Autolens

Small bug fixes and optimizations for Euclid lens modeling pipeline.

Minor release with stability updates and one main feature.

- Extra Galaxies API for modeling multiple galaxies at once: https://github.com/Jammy2211/autolens_workspace/blob/release/notebooks/features/extra_galaxies.ipynb

- Multiwavelength lens modeling with SLaM multi wavelength pipelines: https://github.com/Jammy2211/autolens_workspace/tree/main/scripts/advanced/chaining/slam/multi

- More improvements to Point source solver and Shape solver.

- Sensitivity mapping improvements which will be fully documented in the future.

This release updates all projects to support Python 3.12, with support tested for Python 3.9 - 3.12 and 3.11 regarded as most stable.

This includes many project dependency updates:

https://github.com/rhayes777/PyAutoFit/blob/main/requirements.txt
https://github.com/rhayes777/PyAutoFit/blob/main/optional_requirements.txt
https://github.com/Jammy2211/PyAutoGalaxy/blob/main/requirements.txt
https://github.com/Jammy2211/PyAutoGalaxy/blob/main/optional_requirements.txt
https://github.com/Jammy2211/PyAutoLens/blob/main/requirements.txt
https://github.com/Jammy2211/PyAutoLens/blob/main/optional_requirements.txt

**Workspace Restructure:**

This release has a workspace restructure, which is now grouped at a high level by tasks (e.g. `modeling`, `simulators`) rather than datasets:

https://github.com/Jammy2211/autolens_workspace

The readthedocs have been greatly simplified and include a **new user guide** to help navigate the new workspace:

https://pyautolens.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html

**PyAutoLens:**

- Point source modeling significantly improved with triangle tracing method, image plane chi squared supported: https://github.com/Jammy2211/autolens_workspace/tree/release/notebooks/modeling/point_source
- Shape based point-source modeling for magnification calculations: https://github.com/Jammy2211/PyAutoLens/pull/300
- Improved Cosmology wrapper to support new `astropy` and easier to use in models: https://github.com/Jammy2211/PyAutoGalaxy/pull/193
- Ellipse Fitting: https://github.com/Jammy2211/autogalaxy_workspace/tree/release/notebooks/advanced/misc/ellipse


**PyAutoFit:**

https://github.com/rhayes777/PyAutoFit/pulls?q=is%3Apr+is%3Aclosed

- Improvements to HowToFit lectures: https://github.com/rhayes777/PyAutoFit/pull/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: https://github.com/rhayes777/PyAutoFit/pull/1021
- Name of `optimize` searches renamed to `mle`, for maximum likelihood estimator, with improvements to visualization: https://github.com/rhayes777/PyAutoFit/pull/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.

**PyAutoFit:**

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

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

**PyAutoGalaxy / PyAutoLens:**

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

- 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)

- 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.