def test__inputs_are_other_python_types__converted_correctly():
point_dataset_0 = al.PointDataset(
name="source_1", positions=[[1.0, 1.0]], positions_noise_map=[1.0]
)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0])
assert point_dict["source_1"].name == "source_1"
assert point_dict["source_1"].positions.in_list == [(1.0, 1.0)]
assert point_dict["source_1"].positions_noise_map.in_list == [1.0]
assert point_dict["source_1"].fluxes == None
assert point_dict["source_1"].fluxes_noise_map == None
point_dataset_0 = al.PointDataset(
name="source_1",
positions=[(1.0, 1.0), (2.0, 2.0)],
positions_noise_map=[1.0],
fluxes=[2.0],
fluxes_noise_map=[3.0],
)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0])
assert point_dict["source_1"].name == "source_1"
assert point_dict["source_1"].positions.in_list == [(1.0, 1.0), (2.0, 2.0)]
assert point_dict["source_1"].positions_noise_map.in_list == [1.0]
assert point_dict["source_1"].fluxes.in_list == [2.0]
assert point_dict["source_1"].fluxes_noise_map.in_list == [3.0]
def test__fits_dataset__positions_only():
point_source = al.ps.Point(centre=(0.1, 0.1))
galaxy_point_source = al.Galaxy(redshift=1.0, point_0=point_source)
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5), galaxy_point_source])
positions = al.Grid2DIrregular([(0.0, 0.0), (3.0, 4.0)])
noise_map = al.ValuesIrregular([0.5, 1.0])
model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)])
point_solver = al.m.MockPointSolver(model_positions=model_positions)
point_dataset_0 = al.PointDataset(name="point_0",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert fit["point_0"].positions.log_likelihood == pytest.approx(
-22.14472, 1.0e-4)
assert fit["point_0"].flux == None
assert fit.log_likelihood == fit["point_0"].positions.log_likelihood
point_dataset_1 = al.PointDataset(name="point_1",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(
point_dataset_list=[point_dataset_0, point_dataset_1])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert fit["point_0"].positions.log_likelihood == pytest.approx(
-22.14472, 1.0e-4)
assert fit["point_0"].flux == None
assert fit["point_1"].positions == None
assert fit["point_1"].flux == None
assert fit.log_likelihood == fit["point_0"].positions.log_likelihood
def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, positions_x2, positions_x2_noise_map):
point_dataset = al.PointDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
)
point_dict = al.PointDict(point_dataset_list=[point_dataset])
model = af.Collection(galaxies=af.Collection(lens=al.Galaxy(
redshift=0.5, point_0=al.ps.Point(centre=(0.0, 0.0)))))
solver = al.m.MockPointSolver(model_positions=positions_x2)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_solver=solver,
)
assert fit_positions.chi_squared == 0.0
assert fit_positions.log_likelihood == analysis_log_likelihood
model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)])
solver = al.m.MockPointSolver(model_positions=model_positions)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_solver=solver,
)
assert fit_positions.residual_map.in_list == [1.0, 1.0]
assert fit_positions.chi_squared == 2.0
assert fit_positions.log_likelihood == analysis_log_likelihood
def test__point_dataset_structures_as_dict():
point_dataset_0 = al.PointDataset(
name="source_1",
positions=al.Grid2DIrregular([[1.0, 1.0]]),
positions_noise_map=al.ValuesIrregular([1.0]),
)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0])
assert point_dict["source_1"].name == "source_1"
assert point_dict["source_1"].positions.in_list == [(1.0, 1.0)]
assert point_dict["source_1"].positions_noise_map.in_list == [1.0]
assert point_dict["source_1"].fluxes == None
assert point_dict["source_1"].fluxes_noise_map == None
point_dataset_1 = al.PointDataset(
name="source_2",
positions=al.Grid2DIrregular([[1.0, 1.0]]),
positions_noise_map=al.ValuesIrregular([1.0]),
fluxes=al.ValuesIrregular([2.0, 3.0]),
fluxes_noise_map=al.ValuesIrregular([4.0, 5.0]),
)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0, point_dataset_1])
assert point_dict["source_1"].name == "source_1"
assert point_dict["source_1"].positions.in_list == [(1.0, 1.0)]
assert point_dict["source_1"].positions_noise_map.in_list == [1.0]
assert point_dict["source_1"].fluxes == None
assert point_dict["source_1"].fluxes_noise_map == None
assert point_dict["source_2"].name == "source_2"
assert point_dict["source_2"].positions.in_list == [(1.0, 1.0)]
assert point_dict["source_2"].positions_noise_map.in_list == [1.0]
assert point_dict["source_2"].fluxes.in_list == [2.0, 3.0]
assert point_dict["source_2"].fluxes_noise_map.in_list == [4.0, 5.0]
assert (point_dict.positions_list[0] == np.array([1.0, 1.0])).all()
assert (point_dict.positions_list[1] == np.array([1.0, 1.0])).all()
def test__from_json_and_output_to_json():
point_dataset_0 = al.PointDataset(
name="source_1",
positions=al.Grid2DIrregular([[1.0, 1.0]]),
positions_noise_map=al.ValuesIrregular([1.0]),
)
point_dataset_1 = al.PointDataset(
name="source_2",
positions=al.Grid2DIrregular([[1.0, 1.0]]),
positions_noise_map=al.ValuesIrregular([1.0]),
fluxes=al.ValuesIrregular([2.0, 3.0]),
fluxes_noise_map=al.ValuesIrregular([4.0, 5.0]),
)
point_dict = al.PointDict(point_dataset_list=[point_dataset_0, point_dataset_1])
dir_path = path.join("{}".format(path.dirname(path.realpath(__file__))), "files")
if path.exists(dir_path):
shutil.rmtree(dir_path)
os.makedirs(dir_path)
file_path = path.join(dir_path, "point_dict.json")
point_dict.output_to_json(file_path=file_path, overwrite=True)
point_dict_via_json = al.PointDict.from_json(file_path=file_path)
assert point_dict_via_json["source_1"].name == "source_1"
assert point_dict_via_json["source_1"].positions.in_list == [(1.0, 1.0)]
assert point_dict_via_json["source_1"].positions_noise_map.in_list == [1.0]
assert point_dict_via_json["source_1"].fluxes == None
assert point_dict_via_json["source_1"].fluxes_noise_map == None
assert point_dict_via_json["source_2"].name == "source_2"
assert point_dict_via_json["source_2"].positions.in_list == [(1.0, 1.0)]
assert point_dict_via_json["source_2"].positions_noise_map.in_list == [1.0]
assert point_dict_via_json["source_2"].fluxes.in_list == [2.0, 3.0]
assert point_dict_via_json["source_2"].fluxes_noise_map.in_list == [4.0, 5.0]
def test__model_has_image_and_source_chi_squared__fits_both_correctly():
galaxy_point_image = al.Galaxy(redshift=1.0,
point_0=al.ps.Point(centre=(0.1, 0.1)))
galaxy_point_source = al.Galaxy(redshift=1.0,
point_1=al.ps.PointSourceChi(centre=(0.1,
0.1)))
tracer = al.Tracer.from_galaxies(galaxies=[
al.Galaxy(redshift=0.5), galaxy_point_image, galaxy_point_source
])
positions = al.Grid2DIrregular([(0.0, 0.0), (3.0, 4.0)])
noise_map = al.ValuesIrregular([0.5, 1.0])
model_positions = al.Grid2DIrregular([(3.0, 1.0), (2.0, 3.0)])
point_solver = al.m.MockPointSolver(model_positions=model_positions)
point_dataset_0 = al.PointDataset(name="point_0",
positions=positions,
positions_noise_map=noise_map)
point_dataset_1 = al.PointDataset(name="point_1",
positions=positions,
positions_noise_map=noise_map)
point_dict = al.PointDict(
point_dataset_list=[point_dataset_0, point_dataset_1])
fit = al.FitPointDict(point_dict=point_dict,
tracer=tracer,
point_solver=point_solver)
assert isinstance(fit["point_0"].positions, al.FitPositionsImage)
assert isinstance(fit["point_1"].positions, al.FitPositionsSource)
assert fit[
"point_0"].positions.model_positions.in_list == model_positions.in_list
assert fit[
"point_1"].positions.model_positions.in_list == positions.in_list
fluxes = [flux * np.abs(magnification) for magnification in magnifications]
fluxes = al.ValuesIrregular(values=fluxes)
"""
Create a point-source dictionary data object and output this to a `.json` file, which is the format used to load and
analyse the dataset.
"""
point_dataset = al.PointDataset(
name="point_0",
positions=positions,
positions_noise_map=positions.values_via_value_from(
value=grid.pixel_scale),
fluxes=fluxes,
fluxes_noise_map=al.ValuesIrregular(values=[1.0, 1.0, 1.0, 1.0]),
)
point_dict = al.PointDict(point_dataset_list=[point_dataset])
point_dict.output_to_json(file_path=path.join(dataset_path, "point_dict.json"),
overwrite=True)
"""
__Imaging__
We can now pass this simulator a tracer, which creates the ray-traced image plotted above and simulates it as an
imaging dataset.
"""
imaging = simulator.via_tracer_from(tracer=tracer, grid=grid)
"""
Lets plot the simulated `Imaging` dataset before we output it to fits, including the (y,x) coordinates of the multiple
images in the image-plane.
"""
visuals_2d = aplt.Visuals2D(multiple_images=positions)
def test__figure_of_merit__includes_fit_fluxes(self, positions_x2,
positions_x2_noise_map,
fluxes_x2,
fluxes_x2_noise_map):
point_dataset = al.PointDataset(
name="point_0",
positions=positions_x2,
positions_noise_map=positions_x2_noise_map,
fluxes=fluxes_x2,
fluxes_noise_map=fluxes_x2_noise_map,
)
point_dict = al.PointDict(point_dataset_list=[point_dataset])
model = af.Collection(galaxies=af.Collection(lens=al.Galaxy(
redshift=0.5,
sis=al.mp.SphIsothermal(einstein_radius=1.0),
point_0=al.ps.PointFlux(flux=1.0),
)))
solver = al.m.MockPointSolver(model_positions=positions_x2)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
tracer = analysis.tracer_via_instance_from(instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_solver=solver,
)
fit_fluxes = al.FitFluxes(
name="point_0",
fluxes=fluxes_x2,
noise_map=fluxes_x2_noise_map,
positions=positions_x2,
tracer=tracer,
)
assert (fit_positions.log_likelihood +
fit_fluxes.log_likelihood == analysis_log_likelihood)
model_positions = al.Grid2DIrregular([(0.0, 1.0), (1.0, 2.0)])
solver = al.m.MockPointSolver(model_positions=model_positions)
analysis = al.AnalysisPoint(point_dict=point_dict, solver=solver)
instance = model.instance_from_unit_vector([])
analysis_log_likelihood = analysis.log_likelihood_function(
instance=instance)
fit_positions = al.FitPositionsImage(
name="point_0",
positions=positions_x2,
noise_map=positions_x2_noise_map,
tracer=tracer,
point_solver=solver,
)
fit_fluxes = al.FitFluxes(
name="point_0",
fluxes=fluxes_x2,
noise_map=fluxes_x2_noise_map,
positions=positions_x2,
tracer=tracer,
)
assert fit_positions.residual_map.in_list == [1.0, 1.0]
assert fit_positions.chi_squared == 2.0
assert (fit_positions.log_likelihood +
fit_fluxes.log_likelihood == analysis_log_likelihood)
def test_dicts(self, dataset, dataset_dict):
point_dict = al.PointDict([dataset])
assert point_dict.dicts == [dataset_dict]
def make_point_dict():
return al.PointDict(point_dataset_list=[make_point_dataset()])
