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.

March 2023 (2023.3.21.5)

This is the latest version, which primarily brings in stability upgrades and fixes bugs.

July 11 2022 Release

• Starting point API for starting an MCMC fit with walkers in certain positions or maximum likelihood estimator fit with a start point implemented (#562). The example tutorial script for this feature is not written yet.

• Dynamic delta scaling in expectation propagation fits, which prevent over confident result due to error underestimation (#559). The example tutorial script for this feature is not written yet.

• Faster generation of models by sampling better within the prior limits (#558).

2022.05.02.1

• Can make a parameter free across al combined analysis objects (docs / cookbook to be written):

analysis = sum(analysis_list)
analysis = analysis.with_free_parameters(
    model.parameter, 
)

• Model composition using relations (cookbook to be written):

x_list = [464, 658, 806]

m = af.UniformPrior(lower_limit=-0.1, upper_limit=0.1)
c = af.UniformPrior(lower_limit=-10.0, upper_limit=10.0)

analysis_list = []

for x, imaging in zip(x_list, imaging_list):

    y = af.Add(af.Multiply(x, m), c)

    analysis_list.append(
        al.AnalysisImaging(dataset=imaging).with_model(
            model.replacing(
                {
                    model.gaussian.x: gaussian.x,
                }
            )
        )
    )

• Tutorials for fitting a hierarchical model outside of EP.

https://github.com/Jammy2211/autofit_workspace/tree/release/notebooks/howtofit/chapter_graphical_models

• Stability upgrades to EP framework.

March 30 2022

• Support for Python 3.9, 3.10.
• LogGaussianPrior implemented.
• Simultaneous fitting of hieraerchical models (E.g. not just via EP) supported.
• Minor updates to graphical model API.

March 2022

• Sensitivity mapping now have options for customizing priors on the sensitivity component.
• New API for aspects of graphical models.

Winter 2022 Release

This release primarily includes a lot of continued develop of the graphical modeling framework:

https://pyautofit.readthedocs.io/en/latest/features/graphical.html

There are now 4 fully functional tutorials on graphical models in the autofit_workspace, which include expectation propagation and hierarchical models:

https://github.com/Jammy2211/autofit_workspace/tree/release/notebooks/howtofit/chapter_graphical_models

The release contains a lot of small improvements and additional features to the database, search grid search and general model-fitting. These are documented throughout the autofit_workspace.

Summer Release

We have switched to a date-based numbering system, with a long-term to do an overnight build nightly.

• Add scipy LBFGS as optimizer.
• Fixed bugs where building of database via scrape method would not support all queries.
• Database and Aggregator support for GridSearch objects and results.
• Improvements to unique identifier including option to exclude certain values from being tracked.
• Interal storage of mappings between priors and model now uses the string representation of the prior with an id, to ensure there is no ambuigity in matching,
• Further development on graphical modeling framework, primarily refactoring of existing code.