def test__positions_found_for_multi_plane_tracer(self):
grid = al.Grid2D.uniform(shape_native=(100, 100),
pixel_scales=0.05,
sub_size=1)
g0 = al.Galaxy(
redshift=0.5,
mass=al.mp.EllIsothermal(
centre=(0.001, 0.001),
einstein_radius=1.0,
elliptical_comps=(0.0, 0.111111),
),
)
g1 = al.Galaxy(redshift=1.0)
g2 = al.Galaxy(redshift=2.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1, g2])
solver = PointSolver(grid=grid, pixel_scale_precision=0.01)
coordinates_to_plane_2 = solver.solve(lensing_obj=tracer,
source_plane_coordinate=(0.0,
0.0))
coordinates_to_plane_1 = solver.solve(lensing_obj=tracer,
source_plane_coordinate=(0.0,
0.0),
upper_plane_index=1)
assert coordinates_to_plane_1[0][0] != coordinates_to_plane_2[0][0]
scaling_factor = al.util.cosmology.scaling_factor_between_redshifts_from(
redshift_0=0.5,
redshift_1=1.0,
redshift_final=2.0,
cosmology=tracer.cosmology,
)
assert coordinates_to_plane_1[0][0] == pytest.approx(
coordinates_to_plane_2[0][0] * scaling_factor, 1.0e-1)
coordinates_to_plane_2_with_index = solver.solve(
lensing_obj=tracer,
source_plane_coordinate=(0.0, 0.0),
upper_plane_index=2)
assert coordinates_to_plane_2[0][
0] == coordinates_to_plane_2_with_index[0][0]
def __init__(
self,
name: str,
positions: aa.Grid2DIrregular,
noise_map: aa.ValuesIrregular,
tracer: Tracer,
point_solver: PointSolver,
point_profile: Optional[ag.ps.Point] = None,
):
"""
A lens position fitter, which takes a set of positions (e.g. from a plane in the tracer) and computes \
their maximum separation, such that points which tracer closer to one another have a higher log_likelihood.
Parameters
-----------
positions : Grid2DIrregular
The (y,x) arc-second coordinates of positions which the maximum distance and log_likelihood is computed using.
noise_value
The noise-value assumed when computing the log likelihood.
"""
self.name = name
if point_profile is None:
point_profile = tracer.extract_profile(profile_name=name)
self.point_profile = point_profile
self.point_solver = point_solver
if self.point_profile is None:
raise exc.PointExtractionException(
f"For the point-source named {name} there was no matching point source profile "
f"in the tracer (make sure your tracer's point source name is the same the dataset name."
)
self.source_plane_coordinate = self.point_profile.centre
if len(tracer.planes) > 2:
upper_plane_index = tracer.extract_plane_index_of_profile(
profile_name=name)
else:
upper_plane_index = None
model_positions = point_solver.solve(
lensing_obj=tracer,
source_plane_coordinate=self.source_plane_coordinate,
upper_plane_index=upper_plane_index,
)
model_positions = model_positions.grid_of_closest_from(
grid_pair=positions)
super().__init__(
data=positions,
noise_map=noise_map,
model_data=model_positions,
mask=None,
inversion=None,
)
def test__positions_found_for_simple_mass_profile_list(self):
grid = al.Grid2D.uniform(shape_native=(100, 100), pixel_scales=0.05)
sis = al.mp.SphIsothermal(centre=(0.0, 0.0), einstein_radius=1.0)
solver = al.PointSolver(grid=grid, pixel_scale_precision=0.01)
positions = solver.solve(lensing_obj=sis,
source_plane_coordinate=(0.0, 0.11))
assert positions[0] == pytest.approx(np.array([0.003125, -0.890625]),
1.0e-4)
assert positions[3] == pytest.approx(np.array([-0.003125, 1.109375]),
1.0e-4)
grid = al.Grid2D.uniform(shape_native=(100, 100),
pixel_scales=0.05,
sub_size=1)
g0 = al.Galaxy(
redshift=0.5,
mass=al.mp.EllIsothermal(
centre=(0.001, 0.001),
einstein_radius=1.0,
elliptical_comps=(0.0, 0.111111),
),
)
g1 = al.Galaxy(redshift=1.0)
tracer = al.Tracer.from_galaxies(galaxies=[g0, g1])
solver = PointSolver(grid=grid, pixel_scale_precision=0.01)
coordinates = solver.solve(lensing_obj=tracer,
source_plane_coordinate=(0.0, 0.0))
assert coordinates.in_list[0] == pytest.approx((1.028125, -0.003125),
1.0e-4)
assert coordinates.in_list[1] == pytest.approx((0.009375, -0.95312),
1.0e-4)
assert coordinates.in_list[2] == pytest.approx((0.009375, 0.95312),
1.0e-4)
assert coordinates.in_list[3] == pytest.approx((-1.028125, -0.003125),
1.0e-4)
def image_plane_multiple_image_positions(self) -> aa.Grid2DIrregular:
"""
Backwards ray-trace the source-plane centres (see above) to the image-plane via the mass model, to determine
the multiple image position of the source(s) in the image-plane.
These image-plane positions are used by the next search in a pipeline if automatic position updating is turned
on."""
# TODO : In the future, the multiple image positions functioon wil use an in-built adaptive grid.
grid = self.analysis.dataset.mask.unmasked_grid_sub_1
solver = PointSolver(grid=grid, pixel_scale_precision=0.001)
multiple_images = solver.solve(
lensing_obj=self.max_log_likelihood_tracer,
source_plane_coordinate=self.source_plane_centre.in_list[0],
)
return aa.Grid2DIrregular(grid=multiple_images)