def test__fit_interferometer_generator_from_aggregator(interferometer_7,
mask_2d_7x7, samples,
model):
path_prefix = "aggregator_fit_interferometer"
database_file = path.join(conf.instance.output_path,
"fit_interferometer.sqlite")
result_path = path.join(conf.instance.output_path, path_prefix)
clean(database_file=database_file, result_path=result_path)
search = mock.MockSearch(samples=samples)
search.paths = af.DirectoryPaths(path_prefix=path_prefix)
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
search.fit(model=model, analysis=analysis)
agg = af.Aggregator.from_database(filename=database_file)
agg.add_directory(directory=result_path)
fit_interferometer_gen = ag.agg.FitInterferometer(aggregator=agg)
for fit_interferometer in fit_interferometer_gen:
assert (fit_interferometer.interferometer.visibilities ==
interferometer_7.visibilities).all()
assert (fit_interferometer.interferometer.real_space_mask ==
mask_2d_7x7).all()
clean(database_file=database_file, result_path=result_path)
def test__fit_figure_of_merit__includes_hyper_image_and_noise__matches_fit(
self, interferometer_7
):
hyper_background_noise = ag.hyper_data.HyperBackgroundNoise(noise_scale=1.0)
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=0.1))
model = af.Collection(
hyper_background_noise=hyper_background_noise,
galaxies=af.Collection(galaxy=galaxy),
)
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
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.FitInterferometer(
interferometer=interferometer_7,
plane=plane,
hyper_background_noise=hyper_background_noise,
)
assert fit.log_likelihood == fit_figure_of_merit
def test__interferometer_generator_from_aggregator(
visibilities_7,
visibilities_noise_map_7,
uv_wavelengths_7x2,
mask_2d_7x7,
samples,
model,
):
path_prefix = "aggregator_interferometer"
database_file = path.join(conf.instance.output_path,
"interferometer.sqlite")
result_path = path.join(conf.instance.output_path, path_prefix)
clean(database_file=database_file, result_path=result_path)
interferometer_7 = ag.Interferometer(
visibilities=visibilities_7,
noise_map=visibilities_noise_map_7,
uv_wavelengths=uv_wavelengths_7x2,
real_space_mask=mask_2d_7x7,
settings=ag.SettingsInterferometer(
grid_class=ag.Grid2DIterate,
grid_inversion_class=ag.Grid2DIterate,
fractional_accuracy=0.5,
sub_steps=[2],
transformer_class=ag.TransformerDFT,
),
)
search = mock.MockSearch(samples=samples)
search.paths = af.DirectoryPaths(path_prefix=path_prefix)
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
search.fit(model=model, analysis=analysis)
agg = af.Aggregator.from_database(filename=database_file)
agg.add_directory(directory=result_path)
interferometer_agg = ag.agg.InterferometerAgg(aggregator=agg)
interferometer_gen = interferometer_agg.interferometer_gen()
for interferometer in interferometer_gen:
assert (interferometer.visibilities == interferometer_7.visibilities
).all()
assert (interferometer.real_space_mask == mask_2d_7x7).all()
assert isinstance(interferometer.grid, ag.Grid2DIterate)
assert isinstance(interferometer.grid_inversion, ag.Grid2DIterate)
assert interferometer.grid.sub_steps == [2]
assert interferometer.grid.fractional_accuracy == 0.5
assert isinstance(interferometer.transformer, ag.TransformerDFT)
clean(database_file=database_file, result_path=result_path)
def test__make_result__result_interferometer_is_returned(self, interferometer_7):
model = af.Collection(galaxies=af.Collection(galaxy_0=ag.Galaxy(redshift=0.5)))
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, res.ResultInterferometer)
def test__fit_figure_of_merit__matches_correct_fit_given_galaxy_profiles(
self, interferometer_7
):
galaxy = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=0.1))
model = af.Collection(galaxies=af.Collection(galaxy=galaxy))
analysis = ag.AnalysisInterferometer(dataset=interferometer_7)
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.FitInterferometer(interferometer=interferometer_7, plane=plane)
assert fit.log_likelihood == fit_figure_of_merit
def make_analysis_interferometer_7():
return ag.AnalysisInterferometer(dataset=make_interferometer_7())
