def test__in_order_of_class_constructor(self):
mapper = af.ModelMapper(mock_cls_0=af.m.MockChildTuplex2)
model_map = mapper.instance_from_unit_vector([0.25, 0.5, 0.75, 1.0])
assert model_map.mock_cls_0.tup == (0.25, 0.5)
assert model_map.mock_cls_0.one == 1.5
assert model_map.mock_cls_0.two == 2.0
mapper = af.ModelMapper(
mock_cls_0=af.m.MockChildTuplex2,
mock_cls_1=af.m.MockChildTuple,
mock_cls_2=af.m.MockChildTuplex2,
)
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 == 0.6
assert model_map.mock_cls_0.two == 0.8
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 == 1.8
assert model_map.mock_cls_2.two == 2.0
def test__instance_from_prior_medians(self):
mapper = af.ModelMapper(mock_cls_0=af.m.MockChildTuplex2)
model_map = mapper.instance_from_prior_medians()
model_2 = mapper.instance_from_unit_vector([0.5, 0.5, 0.5, 0.5])
assert model_map.mock_cls_0.tup == model_2.mock_cls_0.tup == (0.5, 0.5)
assert model_map.mock_cls_0.one == model_2.mock_cls_0.one == 1.0
assert model_map.mock_cls_0.two == model_2.mock_cls_0.two == 1.0
mapper = af.ModelMapper(
mock_cls_0=af.m.MockChildTuplex2,
mock_cls_1=af.m.MockChildTuple,
mock_cls_2=af.m.MockChildTuplex2,
)
model_map = mapper.instance_from_prior_medians()
model_2 = mapper.instance_from_unit_vector(
[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
assert model_map.mock_cls_0.tup == model_2.mock_cls_0.tup == (0.5, 0.5)
assert model_map.mock_cls_0.one == model_2.mock_cls_0.one == 1.0
assert model_map.mock_cls_0.two == model_2.mock_cls_0.two == 1.0
assert model_map.mock_cls_1.tup == model_2.mock_cls_1.tup == (0.5, 0.5)
assert model_map.mock_cls_2.tup == model_2.mock_cls_2.tup == (0.5, 0.5)
assert model_map.mock_cls_2.one == model_2.mock_cls_2.one == 1.0
assert model_map.mock_cls_2.two == model_2.mock_cls_2.two == 1.0
def test_no_override(self):
mapper = af.ModelMapper()
mapper.one = af.PriorModel(af.m.MockClassx2)
af.ModelMapper()
assert mapper.one is not None
def test_mapper_plus_mapper(self):
one = af.ModelMapper()
two = af.ModelMapper()
one.a = af.PriorModel(mock.MockClassx2)
two.b = af.PriorModel(mock.MockClassx2)
three = one + two
assert three.prior_count == 4
def test_numerical(self):
model = af.ModelMapper()
model.add_assertion(True)
model.instance_from_unit_vector([])
model = af.ModelMapper()
model.add_assertion(False)
with pytest.raises(exc.FitException):
model.instance_from_unit_vector([])
def test__covariance_matrix():
log_likelihood_list = list(range(3))
weight_list = 3 * [0.1]
parameters = [[2.0, 2.0], [1.0, 1.0], [0.0, 0.0]]
model = af.ModelMapper(mock_class=af.m.MockClassx2)
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=3 * [0.0],
weight_list=weight_list,
),
)
assert samples_x5.covariance_matrix() == pytest.approx(
np.array([[1.0, 1.0], [1.0, 1.0]]), 1.0e-4)
parameters = [[0.0, 2.0], [1.0, 1.0], [2.0, 0.0]]
model = af.ModelMapper(mock_class=af.m.MockClassx2)
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=3 * [0.0],
weight_list=weight_list,
),
)
assert samples_x5.covariance_matrix() == pytest.approx(
np.array([[1.0, -1.0], [-1.0, 1.0]]), 1.0e-4)
weight_list = [0.1, 0.2, 0.3]
model = af.ModelMapper(mock_class=af.m.MockClassx2)
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=10 * [0.0],
weight_list=weight_list,
),
)
assert samples_x5.covariance_matrix() == pytest.approx(
np.array([[0.90909, -0.90909], [-0.90909, 0.90909]]), 1.0e-4)
def test__make_pixelization_model(self):
instance = af.ModelInstance()
mapper = af.ModelMapper()
mapper.lens_galaxy = al.GalaxyModel(
redshift=al.Redshift,
pixelization=al.pix.Rectangular,
regularization=al.reg.Constant,
)
mapper.source_galaxy = al.GalaxyModel(
redshift=al.Redshift, light=al.lp.EllipticalLightProfile)
assert mapper.prior_count == 10
instance.lens_galaxy = al.Galaxy(
pixelization=al.pix.Rectangular(),
regularization=al.reg.Constant(),
redshift=1.0,
)
instance.source_galaxy = al.Galaxy(
redshift=1.0, light=al.lp.EllipticalLightProfile())
# noinspection PyTypeChecker
phase = al.ModelFixingHyperPhase(
MockPhase(),
"mock_phase",
model_classes=(al.pix.Pixelization, al.reg.Regularization),
)
mapper = mapper.copy_with_fixed_priors(instance, phase.model_classes)
assert mapper.prior_count == 3
assert mapper.lens_galaxy.redshift == 1.0
assert mapper.source_galaxy.light.axis_ratio == 1.0
def test__vector_drawn_randomly_from_pdf():
parameters = [
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[21.0, 22.0, 23.0, 24.0],
[0.0, 1.0, 2.0, 3.0],
]
model = af.ModelMapper(mock_class_1=af.m.MockClassx4)
samples_x5 = af.m.MockSamples(
model=model,
sample_list=af.Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[1.0, 2.0, 3.0, 4.0, 5.0],
log_prior_list=5 * [0.0],
weight_list=[0.0, 0.0, 0.0, 1.0, 0.0],
),
)
vector = samples_x5.vector_drawn_randomly_from_pdf()
assert vector == [21.0, 22.0, 23.0, 24.0]
instance = samples_x5.instance_drawn_randomly_from_pdf()
assert vector == [21.0, 22.0, 23.0, 24.0]
assert instance.mock_class_1.one == 21.0
assert instance.mock_class_1.two == 22.0
assert instance.mock_class_1.three == 23.0
assert instance.mock_class_1.four == 24.0
def __init__(
self,
mask=None,
model_image=None,
galaxy_images=(),
model_visibilities=None,
galaxy_visibilities=(),
instance=None,
analysis=None,
optimizer=None,
pixelization=None,
):
self.mask_2d = mask
self.model_image = model_image
self.unmasked_model_image = model_image
self.galaxy_images = galaxy_images
self.model_visibilities = model_visibilities
self.galaxy_visibilities = galaxy_visibilities
self.instance = instance or af.ModelInstance()
self.model = af.ModelMapper()
self.analysis = analysis
self.optimizer = optimizer
self.pixelization = pixelization
self.hyper_combined = MockHyperCombinedPhase()
self.use_as_hyper_dataset = False
def test__samples_from_model(self):
pyswarms = af.PySwarmsGlobal()
pyswarms.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "pyswarms"))
pyswarms.paths._identifier = "tag"
model = af.ModelMapper(mock_class=af.m.MockClassx3)
model.mock_class.one = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
model.mock_class.two = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
model.mock_class.three = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
# model.mock_class.four = af.LogUniformPrior(lower_limit=1e-8, upper_limit=100.0)
samples = pyswarms.samples_from(model=model)
assert isinstance(samples.parameter_lists, list)
assert isinstance(samples.parameter_lists[0], list)
assert isinstance(samples.log_likelihood_list, list)
assert isinstance(samples.log_prior_list, list)
assert isinstance(samples.log_posterior_list, list)
assert samples.parameter_lists[0] == pytest.approx(
[50.1254, 1.04626, 10.09456], 1.0e-4)
assert samples.log_likelihood_list[0] == pytest.approx(
-5071.80777, 1.0e-4)
assert samples.log_posterior_list[0] == pytest.approx(
-5070.73298, 1.0e-4)
assert samples.weight_list[0] == 1.0
assert len(samples.parameter_lists) == 500
assert len(samples.log_likelihood_list) == 500
def test__samples_from_model(self):
lbfgs = af.LBFGS()
lbfgs.paths = af.DirectoryPaths(path_prefix=path.join("non_linear", "LBFGS"))
lbfgs.paths._identifier = "tag"
model = af.ModelMapper(mock_class=af.m.MockClassx3)
model.mock_class.one = af.LogUniformPrior(lower_limit=1e-8, upper_limit=100.0)
model.mock_class.two = af.LogUniformPrior(lower_limit=1e-8, upper_limit=100.0)
model.mock_class.three = af.LogUniformPrior(lower_limit=1e-8, upper_limit=100.0)
samples = lbfgs.samples_from(model=model)
assert isinstance(samples.parameter_lists, list)
assert isinstance(samples.parameter_lists[0], list)
assert isinstance(samples.log_likelihood_list, list)
assert isinstance(samples.log_prior_list, list)
assert isinstance(samples.log_posterior_list, list)
assert samples.parameter_lists[0] == pytest.approx(
[50.005469, 25.143677, 10.06950], 1.0e-4
)
assert samples.log_likelihood_list[0] == pytest.approx(-45.134121, 1.0e-4)
assert samples.log_posterior_list[0] == pytest.approx(-44.97504284, 1.0e-4)
assert samples.weight_list[0] == 1.0
assert len(samples.parameter_lists) == 1
assert len(samples.log_likelihood_list) == 1
def make_mapper_with_list():
mapper = af.ModelMapper()
mapper.list = [
af.PriorModel(af.m.MockClassx2),
af.PriorModel(af.m.MockClassx2)
]
return mapper
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_attribution(self):
mapper = af.ModelMapper()
mapper.mock_class = af.m.MockClassx2
assert hasattr(mapper, "mock_class")
assert hasattr(mapper.mock_class, "one")
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___image_dict(self, analysis_imaging_7x7):
galaxies = af.ModelInstance()
galaxies.lens = al.Galaxy(redshift=0.5)
galaxies.source = al.Galaxy(redshift=1.0)
instance = af.ModelInstance()
instance.galaxies = galaxies
result = ResultImaging(
samples=mock.MockSamples(max_log_likelihood_instance=instance),
model=af.ModelMapper(),
analysis=analysis_imaging_7x7,
search=None,
)
image_dict = result.image_galaxy_dict
assert isinstance(image_dict[("galaxies", "lens")], np.ndarray)
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
result.instance.galaxies.lens = al.Galaxy(redshift=0.5)
image_dict = result.image_galaxy_dict
assert (image_dict[("galaxies", "lens")].native == np.zeros(
(7, 7))).all()
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
def __init__(self, most_likely_fit=None):
self.most_likely_fit = most_likely_fit
self.analysis = MockAnalysis()
self.path_galaxy_tuples = []
self.model = af.ModelMapper()
self.mask = None
self.positions = None
def test__samples_from_model(self):
drawer = af.Drawer()
drawer.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "drawer"))
drawer.paths._identifier = "tag"
model = af.ModelMapper(mock_class=af.m.MockClassx3)
model.mock_class.one = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
model.mock_class.two = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
model.mock_class.three = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=100.0)
samples = drawer.samples_from(model=model)
assert isinstance(samples.parameter_lists, list)
assert isinstance(samples.parameter_lists[0], list)
assert isinstance(samples.log_likelihood_list, list)
assert isinstance(samples.log_prior_list, list)
assert isinstance(samples.log_posterior_list, list)
assert samples.parameter_lists[0] == pytest.approx(
[49.507679, 49.177471, 14.76753], 1.0e-4)
assert samples.log_likelihood_list[0] == pytest.approx(
-2763.925766, 1.0e-4)
assert samples.log_posterior_list[0] == pytest.approx(
-2763.817517, 1.0e-4)
assert samples.weight_list[0] == 1.0
assert len(samples.parameter_lists) == 3
assert len(samples.log_likelihood_list) == 3
def test__samples_from_model(self):
emcee = af.Emcee()
emcee.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "emcee"))
emcee.paths._identifier = "tag"
model = af.ModelMapper(mock_class=af.m.MockClassx4)
model.mock_class.two = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=10.0)
samples = emcee.samples_from(model=model)
assert isinstance(samples.parameter_lists, list)
assert isinstance(samples.parameter_lists[0], list)
assert isinstance(samples.log_likelihood_list, list)
assert isinstance(samples.log_prior_list, list)
assert isinstance(samples.log_posterior_list, list)
assert isinstance(samples.weight_list, list)
assert samples.parameter_lists[0] == pytest.approx(
[0.173670, 0.162607, 3095.28, 0.62104], 1.0e-4)
assert samples.log_likelihood_list[0] == pytest.approx(
-17257775239.32677, 1.0e-4)
assert samples.log_prior_list[0] == pytest.approx(
1.6102016075510708, 1.0e-4)
assert samples.weight_list[0] == pytest.approx(1.0, 1.0e-4)
assert samples.total_steps == 1000
assert samples.total_walkers == 10
assert samples.auto_correlations.times[0] == pytest.approx(
31.98507, 1.0e-4)
def test___image_dict(self, masked_imaging_7x7):
galaxies = af.ModelInstance()
galaxies.lens = al.Galaxy(redshift=0.5)
galaxies.source = al.Galaxy(redshift=1.0)
instance = af.ModelInstance()
instance.galaxies = galaxies
analysis = al.PhaseImaging.Analysis(
masked_imaging=masked_imaging_7x7,
settings=al.SettingsPhaseImaging(),
results=mock.MockResults(),
cosmology=cosmo.Planck15,
)
result = al.PhaseImaging.Result(
samples=mock.MockSamples(max_log_likelihood_instance=instance),
previous_model=af.ModelMapper(),
analysis=analysis,
search=None,
)
image_dict = result.image_galaxy_dict
assert isinstance(image_dict[("galaxies", "lens")], np.ndarray)
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
result.instance.galaxies.lens = al.Galaxy(redshift=0.5)
image_dict = result.image_galaxy_dict
assert (image_dict[("galaxies", "lens")].native == np.zeros(
(7, 7))).all()
assert isinstance(image_dict[("galaxies", "source")], np.ndarray)
def test_samples(self):
db.Object.from_object(
OptimizerSamples(
af.ModelMapper(),
None
)
)()
def test__offset_vector_from_input_vector(self):
model = af.ModelMapper(mock_class_1=MockClassx4)
parameters = [
[1.1, 2.1, 3.1, 4.1],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.1],
]
weight_list = [0.3, 0.2, 0.2, 0.2, 0.1]
log_likelihood_list = list(
map(lambda weight: 10.0 * weight, weight_list))
samples = MockSamples(model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=10 * [0.0],
weight_list=weight_list,
))
offset_values = samples.offset_vector_from_input_vector(
input_vector=[1.0, 1.0, 2.0, 3.0])
assert offset_values == pytest.approx([0.0, 1.0, 1.0, 1.025], 1.0e-4)
def test__latex(samples):
latex_results_at_sigma = samples_text.latex(samples=samples, sigma=3.0)
assert r"one_label^{\rm{o}} = 1.00^{+0.20}_{-0.00} & " in latex_results_at_sigma
assert r"two_label^{\rm{o}} = 2.00^{+0.20}_{-0.00}" in latex_results_at_sigma
latex_results_at_sigma = samples_text.latex(samples=samples,
sigma=3.0,
include_quickmath=True)
assert r"$one_label^{\rm{o}} = 1.00^{+0.20}_{-0.00}$ & " in latex_results_at_sigma
assert r"$two_label^{\rm{o}} = 2.00^{+0.20}_{-0.00}$" in latex_results_at_sigma
model = af.ModelMapper(mock_class=af.m.MockClassx2FormatExp)
parameters = [[1.0, 200.0], [1.2, 200.0]]
log_likelihood_list = [1.0, 0.0]
samples_exp = af.m.MockSamples(model=model,
sample_list=af.Sample.from_lists(
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=[0.0, 0.0],
weight_list=log_likelihood_list,
model=model))
latex_results_at_sigma = samples_text.latex(samples=samples_exp,
sigma=3.0,
include_quickmath=True)
print(latex_results_at_sigma)
assert r"$one_label^{\rm{o}} = 1.00^{+0.20}_{-0.00}$ & " in latex_results_at_sigma
assert r"$one_label^{\rm{o}} = 1.00^{+0.20}_{-0.00}$ & $2.00^{+0.00}_{-0.00} \times 10^{2}$" in latex_results_at_sigma
def test__log_prior_list_and_max_log_posterior_vector_and_instance(self):
model = af.ModelMapper(mock_class_1=MockClassx4)
parameters = [
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[0.0, 1.0, 2.0, 3.0],
[21.0, 22.0, 23.0, 24.0],
]
samples = MockSamples(
model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[1.0, 2.0, 3.0, 0.0, 5.0],
log_prior_list=[1.0, 2.0, 3.0, 10.0, 6.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
))
assert samples.log_posterior_list == [2.0, 4.0, 6.0, 10.0, 11.0]
assert samples.max_log_posterior_vector == [21.0, 22.0, 23.0, 24.0]
instance = samples.max_log_posterior_instance
assert instance.mock_class_1.one == 21.0
assert instance.mock_class_1.two == 22.0
assert instance.mock_class_1.three == 23.0
assert instance.mock_class_1.four == 24.0
def test__unconverged_sample_size__uses_value_unless_fewer_samples(self):
model = af.ModelMapper(mock_class_1=MockClassx4)
log_likelihood_list = 4 * [0.0] + [1.0]
weight_list = 4 * [0.0] + [1.0]
samples = MockSamples(
model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=5 * [[]],
log_likelihood_list=log_likelihood_list,
log_prior_list=[1.0, 1.0, 1.0, 1.0, 1.0],
weight_list=weight_list,
),
unconverged_sample_size=2,
)
assert samples.pdf_converged is False
assert samples.unconverged_sample_size == 2
samples = MockSamples(
model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=5 * [[]],
log_likelihood_list=log_likelihood_list,
log_prior_list=[1.0, 1.0, 1.0, 1.0, 1.0],
weight_list=weight_list,
),
unconverged_sample_size=6,
)
assert samples.pdf_converged is False
assert samples.unconverged_sample_size == 5
def test__unconverged__median_pdf_vector(self):
parameters = [
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.0, 2.0],
[1.1, 2.1],
[0.9, 1.9],
]
log_likelihood_list = 9 * [0.0] + [1.0]
weight_list = 9 * [0.0] + [1.0]
model = af.ModelMapper(mock_class=MockClassx2)
samples = MockSamples(model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=log_likelihood_list,
log_prior_list=10 * [0.0],
weight_list=weight_list,
))
assert samples.pdf_converged is False
median_pdf_vector = samples.median_pdf_vector
assert median_pdf_vector[0] == pytest.approx(0.9, 1.0e-4)
assert median_pdf_vector[1] == pytest.approx(1.9, 1.0e-4)
def test__instance_from_sample_index(self):
model = af.ModelMapper(mock_class=MockClassx4)
parameters = [
[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.1, 2.1, 3.1, 4.1],
]
samples = MockSamples(
model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[0.0, 0.0, 0.0, 0.0, 0.0],
log_prior_list=[0.0, 0.0, 0.0, 0.0, 0.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
))
instance = samples.instance_from_sample_index(sample_index=0)
assert instance.mock_class.one == 1.0
assert instance.mock_class.two == 2.0
assert instance.mock_class.three == 3.0
assert instance.mock_class.four == 4.0
instance = samples.instance_from_sample_index(sample_index=1)
assert instance.mock_class.one == 5.0
assert instance.mock_class.two == 6.0
assert instance.mock_class.three == 7.0
assert instance.mock_class.four == 8.0
def test__gaussian_priors(self):
parameters = [
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.1],
[1.0, 2.0, 3.0, 4.1],
[0.88, 1.88, 2.88, 3.88],
[1.12, 2.12, 3.12, 4.32],
]
model = af.ModelMapper(mock_class=MockClassx4)
samples = MockSamples(
model=model,
samples=Sample.from_lists(
model=model,
parameter_lists=parameters,
log_likelihood_list=[10.0, 0.0, 0.0, 0.0, 0.0],
log_prior_list=[0.0, 0.0, 0.0, 0.0, 0.0],
weight_list=[1.0, 1.0, 1.0, 1.0, 1.0],
))
gaussian_priors = samples.gaussian_priors_at_sigma(sigma=1.0)
assert gaussian_priors[0][0] == 1.0
assert gaussian_priors[1][0] == 2.0
assert gaussian_priors[2][0] == 3.0
assert gaussian_priors[3][0] == 4.0
assert gaussian_priors[0][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[1][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[2][1] == pytest.approx(0.12, 1.0e-4)
assert gaussian_priors[3][1] == pytest.approx(0.32, 1.0e-4)
def test_create_instance(self):
mapper = af.ModelMapper()
mapper.complex = af.m.MockComplexClass
instance = mapper.instance_from_unit_vector([1.0, 0.0])
assert instance.complex.simple.one == 1.0
assert instance.complex.simple.two == 0.0
def test_with_tuple(self):
mm = af.ModelMapper()
mm.tuple = (0, 1)
assert (
mm.info ==
"tuple (0, 1)"
)
