Autolens

This release switches our versionin scheme to dates of the format `year.month.day.release`. This version is shared across all `PyAuto` projects.

There is no major new functionality in this release.

This release brings in a number of features for improved model-fitting, all of which come from an updated to **PyAutoFit**:

- First class support for parallel Dynesty and Emcee model-fits. Previously, parallel fits were slow due to communication overheads, which are now handled correctly with **PyAutoFit**. One can expect a speed up close to the number of CPUs, for example a fit on 4 CPUs should take ~x4 less time to run. The API to perform a parallel fit is as follows:


search = af.DynestyStatic(
path_prefix=path.join("imaging", "modeling"),
name="mass[sie]_source[bulge]",
unique_tag=dataset_name,
nlive=50,
number_of_cores=1, Number of cores controls parallelization
)


- In-built visualization tools for a non-linear search, using each non-linear search's inbuilt visualization libraries. Examples of each visualization are provided at the following link:

https://github.com/Jammy2211/autolens_workspace/tree/release/scripts/plot/search

- Updated to the unique tag generation, which control the output model folder based on the model, search and name of the dataset.

- Improved database tools for querying, including queries based on the name of the specific fit of the non-linear search and the dataset name unique tag.

- Improved visualization for interferometer modeling, including dirty image, noise-map, residuals, etc.
- Unique identifier now uses specific settings of a search.

**MAJOR RELEASE:**

This release is a major overhaul of the PyAutoLens lens modeling API, and as such means that all results are not backwards compatible. Do not update to this version of autolens if you need to use previous results!

The main purpose of this release are major changes to the PyAutoLens source code that utilize the ‘.sqlite’ database output feature. Large suits of lens modeling results can be output directly to a ‘sqlite’ database, such that the results of model-fits to many tens of thousands of lenses can be efficiently loaded and manipulated in a Jupyter notebook. For those who have used the Aggregator, the Aggregator now interacts with the database.

The database has allowed us to remove some core components and features of PyAutoLens, in particular the removal of phases, output-path tagging and Setup objects. The new API for these features is nevertheless easily recognisable for existing users, and the autolens_workspace contains numerous example scripts for the new API.

Over the next month, we will be fully testing the database feature and are likely to make a number of changes to it. Therefore, if you wish to use the database now you should speak to me on SLACK first.

**Phase Removal:**

Phases have been completed removing from PyAutoLens, meaning that the PhaseImaging object used to perform model-fitting no longer exists. The following code which would have been used to perform a model fit:


phase = al.PhaseImaging(
search=af.DynestyStatic(name="phase[example]",n_live_points=50),
galaxies=dict(lens=lens_galaxy_model, source=source_galaxy_model),
)

result = phase.run(dataset=imaging, mask=mask)


Is now performed as follows:

search = af.DynestyStatic(name="search[example]", nlive=50)
analysis = al.AnalysisImaging(dataset=imaging)
result = search.fit(model=model, analysis=analysis)

**Tagging Removal + Unique Identifier:**

The tagging of output folders, where their name was customized based on the model fitted and search used, has been removed. This has been replaced with a unique identifier string which forms the inner folder of the model-fit output path.

This makes the output paths of results consist of many less folders.

**SQLite Database:**

The database moves the results that are output from the output folder to a ‘.sqlite’ database file, which can then be access like the Aggregator. This database runs significantly faster than the aggregator and supports advanced queries. Information on the database can be found in the database folder on the workspace.

**Pipelines + SLaM:**

The removal of phases and use of a unique identifier has allowed us to completely change how pipelines runner scripts are written, in a significantly more concise and readable way. If you are familiar with pipelines, the new API should be instantly familiar, but allows for a lot more customization is how the model-fit is performed. An example of a SLAM runner is linked to below:

**HowToLens:**

I have improved the HowToLens lectures (chapter 1 in particular) and updated them for the new API. In particular, chapter 2 now focused on just lens modeling whereas chapter 3 focused entirely on search chaining + pipelines.

**GalaxyModel Removal:**

The GalaxyModel object has been removed, and can now be created via the `af.Model` command:

lens_galaxy_model = af.Model(al.Galaxy, redshift=0.5, mass=al.mp.SphIsothermal)

**1D Data Structures + Figures:**

One dimensional data structures (e.g. `Array1D`, `Grid1D`) have been added. These can be input into Profile objects to compute quantities like the convergence in 1D, e.g. `convergence_1d_from_grid(grid=grid)`.

If a 2D gird is input into a 1D function, the 2D grid is projected to 1D from the centre of the profile and along its major axis, such that this quantity can be cleanly plotted in 1D.

One dimensional plots of the convergence and potential are now accessible in ProfilePlotter and GalaxyPlotter objects.

**Name changes:**

The following names of objects have been changed:


`Elliptical` -> `Ell` (e.g. `EllipticalIsothermal` -> `ElIIsothermal`)
`Sphericall` -> `Sph` (e.g. `SphericalIsothermal` -> `SphIsothermal`)
`CollectionPriorModel` -> `Collection`
`PriorModel` -> `Model`
`image_from_grid` -> `image_2d_from_grid`
`convergence_from_grid` -> `convergence_2d_from_grid`
`potential_from_grid` -> `potential_2d_from_grid`
`deflections_from_grid` -> `deflections_2d_from_grid`

Tagged release for PyAutoLens JOSS submission.

**API changes:**

A number of data structure objects have been renamed to make their dimensionality explicit (e.g. Grid -> Grid2D , Array -> Array2D). The method in_2d of data structures is now called native whereas in_1d is now slim.

**Visualization:**

- Major visualization refactor with minor change to Plotter API.
- Methods which wrap matploitlib methods now have a dedicated config file for default settings.
- Visuals can be manually added to any figure, or plotted via Include. plotter methods are now Plotter classes instead of plotter functions.
- A complete API reference guide of the new visualization tools can be found at autolens_workspace/notebooks/plot. This includes examples of how to plot every object and how to customize every matplotlib method that is called.

**Decomposed Lens Modeling pipelines:**

Although decomposed (e.g. stellar + dark) modeling has been feasible in PyAutoLens for a while, the SLaM pipelines on the autolens_workspace have now been comprehensively tested on mock and real datasets and are ready for generic adoption to any science use-case.

See autolens_workspace/notebooks/advanced/slam/with_lens_light for example pipelines.

**Interferometer:**

Support for interferometer analysis with PyLops is now fully functionality, which makes analysing millions of visibilities with pixelized sources feasible.