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    "info": {
        "author": "James Nightingale and Richard Hayes",
        "author_email": "richard@rghsoftware.co.uk",
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        "classifiers": [
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: 3.8",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "PyAutoFit: Classy Probabilistic Programming\n===========================================\n\n.. |binder| image:: https://mybinder.org/badge_logo.svg\n   :target: https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/HEAD\n\n.. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02550/status.svg\n   :target: https://doi.org/10.21105/joss.02550\n\n|binder| |JOSS|\n\n`Installation Guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ |\n`readthedocs <https://pyautofit.readthedocs.io/en/latest/index.html>`_ |\n`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_ |\n`HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_\n\nPyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large\ndatasets which:\n\n- Makes it simple to compose and fit mult-level models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_.\n\n- Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis.\n\n- Is built for *big-data* analysis, whereby results are output as a sqlite database which can be queried after model-fitting is complete.\n\n**PyAutoFit** supports advanced statistical methods such as `graphical and hierarchical models <https://pyautofit.readthedocs.io/en/latest/features/graphical.html>`_, `model-fit chaining <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_, `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_ and `massively parallel model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_ .\n\nGetting Started\n---------------\n\nThe following links are useful for new starters:\n\n- `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/release?filepath=introduction.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).\n\n- `The PyAutoFit readthedocs <https://pyautofit.readthedocs.io/en/latest>`_, which includes an `installation guide <https://pyautofit.readthedocs.io/en/latest/installation/overview.html>`_ and an overview of **PyAutoFit**'s core features.\n\n- `The autofit_workspace GitHub repository <https://github.com/Jammy2211/autofit_workspace>`_, which includes example scripts and the `HowToFit Jupyter notebook tutorials <https://github.com/Jammy2211/autofit_workspace/tree/master/notebooks/howtofit>`_ which give new users a step-by-step introduction to **PyAutoFit**.\n\nWhy PyAutoFit?\n--------------\n\n**PyAutoFit** began as an Astronomy project for fitting large imaging datasets of galaxies after the developers found that existing PPLs\n(e.g., `PyMC3 <https://github.com/pymc-devs/pymc3>`_, `Pyro <https://github.com/pyro-ppl/pyro>`_, `STAN <https://github.com/stan-dev/stan>`_)\nwere not suited to the model fitting problems many Astronomers faced. This includes:\n\n- Efficiently analysing large and homogenous datasets with an identical model fitting procedure, with tools for processing the large libraries of results output.\n\n- Problems where likelihood evaluations are expensive (e.g. run times of days per model-fit), necessitating highly customizable model-fitting pipelines with support for massively parallel computing.\n\n- Fitting many different models to the same dataset with tools that streamline model comparison.\n\nIf these challenges sound familiar, then **PyAutoFit** may be the right software for your model-fitting needs!\n\nAPI Overview\n------------\n\nTo illustrate the **PyAutoFit** API, we'll use an illustrative toy model of fitting a one-dimensional Gaussian to\nnoisy 1D data. Here's the ``data`` (black) and the model (red) we'll fit:\n\n.. image:: https://raw.githubusercontent.com/rhayes777/PyAutoFit/master/files/toy_model_fit.png\n  :width: 400\n  :alt: Alternative text\n\nWe define our model, a 1D Gaussian by writing a Python class using the format below:\n\n.. code-block:: python\n\n    class Gaussian:\n\n        def __init__(\n            self,\n            centre=0.0,        # <- PyAutoFit recognises these\n            normalization=0.1, # <- constructor arguments are\n            sigma=0.01,        # <- the Gaussian's parameters.\n        ):\n            self.centre = centre\n            self.normalization = normalization\n            self.sigma = sigma\n\n        \"\"\"\n        An instance of the Gaussian class will be available during model fitting.\n\n        This method will be used to fit the model to data and compute a likelihood.\n        \"\"\"\n\n        def model_data_1d_via_xvalues_from(self, xvalues):\n\n            transformed_xvalues = xvalues - self.centre\n\n            return (self.normalization / (self.sigma * (2.0 * np.pi) ** 0.5)) * \\\n                    np.exp(-0.5 * (transformed_xvalues / self.sigma) ** 2.0)\n\n**PyAutoFit** recognises that this Gaussian may be treated as a model component whose parameters can be fitted for via\na non-linear search like `emcee <https://github.com/dfm/emcee>`_.\n\nTo fit this Gaussian to the ``data`` we create an Analysis object, which gives **PyAutoFit** the ``data`` and a\n``log_likelihood_function`` describing how to fit the ``data`` with the model:\n\n.. code-block:: python\n\n    class Analysis(af.Analysis):\n\n        def __init__(self, data, noise_map):\n\n            self.data = data\n            self.noise_map = noise_map\n\n        def log_likelihood_function(self, instance):\n\n            \"\"\"\n            The 'instance' that comes into this method is an instance of the Gaussian class\n            above, with the parameters set to values chosen by the non-linear search.\n            \"\"\"\n\n            print(\"Gaussian Instance:\")\n            print(\"Centre = \", instance.centre)\n            print(\"normalization = \", instance.normalization)\n            print(\"Sigma = \", instance.sigma)\n\n            \"\"\"\n            We fit the ``data`` with the Gaussian instance, using its\n            \"model_data_1d_via_xvalues_from\" function to create the model data.\n            \"\"\"\n\n            xvalues = np.arange(self.data.shape[0])\n\n            model_data = instance.model_data_1d_via_xvalues_from(xvalues=xvalues)\n            residual_map = self.data - model_data\n            chi_squared_map = (residual_map / self.noise_map) ** 2.0\n            log_likelihood = -0.5 * sum(chi_squared_map)\n\n            return log_likelihood\n\nWe can now fit our model to the ``data`` using a non-linear search:\n\n.. code-block:: python\n\n    model = af.Model(Gaussian)\n\n    analysis = Analysis(data=data, noise_map=noise_map)\n\n    emcee = af.Emcee(nwalkers=50, nsteps=2000)\n\n    result = emcee.fit(model=model, analysis=analysis)\n\nThe ``result`` contains information on the model-fit, for example the parameter samples, maximum log likelihood\nmodel and marginalized probability density functions.\n\nSupport\n-------\n\nSupport for installation issues, help with Fit modeling and using **PyAutoFit** is available by\n`raising an issue on the GitHub issues page <https://github.com/rhayes777/PyAutoFit/issues>`_.\n\nWe also offer support on the **PyAutoFit** `Slack channel <https://pyautoFit.slack.com/>`_, where we also provide the \nlatest updates on **PyAutoFit**. Slack is invitation-only, so if you'd like to join send \nan `email <https://github.com/Jammy2211>`_ requesting an invite.\n\n",
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