def test__samples_in_test_model(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201)
model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301)
model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401)
initializer = af.InitializerPrior()
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_in_test_mode(
total_points=2,
model=model,
)
assert 0.0 < unit_parameter_lists[0][0] < 1.0
assert 0.0 < unit_parameter_lists[1][0] < 1.0
assert 0.0 < unit_parameter_lists[0][1] < 1.0
assert 0.0 < unit_parameter_lists[1][1] < 1.0
assert 0.0 < unit_parameter_lists[0][2] < 1.0
assert 0.0 < unit_parameter_lists[1][2] < 1.0
assert 0.0 < unit_parameter_lists[0][3] < 1.0
assert 0.0 < unit_parameter_lists[1][3] < 1.0
assert 0.099 < parameter_lists[0][0] < 0.101
assert 0.099 < parameter_lists[1][0] < 0.101
assert 0.199 < parameter_lists[0][1] < 0.201
assert 0.199 < parameter_lists[1][1] < 0.201
assert 0.299 < parameter_lists[0][2] < 0.301
assert 0.299 < parameter_lists[1][2] < 0.301
assert 0.399 < parameter_lists[0][3] < 0.401
assert 0.399 < parameter_lists[1][3] < 0.401
assert figure_of_merit_list == [-1.0e99, -1.0e99]
def test_different_prior_width(self, grid_search, mapper):
mapper.component.one_tuple.one_tuple_0 = af.UniformPrior(0.0, 2.0)
mappers = list(
grid_search.model_mappers(
grid_priors=[mapper.component.one_tuple.one_tuple_0], model=mapper
)
)
assert len(mappers) == 10
assert mappers[0].component.one_tuple.one_tuple_0.lower_limit == 0.0
assert mappers[0].component.one_tuple.one_tuple_0.upper_limit == 0.2
assert mappers[-1].component.one_tuple.one_tuple_0.lower_limit == 1.8
assert mappers[-1].component.one_tuple.one_tuple_0.upper_limit == 2.0
mapper.component.one_tuple.one_tuple_0 = af.UniformPrior(1.0, 1.5)
mappers = list(
grid_search.model_mappers(
mapper, grid_priors=[mapper.component.one_tuple.one_tuple_0]
)
)
assert len(mappers) == 10
assert mappers[0].component.one_tuple.one_tuple_0.lower_limit == 1.0
assert mappers[0].component.one_tuple.one_tuple_0.upper_limit == 1.05
assert mappers[-1].component.one_tuple.one_tuple_0.lower_limit == 1.45
assert mappers[-1].component.one_tuple.one_tuple_0.upper_limit == 1.5
def test__prior__samples_sample_priors(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.099, upper_limit=0.101)
model.two = af.UniformPrior(lower_limit=0.199, upper_limit=0.201)
model.three = af.UniformPrior(lower_limit=0.299, upper_limit=0.301)
model.four = af.UniformPrior(lower_limit=0.399, upper_limit=0.401)
initializer = af.InitializerPrior()
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.0 < unit_parameter_lists[0][0] < 1.0
assert 0.0 < unit_parameter_lists[1][0] < 1.0
assert 0.0 < unit_parameter_lists[0][1] < 1.0
assert 0.0 < unit_parameter_lists[1][1] < 1.0
assert 0.0 < unit_parameter_lists[0][2] < 1.0
assert 0.0 < unit_parameter_lists[1][2] < 1.0
assert 0.0 < unit_parameter_lists[0][3] < 1.0
assert 0.0 < unit_parameter_lists[1][3] < 1.0
assert 0.099 < parameter_lists[0][0] < 0.101
assert 0.099 < parameter_lists[1][0] < 0.101
assert 0.199 < parameter_lists[0][1] < 0.201
assert 0.199 < parameter_lists[1][1] < 0.201
assert 0.299 < parameter_lists[0][2] < 0.301
assert 0.299 < parameter_lists[1][2] < 0.301
assert 0.399 < parameter_lists[0][3] < 0.401
assert 0.399 < parameter_lists[1][3] < 0.401
assert figure_of_merit_list == [1.0, 1.0]
def test_mapper_from_prior_arguments_simple_collection():
old = af.UniformPrior()
new = af.UniformPrior()
collection = af.Collection(value=old)
collection = collection.mapper_from_prior_arguments({old: new})
assert collection.value == new
def test__result_derived_properties(self):
lower_limit_lists = [[0.0, 0.0], [0.0, 0.5], [0.5, 0.0], [0.5, 0.5]]
# noinspection PyTypeChecker
grid_search_result = af.GridSearchResult(
results=None,
grid_priors=[
af.UniformPrior(
lower_limit=-2.0,
upper_limit=2.0
),
af.UniformPrior(
lower_limit=-3.0,
upper_limit=3.0
)
],
lower_limits_lists=lower_limit_lists,
)
assert grid_search_result.shape == (2, 2)
assert grid_search_result.physical_step_sizes == (2.0, 3.0)
assert grid_search_result.physical_centres_lists == [
[-1.0, -1.5],
[-1.0, 1.5],
[1.0, -1.5],
[1.0, 1.5],
]
assert grid_search_result.physical_upper_limits_lists == [
[0.0, 0.0],
[0.0, 3.0],
[2.0, 0.0],
[2.0, 3.0],
]
def test_collection():
identifier = af.CollectionPriorModel(
gaussian=af.PriorModel(Gaussian, centre=af.UniformPrior())).identifier
assert identifier == af.CollectionPriorModel(
gaussian=af.PriorModel(Gaussian, centre=af.UniformPrior())).identifier
assert identifier != af.CollectionPriorModel(gaussian=af.PriorModel(
Gaussian, centre=af.UniformPrior(upper_limit=0.5))).identifier
def make_factor_graph_model():
model_factor_1 = g.AnalysisFactor(af.Collection(one=af.UniformPrior()),
af.m.MockAnalysis())
model_factor_2 = g.AnalysisFactor(af.Collection(one=af.UniformPrior()),
af.m.MockAnalysis())
return g.FactorGraphModel(model_factor_1, model_factor_2)
def _test_gaussian():
n_observations = 100
x = np.arange(n_observations)
y = make_data(Gaussian(centre=50.0, normalization=25.0, sigma=10.0), x)
prior_model = af.PriorModel(
Gaussian,
# centre=af.GaussianPrior(mean=50, sigma=10),
# normalization=af.GaussianPrior(mean=25, sigma=10),
sigma=af.GaussianPrior(mean=10, sigma=10),
centre=af.UniformPrior(lower_limit=30, upper_limit=70),
normalization=af.UniformPrior(lower_limit=15, upper_limit=35),
# sigma=af.UniformPrior(lower_limit=5, upper_limit=15),
)
factor_model = ep.AnalysisFactor(prior_model, analysis=Analysis(x=x, y=y))
# optimiser = ep.LaplaceOptimiser(
# transform_cls=DiagonalMatrix
# )
optimiser = af.DynestyStatic()
model = factor_model.optimise(optimiser)
assert model.centre.mean == pytest.approx(50, rel=0.1)
assert model.normalization.mean == pytest.approx(25, rel=0.1)
assert model.sigma.mean == pytest.approx(10, rel=0.1)
def test__check_order_for_different_unit_values(self):
mapper = af.ModelMapper(
mock_cls_0=af.m.MockChildTuplex2,
mock_cls_1=af.m.MockChildTuple,
mock_cls_2=af.m.MockChildTuplex2,
)
mapper.mock_cls_0.tup.tup_0 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_0.tup.tup_1 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_0.one = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_0.two = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_1.tup.tup_0 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_1.tup.tup_1 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_2.tup.tup_0 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_2.tup.tup_1 = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_2.one = af.UniformPrior(0.0, 1.0)
mapper.mock_cls_2.two = af.UniformPrior(0.0, 1.0)
model_map = mapper.instance_from_unit_vector(
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
assert model_map.mock_cls_0.tup == (0.1, 0.2)
assert model_map.mock_cls_0.one == 0.3
assert model_map.mock_cls_0.two == 0.4
assert model_map.mock_cls_1.tup == (0.5, 0.6)
assert model_map.mock_cls_2.tup == (0.7, 0.8)
assert model_map.mock_cls_2.one == 0.9
assert model_map.mock_cls_2.two == 1.0
def test__check_order_for_different_unit_values(self):
mapper = af.ModelMapper(
profile_1=mock_real.EllProfile,
profile_2=mock_real.SphProfile,
profile_3=mock_real.EllProfile,
)
mapper.profile_1.centre.centre_0 = af.UniformPrior(0.0, 1.0)
mapper.profile_1.centre.centre_1 = af.UniformPrior(0.0, 1.0)
mapper.profile_1.axis_ratio = af.UniformPrior(0.0, 1.0)
mapper.profile_1.phi = af.UniformPrior(0.0, 1.0)
mapper.profile_2.centre.centre_0 = af.UniformPrior(0.0, 1.0)
mapper.profile_2.centre.centre_1 = af.UniformPrior(0.0, 1.0)
mapper.profile_3.centre.centre_0 = af.UniformPrior(0.0, 1.0)
mapper.profile_3.centre.centre_1 = af.UniformPrior(0.0, 1.0)
mapper.profile_3.axis_ratio = af.UniformPrior(0.0, 1.0)
mapper.profile_3.phi = af.UniformPrior(0.0, 1.0)
model_map = mapper.instance_from_unit_vector(
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
assert model_map.profile_1.centre == (0.1, 0.2)
assert model_map.profile_1.axis_ratio == 0.3
assert model_map.profile_1.phi == 0.4
assert model_map.profile_2.centre == (0.5, 0.6)
assert model_map.profile_3.centre == (0.7, 0.8)
assert model_map.profile_3.axis_ratio == 0.9
assert model_map.profile_3.phi == 1.0
def test_model():
identifier = af.PriorModel(af.Gaussian,
centre=af.UniformPrior()).identifier
assert identifier == af.PriorModel(af.Gaussian,
centre=af.UniformPrior()).identifier
assert identifier != af.PriorModel(
af.Gaussian, centre=af.UniformPrior(upper_limit=0.5)).identifier
def test__update_stellar_mass_priors_using_einstein_radius(self):
grid = al.Grid2D.uniform(shape_native=(200, 200), pixel_scales=0.05)
tracer = mock.MockTracer(einstein_radius=1.0, einstein_mass=4.0)
fit = mock.MockFit(grid=grid)
result = mock.MockResult(max_log_likelihood_tracer=tracer,
max_log_likelihood_fit=fit)
bulge_prior_model = af.PriorModel(al.lmp.SphericalSersic)
bulge_prior_model.intensity = af.UniformPrior(lower_limit=0.99,
upper_limit=1.01)
bulge_prior_model.effective_radius = af.UniformPrior(lower_limit=0.99,
upper_limit=1.01)
bulge_prior_model.sersic_index = af.UniformPrior(lower_limit=2.99,
upper_limit=3.01)
setup = al.SetupMassLightDark(bulge_prior_model=bulge_prior_model)
setup.update_stellar_mass_priors_from_result(
prior_model=bulge_prior_model,
result=result,
einstein_mass_range=[0.001, 10.0],
bins=10,
)
assert setup.bulge_prior_model.mass_to_light_ratio.lower_limit == pytest.approx(
0.00040519, 1.0e-1)
assert setup.bulge_prior_model.mass_to_light_ratio.upper_limit == pytest.approx(
4.051935, 1.0e-1)
def test_prior():
identifier = af.UniformPrior().identifier
assert identifier == af.UniformPrior().identifier
assert identifier != af.UniformPrior(
lower_limit=0.5
).identifier
assert identifier != af.UniformPrior(
upper_limit=0.5
).identifier
def customize_priors(self, results):
### Lens Subhalo, Adjust priors to physical masses (10^6 - 10^10) and concentrations (6-24)
self.galaxies.subhalo.mass.kappa_s = af.UniformPrior(
lower_limit=0.0005, upper_limit=0.2)
self.galaxies.subhalo.mass.centre_0 = af.UniformPrior(
lower_limit=-2.0, upper_limit=2.0)
self.galaxies.subhalo.mass.centre_1 = af.UniformPrior(
lower_limit=-2.0, upper_limit=2.0)
def test_tuple_prior():
centre = af.TuplePrior()
centre.centre_0 = af.UniformPrior()
centre.centre_1 = af.UniformPrior()
model = af.Model(af.Gaussian, centre=centre)
parameterization = model.parameterization
assert parameterization == (
'model Gaussian (N=4)'
)
def customize_priors(self, results):
self.galaxies.lens.sersic.centre_0 = 0.0
self.galaxies.lens.sersic.centre_1 = 0.0
self.galaxies.lens.sersic.axis_ratio = af.UniformPrior(
lower_limit=-0.5, upper_limit=0.1)
self.galaxies.lens.sersic.phi = 90.0
self.galaxies.lens.sersic.intensity = af.UniformPrior(
lower_limit=-0.5, upper_limit=0.1)
self.galaxies.lens.sersic.effective_radius = 1.3
self.galaxies.lens.sersic.sersic_index = 3.0
def test_model_order_no_complicated():
model_1 = af.Model(af.Gaussian,
centre=1.0,
normalization=af.UniformPrior(0.0, 1.0))
model_2 = af.Model(af.Gaussian,
centre=2.0,
normalization=af.UniformPrior(0.0, 0.5))
model_3 = af.Model(af.Gaussian,
centre=2.0,
normalization=af.UniformPrior(0.0, 1.0))
assert model_1.order_no < model_2.order_no < model_3.order_no
def make_pipeline(name, folders, search=af.DynestyStatic()):
lens = al.GalaxyModel(redshift=0.5, mass=al.mp.EllipticalIsothermal)
lens.mass.centre_0 = af.UniformPrior(lower_limit=-0.01, upper_limit=0.01)
lens.mass.centre_1 = af.UniformPrior(lower_limit=-0.01, upper_limit=0.01)
lens.mass.einstein_radius = af.UniformPrior(lower_limit=1.55,
upper_limit=1.65)
source = al.GalaxyModel(redshift=1.0, light=al.lp.EllipticalSersic)
source.light.centre_0 = af.UniformPrior(lower_limit=-0.01,
upper_limit=0.01)
source.light.centre_1 = af.UniformPrior(lower_limit=-0.01,
upper_limit=0.01)
source.light.intensity = af.UniformPrior(lower_limit=0.35,
upper_limit=0.45)
source.light.effective_radius = af.UniformPrior(lower_limit=0.45,
upper_limit=0.55)
source.light.sersic_index = af.UniformPrior(lower_limit=0.9,
upper_limit=1.1)
class GridPhase(
af.as_grid_search(phase_class=al.PhaseImaging, parallel=True)):
@property
def grid_priors(self):
return [
self.model.galaxies.subhalo.mass.centre_0,
self.model.galaxies.subhalo.mass.centre_1,
]
subhalo = al.GalaxyModel(redshift=0.5,
mass=al.mp.SphericalTruncatedNFWMCRLudlow)
subhalo.mass.mass_at_200 = af.LogUniformPrior(lower_limit=1.0e6,
upper_limit=1.0e11)
subhalo.mass.centre_0 = af.UniformPrior(lower_limit=-2.5, upper_limit=2.5)
subhalo.mass.centre_1 = af.UniformPrior(lower_limit=-2.5, upper_limit=2.5)
phase1 = GridPhase(
phase_name="phase_1",
folders=folders,
galaxies=dict(lens=lens, subhalo=subhalo, source=source),
search=search,
settings=al.SettingsPhaseImaging(),
number_of_steps=2,
)
return al.PipelineDataset(name, phase1)
def customize_priors(self, results):
self.galaxies.lens.light.centre_0 = 0.0
self.galaxies.lens.light.centre_1 = 0.0
self.galaxies.lens.light.axis_ratio = results.from_phase(
"phase_1").instance.lens.light.axis_ratio
self.galaxies.lens.light.phi = results.from_phase(
"phase_1").instance.lens.light.phi
self.galaxies.lens.light.intensity = results.from_phase(
"phase_1").instance.lens.light.intensity
self.galaxies.lens.light.effective_radius = af.UniformPrior(
lower_limit=0.0, upper_limit=4.0)
self.galaxies.lens.light.sersic_index = af.UniformPrior(
lower_limit=1.0, upper_limit=8.0)
def test__ball__samples_sample_centre_of_priors(self):
model = af.PriorModel(af.m.MockClassx4)
model.one = af.UniformPrior(lower_limit=0.0, upper_limit=1.0)
model.two = af.UniformPrior(lower_limit=0.0, upper_limit=2.0)
model.three = af.UniformPrior(lower_limit=0.0, upper_limit=3.0)
model.four = af.UniformPrior(lower_limit=0.0, upper_limit=4.0)
initializer = af.InitializerBall(lower_limit=0.4999,
upper_limit=0.5001)
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.4999 < unit_parameter_lists[0][0] < 0.5001
assert 0.4999 < unit_parameter_lists[1][0] < 0.5001
assert 0.4999 < unit_parameter_lists[0][1] < 0.5001
assert 0.4999 < unit_parameter_lists[1][1] < 0.5001
assert 0.4999 < unit_parameter_lists[0][2] < 0.5001
assert 0.4999 < unit_parameter_lists[1][2] < 0.5001
assert 0.4999 < unit_parameter_lists[0][3] < 0.5001
assert 0.4999 < unit_parameter_lists[1][3] < 0.5001
assert 0.499 < parameter_lists[0][0] < 0.501
assert 0.499 < parameter_lists[1][0] < 0.501
assert 0.999 < parameter_lists[0][1] < 1.001
assert 0.999 < parameter_lists[1][1] < 1.001
assert 1.499 < parameter_lists[0][2] < 1.501
assert 1.499 < parameter_lists[1][2] < 1.501
assert 1.999 < parameter_lists[0][3] < 2.001
assert 1.999 < parameter_lists[1][3] < 2.001
initializer = af.InitializerBall(lower_limit=0.7999,
upper_limit=0.8001)
unit_parameter_lists, parameter_lists, figure_of_merit_list = initializer.samples_from_model(
total_points=2, model=model, fitness_function=MockFitness())
assert 0.799 < parameter_lists[0][0] < 0.801
assert 0.799 < parameter_lists[1][0] < 0.801
assert 1.599 < parameter_lists[0][1] < 1.601
assert 1.599 < parameter_lists[1][1] < 1.601
assert 2.399 < parameter_lists[0][2] < 2.401
assert 2.399 < parameter_lists[1][2] < 2.401
assert 3.199 < parameter_lists[0][3] < 3.201
assert 3.199 < parameter_lists[1][3] < 3.201
assert figure_of_merit_list == 2 * [1.0]
def make_model():
return af.Model(
Gaussian,
centre=af.UniformPrior(
upper_limit=2.0
)
)
def test_graph(hierarchical_factor):
graph = g.FactorGraphModel(hierarchical_factor)
assert len(graph.model_factors) == 1
hierarchical_factor.add_drawn_variable(
af.UniformPrior(lower_limit=0.0, upper_limit=1.0))
assert len(graph.model_factors) == 2
def test_simple(self):
uniform_prior = af.UniformPrior(0.0, 1.0)
assert uniform_prior.mean == 0.5
uniform_prior.mean = 1.0
assert uniform_prior.lower_limit == 0.5
assert uniform_prior.upper_limit == 1.5
def test_higher(self):
uniform_prior = af.UniformPrior(1.0, 2.0)
assert uniform_prior.mean == 1.5
uniform_prior.mean = 2.0
assert uniform_prior.lower_limit == 1.5
assert uniform_prior.upper_limit == 2.5
def set_upper_limit_of_pixelization_pixels_prior(hyper_model: af.Collection,
result: af.Result):
"""
If the pixelization being fitted in the hyper-model fit is a `VoronoiBrightnessImage` pixelization, this function
sets the upper limit of its `pixels` prior to the number of data points in the mask.
This ensures the KMeans algorithm does not raise an exception due to having fewer data points than source pixels.
Parameters
----------
hyper_model : Collection
The hyper model used by the hyper-fit, which models hyper-components like a `Pixelization` or `HyperGalaxy`'s.
result
The result of a previous `Analysis` search whose maximum log likelihood model forms the basis of the hyper model.
"""
if hasattr(hyper_model, "galaxies"):
pixels_in_mask = result.analysis.dataset.mask.pixels_in_mask
if pixels_in_mask < hyper_model.galaxies.source.pixelization.pixels.upper_limit:
if (hyper_model.galaxies.source.pixelization.cls is
aa.pix.VoronoiBrightnessImage):
lower_limit = (hyper_model.galaxies.source.pixelization.pixels.
lower_limit)
hyper_model.galaxies.source.pixelization.pixels = af.UniformPrior(
lower_limit=lower_limit, upper_limit=pixels_in_mask)
def test_prior_model(self, prior_model):
prior_model_copy = deepcopy(prior_model)
assert prior_model == prior_model_copy
prior_model_copy.centre_0 = af.UniformPrior()
assert prior_model != prior_model_copy
def test_prior_with_limits(self):
prior = af.UniformPrior(
lower_limit=0,
upper_limit=10,
).with_limits(3, 5)
assert prior.lower_limit == 3
assert prior.upper_limit == 5
def test_projected_model():
model = af.Model(
af.Gaussian,
centre=af.UniformPrior()
)
samples = af.Samples(
model,
[
af.Sample(
-1.0, -1.0,
weight=random(),
kwargs={
("centre",): random(),
("normalization",): random(),
("sigma",): random(),
}
)
for _ in range(100)
]
)
result = af.Result(
samples=samples,
model=model
)
projected_model = result.projected_model
assert projected_model.prior_count == 3
assert projected_model.centre is not model.centre
assert projected_model.centre.id == model.centre.id
assert isinstance(
projected_model.centre,
af.UniformPrior
)
def make_factor_model(centre: float,
sigma: float,
optimiser=None) -> ep.AnalysisFactor:
y = make_data(
Gaussian(centre=centre, normalization=normalization, sigma=sigma),
x)
prior_model = af.PriorModel(
Gaussian,
centre=af.UniformPrior(lower_limit=10, upper_limit=100),
normalization=normalization_prior,
sigma=af.UniformPrior(lower_limit=0, upper_limit=20),
)
return ep.AnalysisFactor(prior_model,
analysis=Analysis(x=x, y=y),
optimiser=optimiser)
def test_list_prior_model(self, prior_model):
list_prior_model = af.CollectionPriorModel([prior_model])
list_prior_model_copy = deepcopy(list_prior_model)
assert list_prior_model == list_prior_model_copy
list_prior_model[0].centre_0 = af.UniformPrior()
assert list_prior_model != list_prior_model_copy
