def test__likelihood__is_sum_of_separations_divided_by_noise(self):
positions = al.GridCoordinates([
[(0.0, 0.0), (0.0, 1.0), (0.0, 0.5)],
[(0.0, 0.0), (0.0, 0.0), (3.0, 3.0)],
[(0.0, 0.0), (1.0, 1.0), (3.0, 3.0)],
])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.chi_squared_map[0] == 1.0
assert fit.chi_squared_map[1] == pytest.approx(18.0, 1e-4)
assert fit.chi_squared_map[2] == pytest.approx(18.0, 1e-4)
assert fit.figure_of_merit == pytest.approx(-0.5 * (1.0 + 18 + 18),
1e-4)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=2.0)
assert fit.chi_squared_map[0] == (1.0 / 2.0)**2.0
assert fit.chi_squared_map[1] == pytest.approx(
(np.sqrt(18.0) / 2.0)**2.0, 1e-4)
assert fit.chi_squared_map[2] == pytest.approx(
(np.sqrt(18.0) / 2.0)**2.0, 1e-4)
assert fit.figure_of_merit == pytest.approx(
-0.5 * ((1.0 / 2.0)**2.0 + (np.sqrt(18.0) / 2.0)**2.0 +
(np.sqrt(18.0) / 2.0)**2.0),
1e-4,
)
def test__positions_do_not_trace_within_threshold__raises_exception(
self, phase_interferometer_7, interferometer_7, mask_7x7, visibilities_mask_7x2
):
interferometer_7.positions = al.GridCoordinates([[(1.0, 100.0), (200.0, 2.0)]])
phase_interferometer_7 = al.PhaseInterferometer(
phase_name="test_phase",
real_space_mask=mask_7x7,
galaxies=dict(
lens=al.Galaxy(redshift=0.5, mass=al.mp.SphericalIsothermal()),
source=al.Galaxy(redshift=1.0),
),
settings=al.SettingsPhaseInterferometer(
settings_lens=al.SettingsLens(positions_threshold=0.01)
),
search=mock.MockSearch(),
)
analysis = phase_interferometer_7.make_analysis(
dataset=interferometer_7,
mask=visibilities_mask_7x2,
results=mock.MockResults(),
)
instance = phase_interferometer_7.model.instance_from_unit_vector([])
with pytest.raises(exc.RayTracingException):
analysis.log_likelihood_function(instance=instance)
def test__results_of_phase_include_positions__available_as_property(
self, imaging_7x7, mask_7x7, samples_with_result
):
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase", search=mock.MockSearch(samples=samples_with_result)
)
result = phase_imaging_7x7.run(
dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults()
)
assert result.positions == None
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
galaxies=dict(lens=al.Galaxy(redshift=0.5), source=al.Galaxy(redshift=1.0)),
search=mock.MockSearch(samples=samples_with_result),
settings=al.SettingsPhaseImaging(
settings_lens=al.SettingsLens(positions_threshold=1.0)
),
)
imaging_7x7.positions = al.GridCoordinates([[(1.0, 1.0)]])
result = phase_imaging_7x7.run(
dataset=imaging_7x7, mask=mask_7x7, results=mock.MockResults()
)
assert (result.positions[0] == np.array([1.0, 1.0])).all()
def test_multiple_positions__mock_position_tracer__maximum_separation_is_correct(
self):
positions = al.GridCoordinates([[(0.0, 0.0), (0.0, 1.0), (0.0, 0.5)]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == 1.0
positions = al.GridCoordinates([[(0.0, 0.0), (0.0, 0.0), (3.0, 3.0)]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == np.sqrt(18)
al.GridCoordinates([[(0.0, 0.0), (1.0, 1.0), (3.0, 3.0)]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == np.sqrt(18)
positions = al.GridCoordinates([[
(-2.0, -4.0),
(1.0, 3.0),
(0.1, 0.1),
(-0.1, -0.1),
(0.3, 0.4),
(-0.6, 0.5),
]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == np.sqrt(
np.square(3.0) + np.square(7.0))
positions = al.GridCoordinates([[(8.0, 4.0), (8.0, 4.0),
(-9.0, -4.0)]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == np.sqrt(
np.square(17.0) + np.square(8.0))
def test__threshold__if_not_met_returns_ray_tracing_exception(self):
positions = al.GridCoordinates([[(0.0, 0.0), (0.0, 1.0)]])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separation_within_threshold(threshold=100.0)
assert not fit.maximum_separation_within_threshold(threshold=0.1)
def test__above_with_real_tracer(self):
tracer = al.Tracer.from_galaxies(galaxies=[
al.Galaxy(redshift=0.5,
mass=al.mp.SphericalIsothermal(einstein_radius=1.0)),
al.Galaxy(redshift=1.0),
])
positions = al.GridCoordinates([[(1.0, 0.0), (-1.0, 0.0)]])
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separation_within_threshold(threshold=0.01)
positions = al.GridCoordinates([[(1.2, 0.0), (-1.0, 0.0)]])
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separation_within_threshold(threshold=0.3)
assert not fit.maximum_separation_within_threshold(threshold=0.15)
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_do_not_trace_within_threshold__raises_exception(
self, ):
tracer = al.Tracer.from_galaxies(galaxies=[
al.Galaxy(redshift=0.5, mass=al.mp.SphericalIsothermal()),
al.Galaxy(redshift=1.0),
])
settings = al.SettingsLens(positions_threshold=50.0)
settings.check_positions_trace_within_threshold_via_tracer(
tracer=tracer,
positions=al.GridCoordinates([[(1.0, 1.0), (2.0, 2.0)]]))
settings = al.SettingsLens(positions_threshold=0.0)
with pytest.raises(exc.RayTracingException):
settings.check_positions_trace_within_threshold_via_tracer(
tracer=tracer,
positions=al.GridCoordinates([[(1.0, 1.0), (2.0, 2.0)]]))
# No mass profile - doesnt raise exception
tracer = al.Tracer.from_galaxies(
galaxies=[al.Galaxy(redshift=0.5),
al.Galaxy(redshift=1.0)])
settings.check_positions_trace_within_threshold_via_tracer(
tracer=tracer,
positions=al.GridCoordinates([[(1.0, 1.0), (2.0, 2.0)]]))
# Single plane - doesnt raise exception
tracer = al.Tracer.from_galaxies(galaxies=[
al.Galaxy(redshift=0.5, mass=al.mp.SphericalIsothermal())
])
settings.check_positions_trace_within_threshold_via_tracer(
tracer=tracer,
positions=al.GridCoordinates([[(1.0, 1.0), (2.0, 2.0)]]))
def test_multiple_sets_of_positions__multiple_sets_of_max_distances(self):
positions = al.GridCoordinates([
[(0.0, 0.0), (0.0, 1.0), (0.0, 0.5)],
[(0.0, 0.0), (0.0, 0.0), (3.0, 3.0)],
[(0.0, 0.0), (1.0, 1.0), (3.0, 3.0)],
])
tracer = MockTracerPositions(positions=positions)
fit = al.FitPositionsSourcePlaneMaxSeparation(positions=positions,
tracer=tracer,
noise_value=1.0)
assert fit.maximum_separations[0] == 1.0
assert fit.maximum_separations[1] == np.sqrt(18)
assert fit.maximum_separations[2] == np.sqrt(18)
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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates([[(1.0, 0.0), (-1.0, 0.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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_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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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_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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates(coordinates=[[(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_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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates(coordinates=[[(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_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.GridCoordinates([[(1.0, 1.0)]]),
)
phase_imaging_7x7 = al.PhaseImaging(
phase_name="test_phase",
search=mock.MockSearch(),
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.GridCoordinates(coordinates=[[(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_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.GridCoordinates([[(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=0.1)),
)
results = mock.MockResults(
max_log_likelihood_tracer=tracer,
positions=al.GridCoordinates(coordinates=[[(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_list == [[(3.0, 3.0),
(4.0, 4.0)]]
positions = solver.solve(
lensing_obj=tracer,
source_plane_coordinate=tracer.source_plane.galaxies[0].light.centre,
)
positions_true = al.GridCoordinates.load(file_path=pickle_path,
filename=f"positions_{str(i)}")
minimum_separations = util.minimum_separations_from(
positions_true=positions, positions=positions_true)
in_positions_true = util.check_if_positions_in_positions_true(
positions_true=positions_true, positions=positions, threshold=0.1)
print()
print(positions_true.in_list)
print(positions.in_list)
print(minimum_separations)
print(in_positions_true)
positions_plot = al.GridCoordinates(
coordinates=[positions.in_list[0], positions_true.in_list[0]])
aplt.Tracer.image(
tracer=tracer,
grid=grid,
positions=positions_plot,
include=aplt.Include(origin=False,
critical_curves=True,
caustics=False),
)
mass-model is clearly not accurate.
2) By removing these solutions, a global-maximum solution may be reached instead of a local-maxima. This is
because removing the incorrect mass models makes the non-linear parameter space less complex.
We can easily check the image-positions are accurate by plotting them using our _Imaging_ _Plotter_ (they are the magenta
dots on the image).
To specify these positions, we use the *GridCoordinates* object, which is used by __PyAutoLens__ in general to specify (y,x)
coordinates.
"""
# %%
positions = al.GridCoordinates(coordinates=[(1.6,
0.0), (0.0,
1.6), (-1.6,
0.0), (0.0, -1.6)])
aplt.Imaging.subplot_imaging(imaging=imaging, positions=positions)
# %%
"""
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,
