def make__gaussian__sub_gaussian(sub_size):
data_type = "gaussian__sub_gaussian"
# This lens-only system has a Dev Vaucouleurs spheroid / bulge.
gaussian_0 = toy.SphericalGaussian(centre=(0.0, 0.0),
intensity=10.0,
sigma=1.0)
gaussian_1 = toy.SphericalGaussian(centre=(0.5, 0.5),
intensity=0.2,
sigma=0.1)
simulate_imaging_from_gaussian_and_output_to_fits(
gaussians=[gaussian_0, gaussian_1],
pixel_scales=0.1,
shape_2d=(40, 40),
data_type=data_type,
sub_size=sub_size,
)
def test__customize(self, mask_7x7, results_7x7, results_collection_7x7,
imaging_7x7):
class MyPlanePhaseAnd(toy.PhaseImaging):
def customize_priors(self, results):
self.gaussians = results.last.instance.gaussians
gaussian = toy.SphericalGaussian()
gaussian_model = af.PriorModel(cls=toy.SphericalGaussian)
setattr(results_7x7.instance, "gaussians", [gaussian])
setattr(results_7x7.model, "gaussians", [gaussian_model])
phase_dataset_7x7 = MyPlanePhaseAnd(
phase_name="test_phase", optimizer_class=mock_pipeline.MockNLO)
phase_dataset_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7,
results=results_collection_7x7)
phase_dataset_7x7.customize_priors(results_collection_7x7)
assert phase_dataset_7x7.gaussians == [gaussian]
class MyPlanePhaseAnd(toy.PhaseImaging):
def customize_priors(self, results):
self.gaussians = results.last.model.gaussians
gaussian = toy.SphericalGaussian()
gaussian_model = af.PriorModel(cls=toy.SphericalGaussian)
setattr(results_7x7.instance, "gaussians", [gaussian])
setattr(results_7x7.model, "gaussians", [gaussian_model])
phase_dataset_7x7 = MyPlanePhaseAnd(
phase_name="test_phase", optimizer_class=mock_pipeline.MockNLO)
phase_dataset_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7,
results=results_collection_7x7)
phase_dataset_7x7.customize_priors(results_collection_7x7)
assert phase_dataset_7x7.gaussians == [gaussian_model]
def test__results_of_phase_are_available_as_properties(
self, imaging_7x7, mask_7x7):
clean_images()
phase_dataset_7x7 = toy.PhaseImaging(
optimizer_class=mock_pipeline.MockNLO,
gaussians=[toy.SphericalGaussian()],
phase_name="test_phase_2",
)
result = phase_dataset_7x7.run(dataset=imaging_7x7, mask=mask_7x7)
assert isinstance(result, toy.AbstractPhase.Result)
def make__gaussian_x1__input_sigma(sub_size, sigma):
data_type = "gaussian_x1__sigma_" + str(sigma)
# This lens-only system has a Dev Vaucouleurs spheroid / bulge.
gaussian = toy.SphericalGaussian(centre=(0.0, 0.0),
intensity=10.0,
sigma=sigma)
simulate_imaging_from_gaussian_and_output_to_fits(
gaussians=[gaussian],
pixel_scales=0.1,
shape_2d=(25, 25),
data_type=data_type,
sub_size=sub_size,
)
def test__fit_figure_of_merit__matches_correct_fit_given_gaussian_profiles(
self, imaging_7x7, mask_7x7):
gaussian = toy.SphericalGaussian(intensity=0.1)
phase_imaging_7x7 = toy.PhaseImaging(gaussians=[gaussian],
sub_size=2,
phase_name="test_phase")
analysis = phase_imaging_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7)
instance = phase_imaging_7x7.model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.fit(instance=instance)
mask = phase_imaging_7x7.meta_imaging_fit.mask_with_phase_sub_size_from_mask(
mask=mask_7x7)
masked_imaging = aa.masked.imaging(imaging=imaging_7x7, mask=mask)
model_image = gaussian.profile_image_from_grid(
grid=masked_imaging.grid)
fit = fit_masked_dataset(masked_dataset=masked_imaging,
model_data=model_image.in_1d_binned)
assert fit.likelihood == fit_figure_of_merit
def customize_priors(self, results):
self.gaussians[0] = toy.SphericalGaussian()
def make_gaussian_1():
# noinspection PyTypeChecker
return toy.SphericalGaussian(centre=(0.5, 0.5), intensity=2.0, sigma=1.0)
def test__set_instances(self, phase_dataset_7x7):
phase_dataset_7x7.gaussians = [toy.SphericalGaussian()]
assert phase_dataset_7x7.model.gaussians == [toy.SphericalGaussian()]
