Releases · Jammy2211/PyAutoLens

Improvements to HowToFit lectures: rhayes777/PyAutoFit#1022
Support for NumPy arrays in model composition and prior creation, for example creating an ndarray of input shape where each value is a free parameter in the seach: rhayes777/PyAutoFit#1021
Name of optimize searches renamed to mle, for maximum likelihood estimator, with improvements to visualization: rhayes777/PyAutoFit#1029
Improvement to sensitivity mapping functionality and results: https://github.com/rhayes777/PyAutoFit/pulls?q=is%3Apr+is%3Aclosed
More improvements to JAX Pytree interface, documentation still to come.

Assets 2

16 May 10:37

Jammy2211

2024.5.16.0

166f4bd

May 2024

PyAutoFit:

Nautilus now outputs results on the fly: rhayes777/PyAutoFit#961
Output latent samples of a model-fit, which are parameters derived from a model which may be marginalized over:

PR: rhayes777/PyAutoFit#994
Example: https://github.com/Jammy2211/autofit_workspace/blob/release/notebooks/cookbooks/analysis.ipynb

model.info file displays complex models in a more concise and readable way: rhayes777/PyAutoFit#1012
All samples with a weight below an input value are now removed from samples.csv to save hard disk space: rhayes777/PyAutoFit#979
Documentation describing autofit scientific workflow: rhayes777/PyAutoFit#1011
Refactor visualization into stand alone module: rhayes777/PyAutoFit#995
Refactor how results are returned after a search: rhayes777/PyAutoFit#989
Improved parallelism logging: rhayes777/PyAutoFit#1009
Likelihood consistency check now performed internally: rhayes777/PyAutoFit#987
Generation of initial search samples is now performed in parallel: rhayes777/PyAutoFit#997
No longer store search_internal on hard-disk. simplifying source code internals: rhayes777/PyAutoFit#938
Multiple small bug fixes and improvements to interface.

PyAutoGalaxy / PyAutoLens:

Remove Plane object and replace with Galaxies object
Shapelets improvements: Jammy2211/PyAutoGalaxy#173
Adaptive over sampling of grids for a pixelization: Jammy2211/PyAutoGalaxy#168
BasisPlotter which plots each basis (e.g. each Gaussian of an MGE): Jammy2211/PyAutoGalaxy#173
Plot mappings between source and image plane of a pixelization as lines: Jammy2211/PyAutoGalaxy#172
For multi-wavelength datasets model offsets between each dataset: Jammy2211/PyAutoGalaxy#171
Modeling of background sky: Jammy2211/PyAutoGalaxy#170
Improvements to use of adapt images for adaptive pixelizations: Jammy2211/PyAutoGalaxy#160
Improved angle conversions for computing errors on mass profile and shear angles from ell_comps: Jammy2211/PyAutoGalaxy#169
Remove sub_size from all classes (e.g. Array2D, Mask2D) to simplify API.
MaternKernel added: Jammy2211/PyAutoGalaxy#148
Refactor of tracer module: #255

Assets 2

27 Jan 20:05

Jammy2211

2024.1.27.4

dcbbbac

January 2024 (2024.1.27.4)

Log10 plots implemented in 1D and 2D, which show certain quantities (e.g. galaxy convergence) more clear and include contours showing log10 values:

Improved subplots including addition of log10 panels:

Pixelization API now has separate entry for an image_mesh, defining how the source pixel centres are computed (E.g. using a KMeans clustering) and the mesh is now just the method (e.g. Delaunay):

pixelization = al.Pixelization(
    image_mesh=al.image_mesh.Overlay(shape=(25, 25)),
    mesh=al.mesh.Delaunay(),
    regularization=al.reg.Constant(coefficient=1.0),
)

Implemented Hilbert image-mesh which is a significant improvement on KMeans clustering for creating the distribution of source pixels for a pixelization and inversion.
Simplified adapt_dataset API to now only pass via an AdaptImage class, which is not passed as Galaxy attributes anymore but instead through the FitImaging object.
Removed SetupAdapt object and pass image_mesh_pixels as an integer through pipelines.
Added Exponential / Gaussian smoothing kernels for regularization documented in Vernardos 2022 (https://arxiv.org/abs/2202.09378)

Assets 2

23 Oct 13:07

Jammy2211

2023.10.23.3

3d5a0c3

October 2023 (2023.10.23.3)

Support for Python 3.11 by updating requirement on core libraries (e.g. numpy, scipy, scikit-learn).
Fix issues with sqlite database following switch from .pickle outputs to .json / .fits / .csv.
Database use of Samples object much more efficient.
Methods to output classes to hard-disk (e.g. output_to_json, from_json, to_dict) are now all handled and called from autoconf.
Fix bug where nautilus parallel fits sometimes crashed.
Fix bug where nautilus single CPU fits did not work.

Assets 2

18 Sep 13:16

Jammy2211

2023.9.18.4

b486fc8

September (v2023.9.18.4)

This release implements two major changes to PyAutoLens:

Nautilus:

For the past ~3 years, lens model fitting has used the nested sampling algorithm Dynesty.

Recently, a new nested sampler, Nautilus (https://nautilus-sampler.readthedocs.io/en/stable/), was released, which uses machine-learning based techniques to improve sampling.

Extensive testing of lens modeling with Nautilus has revealed that it:

Speeds up the fitting of simple lens models by ~x2 - x3.
Speeds up the fitting of complex lens models by ~x3 - x5+.
Is more robust and reliable (e.g less likely to infer a local maxima, can fit more complex lens models).
Controlled predominantly by just one parameter n_live, so is simpler to use than dynesty.
Parallelization using Python multiprocessing is more efficient than dynesty and now supports proper error handling.

Nautilus is therefore now the default lens modeler, with all workspace examples updated accordingly.

NOTE: Nautilus does not currently support on-the-fly output and to get the results of a lens model mid-fit a user can instead cancel the run (e.g. via Ctrl + C) and restart it, where the maximum likelihood model will be output.

Results Output

Result metadata was previously output as .pickle files, which were not human readable and depended on project imports, hurting backwards compatibility.

All metadata is now output as human readable .json files and dataset as .fits files, making it a lot more straight forward for a user to interpret how data is stored internally within PyAutoLens:

Here is an example of the search.json file:

All internal functionality (e.g. the sqlite database) has been updated to use these files.

All workspace documentation has been updated accordingly.

Other:

imaging/modeling/features split to make linear light profiles and multi gaussian expansion more visible.
Improved HowToLens tutorial 5 on linear light profiles.
Power law with multipole parameterization updated, now supports multipoles of any order (Jammy2211/PyAutoGalaxy#115).
Update certain requirements (e.g. PyYAML) to mitigate installation issues (rhayes777/PyAutoConf#41).
Lots of quality-of-life improvements thoughout the code bases.

Assets 2

05 Jul 15:32

Jammy2211

2023.7.5.2

7559fc6

July (2023.5.7.2)

Bug fixes for new MacOS parallelization.

No new features.

Assets 2

18 Jun 22:27

Jammy2211

2023.6.18.3

34434c6

June 2023 (2023.6.18.3)

Fixes bug so that the all_at_end_png and all_at_end_fits visualization configuration options now actually do output all images at the end of a model-fit as .png and .fits files.
Fixes bug so that pixelized source reconstructions are output as .fits files at the end.
Fixes bug so that visuals at end display correctly.