Autofit

Minor release with stability updates and one main feature.

- Sensitivity Mapping improvements.

- Start point for MCMC.

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

**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.

- Stability upgrades for change from .pickle to .json files.
- JAX implementation improved, still in development.
- Sensitivity mapping improvements.

- 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.
- Fix bug where `nautilus` parallel fits sometimes crashed.
- Fix bug where `nautilus` single CPU fits did not work.

This release implements two major changes to **PyAutoFit**:

**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 **PyAutoFit**:


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.

**Nautilus**

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

This release implements this.