Autofit

Bug fixes for new MacOS parallelization.

No new features.

- 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:

https://github.com/rhayes777/PyAutoFit/pull/715
https://github.com/rhayes777/PyAutoFit/pull/703
https://github.com/rhayes777/PyAutoFit/pull/701
https://github.com/rhayes777/PyAutoFit/pull/696

- Database support for combined analyses:

https://github.com/rhayes777/PyAutoFit/pull/708

- Sensitivity mapping visualization improvements:

https://github.com/rhayes777/PyAutoFit/pull/711

- Improvements to graphical models:

https://github.com/rhayes777/PyAutoFit/pull/712
https://github.com/rhayes777/PyAutoFit/pull/709

- [Update to `dynesty` 2.1.0, which avoids errors when sampling flat likelihood functions](https://github.com/rhayes777/PyAutoFit/pull/693).
- [Support filtering for the as_model function which generates a model from a previous result](https://github.com/rhayes777/PyAutoFit/pull/691)
- [Cubic spline interpolation of model results with shared parameter (e.g. time varying models)](https://github.com/rhayes777/PyAutoFit/pull/687)
- General bug fixes.

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

- Starting point API for starting an MCMC fit with walkers in certain positions or maximum likelihood estimator fit with a start point implemented (https://github.com/rhayes777/PyAutoFit/pull/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 (https://github.com/rhayes777/PyAutoFit/pull/559). The example tutorial script for this feature is not written yet.

- Faster generation of models by sampling better within the prior limits (https://github.com/rhayes777/PyAutoFit/pull/558).

- 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.