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__plane_generator_from_aggregator(masked_imaging_7x7, samples, model):
path_prefix = "aggregator_plane_gen"
database_file = path.join(conf.instance.output_path, "plane.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.AnalysisImaging(dataset=masked_imaging_7x7)
search.fit(model=model, analysis=analysis)
agg = af.Aggregator.from_database(filename=database_file)
agg.add_directory(directory=result_path)
plane_gen = ag.agg.Plane(aggregator=agg)
for plane in plane_gen:
assert plane.galaxies[0].redshift == 0.5
assert plane.galaxies[0].light.centre == (0.0, 1.0)
assert plane.galaxies[1].redshift == 1.0
clean(database_file=database_file, result_path=result_path)
def test__fit_imaging_generator_from_aggregator(masked_imaging_7x7, samples,
model):
path_prefix = "aggregator_fit_imaging_gen"
database_file = path.join(conf.instance.output_path, "fit_imaging.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.AnalysisImaging(dataset=masked_imaging_7x7)
search.fit(model=model, analysis=analysis)
agg = af.Aggregator.from_database(filename=database_file)
agg.add_directory(directory=result_path)
fit_imaging_gen = ag.agg.FitImaging(aggregator=agg)
for fit_imaging in fit_imaging_gen:
assert (fit_imaging.image == masked_imaging_7x7.image).all()
clean(database_file=database_file, result_path=result_path)
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_imaging_is_returned(self, masked_imaging_7x7):
model = af.Collection(galaxies=af.Collection(galaxy_0=ag.Galaxy(redshift=0.5)))
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7)
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, res.ResultImaging)
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__make_result__result_quantity_is_returned(
self, dataset_quantity_7x7_array_2d):
model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(
redshift=0.5)))
analysis = al.AnalysisQuantity(dataset=dataset_quantity_7x7_array_2d,
func_str="convergence_2d_from")
search = mock.MockSearch(name="test_search")
result = search.fit(model=model, analysis=analysis)
assert isinstance(result, ResultQuantity)
def test__imaging_generator_from_aggregator(imaging_7x7, mask_2d_7x7, samples, model):
path_prefix = "aggregator_imaging_gen"
database_file = path.join(conf.instance.output_path, "imaging.sqlite")
result_path = path.join(conf.instance.output_path, path_prefix)
clean(database_file=database_file, result_path=result_path)
masked_imaging_7x7 = imaging_7x7.apply_mask(mask=mask_2d_7x7)
masked_imaging_7x7 = masked_imaging_7x7.apply_settings(
settings=ag.SettingsImaging(
grid_class=ag.Grid2DIterate,
grid_inversion_class=ag.Grid2DIterate,
fractional_accuracy=0.5,
sub_steps=[2],
)
)
search = mock.MockSearch(samples=samples)
search.paths = af.DirectoryPaths(path_prefix=path_prefix)
analysis = ag.AnalysisImaging(dataset=masked_imaging_7x7)
search.fit(model=model, analysis=analysis)
agg = af.Aggregator.from_database(filename=database_file)
agg.add_directory(directory=result_path)
imaging_agg = ag.agg.ImagingAgg(aggregator=agg)
imaging_gen = imaging_agg.imaging_gen()
for imaging in imaging_gen:
assert (imaging.image == masked_imaging_7x7.image).all()
assert isinstance(imaging.grid, ag.Grid2DIterate)
assert isinstance(imaging.grid_inversion, ag.Grid2DIterate)
assert imaging.grid.sub_steps == [2]
assert imaging.grid.fractional_accuracy == 0.5
clean(database_file=database_file, result_path=result_path)
def test__max_log_likelihood_plane_available_as_result(self, analysis_imaging_7x7):
galaxy_0 = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=1.0))
galaxy_1 = ag.Galaxy(redshift=0.5, light=ag.lp.EllSersic(intensity=2.0))
model = af.Collection(
galaxies=af.Collection(galaxy_0=galaxy_0, galaxy_1=galaxy_1)
)
max_log_likelihood_plane = ag.Plane(galaxies=[galaxy_0, galaxy_1])
search = mock.MockSearch(
name="test_search",
samples=mock.MockSamples(
max_log_likelihood_instance=max_log_likelihood_plane
),
)
result = search.fit(model=model, analysis=analysis_imaging_7x7)
assert isinstance(result.max_log_likelihood_plane, ag.Plane)
assert result.max_log_likelihood_plane.galaxies[0].light.intensity == 1.0
assert result.max_log_likelihood_plane.galaxies[1].light.intensity == 2.0
