def test__positions_are_input__are_used_in_analysis(
self, image_7x7, noise_map_7x7, mask_7x7):
# If position threshold is input (not None) and positions are input, make the positions part of the lens dataset.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridCoordinates([[(1.0, 1.0), (2.0, 2.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
settings=al.SettingsPhaseImaging(settings_lens=al.SettingsLens(
positions_threshold=0.2)),
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7,
results=mock.MockResults())
phase_imaging_7x7.modify_settings(dataset=imaging_7x7,
results=mock.MockResults())
analysis = phase_imaging_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7,
results=mock.MockResults())
assert (analysis.masked_dataset.positions.in_list[0][0] == np.array(
[1.0, 1.0])).all()
assert (analysis.masked_dataset.positions.in_list[0][1] == np.array(
[2.0, 2.0])).all()
assert analysis.settings.settings_lens.positions_threshold == 0.2
# If position threshold is input (not None) and but no positions are supplied, raise an error
with pytest.raises(exc.PhaseException):
imaging_7x7 = al.Imaging(image=image_7x7,
noise_map=noise_map_7x7,
positions=None)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
settings=al.SettingsPhaseImaging(settings_lens=al.SettingsLens(
positions_threshold=0.2)),
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7,
results=mock.MockResults())
phase_imaging_7x7.modify_settings(dataset=imaging_7x7,
results=mock.MockResults())
def test__positions_are_input__are_used_in_analysis(
self, image_7x7, noise_map_7x7, mask_7x7):
# If position threshold is input (not None) and positions are input, make the positions part of the lens dataset.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0), (2.0, 2.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(settings_lens=al.SettingsLens(
positions_threshold=0.2)),
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7,
results=mock.MockResults())
phase_imaging_7x7.modify_settings(dataset=imaging_7x7,
results=mock.MockResults())
analysis = phase_imaging_7x7.make_analysis(dataset=imaging_7x7,
mask=mask_7x7,
results=mock.MockResults())
assert (analysis.masked_dataset.positions.in_grouped_list[0][0] ==
np.array([1.0, 1.0])).all()
assert (analysis.masked_dataset.positions.in_grouped_list[0][1] ==
np.array([2.0, 2.0])).all()
assert analysis.settings.settings_lens.positions_threshold == 0.2
def make_imaging_gen(fit):
data = fit.value(name="data")
noise_map = fit.value(name="noise_map")
psf = fit.value(name="psf")
settings_imaging = fit.value(name="settings_dataset")
imaging = al.Imaging(
image=data,
noise_map=noise_map,
psf=psf,
settings=settings_imaging,
pad_for_convolver=True,
)
imaging.apply_settings(settings=settings_imaging)
return imaging
The individual masked `data`, `noise_map` and `psf` are stored in the database, as opposed to the `Imaging` object,
which saves of hard-disk space used. Thus, we need to create the `Imaging` object ourselves to inspect it.
"""
data_gen = agg.values(name="data")
noise_map_gen = agg.values(name="noise_map")
psf_gen = agg.values(name="psf")
settings_imaging_gen = agg.values(name="settings_dataset")
for (data, noise_map, psf, settings_imaging) in zip(data_gen, noise_map_gen,
psf_gen,
settings_imaging_gen):
imaging = al.Imaging(
image=data,
noise_map=noise_map,
psf=psf,
settings=settings_imaging,
pad_for_convolver=True,
)
imaging_plotter = aplt.ImagingPlotter(imaging=imaging)
imaging_plotter.subplot_imaging()
"""
__Manual Fit via Generators (Optional / Advanced)__
I now illustrate how one can create fits via generators. There may be occasions where the functionality of
the `FitImagingAgg` object is insufficient to perform the calculation you require. You can therefore write your own
generator to do this.
This section is optional, and I advise you only follow it if the `FitImagingAgg` object is sufficient for your use-case.
"""
def test__uses_auto_update_factor(self, image_7x7, noise_map_7x7, mask_7x7):
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5), al.Galaxy(redshift=1.0)]
)
# Auto positioning is OFF, so use input positions + threshold.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(positions_threshold=0.1)
),
)
results = mock.MockResults(max_log_likelihood_tracer=tracer)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
phase_imaging_7x7.modify_settings(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.settings.settings_lens.positions_threshold == 0.1
# Auto positioning is ON, but there are no previous results, so use separate of postiions x positions factor..
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.1, auto_positions_factor=1.0
)
),
)
results = mock.MockResults(max_log_likelihood_tracer=tracer)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
phase_imaging_7x7.modify_settings(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.settings.settings_lens.positions_threshold == 2.0
# Auto position is ON, and same as above but with a factor of 3.0 which increases the threshold.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2, auto_positions_factor=3.0
)
),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer, updated_positions_threshold=0.2
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
phase_imaging_7x7.modify_settings(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.settings.settings_lens.positions_threshold == 6.0
# Auto position is ON, and same as above but with a minimum auto positions threshold that rounds the value up.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2,
auto_positions_factor=3.0,
auto_positions_minimum_threshold=10.0,
)
),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer, updated_positions_threshold=0.2
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
phase_imaging_7x7.modify_settings(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.settings.settings_lens.positions_threshold == 10.0
# Auto positioning is ON, but positions are None and it cannot find new positions so no threshold.
imaging_7x7 = al.Imaging(
image=image_7x7, noise_map=noise_map_7x7, positions=None
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(auto_positions_factor=1.0)
),
)
results = mock.MockResults(max_log_likelihood_tracer=tracer)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
phase_imaging_7x7.modify_settings(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.settings.settings_lens.positions_threshold == None
def test__updates_correct_using_factor(
self, imaging_7x7, image_7x7, noise_map_7x7, mask_7x7
):
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5), al.Galaxy(redshift=1.0)]
)
# Auto positioning is OFF, so use input positions + threshold.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(positions_threshold=0.1)
),
)
results = mock.MockResults(max_log_likelihood_tracer=tracer)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [[(1.0, 1.0)]]
# Auto positioning is ON, but there are no previous results, so use input positions.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2, auto_positions_factor=2.0
)
),
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [[(1.0, 1.0)]]
# Auto positioning is ON, there are previous results so use their new positions and threshold (which is
# multiplied by the auto_positions_factor). However, only one set of positions is computed from the previous
# result, to use input positions.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2, auto_positions_factor=2.0
)
),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer,
updated_positions=al.GridIrregularGrouped(grid=[[(2.0, 2.0)]]),
updated_positions_threshold=0.3,
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [[(1.0, 1.0)]]
# Auto positioning is ON, but the tracer only has a single plane and thus no lensing, so use input positions.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2, auto_positions_factor=1.0
)
),
)
tracer_x1_plane = al.Tracer.from_galaxies(galaxies=[al.Galaxy(redshift=0.5)])
results = mock.MockResults(
max_log_likelihood_tracer=tracer_x1_plane,
updated_positions=al.GridIrregularGrouped(grid=[[(2.0, 2.0), (3.0, 3.0)]]),
updated_positions_threshold=0.3,
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [[(1.0, 1.0)]]
# Auto positioning is ON, there are previous results so use their new positions and threshold (which is
# multiplied by the auto_positions_factor). Multiple positions are available so these are now used.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(
positions_threshold=0.2, auto_positions_factor=2.0
)
),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer,
updated_positions=al.GridIrregularGrouped(grid=[[(2.0, 2.0), (3.0, 3.0)]]),
updated_positions_threshold=0.3,
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [
[(2.0, 2.0), (3.0, 3.0)]
]
# Auto positioning is Off, but there are previous results with updated positions relative to the input
# positions, so use those with their positions threshold.
imaging_7x7 = al.Imaging(
image=image_7x7,
noise_map=noise_map_7x7,
positions=al.GridIrregularGrouped([[(2.0, 2.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
search=mock.MockSearch("test_phase"),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(positions_threshold=0.1)
),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer,
positions=al.GridIrregularGrouped(grid=[[(3.0, 3.0), (4.0, 4.0)]]),
updated_positions_threshold=0.3,
)
phase_imaging_7x7.modify_dataset(dataset=imaging_7x7, results=results)
analysis = phase_imaging_7x7.make_analysis(
dataset=imaging_7x7, mask=mask_7x7, results=results
)
assert analysis.masked_dataset.positions.in_grouped_list == [
[(3.0, 3.0), (4.0, 4.0)]
]
imaging_plotter = aplt.ImagingPlotter(imaging=imaging, visuals_2d=visuals_2d)
imaging_plotter.subplot_imaging()
# %%
"""
For the positions to be used in a Phase, we associate them with the `Imaging` data.
We also pass the `SettingsPhaseImaging` a new object, `SettingsLens`. This includes the *position_threshold* used
during modeling to resample inaccurate mass models.
"""
# %%
imaging = al.Imaging(
image=imaging.image,
noise_map=imaging.noise_map,
psf=imaging.psf,
positions=positions,
)
settings_masked_imaging = al.SettingsMaskedImaging(grid_class=al.Grid,
sub_size=2)
settings_lens = al.SettingsLens(positions_threshold=2.0)
settings = al.SettingsPhaseImaging(
settings_masked_imaging=settings_masked_imaging,
settings_lens=settings_lens)
# %%
"""
We can then tell our phase to use these positions in the analysis.
"""