November 2024 update

Garbage collection and other things to improve memory use.

November 2024

Minor release with stability updates and one main feature.

• Sensitivity Mapping improvements.

• Start point for MCMC.

September 2024

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: #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: #1021
• Name of optimize searches renamed to mle, for maximum likelihood estimator, with improvements to visualization: #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.

May 2024

PyAutoFit:

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

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

January 2024 (2024.1.27.4)

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

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

September (v2023.9.18.4)

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.

July (2023.5.7.2)

Bug fixes for new MacOS parallelization.

No new features.

June 2023 (2023.6.12.5)

• Improvements to combined analyses (e.g. summed Analysis objects to fit multipole datasets), for example better output paths for visualization, options to visualize before a fit and making combined figures across analyses:

#715
#703
#701
#696

• Database support for combined analyses:

#708

• Sensitivity mapping visualization improvements:

#711

• Improvements to graphical models:

#712
#709

March 2023 (2023.3.27.1)

• Update to dynesty 2.1.0, which avoids errors when sampling flat likelihood functions.
• Support filtering for the as_model function which generates a model from a previous result
• Cubic spline interpolation of model results with shared parameter (e.g. time varying models)
• General bug fixes.