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PyAutoFit Workspace ==================== .. |binder| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD .. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02550/status.svg :target: https://doi.org/10.21105/joss.02550 |binder| |JOSS| `Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ | `readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ | `Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=notebooks/overview/overview_1_the_basics.ipynb>`_ | `HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_ Welcome to the **PyAutoFit** Workspace! Getting Started --------------- You can get set up on your personal computer by following the installation guide on our `readthedocs <https://pyautofit.readthedocs.io/>`_. Alternatively, you can try **PyAutoFit** out in a web browser by going to the `autofit workspace Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=notebooks/overview/overview_1_the_basics.ipynb>`_. Where To Go? ------------ We recommend that you start with the ``autofit_workspace/notebooks/overview/overview_1_the_basics.ipynb`` notebook, which will give you a concise overview of **PyAutoFit**'s core features and API. Next, read through the overview example notebooks of features you are interested in, in the folder: ``autofit_workspace/notebooks/overview``. Then, you may wish to implement your own model in **PyAutoFit**, using the ``cookbooks`` for help with the API. Alternative, you may want to checkout the ``features`` package for a list of advanced statistical modeling features. HowToFit -------- For users less familiar with Bayesian inference and scientific analysis you may wish to read through the **HowToFits** lectures. These teach you the basic principles of Bayesian inference, with the content pitched at undergraduate level and above. A complete overview of the lectures `is provided on the HowToFit readthedocs page <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.htmll>`_ Workspace Structure ------------------- The workspace includes the following main directories: - ``notebooks``: **PyAutoFit** examples written as Jupyter notebooks. - ``scipts``: **PyAutoFit** examples written as Python scripts. - ``projects``: Example projects which use **PyAutoFit**, which serve as a illustration of model-fitting problems and the **PyAutoFit** API. - ``config``: Configuration files which customize **PyAutoFit**'s behaviour. - ``dataset``: Where data is stored, including example datasets distributed with **PyAutoFit**. - ``output``: Where the **PyAutoFit** analysis and visualization are output. The **examples** in the notebooks and scripts folders are structured as follows: - ``overview``: Examples using **PyAutoFit** to compose and fit a model to data via a non-linear search. - ``cookbooks``: Concise API reference guides for **PyAutoFit**'s core features. - ``features``: Examples of **PyAutoFit**'s advanced modeling features. - ``howtofit``: Detailed step-by-step tutorials. - ``searches``: Example scripts of every non-linear search supported by **PyAutoFit**. - ``plot``: An API reference guide for **PyAutoFits**'s plotting tools. The following **projects** are available in the project folder: - ``astro``: An Astronomy project which fits images of gravitationally lensed galaxies. Workspace Version ----------------- This version of the workspace are built and tested for using **PyAutoFit v2023.7.5.2**. Support ------- Support for installation issues and integrating your modeling software with **PyAutoFit** is available by `raising an issue on the autofit_workspace GitHub page <https://github.com/Jammy2211/autofit_workspace/issues>`_. or joining the **PyAutoFit** `Slack channel <https://pyautofit.slack.com/>`_, where we also provide the latest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send an `email <https://github.com/Jammy2211>`_ requesting an invite.