] = None,
):
"""
An lens fitter, which contains the tracer's used to perform the fit and functions to manipulate \
the lens dataset's hyper_galaxies.
Parameters
-----------
tracer : Tracer
The tracer, which describes the ray-tracing and strong lens configuration.
"""
self.tracer = tracer
self.hyper_image_sky = hyper_image_sky
self.hyper_background_noise = hyper_background_noise
self.use_hyper_scaling = use_hyper_scaling
self.settings_pixelization = settings_pixelization
self.settings_inversion = settings_inversion
self.preloads = preloads
self.profiling_dict = profiling_dict
if use_hyper_scaling:
image = hyper_image_from(image=dataset.image,
hyper_image_sky=hyper_image_sky)
noise_map = hyper_noise_map_from(
noise_map=dataset.noise_map,
tracer=tracer,
hyper_background_noise=hyper_background_noise,
)
else:
image = dataset.image
noise_map = dataset.noise_map
if preloads.blurred_image is None:
self.blurred_image = self.tracer.blurred_image_2d_via_convolver_from(
grid=dataset.grid,
convolver=dataset.convolver,
blurring_grid=dataset.blurring_grid,
)
else:
self.blurred_image = preloads.blurred_image
self.profile_subtracted_image = image - self.blurred_image
if not tracer.has_pixelization:
inversion = None
model_image = self.blurred_image
else:
inversion = tracer.inversion_imaging_from(
grid=dataset.grid_inversion,
image=self.profile_subtracted_image,
noise_map=noise_map,
convolver=dataset.convolver,
w_tilde=dataset.w_tilde,
settings_pixelization=settings_pixelization,
settings_inversion=settings_inversion,
preloads=preloads,
)
model_image = self.blurred_image + inversion.mapped_reconstructed_image
fit = aa.FitData(
data=image,
noise_map=noise_map,
model_data=model_image,
mask=dataset.mask,
inversion=inversion,
use_mask_in_fit=False,
profiling_dict=profiling_dict,
)
super().__init__(dataset=dataset,
fit=fit,
profiling_dict=profiling_dict)
def test__interp__manual_shape_native__uses_input_shape_native(self):
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[1.0, 1.0], [1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(1.0, 1.0), 1.0e-4
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([3.0, 3.0, 3.0, 3.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[3.0, 3.0], [3.0, 3.0]])
).all()
assert interpolated_reconstruction.pixel_scales == (1.0, 1.0)
def test__interpolated_reconstruction__config_is_source_grid__grid_is_zoomed_as_uses_mapper_grid(
self,
):
conf.instance = conf.Config(
path.join(directory, path.join("files", "inversion_source_grid")),
path.join(directory, "output"),
)
matrix_shape = (25, 3)
mask = aa.Mask2D.manual(
mask=np.array(
[
[True, True, True, True, True],
[True, True, False, False, True],
[True, False, False, False, True],
[True, False, False, True, True],
[True, True, True, True, True],
]
),
pixel_scales=1.0,
sub_size=1,
)
grid = aa.Grid2D.from_mask(mask=mask)
pixelization_grid = aa.Grid2D.uniform(
shape_native=(3, 3), pixel_scales=1.0, sub_size=1
)
inversion = inversions.InversionImagingMatrix.from_data_mapper_and_regularization(
image=np.ones(25),
noise_map=np.ones(25),
convolver=mock.MockConvolver(matrix_shape),
mapper=mock.MockMapper(
matrix_shape=matrix_shape,
source_grid_slim=grid,
source_pixelization_grid=pixelization_grid,
),
regularization=mock.MockRegularization(matrix_shape),
settings=aa.SettingsInversion(check_solution=False),
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
)
interpolated_reconstruction = (
inversion.interpolated_reconstructed_data_from_shape_native()
)
assert (
interpolated_reconstruction.slim
== np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native
== np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(0.66666, 0.66666), 1.0e-4
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([1.0, 1.0, 1.0, 1.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[1.0, 1.0], [1.0, 1.0]])
).all()
assert interpolated_reconstruction.pixel_scales == pytest.approx(
(1.0, 1.0), 1.0e-4
)
inversion.reconstruction = np.array(
[1.0, 1.0, 1.0, 5.0, 5.0, 5.0, 5.0, 1.0, 1.0]
)
interpolated_reconstruction = inversion.interpolated_reconstructed_data_from_shape_native(
shape_native=(2, 2)
)
assert (
interpolated_reconstruction.slim == np.array([3.0, 3.0, 3.0, 3.0])
).all()
assert (
interpolated_reconstruction.native == np.array([[3.0, 3.0], [3.0, 3.0]])
).all()
assert interpolated_reconstruction.pixel_scales == (1.0, 1.0)
