def solve(self, lensing_obj, source_plane_coordinate):
coordinates_list = self.grid_peaks_from(
lensing_obj=lensing_obj,
grid=self.grid,
source_plane_coordinate=source_plane_coordinate,
)
coordinates_list = self.grid_with_coordinates_from_mass_profile_centre_removed(
lensing_obj=lensing_obj, grid=coordinates_list)
coordinates_list = self.grid_with_points_below_magnification_threshold_removed(
lensing_obj=lensing_obj, grid=coordinates_list)
if not self.use_upscaling:
return grids.GridIrregularGrouped(grid=coordinates_list)
pixel_scale = self.grid.pixel_scale
while pixel_scale > self.pixel_scale_precision:
refined_coordinates_list = []
for coordinate in coordinates_list:
refined_coordinates = self.refined_coordinates_from_coordinate(
coordinate=coordinate,
pixel_scale=pixel_scale,
lensing_obj=lensing_obj,
source_plane_coordinate=source_plane_coordinate,
)
if refined_coordinates is not None:
refined_coordinates_list += refined_coordinates
refined_coordinates_list = grid_remove_duplicates(
grid=np.asarray(refined_coordinates_list))
pixel_scale = pixel_scale / self.upscale_factor
coordinates_list = refined_coordinates_list
coordinates_list = self.grid_within_distance_of_source_plane_centre(
lensing_obj=lensing_obj,
grid=grids.GridIrregularGroupedUniform(grid=coordinates_list,
pixel_scales=(pixel_scale,
pixel_scale)),
source_plane_coordinate=source_plane_coordinate,
distance=self.distance_from_source_centre,
)
coordinates_list = self.grid_with_points_below_magnification_threshold_removed(
lensing_obj=lensing_obj, grid=coordinates_list)
return grids.GridIrregularGrouped(grid=coordinates_list)
def solve_from_tracer(self, tracer):
"""Needs work - idea is it solves for all image plane multiple image positions using the redshift distribution of
the tracer."""
return grids.GridIrregularGrouped(grid=[
self.solve(lensing_obj=tracer, source_plane_coordinate=centre)
for centre in tracer.light_profile_centres.in_grouped_list[-1]
])
def image_plane_multiple_image_positions_of_source_plane_centres(
self,
) -> grids.GridIrregularGrouped:
"""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 phase 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.masked_dataset.mask.geometry.unmasked_grid_sub_1
solver = pos.PositionsFinder(grid=grid, pixel_scale_precision=0.001)
try:
multiple_images = [
solver.solve(
lensing_obj=self.max_log_likelihood_tracer,
source_plane_coordinate=centre,
)
for centre in self.source_plane_centres.in_grouped_list[0]
]
return grids.GridIrregularGrouped(grid=multiple_images)
except IndexError:
return None
def source_plane_centres(self) -> grids.GridIrregularGrouped:
"""Combine the source-plane light profile and inversion centres (see above) into a single list of source-plane
centres.
These centres are used by automatic position updating to determine the multiple-images of a best-fit lens model
(and thus tracer) by back-tracing the centres to the image plane via the mass model."""
centres = list(self.source_plane_light_profile_centres) + list(
self.source_plane_inversion_centres)
return grids.GridIrregularGrouped(grid=centres)
def mass_profile_centres(self):
"""
Returns the mass profile centres of the tracer as a `GridIrregularGrouped` object, which structures the centres
in lists according to which plane they come from.
Fo example, if the tracer has two planes, the first with one mass profile and second with two mass profiles
this returns:
[[(y0, x0)], [(y0, x0), (y1, x1)]]
This is used for visualization, for example plotting the centres of all mass profiles colored by their galaxy.
The centres of mass-sheets are filtered out, as their centres are not relevant to lensing calculations
"""
return grids.GridIrregularGrouped([
list(plane.mass_profile_centres) for plane in self.planes
if plane.has_mass_profile
])
