diff --git a/autolens/__init__.py b/autolens/__init__.py index aafada13f..7f17e1e8f 100644 --- a/autolens/__init__.py +++ b/autolens/__init__.py @@ -84,6 +84,7 @@ from .point.point_solver import PointSolver from .quantity.fit_quantity import FitQuantity from .quantity.model.analysis import AnalysisQuantity +from . import mock as m from . import util from autoconf import conf diff --git a/autolens/mock/fixtures.py b/autolens/fixtures.py similarity index 91% rename from autolens/mock/fixtures.py rename to autolens/fixtures.py index 4602a7751..cc375756b 100644 --- a/autolens/mock/fixtures.py +++ b/autolens/fixtures.py @@ -1,8 +1,6 @@ import autolens as al -from autogalaxy.mock.fixtures import * -from autofit.mock.mock import MockSearch -from autolens.mock.mock import MockPointSolver +from autogalaxy.fixtures import * def make_positions_x2(): @@ -154,5 +152,5 @@ def make_analysis_interferometer_7(): def make_analysis_point_x2(): return al.AnalysisPoint( point_dict=make_point_dict(), - solver=MockPointSolver(model_positions=make_positions_x2()), + solver=al.m.MockPointSolver(model_positions=make_positions_x2()), ) diff --git a/autolens/imaging/fit_imaging.py b/autolens/imaging/fit_imaging.py index 7f39530ad..e62fdc28d 100644 --- a/autolens/imaging/fit_imaging.py +++ b/autolens/imaging/fit_imaging.py @@ -15,7 +15,7 @@ def __init__( tracer, hyper_image_sky=None, hyper_background_noise=None, - use_hyper_scaling=True, + use_hyper_scalings=True, settings_pixelization=aa.SettingsPixelization(), settings_inversion=aa.SettingsInversion(), preloads=Preloads(), @@ -31,11 +31,13 @@ def __init__( The tracer, which describes the ray-tracing and strong lens configuration. """ + super().__init__(dataset=dataset, profiling_dict=profiling_dict) + 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.use_hyper_scalings = use_hyper_scalings self.settings_pixelization = settings_pixelization self.settings_inversion = settings_inversion @@ -44,68 +46,97 @@ def __init__( self.profiling_dict = profiling_dict - if use_hyper_scaling: + @property + def data(self): + """ + Returns the imaging data, which may have a hyper scaling performed which rescales the background sky level + in order to account for uncertainty in the background sky subtraction. + """ + if self.use_hyper_scalings: - image = hyper_image_from( - image=dataset.image, hyper_image_sky=hyper_image_sky + return hyper_image_from( + image=self.dataset.image, hyper_image_sky=self.hyper_image_sky ) - noise_map = hyper_noise_map_from( - noise_map=dataset.noise_map, - tracer=tracer, - hyper_background_noise=hyper_background_noise, - ) + return self.dataset.data - else: + @property + def noise_map(self): + """ + Returns the imaging noise-map, which may have a hyper scaling performed which increase the noise in regions of + the data that are poorly fitted in order to avoid overfitting. + """ + if self.use_hyper_scalings: - image = dataset.image - noise_map = dataset.noise_map + return hyper_noise_map_from( + noise_map=self.dataset.noise_map, + tracer=self.tracer, + hyper_background_noise=self.hyper_background_noise, + ) - if preloads.blurred_image is None: + return self.dataset.noise_map - self.blurred_image = self.tracer.blurred_image_2d_via_convolver_from( - grid=dataset.grid, - convolver=dataset.convolver, - blurring_grid=dataset.blurring_grid, - ) + @property + def blurred_image(self): + """ + Returns the image of all light profiles in the fit's tracer convolved with the imaging dataset's PSF. - else: + For certain lens models the blurred image does not change (for example when all light profiles in the tracer + are fixed in the lens model). For faster run-times the blurred image can be preloaded. + """ - self.blurred_image = preloads.blurred_image + if self.preloads.blurred_image is None: - self.profile_subtracted_image = image - self.blurred_image + return self.tracer.blurred_image_2d_via_convolver_from( + grid=self.dataset.grid, + convolver=self.dataset.convolver, + blurring_grid=self.dataset.blurring_grid, + ) + return self.preloads.blurred_image - if not tracer.has_pixelization: + @property + def profile_subtracted_image(self): + """ + Returns the dataset's image with all blurred light profile images in the fit's tracer subtracted. + """ + return self.image - self.blurred_image - inversion = None - model_image = self.blurred_image + @property + def inversion(self): + """ + If the tracer has linear objects which are used to fit the data (e.g. a pixelization) this function returns + the linear inversion. - else: + The image passed to this function is the dataset's image with all light profile images of the tracer subtracted. + """ + if self.tracer.has_pixelization: - inversion = tracer.inversion_imaging_from( - grid=dataset.grid_inversion, + return self.tracer.inversion_imaging_from( + grid=self.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, + noise_map=self.noise_map, + convolver=self.dataset.convolver, + w_tilde=self.dataset.w_tilde, + settings_pixelization=self.settings_pixelization, + settings_inversion=self.settings_inversion, ) - model_image = self.blurred_image + inversion.mapped_reconstructed_image + @property + def model_data(self): + """ + Returns the model-image that is used to fit the data. + + If the tracer does not have any linear objects and therefore omits an inversion, the model image is the + sum of all light profile images. - 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, - ) + If a inversion is included it is the sum of this sum and the inversion's reconstruction of the image. + """ - super().__init__(dataset=dataset, fit=fit, profiling_dict=profiling_dict) + if self.tracer.has_pixelization: + + return self.blurred_image + self.inversion.mapped_reconstructed_data + + return self.blurred_image @property def grid(self): @@ -192,20 +223,18 @@ def total_mappers(self): def refit_with_new_preloads(self, preloads, settings_inversion=None): - if self.profiling_dict is not None: - profiling_dict = {} - else: - profiling_dict = None + profiling_dict = {} if self.profiling_dict is not None else None - if settings_inversion is None: - settings_inversion = self.settings_inversion + settings_inversion = ( + self.settings_inversion if settings_inversion is None else None + ) return FitImaging( dataset=self.imaging, tracer=self.tracer, hyper_image_sky=self.hyper_image_sky, hyper_background_noise=self.hyper_background_noise, - use_hyper_scaling=self.use_hyper_scaling, + use_hyper_scalings=self.use_hyper_scalings, settings_pixelization=self.settings_pixelization, settings_inversion=settings_inversion, preloads=preloads, diff --git a/autolens/mock/__init__.py b/autolens/imaging/mock/__init__.py similarity index 100% rename from autolens/mock/__init__.py rename to autolens/imaging/mock/__init__.py diff --git a/autolens/imaging/mock/mock_fit_imaging.py b/autolens/imaging/mock/mock_fit_imaging.py new file mode 100644 index 000000000..bed1d9bc0 --- /dev/null +++ b/autolens/imaging/mock/mock_fit_imaging.py @@ -0,0 +1,23 @@ +import autoarray as aa + + +class MockFitImaging(aa.m.MockFitImaging): + def __init__( + self, + tracer=None, + dataset=aa.m.MockDataset(), + inversion=None, + noise_map=None, + grid=None, + blurred_image=None, + ): + + super().__init__( + dataset=dataset, + inversion=inversion, + noise_map=noise_map, + blurred_image=blurred_image, + ) + + self.tracer = tracer + self.grid = grid diff --git a/autolens/imaging/model/analysis.py b/autolens/imaging/model/analysis.py index 1644adcbb..206768409 100644 --- a/autolens/imaging/model/analysis.py +++ b/autolens/imaging/model/analysis.py @@ -226,7 +226,7 @@ def fit_imaging_for_tracer( tracer=tracer, hyper_image_sky=hyper_image_sky, hyper_background_noise=hyper_background_noise, - use_hyper_scaling=use_hyper_scalings, + use_hyper_scalings=use_hyper_scalings, settings_pixelization=self.settings_pixelization, settings_inversion=self.settings_inversion, preloads=preloads, @@ -490,7 +490,7 @@ def save_results_for_aggregator( def make_result( self, samples: af.PDFSamples, model: af.Collection, search: af.NonLinearSearch - ): + ) -> ResultImaging: """ After the non-linear search is complete create its `Result`, which includes: diff --git a/autolens/interferometer/fit_interferometer.py b/autolens/interferometer/fit_interferometer.py index 23b07264b..a2358e763 100644 --- a/autolens/interferometer/fit_interferometer.py +++ b/autolens/interferometer/fit_interferometer.py @@ -13,13 +13,14 @@ def __init__( dataset, tracer, hyper_background_noise=None, - use_hyper_scaling=True, + use_hyper_scalings=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 \ + """ + An lens fitter, which contains the tracer's used to perform the fit and functions to manipulate \ the lens dataset's hyper_galaxies. Parameters @@ -31,7 +32,7 @@ def __init__( self.tracer = tracer self.hyper_background_noise = hyper_background_noise - self.use_hyper_scaling = use_hyper_scaling + self.use_hyper_scalings = use_hyper_scalings self.settings_pixelization = settings_pixelization self.settings_inversion = settings_inversion @@ -40,61 +41,76 @@ def __init__( 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: + super().__init__(dataset=dataset, profiling_dict=profiling_dict) - noise_map = dataset.noise_map + @property + def noise_map(self): + """ + Returns the interferometer's noise-map, which may have a hyper scaling performed which increase the noise in + regions of the data that are poorly fitted in order to avoid overfitting. + """ + if self.use_hyper_scalings and self.hyper_background_noise is not None: - self.tracer = tracer + return self.hyper_background_noise.hyper_noise_map_complex_from( + noise_map=self.dataset.noise_map + ) - self.profile_visibilities = self.tracer.visibilities_via_transformer_from( - grid=dataset.grid, transformer=dataset.transformer - ) + return self.dataset.noise_map - self.profile_subtracted_visibilities = ( - dataset.visibilities - self.profile_visibilities + @property + def profile_visibilities(self): + """ + Returns the visibilities of every light profile in the plane, which are computed by performing a Fourier + transform to the sum of light profile images. + """ + return self.tracer.visibilities_via_transformer_from( + grid=self.dataset.grid, transformer=self.dataset.transformer ) - if not tracer.has_pixelization: + @property + def profile_subtracted_visibilities(self): + """ + Returns the interferomter dataset's visibilities with all transformed light profile images in the fit's + plane subtracted. + """ + return self.visibilities - self.profile_visibilities - inversion = None - model_visibilities = self.profile_visibilities + @property + def inversion(self): + """ + If the plane has linear objects which are used to fit the data (e.g. a pixelization) this function returns + the linear inversion. - else: + The image passed to this function is the dataset's image with all light profile images of the plane subtracted. + """ + if self.tracer.has_pixelization: - inversion = tracer.inversion_interferometer_from( - grid=dataset.grid_inversion, + return self.tracer.inversion_interferometer_from( + grid=self.dataset.grid_inversion, visibilities=self.profile_subtracted_visibilities, - noise_map=noise_map, - transformer=dataset.transformer, - w_tilde=dataset.w_tilde, - settings_pixelization=settings_pixelization, - settings_inversion=settings_inversion, - preloads=preloads, + noise_map=self.noise_map, + transformer=self.dataset.transformer, + w_tilde=self.dataset.w_tilde, + settings_pixelization=self.settings_pixelization, + settings_inversion=self.settings_inversion, ) - model_visibilities = ( - self.profile_visibilities + inversion.mapped_reconstructed_data - ) + @property + def model_data(self): + """ + Returns the model-image that is used to fit the 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, - ) + If the plane does not have any linear objects and therefore omits an inversion, the model image is the + sum of all light profile images. + + If a inversion is included it is the sum of this sum and the inversion's reconstruction of the image. + """ + + if self.tracer.has_pixelization: + + return self.profile_visibilities + self.inversion.mapped_reconstructed_data - super().__init__(dataset=dataset, fit=fit, profiling_dict=profiling_dict) + return self.profile_visibilities @property def grid(self): @@ -179,7 +195,7 @@ def refit_with_new_preloads(self, preloads, settings_inversion=None): dataset=self.interferometer, tracer=self.tracer, hyper_background_noise=self.hyper_background_noise, - use_hyper_scaling=self.use_hyper_scaling, + use_hyper_scalings=self.use_hyper_scalings, settings_pixelization=self.settings_pixelization, settings_inversion=settings_inversion, preloads=preloads, diff --git a/autolens/interferometer/model/analysis.py b/autolens/interferometer/model/analysis.py index fa740fb72..638bb3206 100644 --- a/autolens/interferometer/model/analysis.py +++ b/autolens/interferometer/model/analysis.py @@ -339,7 +339,7 @@ def fit_interferometer_for_tracer( dataset=self.dataset, tracer=tracer, hyper_background_noise=hyper_background_noise, - use_hyper_scaling=use_hyper_scalings, + use_hyper_scalings=use_hyper_scalings, settings_pixelization=self.settings_pixelization, settings_inversion=self.settings_inversion, preloads=preloads, diff --git a/autolens/lens/mock/__init__.py b/autolens/lens/mock/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/autolens/lens/mock/mock_tracer.py b/autolens/lens/mock/mock_tracer.py new file mode 100644 index 000000000..93ef328f4 --- /dev/null +++ b/autolens/lens/mock/mock_tracer.py @@ -0,0 +1,70 @@ +class MockTracer: + def __init__( + self, traced_grids_of_planes=None, sparse_image_plane_grid_pg_list=None + ): + + self.traced_grids_of_planes = traced_grids_of_planes + self.sparse_image_plane_grid_pg_list = sparse_image_plane_grid_pg_list + + def traced_grid_list_from(self, grid): + + return self.traced_grids_of_planes + + def sparse_image_plane_grid_pg_list_from(self, grid): + + return self.sparse_image_plane_grid_pg_list + + +class MockTracerPoint(MockTracer): + def __init__( + self, + sparse_image_plane_grid_pg_list=None, + traced_grid=None, + attribute=None, + profile=None, + magnification=None, + einstein_radius=None, + einstein_mass=None, + ): + + super().__init__( + sparse_image_plane_grid_pg_list=sparse_image_plane_grid_pg_list + ) + + self.positions = traced_grid + + self.attribute = attribute + self.profile = profile + + self.magnification = magnification + self.einstein_radius = einstein_radius + self.einstein_mass = einstein_mass + + @property + def planes(self): + return [0, 1] + + def deflections_yx_2d_from(self): + pass + + @property + def has_mass_profile(self): + return True + + def extract_attribute(self, cls, attr_name): + return [self.attribute] + + def extract_profile(self, profile_name): + return self.profile + + def traced_grid_list_from(self, grid, plane_index_limit=None): + return [self.positions] + + def magnification_2d_via_hessian_from(self, grid, deflections_func=None): + return self.magnification + + def einstein_radius_from(self, grid): + return self.einstein_radius + + def einstein_mass_angular_from(self, grid): + return self.einstein_mass diff --git a/autolens/lens/model/mock/__init__.py b/autolens/lens/model/mock/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/autolens/mock/mock.py b/autolens/lens/model/mock/mock_result.py similarity index 59% rename from autolens/mock/mock.py rename to autolens/lens/model/mock/mock_result.py index ff339a675..8d0dcea56 100644 --- a/autolens/mock/mock.py +++ b/autolens/lens/model/mock/mock_result.py @@ -1,14 +1,7 @@ import autofit as af -from autofit.mock.mock import MockSearch, MockSamples -from autoarray.mock.mock import MockMask, MockDataset, MockFit as AAMockFit -from autogalaxy.mock.mock import MockLightProfile, MockMassProfile -from autofit.mock.mock import * -from autofit.mock import mock as af_m - - -class MockResult(af_m.MockResult): +class MockResult(af.m.MockResult): def __init__( self, samples=None, @@ -149,102 +142,3 @@ def __getitem__(self, item): def __len__(self): return len(self.__result_list) - - -class MockFit(AAMockFit): - def __init__( - self, - tracer=None, - dataset=MockDataset(), - inversion=None, - noise_map=None, - grid=None, - blurred_image=None, - ): - - super().__init__(dataset=dataset, inversion=inversion, noise_map=noise_map) - - self.tracer = tracer - self.grid = grid - self.blurred_image = blurred_image - - -class MockTracer: - def __init__( - self, traced_grids_of_planes=None, sparse_image_plane_grid_pg_list=None - ): - - self.traced_grids_of_planes = traced_grids_of_planes - self.sparse_image_plane_grid_pg_list = sparse_image_plane_grid_pg_list - - def traced_grid_list_from(self, grid): - - return self.traced_grids_of_planes - - def sparse_image_plane_grid_pg_list_from(self, grid): - - return self.sparse_image_plane_grid_pg_list - - -class MockTracerPoint(MockTracer): - def __init__( - self, - sparse_image_plane_grid_pg_list=None, - traced_grid=None, - attribute=None, - profile=None, - magnification=None, - einstein_radius=None, - einstein_mass=None, - ): - - super().__init__( - sparse_image_plane_grid_pg_list=sparse_image_plane_grid_pg_list - ) - - self.positions = traced_grid - - self.attribute = attribute - self.profile = profile - - self.magnification = magnification - self.einstein_radius = einstein_radius - self.einstein_mass = einstein_mass - - @property - def planes(self): - return [0, 1] - - def deflections_yx_2d_from(self): - pass - - @property - def has_mass_profile(self): - return True - - def extract_attribute(self, cls, attr_name): - return [self.attribute] - - def extract_profile(self, profile_name): - return self.profile - - def traced_grid_list_from(self, grid, plane_index_limit=None): - return [self.positions] - - def magnification_2d_via_hessian_from(self, grid, deflections_func=None): - return self.magnification - - def einstein_radius_from(self, grid): - return self.einstein_radius - - def einstein_mass_angular_from(self, grid): - return self.einstein_mass - - -class MockPointSolver: - def __init__(self, model_positions): - - self.model_positions = model_positions - - def solve(self, lensing_obj, source_plane_coordinate, upper_plane_index=None): - return self.model_positions diff --git a/autolens/mock.py b/autolens/mock.py new file mode 100644 index 000000000..025e500ad --- /dev/null +++ b/autolens/mock.py @@ -0,0 +1,10 @@ +from autofit.mock import * +from autoarray.mock import * +from autogalaxy.mock import * + +from autolens.imaging.mock.mock_fit_imaging import MockFitImaging +from autolens.lens.mock.mock_tracer import MockTracer +from autolens.lens.mock.mock_tracer import MockTracerPoint +from autolens.lens.model.mock.mock_result import MockResult +from autolens.lens.model.mock.mock_result import MockResults +from autolens.point.mock.mock_point_solver import MockPointSolver diff --git a/autolens/point/fit_point.py b/autolens/point/fit_point.py index 31f5f7ff6..4ed32dd97 100644 --- a/autolens/point/fit_point.py +++ b/autolens/point/fit_point.py @@ -113,7 +113,7 @@ def log_likelihood(self) -> float: return log_likelihood_positions + log_likelihood_flux -class FitPositionsImage(aa.FitData): +class FitPositionsImage(aa.FitDataset): def __init__( self, name: str, @@ -135,6 +135,8 @@ def __init__( The noise-value assumed when computing the log likelihood. """ + super().__init__(dataset=positions) + self.name = name if point_profile is None: @@ -189,7 +191,7 @@ def residual_map(self) -> aa.ValuesIrregular: return residual_positions.distances_to_coordinate(coordinate=(0.0, 0.0)) -class FitPositionsSource(aa.FitData): +class FitPositionsSource(aa.FitDataset): def __init__( self, name: str, @@ -210,6 +212,8 @@ def __init__( The noise-value assumed when computing the log likelihood. """ + super().__init__(dataset=positions) + self.name = name if point_profile is None: @@ -265,7 +269,7 @@ def residual_map(self) -> aa.ValuesIrregular: ) -class FitFluxes(aa.FitData): +class FitFluxes(aa.FitDataset): def __init__( self, name: str, @@ -276,6 +280,8 @@ def __init__( point_profile: Optional[ag.ps.Point] = None, ): + super().__init__(dataset=fluxes) + self.tracer = tracer self.name = name diff --git a/autolens/point/mock/__init__.py b/autolens/point/mock/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/autolens/point/mock/mock_point_solver.py b/autolens/point/mock/mock_point_solver.py new file mode 100644 index 000000000..0650bf10c --- /dev/null +++ b/autolens/point/mock/mock_point_solver.py @@ -0,0 +1,11 @@ +import autofit as af +import autoarray as aa + + +class MockPointSolver: + def __init__(self, model_positions): + + self.model_positions = model_positions + + def solve(self, lensing_obj, source_plane_coordinate, upper_plane_index=None): + return self.model_positions diff --git a/docs/installation/source.rst b/docs/installation/source.rst index 8ae5faeef..43b301e40 100644 --- a/docs/installation/source.rst +++ b/docs/installation/source.rst @@ -21,6 +21,12 @@ projects. We include below instructions for building just **PyAutoLens** from so Building Only PyAutoLens ------------------------ +We upgrade pip to ensure certain libraries install: + +.. code-block:: bash + + pip install --upgrade pip + First, clone (or fork) the **PyAutoLens** GitHub repository: .. code-block:: bash @@ -73,6 +79,12 @@ Finally, check the **PyAutoLens** unit tests run and pass (you may need to insta Building All Projects --------------------- +We upgrade pip to ensure certain libraries install: + +.. code-block:: bash + + pip install --upgrade pip + First, clone (or fork) all 4 GitHub repositories: .. code-block:: bash diff --git a/test_autolens/conftest.py b/test_autolens/conftest.py index 9bbd022ec..f2bcb000f 100644 --- a/test_autolens/conftest.py +++ b/test_autolens/conftest.py @@ -5,7 +5,7 @@ from matplotlib import pyplot from autofit import conf -from autolens.mock import fixtures +from autolens import fixtures directory = path.dirname(path.realpath(__file__)) diff --git a/test_autolens/imaging/model/test_aggregator_imaging.py b/test_autolens/imaging/model/test_aggregator_imaging.py index 0941ed26b..69ca4688d 100644 --- a/test_autolens/imaging/model/test_aggregator_imaging.py +++ b/test_autolens/imaging/model/test_aggregator_imaging.py @@ -8,9 +8,6 @@ import autolens as al from autofit.non_linear.samples import Sample -from autofit.mock.mock import MockSearch, MockSamples -from autolens.mock.mock import MockResult - directory = path.dirname(path.realpath(__file__)) @@ -47,7 +44,7 @@ def make_samples(model): weight_list=[0.0, 1.0], ) - return MockSamples( + return al.m.MockSamples( model=model, sample_list=sample_list, max_log_likelihood_instance=tracer ) @@ -72,7 +69,7 @@ class TestFitImagingAgg: # # clean(database_file=database_file, result_path=result_path) # - # search = MockSearch(samples=samples) + # search = al.m.MockSearch(samples=samples) # search.paths = af.DirectoryPaths(path_prefix=path_prefix) # analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) # search.fit(model=model, analysis=analysis) @@ -100,8 +97,8 @@ def test__fit_imaging_randomly_drawn_via_pdf_gen_from( clean(database_file=database_file, result_path=result_path) - search = MockSearch( - samples=samples, result=MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) @@ -142,8 +139,8 @@ def test__fit_imaging_all_above_weight_gen( clean(database_file=database_file, result_path=result_path) - search = MockSearch( - samples=samples, result=MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) diff --git a/test_autolens/imaging/model/test_analysis_imaging.py b/test_autolens/imaging/model/test_analysis_imaging.py index 76af813b0..e7f31d4ff 100644 --- a/test_autolens/imaging/model/test_analysis_imaging.py +++ b/test_autolens/imaging/model/test_analysis_imaging.py @@ -9,7 +9,6 @@ from autolens.imaging.model.result import ResultImaging from autolens import exc -from autolens.mock import mock directory = path.dirname(path.realpath(__file__)) @@ -22,9 +21,9 @@ def test__make_result__result_imaging_is_returned(self, masked_imaging_7x7): instance = model.instance_from_prior_medians() - samples = mock.MockSamples(max_log_likelihood_instance=instance) + samples = al.m.MockSamples(max_log_likelihood_instance=instance) - search = mock.MockSearch(name="test_search", samples=samples) + search = al.m.MockSearch(name="test_search", samples=samples) analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) @@ -126,7 +125,7 @@ def test__uses_hyper_fit_correctly(self, masked_imaging_7x7): hyper_galaxy_image_path_dict = {("galaxies", "lens"): lens_hyper_image} - result = mock.MockResult( + result = al.m.MockResult( hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict, hyper_model_image=hyper_model_image, ) @@ -172,7 +171,7 @@ def test__sets_up_hyper_galaxy_images__froms(self, masked_imaging_7x7): ), } - result = mock.MockResult( + result = al.m.MockResult( hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict, hyper_model_image=al.Array2D.full( fill_value=3.0, shape_native=(3, 3), pixel_scales=1.0 @@ -210,7 +209,7 @@ def test__stochastic_log_likelihoods_for_instance(self, masked_imaging_7x7): ("galaxies", "source"): source_hyper_image, } - result = mock.MockResult( + result = al.m.MockResult( hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict, hyper_model_image=hyper_model_image, ) diff --git a/test_autolens/imaging/test_fit_imaging.py b/test_autolens/imaging/test_fit_imaging.py index e6ebec05c..9e10e2996 100644 --- a/test_autolens/imaging/test_fit_imaging.py +++ b/test_autolens/imaging/test_fit_imaging.py @@ -1,7 +1,7 @@ -import autolens as al import numpy as np import pytest -from autogalaxy.mock.mock import MockLightProfile + +import autolens as al def test__model_image__with_and_without_psf_blurring( @@ -10,7 +10,9 @@ def test__model_image__with_and_without_psf_blurring( g0 = al.Galaxy( redshift=0.5, - light_profile=MockLightProfile(image_2d_value=1.0, image_2d_first_value=2.0), + light_profile=al.m.MockLightProfile( + image_2d_value=1.0, image_2d_first_value=2.0 + ), ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) @@ -31,7 +33,9 @@ def test__model_image__with_and_without_psf_blurring( def test__noise_map__with_and_without_hyper_galaxy(masked_imaging_7x7_no_blur): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d_value=1.0)) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d_value=1.0) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) @@ -45,7 +49,7 @@ def test__noise_map__with_and_without_hyper_galaxy(masked_imaging_7x7_no_blur): g0 = al.Galaxy( redshift=0.5, - light_profile=MockLightProfile(image_2d_value=1.0), + light_profile=al.m.MockLightProfile(image_2d_value=1.0), hyper_galaxy=al.HyperGalaxy( contribution_factor=1.0, noise_factor=1.0, noise_power=1.0 ), @@ -70,7 +74,7 @@ def test__noise_map__with_hyper_galaxy_reaches_upper_limit(masked_imaging_7x7_no g0 = al.Galaxy( redshift=0.5, - light_profile=MockLightProfile(image_2d_value=1.0), + light_profile=al.m.MockLightProfile(image_2d_value=1.0), hyper_galaxy=al.HyperGalaxy( contribution_factor=1.0, noise_factor=1.0e9, noise_power=1.0 ), @@ -91,14 +95,18 @@ def test__noise_map__with_hyper_galaxy_reaches_upper_limit(masked_imaging_7x7_no def test__image__with_and_without_hyper_background_sky(masked_imaging_7x7_no_blur): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d_value=1.0)) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d_value=1.0) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) fit = al.FitImaging(dataset=masked_imaging_7x7_no_blur, tracer=tracer) assert fit.image.slim == pytest.approx(np.full(fill_value=1.0, shape=(9,)), 1.0e-1) - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d_value=1.0)) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d_value=1.0) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) hyper_image_sky = al.hyper_data.HyperImageSky(sky_scale=1.0) @@ -115,7 +123,9 @@ def test__image__with_and_without_hyper_background_sky(masked_imaging_7x7_no_blu def test__noise_map__with_and_without_hyper_background(masked_imaging_7x7_no_blur): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d_value=1.0)) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d_value=1.0) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) fit = al.FitImaging( @@ -219,7 +229,7 @@ def test__fit_figure_of_merit__include_hyper_methods(masked_imaging_7x7): tracer=tracer, hyper_image_sky=hyper_image_sky, hyper_background_noise=hyper_background_noise, - use_hyper_scaling=True, + use_hyper_scalings=True, settings_inversion=al.SettingsInversion(use_w_tilde=False), ) @@ -535,14 +545,16 @@ def test___blurred_and_model_image_properties(masked_imaging_7x7): def test__subtracted_images_of_planes(masked_imaging_7x7_no_blur): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d=np.ones(1))) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d=np.ones(1)) + ) g1 = al.Galaxy( - redshift=0.75, light_profile=MockLightProfile(image_2d=2.0 * np.ones(1)) + redshift=0.75, light_profile=al.m.MockLightProfile(image_2d=2.0 * np.ones(1)) ) g2 = al.Galaxy( - redshift=1.0, light_profile=MockLightProfile(image_2d=3.0 * np.ones(1)) + redshift=1.0, light_profile=al.m.MockLightProfile(image_2d=3.0 * np.ones(1)) ) tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2]) @@ -553,14 +565,16 @@ def test__subtracted_images_of_planes(masked_imaging_7x7_no_blur): assert fit.subtracted_images_of_planes[1].slim[0] == -3.0 assert fit.subtracted_images_of_planes[2].slim[0] == -2.0 - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d=np.ones(1))) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d=np.ones(1)) + ) g1 = al.Galaxy( - redshift=1.0, light_profile=MockLightProfile(image_2d=2.0 * np.ones(1)) + redshift=1.0, light_profile=al.m.MockLightProfile(image_2d=2.0 * np.ones(1)) ) g2 = al.Galaxy( - redshift=1.0, light_profile=MockLightProfile(image_2d=3.0 * np.ones(1)) + redshift=1.0, light_profile=al.m.MockLightProfile(image_2d=3.0 * np.ones(1)) ) tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2]) @@ -640,7 +654,9 @@ def test__preloads__refit_with_new_preloads(masked_imaging_7x7): def test__preloads__blurred_image_uses_preload_when_passed(masked_imaging_7x7_no_blur): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d=np.ones(1))) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d=np.ones(1)) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) diff --git a/test_autolens/interferometer/model/test_aggregator_interferometer.py b/test_autolens/interferometer/model/test_aggregator_interferometer.py index e48aa059f..dc3d1e6c1 100644 --- a/test_autolens/interferometer/model/test_aggregator_interferometer.py +++ b/test_autolens/interferometer/model/test_aggregator_interferometer.py @@ -7,7 +7,6 @@ import autofit as af import autolens as al from autofit.non_linear.samples import Sample -from autolens.mock import mock directory = path.dirname(path.realpath(__file__)) @@ -44,7 +43,7 @@ def make_samples(model): weight_list=[0.0, 1.0], ) - return mock.MockSamples( + return al.m.MockSamples( model=model, sample_list=sample_list, max_log_likelihood_instance=tracer ) @@ -71,7 +70,7 @@ class TestFitInterferometerAgg: # # clean(database_file=database_file, result_path=result_path) # - # search = mock.MockSearch(samples=samples) + # search = al.m.MockSearch(samples=samples) # search.paths = af.DirectoryPaths(path_prefix=path_prefix) # analysis = al.AnalysisInterferometer(dataset=interferometer_7) # search.fit(model=model, analysis=analysis) @@ -101,8 +100,8 @@ def test__fit_interferometer_randomly_drawn_via_pdf_gen_from( clean(database_file=database_file, result_path=result_path) - search = mock.MockSearch( - samples=samples, result=mock.MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisInterferometer(dataset=interferometer_7) @@ -147,8 +146,8 @@ def test__fit_interferometer_all_above_weight_gen( clean(database_file=database_file, result_path=result_path) - search = mock.MockSearch( - samples=samples, result=mock.MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisInterferometer(dataset=interferometer_7) diff --git a/test_autolens/interferometer/model/test_analysis_interferometer.py b/test_autolens/interferometer/model/test_analysis_interferometer.py index 5124ca10a..6c31216e3 100644 --- a/test_autolens/interferometer/model/test_analysis_interferometer.py +++ b/test_autolens/interferometer/model/test_analysis_interferometer.py @@ -1,12 +1,12 @@ from os import path import numpy as np +import pytest import autofit as af import autolens as al from autolens import exc -import pytest + from autolens.interferometer.model.result import ResultInterferometer -from autolens.mock import mock directory = path.dirname(path.realpath(__file__)) @@ -18,9 +18,9 @@ def test__make_result__result_interferometer_is_returned(self, interferometer_7) instance = model.instance_from_prior_medians() - samples = mock.MockSamples(max_log_likelihood_instance=instance) + samples = al.m.MockSamples(max_log_likelihood_instance=instance) - search = mock.MockSearch(name="test_search", samples=samples) + search = al.m.MockSearch(name="test_search", samples=samples) analysis = al.AnalysisInterferometer(dataset=interferometer_7) @@ -120,7 +120,7 @@ def test__sets_up_hyper_galaxy_visibiltiies__froms(self, interferometer_7): ), } - result = mock.MockResult( + result = al.m.MockResult( hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict, hyper_model_image=al.Array2D.full( fill_value=3.0, shape_native=(3, 3), pixel_scales=1.0 @@ -176,7 +176,7 @@ def test__stochastic_log_likelihoods_for_instance(self, interferometer_7): ("galaxies", "source"): source_hyper_image, } - result = mock.MockResult( + result = al.m.MockResult( hyper_galaxy_image_path_dict=hyper_galaxy_image_path_dict, hyper_model_image=hyper_model_image, ) diff --git a/test_autolens/interferometer/test_fit_interferometer.py b/test_autolens/interferometer/test_fit_interferometer.py index c3d565f96..99fe35ac7 100644 --- a/test_autolens/interferometer/test_fit_interferometer.py +++ b/test_autolens/interferometer/test_fit_interferometer.py @@ -1,13 +1,14 @@ -import autolens as al import numpy as np import pytest -from autogalaxy.mock.mock import MockLightProfile +import autolens as al def test__model_visibilities(interferometer_7): - g0 = al.Galaxy(redshift=0.5, light_profile=MockLightProfile(image_2d=np.ones(2))) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d=np.ones(2)) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer) @@ -20,7 +21,9 @@ def test__model_visibilities(interferometer_7): 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))) + g0 = al.Galaxy( + redshift=0.5, light_profile=al.m.MockLightProfile(image_2d=np.ones(2)) + ) tracer = al.Tracer.from_galaxies(galaxies=[g0]) fit = al.FitInterferometer(dataset=interferometer_7, tracer=tracer) @@ -116,7 +119,7 @@ def test__fit_figure_of_merit__include_hyper_methods(interferometer_7): dataset=interferometer_7, tracer=tracer, hyper_background_noise=hyper_background_noise, - use_hyper_scaling=False, + use_hyper_scalings=False, ) assert fit.noise_map == pytest.approx(interferometer_7.noise_map, 1.0e-4) diff --git a/test_autolens/lens/model/test_aggregator.py b/test_autolens/lens/model/test_aggregator.py index 85e21d3a3..e10b2ae60 100644 --- a/test_autolens/lens/model/test_aggregator.py +++ b/test_autolens/lens/model/test_aggregator.py @@ -7,7 +7,6 @@ import autofit as af import autolens as al from autofit.non_linear.samples import Sample -from autolens.mock import mock directory = path.dirname(path.realpath(__file__)) @@ -44,7 +43,7 @@ def make_samples(model): weight_list=[0.0, 1.0], ) - return mock.MockSamples( + return al.m.MockSamples( model=model, sample_list=sample_list, max_log_likelihood_instance=tracer ) @@ -69,7 +68,7 @@ class TestTracerAgg: # # clean(database_file=database_file, result_path=result_path) # - # search = mock.MockSearch(samples=samples) + # search = al.m.MockSearch(samples=samples) # search.paths = af.DirectoryPaths(path_prefix=path_prefix) # analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) # search.fit(model=model, analysis=analysis) @@ -98,8 +97,8 @@ def test__tracer_randomly_drawn_via_pdf_gen_from( clean(database_file=database_file, result_path=result_path) - search = mock.MockSearch( - samples=samples, result=mock.MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) @@ -136,8 +135,8 @@ def test__tracer_all_above_weight_gen(self, masked_imaging_7x7, samples, model): clean(database_file=database_file, result_path=result_path) - search = mock.MockSearch( - samples=samples, result=mock.MockResult(model=model, samples=samples) + search = al.m.MockSearch( + samples=samples, result=al.m.MockResult(model=model, samples=samples) ) search.paths = af.DirectoryPaths(path_prefix=path_prefix) analysis = al.AnalysisImaging(dataset=masked_imaging_7x7) diff --git a/test_autolens/lens/model/test_preloads.py b/test_autolens/lens/model/test_preloads.py index b4b7bb749..e219c7ef0 100644 --- a/test_autolens/lens/model/test_preloads.py +++ b/test_autolens/lens/model/test_preloads.py @@ -3,39 +3,37 @@ import autofit as af -from autolens.mock.mock import MockTracer -from autolens.mock.mock import MockFit -from autolens.lens.model.preloads import Preloads +import autolens as al def test__set_blurred_image(): # Blurred image is all zeros so preloads as zeros - fit_0 = MockFit(blurred_image=np.zeros(2)) - fit_1 = MockFit(blurred_image=np.zeros(2)) + fit_0 = al.m.MockFitImaging(blurred_image=np.zeros(2)) + fit_1 = al.m.MockFitImaging(blurred_image=np.zeros(2)) - preloads = Preloads(blurred_image=1) + preloads = al.Preloads(blurred_image=1) preloads.set_blurred_image(fit_0=fit_0, fit_1=fit_1) assert (preloads.blurred_image == np.zeros(2)).all() # Blurred image are different, indicating the model parameters change the grid, so no preloading. - fit_0 = MockFit(blurred_image=np.array([1.0])) - fit_1 = MockFit(blurred_image=np.array([2.0])) + fit_0 = al.m.MockFitImaging(blurred_image=np.array([1.0])) + fit_1 = al.m.MockFitImaging(blurred_image=np.array([2.0])) - preloads = Preloads(blurred_image=1) + preloads = al.Preloads(blurred_image=1) preloads.set_blurred_image(fit_0=fit_0, fit_1=fit_1) assert preloads.blurred_image is None # Blurred images are the same meaning they are fixed in the model, so do preload. - fit_0 = MockFit(blurred_image=np.array([1.0])) - fit_1 = MockFit(blurred_image=np.array([1.0])) + fit_0 = al.m.MockFitImaging(blurred_image=np.array([1.0])) + fit_1 = al.m.MockFitImaging(blurred_image=np.array([1.0])) - preloads = Preloads(blurred_image=1) + preloads = al.Preloads(blurred_image=1) preloads.set_blurred_image(fit_0=fit_0, fit_1=fit_1) assert (preloads.blurred_image == np.array([1.0])).all() @@ -45,39 +43,39 @@ def test__set_traced_grids_of_planes(): # traced grids is None so no Preloading. - tracer_0 = MockTracer(traced_grids_of_planes=[None, None]) - tracer_1 = MockTracer(traced_grids_of_planes=[None, None]) + tracer_0 = al.m.MockTracer(traced_grids_of_planes=[None, None]) + tracer_1 = al.m.MockTracer(traced_grids_of_planes=[None, None]) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(traced_grids_of_planes_for_inversion=1) + preloads = al.Preloads(traced_grids_of_planes_for_inversion=1) preloads.set_traced_grids_of_planes_for_inversion(fit_0=fit_0, fit_1=fit_1) assert preloads.traced_grids_of_planes_for_inversion is None # traced grids are different, indiciating the model parameters change the grid, so no preloading. - tracer_0 = MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) - tracer_1 = MockTracer(traced_grids_of_planes=[None, np.array([[2.0]])]) + tracer_0 = al.m.MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) + tracer_1 = al.m.MockTracer(traced_grids_of_planes=[None, np.array([[2.0]])]) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(traced_grids_of_planes_for_inversion=1) + preloads = al.Preloads(traced_grids_of_planes_for_inversion=1) preloads.set_traced_grids_of_planes_for_inversion(fit_0=fit_0, fit_1=fit_1) assert preloads.traced_grids_of_planes_for_inversion is None # traced grids are the same meaning they are fixed in the model, so do preload. - tracer_0 = MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) - tracer_1 = MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) + tracer_0 = al.m.MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) + tracer_1 = al.m.MockTracer(traced_grids_of_planes=[None, np.array([[1.0]])]) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(traced_grids_of_planes_for_inversion=1) + preloads = al.Preloads(traced_grids_of_planes_for_inversion=1) preloads.set_traced_grids_of_planes_for_inversion(fit_0=fit_0, fit_1=fit_1) assert preloads.traced_grids_of_planes_for_inversion[0] is None @@ -88,39 +86,47 @@ def test__set_sparse_grid_of_planes(): # sparse image plane of grids is None so no Preloading. - tracer_0 = MockTracer(sparse_image_plane_grid_pg_list=[None, None]) - tracer_1 = MockTracer(sparse_image_plane_grid_pg_list=[None, None]) + tracer_0 = al.m.MockTracer(sparse_image_plane_grid_pg_list=[None, None]) + tracer_1 = al.m.MockTracer(sparse_image_plane_grid_pg_list=[None, None]) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(sparse_image_plane_grid_pg_list=1) + preloads = al.Preloads(sparse_image_plane_grid_pg_list=1) preloads.set_sparse_image_plane_grid_pg_list(fit_0=fit_0, fit_1=fit_1) assert preloads.sparse_image_plane_grid_pg_list is None # sparse image plane of grids are different, indiciating the model parameters change the grid, so no preloading. - tracer_0 = MockTracer(sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])]) - tracer_1 = MockTracer(sparse_image_plane_grid_pg_list=[None, np.array([[2.0]])]) + tracer_0 = al.m.MockTracer( + sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])] + ) + tracer_1 = al.m.MockTracer( + sparse_image_plane_grid_pg_list=[None, np.array([[2.0]])] + ) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(sparse_image_plane_grid_pg_list=1) + preloads = al.Preloads(sparse_image_plane_grid_pg_list=1) preloads.set_sparse_image_plane_grid_pg_list(fit_0=fit_0, fit_1=fit_1) assert preloads.sparse_image_plane_grid_pg_list is None # sparse image plane of grids are the same meaning they are fixed in the model, so do preload. - tracer_0 = MockTracer(sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])]) - tracer_1 = MockTracer(sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])]) + tracer_0 = al.m.MockTracer( + sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])] + ) + tracer_1 = al.m.MockTracer( + sparse_image_plane_grid_pg_list=[None, np.array([[1.0]])] + ) - fit_0 = MockFit(tracer=tracer_0) - fit_1 = MockFit(tracer=tracer_1) + fit_0 = al.m.MockFitImaging(tracer=tracer_0) + fit_1 = al.m.MockFitImaging(tracer=tracer_1) - preloads = Preloads(sparse_image_plane_grid_pg_list=1) + preloads = al.Preloads(sparse_image_plane_grid_pg_list=1) preloads.set_sparse_image_plane_grid_pg_list(fit_0=fit_0, fit_1=fit_1) assert preloads.sparse_image_plane_grid_pg_list[0] is None @@ -133,7 +139,7 @@ def test__info(): file_preloads = path.join(file_path, "preloads.summary") - preloads = Preloads( + preloads = al.Preloads( blurred_image=np.zeros(3), w_tilde=None, use_w_tilde=False, @@ -179,7 +185,7 @@ def test__info(): assert lines[i] == f"Log Det Regularization Matrix Term = False\n" i += 1 - preloads = Preloads( + preloads = al.Preloads( blurred_image=1, w_tilde=1, use_w_tilde=True, diff --git a/test_autolens/lens/model/test_result.py b/test_autolens/lens/model/test_result.py index cb1bad80a..76aa5b84a 100644 --- a/test_autolens/lens/model/test_result.py +++ b/test_autolens/lens/model/test_result.py @@ -8,8 +8,6 @@ from autolens.lens.model import result as res from autolens.imaging.model.result import ResultImaging -from autolens.mock import mock - directory = os.path.dirname(os.path.realpath(__file__)) @@ -24,7 +22,7 @@ def test__max_log_likelihood_tracer_available_as_result( ) ) - search = mock.MockSearch(name="test_search_2", samples=samples_with_result) + search = al.m.MockSearch(name="test_search_2", samples=samples_with_result) result = search.fit(model=model, analysis=analysis_imaging_7x7) @@ -44,7 +42,7 @@ def test__max_log_likelihood_tracer_source_light_profile_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = res.Result( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -64,7 +62,7 @@ def test__max_log_likelihood_tracer_source_light_profile_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source_0, source_1]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = res.Result( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -82,7 +80,7 @@ def test__max_log_likelihood_tracer_source_light_profile_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source_0, source_1]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = res.Result( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -92,7 +90,7 @@ def test__max_log_likelihood_tracer_source_light_profile_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5)]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = res.Result( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -114,7 +112,7 @@ def test__max_log_likelihood_tracer_source_inversion_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = ResultImaging( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -134,7 +132,7 @@ def test__max_log_likelihood_tracer_source_inversion_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = ResultImaging( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -156,7 +154,7 @@ def test__max_log_likelihood_tracer_source_centres_correct( tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = ResultImaging( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -189,7 +187,7 @@ def test__max_log_likelihood_tracer__multiple_image_positions_of_source_plane_ce tracer = al.Tracer.from_galaxies(galaxies=[lens, source]) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = ResultImaging( samples=samples, analysis=analysis_imaging_7x7, model=None, search=None @@ -220,7 +218,7 @@ def test__image_plane_multiple_image_positions_and_threshold( ] ) - samples = mock.MockSamples(max_log_likelihood_instance=tracer) + samples = al.m.MockSamples(max_log_likelihood_instance=tracer) result = res.Result( samples=samples, model=None, analysis=analysis_imaging_7x7, search=None @@ -291,7 +289,7 @@ def test___image_dict(self, analysis_imaging_7x7): instance.galaxies = galaxies result = ResultImaging( - samples=mock.MockSamples(max_log_likelihood_instance=instance), + samples=al.m.MockSamples(max_log_likelihood_instance=instance), model=af.ModelMapper(), analysis=analysis_imaging_7x7, search=None, diff --git a/test_autolens/lens/test_operate.py b/test_autolens/lens/test_operate.py index 372916b31..bf49ce055 100644 --- a/test_autolens/lens/test_operate.py +++ b/test_autolens/lens/test_operate.py @@ -8,9 +8,6 @@ import autolens as al -from autoarray.mock.mock import MockPixelization, MockRegularization -from autolens.mock.mock import MockMassProfile - test_path = path.join("{}".format(path.dirname(path.realpath(__file__))), "files") diff --git a/test_autolens/lens/test_ray_tracing.py b/test_autolens/lens/test_ray_tracing.py index d1b0b8c56..a25ffeb6c 100644 --- a/test_autolens/lens/test_ray_tracing.py +++ b/test_autolens/lens/test_ray_tracing.py @@ -8,9 +8,6 @@ import autolens as al -from autoarray.mock.mock import MockPixelization, MockRegularization -from autolens.mock.mock import MockMassProfile - test_path = path.join("{}".format(path.dirname(path.realpath(__file__))), "files") @@ -160,8 +157,8 @@ def test__planes_indexes_with_inversion(self): gal = al.Galaxy(redshift=0.5) gal_pix = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies(galaxies=[gal, gal]) @@ -174,8 +171,8 @@ def test__planes_indexes_with_inversion(self): gal_pix = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies(galaxies=[gal_pix, gal]) @@ -184,14 +181,14 @@ def test__planes_indexes_with_inversion(self): gal_pix_0 = al.Galaxy( redshift=0.6, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) gal_pix_1 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) gal0 = al.Galaxy(redshift=0.25) @@ -209,8 +206,8 @@ def test__has_galaxy_with_pixelization(self, sub_grid_2d_7x7): gal_lp = al.Galaxy(redshift=0.5, light_profile=al.lp.LightProfile()) gal_pix = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies(galaxies=[gal, gal]) @@ -238,8 +235,8 @@ def test__has_galaxy_with_regularization(self, sub_grid_2d_7x7): gal_lp = al.Galaxy(redshift=0.5, light_profile=al.lp.LightProfile()) gal_reg = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies(galaxies=[gal, gal]) @@ -337,8 +334,8 @@ def test__hyper_galaxy_image_list_of_planes(self, sub_grid_2d_7x7): gal = al.Galaxy(redshift=0.5) gal_pix = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies(galaxies=[gal, gal]) @@ -351,8 +348,8 @@ def test__hyper_galaxy_image_list_of_planes(self, sub_grid_2d_7x7): gal_pix = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), hyper_galaxy_image=1, ) @@ -366,22 +363,22 @@ def test__hyper_galaxy_image_list_of_planes(self, sub_grid_2d_7x7): gal_pix0 = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), hyper_galaxy_image=1, ) gal_pix1 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), hyper_galaxy_image=2, ) gal_pix2 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization(), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(), hyper_galaxy_image=3, ) @@ -395,8 +392,8 @@ class TestPixelizations: def test__pixelization_list_of_lists(self, sub_grid_2d_7x7): galaxy_pix = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(mapper=1), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(mapper=1), + regularization=al.m.MockRegularization(), ) galaxy_no_pix = al.Galaxy(redshift=0.5) @@ -407,20 +404,20 @@ def test__pixelization_list_of_lists(self, sub_grid_2d_7x7): galaxy_pix_0 = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(mapper=1), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(mapper=1), + regularization=al.m.MockRegularization(), ) galaxy_pix_1 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(mapper=2), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(mapper=2), + regularization=al.m.MockRegularization(), ) galaxy_pix_2 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(mapper=3), - regularization=MockRegularization(), + pixelization=al.m.MockPixelization(mapper=3), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies( @@ -441,8 +438,8 @@ def test__regularization_list_of_lists(self, sub_grid_2d_7x7): galaxy_reg = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(), - regularization=MockRegularization(regularization_matrix=1), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(regularization_matrix=1), ) galaxy_no_reg = al.Galaxy(redshift=0.5) @@ -454,20 +451,20 @@ def test__regularization_list_of_lists(self, sub_grid_2d_7x7): galaxy_reg_0 = al.Galaxy( redshift=0.5, - pixelization=MockPixelization(), - regularization=MockRegularization(regularization_matrix=1), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(regularization_matrix=1), ) galaxy_reg_1 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(), - regularization=MockRegularization(regularization_matrix=2), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(regularization_matrix=2), ) galaxy_reg_2 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization(), - regularization=MockRegularization(regularization_matrix=3), + pixelization=al.m.MockPixelization(), + regularization=al.m.MockRegularization(regularization_matrix=3), ) tracer = al.Tracer.from_galaxies( @@ -1386,10 +1383,10 @@ class TestAbstractTracerData: def test__sparse_image_plane_grid_list_from__x2_planes(self, sub_grid_2d_7x7): galaxy_pix = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=np.array([[1.0, 1.0]]) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_no_pix = al.Galaxy(redshift=0.5) @@ -1406,18 +1403,18 @@ def test__sparse_image_plane_grid_list_from__multi_plane(self, sub_grid_2d_7x7): galaxy_pix0 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=np.array([[1.0, 1.0]]) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix1 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=np.array([[2.0, 2.0]]) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_no_pix_0 = al.Galaxy(redshift=0.25) @@ -1448,12 +1445,12 @@ def test__traced_sparse_grids_list_from__x2_planes(self, sub_grid_2d_7x7): galaxy_pix = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=al.Grid2D.manual_native( grid=[[[1.0, 0.0]]], pixel_scales=(1.0, 1.0) ) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_no_pix = al.Galaxy(redshift=0.5) @@ -1473,22 +1470,22 @@ def test__traced_sparse_grids_list_from__x2_planes(self, sub_grid_2d_7x7): galaxy_pix_0 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=al.Grid2D.manual_native( grid=[[[1.0, 0.0]]], pixel_scales=(1.0, 1.0) ) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix_1 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=al.Grid2D.manual_native( grid=[[[2.0, 0.0]]], pixel_scales=(1.0, 1.0) ) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies( @@ -1511,22 +1508,22 @@ def test__traced_sparse_grids_list_from__multi_plane(self, sub_grid_2d_7x7): galaxy_pix_0 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=al.Grid2D.manual_native( grid=[[[1.0, 1.0]]], pixel_scales=(1.0, 1.0) ) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix_1 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( data_pixelization_grid=al.Grid2D.manual_native( grid=[[[2.0, 2.0]]], pixel_scales=(1.0, 1.0) ) ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_no_pix_0 = al.Galaxy( @@ -1571,25 +1568,25 @@ def test__linear_obj_list_from__x2_planes(self, sub_grid_2d_7x7): galaxy_no_pix = al.Galaxy(redshift=0.5) galaxy_pix_0 = al.Galaxy( redshift=0.5, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( mapper=1, data_pixelization_grid=sub_grid_2d_7x7 ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix_1 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( mapper=2, data_pixelization_grid=sub_grid_2d_7x7 ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix_2 = al.Galaxy( redshift=1.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( mapper=3, data_pixelization_grid=sub_grid_2d_7x7 ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies( @@ -1611,17 +1608,17 @@ def test__linear_obj_list_from__multi_plane(self, sub_grid_2d_7x7): galaxy_pix_0 = al.Galaxy( redshift=0.75, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( mapper=1, data_pixelization_grid=sub_grid_2d_7x7 ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) galaxy_pix_1 = al.Galaxy( redshift=2.0, - pixelization=MockPixelization( + pixelization=al.m.MockPixelization( mapper=2, data_pixelization_grid=sub_grid_2d_7x7 ), - regularization=MockRegularization(), + regularization=al.m.MockRegularization(), ) tracer = al.Tracer.from_galaxies( @@ -1945,12 +1942,16 @@ def test__4_planes__data_grid_and_deflections_stacks_are_correct__sis_mass_profi class TestExtractAttribute: def test__extract_attribute(self): - g0 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.9, value1=(1.0, 1.0))) - g1 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.8, value1=(2.0, 2.0))) + g0 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.9, value1=(1.0, 1.0)) + ) + g1 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.8, value1=(2.0, 2.0)) + ) g2 = al.Galaxy( redshift=0.5, - mp_0=MockMassProfile(value=0.7), - mp_1=MockMassProfile(value=0.6), + mp_0=al.m.MockMassProfile(value=0.7), + mp_1=al.m.MockMassProfile(value=0.6), ) plane_0 = al.Plane(galaxies=[al.Galaxy(redshift=0.5)], redshift=None) @@ -1988,12 +1989,16 @@ def test__extract_attribute(self): def test__extract_attributes_of_planes(self): - g0 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.9, value1=(1.0, 1.0))) - g1 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.8, value1=(2.0, 2.0))) + g0 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.9, value1=(1.0, 1.0)) + ) + g1 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.8, value1=(2.0, 2.0)) + ) g2 = al.Galaxy( redshift=0.5, - mp_0=MockMassProfile(value=0.7), - mp_1=MockMassProfile(value=0.6), + mp_0=al.m.MockMassProfile(value=0.7), + mp_1=al.m.MockMassProfile(value=0.6), ) plane_0 = al.Plane(galaxies=[al.Galaxy(redshift=0.5)], redshift=None) @@ -2051,12 +2056,16 @@ def test__extract_attributes_of_planes(self): def test__extract_attributes_of_galaxies(self): - g0 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.9, value1=(1.0, 1.0))) - g1 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.8, value1=(2.0, 2.0))) + g0 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.9, value1=(1.0, 1.0)) + ) + g1 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.8, value1=(2.0, 2.0)) + ) g2 = al.Galaxy( redshift=0.5, - mp_0=MockMassProfile(value=0.7), - mp_1=MockMassProfile(value=0.6), + mp_0=al.m.MockMassProfile(value=0.7), + mp_1=al.m.MockMassProfile(value=0.6), ) plane_0 = al.Plane(galaxies=[al.Galaxy(redshift=0.5)], redshift=None) @@ -2116,12 +2125,16 @@ def test__extract_attributes_of_galaxies(self): def test__extract_profile(self): - g0 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.9, value1=(1.0, 1.0))) - g1 = al.Galaxy(redshift=0.5, mp_1=MockMassProfile(value=0.8, value1=(2.0, 2.0))) + g0 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.9, value1=(1.0, 1.0)) + ) + g1 = al.Galaxy( + redshift=0.5, mp_1=al.m.MockMassProfile(value=0.8, value1=(2.0, 2.0)) + ) g2 = al.Galaxy( redshift=1.0, - mp_2=MockMassProfile(value=0.7), - mp_3=MockMassProfile(value=0.6), + mp_2=al.m.MockMassProfile(value=0.7), + mp_3=al.m.MockMassProfile(value=0.6), ) tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2], cosmology=None) @@ -2136,14 +2149,16 @@ def test__extract_profile(self): def test__extract_plane_index_of_profile(self): - g0 = al.Galaxy(redshift=0.5, mp_0=MockMassProfile(value=0.9, value1=(1.0, 1.0))) + g0 = al.Galaxy( + redshift=0.5, mp_0=al.m.MockMassProfile(value=0.9, value1=(1.0, 1.0)) + ) g1 = al.Galaxy( - redshift=0.75, mp_1=MockMassProfile(value=0.8, value1=(2.0, 2.0)) + redshift=0.75, mp_1=al.m.MockMassProfile(value=0.8, value1=(2.0, 2.0)) ) g2 = al.Galaxy( redshift=1.0, - mp_2=MockMassProfile(value=0.7), - mp_3=MockMassProfile(value=0.6), + mp_2=al.m.MockMassProfile(value=0.7), + mp_3=al.m.MockMassProfile(value=0.6), ) tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2], cosmology=None) diff --git a/test_autolens/point/model/test_analysis_point.py b/test_autolens/point/model/test_analysis_point.py index f66e3535f..6ea27af25 100644 --- a/test_autolens/point/model/test_analysis_point.py +++ b/test_autolens/point/model/test_analysis_point.py @@ -4,7 +4,6 @@ import autolens as al from autolens.point.model.result import ResultPoint -from autolens.mock import mock directory = path.dirname(path.realpath(__file__)) @@ -18,9 +17,9 @@ def test__make_result__result_imaging_is_returned(self, point_dict): ) ) - search = mock.MockSearch(name="test_search") + search = al.m.MockSearch(name="test_search") - solver = mock.MockPointSolver(model_positions=point_dict["point_0"].positions) + solver = al.m.MockPointSolver(model_positions=point_dict["point_0"].positions) analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) @@ -46,7 +45,7 @@ def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles( ) ) - solver = mock.MockPointSolver(model_positions=positions_x2) + solver = al.m.MockPointSolver(model_positions=positions_x2) analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) @@ -67,7 +66,7 @@ def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles( assert fit_positions.log_likelihood == analysis_log_likelihood model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)]) - solver = mock.MockPointSolver(model_positions=model_positions) + solver = al.m.MockPointSolver(model_positions=model_positions) analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) @@ -109,7 +108,7 @@ def test__figure_of_merit__includes_fit_fluxes( ) ) - solver = mock.MockPointSolver(model_positions=positions_x2) + solver = al.m.MockPointSolver(model_positions=positions_x2) analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) @@ -141,7 +140,7 @@ def test__figure_of_merit__includes_fit_fluxes( ) model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)]) - solver = mock.MockPointSolver(model_positions=model_positions) + solver = al.m.MockPointSolver(model_positions=model_positions) analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver) diff --git a/test_autolens/point/test_fit_point_source.py b/test_autolens/point/test_fit_point_source.py index d2dee4d42..db85eeb30 100644 --- a/test_autolens/point/test_fit_point_source.py +++ b/test_autolens/point/test_fit_point_source.py @@ -4,9 +4,6 @@ import autolens as al -from autolens.mock.mock import MockTracerPoint -from autolens.mock.mock import MockPointSolver - class TestAbstractFitPositionsSourcePlane: def test__furthest_separation_of_source_plane_positions(self): @@ -14,7 +11,7 @@ def test__furthest_separation_of_source_plane_positions(self): positions = al.Grid2DIrregular(grid=[(0.0, 0.0), (0.0, 1.0)]) noise_map = al.ValuesIrregular([[1.0, 1.0]]) - tracer = MockTracerPoint(traced_grid=positions) + tracer = al.m.MockTracerPoint(traced_grid=positions) fit = al.FitPositionsSourceMaxSeparation( positions=positions, noise_map=noise_map, tracer=tracer ) @@ -27,7 +24,7 @@ def test__furthest_separation_of_source_plane_positions(self): positions = al.Grid2DIrregular(grid=[(0.0, 0.0), (0.0, 1.0), (0.0, 3.0)]) noise_map = al.ValuesIrregular([1.0, 1.0, 1.0]) - tracer = MockTracerPoint(traced_grid=positions) + tracer = al.m.MockTracerPoint(traced_grid=positions) fit = al.FitPositionsSourceMaxSeparation( positions=positions, noise_map=noise_map, tracer=tracer ) @@ -80,7 +77,7 @@ def test__two_sets_of_positions__residuals_likelihood_correct(self): noise_map = al.ValuesIrregular([0.5, 1.0]) model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)]) - point_solver = MockPointSolver(model_positions=model_positions) + point_solver = al.m.MockPointSolver(model_positions=model_positions) fit = al.FitPositionsImage( name="point_0", @@ -120,7 +117,7 @@ def test__more_model_positions_than_data_positions__pairs_closest_positions(self [(3.0, 1.0), (2.0, 3.0), (1.0, 0.0), (0.0, 1.0)] ) - point_solver = MockPointSolver(model_positions=model_positions) + point_solver = al.m.MockPointSolver(model_positions=model_positions) fit = al.FitPositionsImage( name="point_0", @@ -256,7 +253,7 @@ def test__multi_plane_position_solving(self): class TestFitFluxes: def test__one_set_of_fluxes__residuals_likelihood_correct(self): - tracer = MockTracerPoint( + tracer = al.m.MockTracerPoint( profile=al.ps.PointFlux(flux=2.0), magnification=al.ValuesIrregular([2.0, 2.0]), ) @@ -369,7 +366,7 @@ def test__fits_dataset__positions_only(self): noise_map = al.ValuesIrregular([0.5, 1.0]) model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)]) - point_solver = MockPointSolver(model_positions=model_positions) + point_solver = al.m.MockPointSolver(model_positions=model_positions) point_dataset_0 = al.PointDataset( name="point_0", positions=positions, positions_noise_map=noise_map @@ -421,7 +418,7 @@ def test__fits_dataset__positions_and_flux(self): fluxes = al.ValuesIrregular([1.0, 2.0]) flux_noise_map = al.ValuesIrregular([3.0, 1.0]) - point_solver = MockPointSolver(model_positions=model_positions) + point_solver = al.m.MockPointSolver(model_positions=model_positions) point_dataset_0 = al.PointDataset( name="point_0", @@ -491,7 +488,7 @@ def test__model_has_image_and_source_chi_squared__fits_both_correctly(self): noise_map = al.ValuesIrregular([0.5, 1.0]) model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)]) - point_solver = MockPointSolver(model_positions=model_positions) + point_solver = al.m.MockPointSolver(model_positions=model_positions) point_dataset_0 = al.PointDataset( name="point_0", positions=positions, positions_noise_map=noise_map diff --git a/test_autolens/quantity/model/test_analysis_quantity.py b/test_autolens/quantity/model/test_analysis_quantity.py index 50687d651..88d0015a1 100644 --- a/test_autolens/quantity/model/test_analysis_quantity.py +++ b/test_autolens/quantity/model/test_analysis_quantity.py @@ -3,8 +3,6 @@ import autofit as af import autolens as al -from autogalaxy.mock import mock - from autolens.quantity.model.result import ResultQuantity directory = path.dirname(path.realpath(__file__)) @@ -21,7 +19,7 @@ def test__make_result__result_quantity_is_returned( dataset=dataset_quantity_7x7_array_2d, func_str="convergence_2d_from" ) - search = mock.MockSearch(name="test_search") + search = al.m.MockSearch(name="test_search") result = search.fit(model=model, analysis=analysis) diff --git a/test_autolens/quantity/test_fit_quantity.py b/test_autolens/quantity/test_fit_quantity.py index 5a99c9822..d8e49882f 100644 --- a/test_autolens/quantity/test_fit_quantity.py +++ b/test_autolens/quantity/test_fit_quantity.py @@ -3,12 +3,10 @@ import autolens as al -from autolens.mock.mock import MockMassProfile - def test__fit_via_mock_profile(dataset_quantity_7x7_array_2d): - model_object = MockMassProfile( + model_object = al.m.MockMassProfile( convergence_2d=al.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0), potential_2d=al.Array2D.full( fill_value=2.0, shape_native=(7, 7), pixel_scales=1.0