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Merge branch 'main' into feature/jax_coordinate_array
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@rhayes777
rhayes777 committed Dec 16, 2024
2 parents a8c933c + d876f51 commit 76730dc
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Showing 25 changed files with 437 additions and 148 deletions.
2 changes: 1 addition & 1 deletion autolens/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,4 +123,4 @@

conf.instance.register(__file__)

__version__ = "2024.11.6.1"
__version__ = "2024.11.13.2"
23 changes: 12 additions & 11 deletions autolens/aggregator/tracer.py
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Expand Up @@ -58,17 +58,18 @@ def _tracer_from(

tracer = Tracer(galaxies=galaxies, cosmology=cosmology)

if len(fit.children) > 0:
logger.info(
"""
Using database for a fit with multiple summed Analysis objects.
Tracer objects do not fully support this yet (e.g. model parameters which vary over analyses may be incorrect)
so proceed with caution!
"""
)

return [tracer] * len(fit.children)
if fit.children is not None:
if len(fit.children) > 0:
logger.info(
"""
Using database for a fit with multiple summed Analysis objects.
Tracer objects do not fully support this yet (e.g. model parameters which vary over analyses may be incorrect)
so proceed with caution!
"""
)

return [tracer] * len(fit.children)

return [tracer]

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93 changes: 93 additions & 0 deletions autolens/analysis/result.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import logging
import os
import numpy as np
from typing import Optional, Union
Expand All @@ -15,6 +16,8 @@
from autolens.lens.tracer import Tracer
from autolens.point.solver import PointSolver

logger = logging.getLogger(__name__)


class Result(AgResultDataset):
@property
Expand Down Expand Up @@ -101,8 +104,76 @@ def image_plane_multiple_image_positions(self) -> aa.Grid2DIrregular:
source_plane_coordinate=self.source_plane_centre.in_list[0],
)

if multiple_images.shape[0] == 1:
return self.image_plane_multiple_image_positions_for_single_image_from()

return aa.Grid2DIrregular(values=multiple_images)

def image_plane_multiple_image_positions_for_single_image_from(
self, increments: int = 20
) -> aa.Grid2DIrregular:
"""
If the standard point solver only locates one multiple image, finds one or more additional images, which are
not technically multiple image in the point source regime, but are close enough to it they can be used
in a position threshold likelihood.
This is performed by incrementally moving the source-plane centre's coordinates towards the centre of the
source-plane at (0.0", 0.0"). This ensures that the centre will eventually go inside the caustic, where
multiple images are formed.
To move the source-plane centre, the original source-plane centre is multiplied by a factor that decreases
from 1.0 to 0.0 in increments of 1/increments. For example, if the source-plane centre is (1.0", -0.5") and
the `factor` is 0.5, the input source-plane centre is (0.5", -0.25").
The multiple images are always computed for the same mass model, thus they will always be valid multiple images
for the model being fitted, but as the factor decrease the multiple images may move furhter from their observed
positions.
Parameters
----------
increments
The number of increments the source-plane centre is moved to compute multiple images.
"""

logger.info(
"""
Could not find multiple images for maximum likelihood lens model.
Incrementally moving source centre inwards towards centre of source-plane until caustic crossing occurs
and multiple images are formed.
"""
)

grid = self.analysis.dataset.mask.derive_grid.all_false

centre = self.source_plane_centre.in_list[0]

solver = PointSolver.for_grid(
grid=grid,
pixel_scale_precision=0.001,
)

for i in range(1, increments):
factor = 1.0 - (1.0 * (i / increments))

multiple_images = solver.solve(
tracer=self.max_log_likelihood_tracer,
source_plane_coordinate=(centre[0] * factor, centre[1] * factor),
)

if multiple_images.shape[0] > 1:
return aa.Grid2DIrregular(values=multiple_images)

logger.info(
"""
Could not find multiple images for maximum likelihood lens model, even after incrementally moving the source
centre inwards to the centre of the source-plane.
Set the multiple image postiions to two images at (1.0", 1.0") so code continues to run.
"""
)
return aa.Grid2DIrregular(values=[(1.0, 1.0), (1.0, 1.0)])

def positions_threshold_from(
self,
factor=1.0,
Expand Down Expand Up @@ -168,6 +239,7 @@ def positions_likelihood_from(
minimum_threshold=None,
use_resample=False,
positions: Optional[aa.Grid2DIrregular] = None,
mass_centre_radial_distance_min: float = None,
) -> Union[PositionsLHPenalty, PositionsLHResample]:
"""
Returns a `PositionsLH` object from the result of a lens model-fit, where the maximum log likelihood mass
Expand All @@ -178,6 +250,11 @@ def positions_likelihood_from(
parametric source) can be used to determine the multiple image positions and threshold for a more complex
subsequent fit (e.g. power-law mass model, pixelized source).
The mass model central image is removed from the solution, as this is rarely physically observed and therefore
should not be included in the likelihood penalty or resampling. It is removed by setting a positive
magnification threshold in the `PointSolver`. For strange lens models the central image may still be
solved for, in which case the `mass_centre_radial_distance_min` parameter can be used to remove it.
Parameters
----------
factor
Expand All @@ -193,6 +270,10 @@ def positions_likelihood_from(
positions
If input, these positions are used instead of the computed multiple image positions from the lens mass
model.
mass_centre_radial_distance_min
The minimum radial distance from the mass model centre that a multiple image position must be to be
included in the likelihood penalty or resampling. If `None` all positions are used. This is an additional
method to remove central images that may make it through the point solver's magnification threshold.
Returns
-------
Expand All @@ -207,6 +288,18 @@ def positions_likelihood_from(
if positions is None
else positions
)

if mass_centre_radial_distance_min is not None:
mass_centre = self.max_log_likelihood_tracer.extract_attribute(
cls=ag.mp.MassProfile, attr_name="centre"
)

distances = positions.distances_to_coordinate_from(
coordinate=mass_centre[0]
)

positions = positions[distances > mass_centre_radial_distance_min]

threshold = self.positions_threshold_from(
factor=factor, minimum_threshold=minimum_threshold, positions=positions
)
Expand Down
2 changes: 1 addition & 1 deletion autolens/config/visualize/plots.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@
all_at_end_png: true # Plot all individual plots listed below as .png (even if False)?
all_at_end_fits: true # Plot all individual plots listed below as .fits (even if False)?
all_at_end_pdf: false # Plot all individual plots listed below as publication-quality .pdf (even if False)?
errors: false
reconstruction_noise_map: false
reconstructed_image: false
reconstruction: false
regularization_weights: false
Expand Down
22 changes: 8 additions & 14 deletions autolens/imaging/plot/fit_imaging_plotters.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@ def figures_2d_of_planes(
subtracted_image: bool = False,
model_image: bool = False,
plane_image: bool = False,
plane_errors: bool = False,
plane_noise_map: bool = False,
plane_signal_to_noise_map: bool = False,
use_source_vmax: bool = False,
zoom_to_brightest: bool = True,
Expand Down Expand Up @@ -180,12 +180,12 @@ def figures_2d_of_planes(
Whether to make a 2D plot (via `imshow`) of the image of a plane in its source-plane (e.g. unlensed).
Depending on how the fit is performed, this could either be an image of light profiles of the reconstruction
of an `Inversion`.
plane_errors
Whether to make a 2D plot (via `imshow`) of the errors of a plane in its source-plane, where the
errors can only be computed when a pixelized source reconstruction is performed and they correspond to
the errors in each reconstructed pixel as given by the inverse curvature matrix.
plane_noise_map
Whether to make a 2D plot of the noise-map of a plane in its source-plane, where the
noise map can only be computed when a pixelized source reconstruction is performed and they correspond to
the noise map in each reconstructed pixel as given by the inverse curvature matrix.
plane_signal_to_noise_map
Whether to make a 2D plot (via `imshow`) of the signal-to-noise map of a plane in its source-plane,
Whether to make a 2D plot of the signal-to-noise map of a plane in its source-plane,
where the signal-to-noise map values can only be computed when a pixelized source reconstruction and they
are the ratio of reconstructed flux to error in each pixel.
use_source_vmax
Expand Down Expand Up @@ -295,12 +295,6 @@ def figures_2d_of_planes(

pix = self.tracer.planes[plane_index].cls_list_from(cls=aa.Pixelization)[0]

if isinstance(pix.mesh, aa.mesh.Delaunay):
try:
self.mat_plot_2d.cmap.kwargs.pop("vmax")
except KeyError:
pass

inversion_plotter = self.inversion_plotter_of_plane(
plane_index=plane_index
)
Expand All @@ -318,7 +312,7 @@ def figures_2d_of_planes(
except KeyError:
pass

if plane_errors:
if plane_noise_map:

if self.tracer.planes[plane_index].has(cls=aa.Pixelization):

Expand All @@ -328,7 +322,7 @@ def figures_2d_of_planes(

inversion_plotter.figures_2d_of_pixelization(
pixelization_index=0,
errors=True,
reconstruction_noise_map=True,
zoom_to_brightest=zoom_to_brightest,
interpolate_to_uniform=interpolate_to_uniform
)
Expand Down
46 changes: 32 additions & 14 deletions autolens/imaging/simulator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,21 +9,30 @@ class SimulatorImaging(aa.SimulatorImaging):

def via_tracer_from(self, tracer : Tracer, grid : aa.type.Grid2DLike) -> aa.Imaging:
"""
Simulate an `Imaging` dataset from an input tracer and grid.
Simulate an `Imaging` dataset from an input `Tracer` object and a 2D grid of (y,x) coordinates.
The tracer is used to perform ray-tracing and generate the image of the strong lens galaxies (e.g.
the lens light, lensed source light, etc) which is simulated.
The mass profiles of each galaxy in the tracer are used to perform ray-tracing of the input 2D grid and
their light profiles are used to generate the image of the galaxies which are simulated.
The steps of the `SimulatorImaging` simulation process (e.g. PSF convolution, noise addition) are
described in the `SimulatorImaging` `__init__` method docstring.
described in the `SimulatorImaging` `__init__` method docstring, found in the PyAutoArray project.
If one of more galaxy light profiles are a `LightProfileSNR` object, the `intensity` of the light profile is
automatically set such that the signal-to-noise ratio of the light profile is equal to its input
`signal_to_noise_ratio` value.
For example, if a `LightProfileSNR` object has a `signal_to_noise_ratio` of 5.0, the intensity of the light
profile is set such that the peak surface brightness of the profile is 5.0 times the background noise level of
the image.
Parameters
----------
tracer
The tracer, which describes the ray-tracing and strong lens configuration used to simulate the imaging
dataset.
dataset as well as the light profiles of the galaxies used to simulate the image of the galaxies.
grid
The image-plane grid which the image of the strong lens is generated on.
The 2D grid of (y,x) coordinates which the mass profiles of the galaxies in the tracer are ray-traced using
in order to generate the image of the galaxies via their light profiles.
"""

tracer.set_snr_of_snr_light_profiles(
Expand All @@ -44,21 +53,30 @@ def via_tracer_from(self, tracer : Tracer, grid : aa.type.Grid2DLike) -> aa.Imag

def via_galaxies_from(self, galaxies : List[ag.Galaxy], grid : aa.type.Grid2DLike) -> aa.Imaging:
"""
Simulate an `Imaging` dataset from an input list of galaxies and grid.
Simulate an `Imaging` dataset from an input list of `Galaxy` objects and a 2D grid of (y,x) coordinates.
The galaxies are used to create a tracer, which performs ray-tracing and generate the image of the strong lens
galaxies (e.g. the lens light, lensed source light, etc) which is simulated.
The galaxies are used to create a `Tracer`. The mass profiles of each galaxy in the tracer are used to
perform ray-tracing of the input 2D grid and their light profiles are used to generate the image of the
galaxies which are simulated.
The steps of the `SimulatorImaging` simulation process (e.g. PSF convolution, noise addition) are
described in the `SimulatorImaging` `__init__` method docstring.
If one of more galaxy light profiles are a `LightProfileSNR` object, the `intensity` of the light profile is
automatically set such that the signal-to-noise ratio of the light profile is equal to its input
`signal_to_noise_ratio` value.
For example, if a `LightProfileSNR` object has a `signal_to_noise_ratio` of 5.0, the intensity of the light
profile is set such that the peak surface brightness of the profile is 5.0 times the background noise level of
the image.
Parameters
----------
galaxies
The galaxies used to create the tracer, which describes the ray-tracing and strong lens configuration
used to simulate the imaging dataset.
grid
The image-plane grid which the image of the strong lens is generated on.
The image-plane 2D grid of (y,x) coordinates grid which the image of the strong lens is generated on.
"""

tracer = Tracer(galaxies=galaxies)
Expand Down Expand Up @@ -87,7 +105,7 @@ def via_deflections_and_galaxies_from(self, deflections : aa.VectorYX2D, galaxie
The galaxies used to create the tracer, which describes the ray-tracing and strong lens configuration
used to simulate the imaging dataset.
grid
The image-plane grid which the image of the strong lens is generated on.
The image-plane 2D grid of (y,x) coordinates grid which the image of the strong lens is generated on.
"""
grid = aa.Grid2D.uniform(
shape_native=deflections.shape_native,
Expand All @@ -105,10 +123,10 @@ def via_source_image_from(self, tracer : Tracer, grid : aa.type.Grid2DLike, sour
Simulate an `Imaging` dataset from an input image of a source galaxy.
This input image is on a uniform and regular 2D array, meaning it can simulate the source's irregular
and assymetric source galaxy morphological features.
and asymmetric source galaxy morphological features.
The typical use case is inputting the image of an irregular galaxy in the source-plane (whose values are
on a uniform array) and using this function computing the lensed image of this source galaxy.
on a uniform array) and using this function to compute the lensed image of this source galaxy.
The tracer is used to perform ray-tracing and generate the image of the strong lens galaxies (e.g.
the lens light, lensed source light, etc) which is simulated.
Expand All @@ -125,7 +143,7 @@ def via_source_image_from(self, tracer : Tracer, grid : aa.type.Grid2DLike, sour
The tracer, which describes the ray-tracing and strong lens configuration used to simulate the imaging
dataset.
grid
The image-plane grid which the image of the strong lens is generated on.
The image-plane 2D grid of (y,x) coordinates grid which the image of the strong lens is generated on.
source_image
The image of the source-plane and source galaxy which is interpolated to compute the lensed image.
"""
Expand Down
16 changes: 8 additions & 8 deletions autolens/interferometer/plot/fit_interferometer_plotters.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -375,7 +375,7 @@ def figures_2d_of_planes(
self,
plane_index: Optional[int] = None,
plane_image: bool = False,
plane_errors: bool = False,
plane_noise_map: bool = False,
plane_signal_to_noise_map: bool = False,
zoom_to_brightest: bool = True,
interpolate_to_uniform: bool = False,
Expand All @@ -398,12 +398,12 @@ def figures_2d_of_planes(
Whether to make a 2D plot (via `imshow`) of the image of a plane in its source-plane (e.g. unlensed).
Depending on how the fit is performed, this could either be an image of light profiles of the reconstruction
of an `Inversion`.
plane_errors
Whether to make a 2D plot (via `imshow`) of the errors of a plane in its source-plane, where the
errors can only be computed when a pixelized source reconstruction is performed and they correspond to
the errors in each reconstructed pixel as given by the inverse curvature matrix.
plane_noise_map
Whether to make a 2D plot of the noise-map of a plane in its source-plane, where the
noise map can only be computed when a pixelized source reconstruction is performed and they correspond to
the noise map in each reconstructed pixel as given by the inverse curvature matrix.
plane_signal_to_noise_map
Whether to make a 2D plot (via `imshow`) of the signal-to-noise map of a plane in its source-plane,
Whether to make a 2D plot of the signal-to-noise map of a plane in its source-plane,
where the signal-to-noise map values can only be computed when a pixelized source reconstruction and they
are the ratio of reconstructed flux to error in each pixel.
zoom_to_brightest
Expand All @@ -430,15 +430,15 @@ def figures_2d_of_planes(
interpolate_to_uniform=interpolate_to_uniform,
)

if plane_errors:
if plane_noise_map:
if self.tracer.planes[plane_index].has(cls=aa.Pixelization):
inversion_plotter = self.inversion_plotter_of_plane(
plane_index=plane_index
)

inversion_plotter.figures_2d_of_pixelization(
pixelization_index=0,
errors=True,
reconstruction_noise_map=True,
zoom_to_brightest=zoom_to_brightest,
interpolate_to_uniform=interpolate_to_uniform,
)
Expand Down
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