def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, dataset_quantity_7x7_array_2d):
galaxy = ag.Galaxy(redshift=0.5,
light=ag.mp.EllIsothermal(einstein_radius=1.0))
model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
analysis = ag.AnalysisQuantity(dataset=dataset_quantity_7x7_array_2d,
func_str="convergence_2d_from")
instance = model.instance_from_unit_vector([])
fit_figure_of_merit = analysis.log_likelihood_function(
instance=instance)
plane = analysis.plane_for_instance(instance=instance)
fit = ag.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
light_mass_obj=plane,
func_str="convergence_2d_from",
)
assert fit.log_likelihood == fit_figure_of_merit
fit = ag.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
light_mass_obj=plane,
func_str="potential_2d_from",
)
assert fit.log_likelihood != fit_figure_of_merit
def make_fit_quantity_7x7_vector_yx_2d():
return ag.FitQuantity(
dataset=make_dataset_quantity_7x7_vector_yx_2d(),
light_mass_obj=make_plane_7x7(),
func_str="deflections_yx_2d_from",
)
def make_fit_quantity_7x7_array_2d():
return ag.FitQuantity(
dataset=make_dataset_quantity_7x7_array_2d(),
light_mass_obj=make_plane_7x7(),
func_str="convergence_2d_from",
)
def test__fit_via_mock_profile(dataset_quantity_7x7_array_2d,
dataset_quantity_7x7_vector_yx_2d):
model_object = MockMassProfile(
convergence_2d=ag.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0),
potential_2d=ag.Array2D.full(fill_value=2.0,
shape_native=(7, 7),
pixel_scales=1.0),
deflections_yx_2d=ag.VectorYX2D.full(fill_value=3.0,
shape_native=(7, 7),
pixel_scales=1.0),
)
plane = ag.Plane(galaxies=[ag.Galaxy(redshift=0.5, mass=model_object)])
fit_quantity = ag.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
light_mass_obj=plane,
func_str="convergence_2d_from",
)
assert fit_quantity.chi_squared == pytest.approx(0.0, 1.0e-4)
assert fit_quantity.log_likelihood == pytest.approx(
-0.5 * 49.0 * np.log(2 * np.pi * 2.0**2.0), 1.0e-4)
fit_quantity = ag.FitQuantity(
dataset=dataset_quantity_7x7_array_2d,
light_mass_obj=plane,
func_str="potential_2d_from",
)
assert fit_quantity.chi_squared == pytest.approx(12.25, 1.0e-4)
assert fit_quantity.log_likelihood == pytest.approx(-85.1171999, 1.0e-4)
fit_quantity = ag.FitQuantity(
dataset=dataset_quantity_7x7_vector_yx_2d,
light_mass_obj=plane,
func_str="deflections_yx_2d_from",
)
assert fit_quantity.chi_squared == pytest.approx(98.0, 1.0e-4)
assert fit_quantity.log_likelihood == pytest.approx(-206.98438, 1.0e-4)
def test__y_x(dataset_quantity_7x7_vector_yx_2d):
model_object = MockMassProfile(deflections_yx_2d=ag.VectorYX2D.full(
fill_value=3.0, shape_native=(7, 7), pixel_scales=1.0))
plane = ag.Plane(galaxies=[ag.Galaxy(redshift=0.5, mass=model_object)])
fit_quantity = ag.FitQuantity(
dataset=dataset_quantity_7x7_vector_yx_2d,
light_mass_obj=plane,
func_str="deflections_yx_2d_from",
)
assert (fit_quantity.y.dataset.data ==
dataset_quantity_7x7_vector_yx_2d.y.data).all()
assert (fit_quantity.x.dataset.data ==
dataset_quantity_7x7_vector_yx_2d.x.data).all()