Metadata-Version: 2.1
Name: autofit
Version: 0.74.0
Summary: Classy Probabilistic Programming
Home-page: https://github.com/rhayes777/AutoFit
Author: James Nightingale and Richard Hayes
Author-email: richard@rghsoftware.co.uk
License: MIT License
Description: PyAutoFit: Classy Probabilistic Programming
        ===========================================
        
        .. |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/26262bc184d0c77795db70636a004c9dce9c52b0?filepath=introduction.ipynb>`_ |
        `HowToFit <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>`_
        
        **PyAutoFit** is a Python-based probabilistic programming language which:
        
        - Makes it simple to compose and fit models using a range of Bayesian inference libraries, such as `emcee <https://github.com/dfm/emcee>`_ and `dynesty <https://github.com/joshspeagle/dynesty>`_.
        
        - Handles the 'heavy lifting' that comes with model-fitting, including model composition & customization, outputting results, model-specific visualization and posterior analysis.
        
        - Is built for *big-data* analysis, whereby results are output as a database which can be loaded after model-fitting is complete.
        
        **PyAutoFit** supports advanced statistical methods such as `massively parallel non-linear search grid-searches <https://pyautofit.readthedocs.io/en/latest/features/search_grid_search.html>`_, `chaining together model-fits <https://pyautofit.readthedocs.io/en/latest/features/search_chaining.html>`_  and `sensitivity mapping <https://pyautofit.readthedocs.io/en/latest/features/sensitivity_mapping.html>`_.
        
        Getting Started
        ---------------
        
        The following links are useful for new starters:
        
        - `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/26262bc184d0c77795db70636a004c9dce9c52b0?filepath=introduction.ipynb>`_, where you can try **PyAutoFit** in a web browser (without installation).
        
        - `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.
        
        - `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**.
        
        Why PyAutoFit?
        --------------
        
        **PyAutoFit** began as an Astronomy project for fitting large imaging datasets of galaxies after the developers found that existing PPLs
        (e.g., `PyMC3 <https://github.com/pymc-devs/pymc3>`_, `Pyro <https://github.com/pyro-ppl/pyro>`_, `STAN <https://github.com/stan-dev/stan>`_)
        were not suited to the type of model fitting problems Astronomers faced. This includes:
        
        - Efficiently analysing large and homogenous datasets with an identical model fitting procedure, with tools for processing the large libraries of results output.
        
        - Problems where likelihood evaluations are expensive, leading to run times of days per fit and necessitating support for massively parallel computing.
        
        - Fitting many different models to the same dataset with tools that streamline model comparison.
        
        If these challenges sound familiar, then **PyAutoFit** may be the right software for your model-fitting needs!
        
        API Overview
        ------------
        
        To illustrate the **PyAutoFit** API, we'll use an illustrative toy model of fitting a one-dimensional Gaussian to
        noisy 1D data. Here's the ``data`` (black) and the model (red) we'll fit:
        
        .. image:: https://raw.githubusercontent.com/rhayes777/PyAutoFit/master/files/toy_model_fit.png
          :width: 400
          :alt: Alternative text
        
        We define our model, a 1D Gaussian by writing a Python class using the format below:
        
        .. code-block:: python
        
            class Gaussian:
        
                def __init__(
                    self,
                    centre=0.0,     # <- PyAutoFit recognises these
                    intensity=0.1,  # <- constructor arguments are
                    sigma=0.01,     # <- the Gaussian's parameters.
                ):
                    self.centre = centre
                    self.intensity = intensity
                    self.sigma = sigma
        
                """
                An instance of the Gaussian class will be available during model fitting.
        
                This method will be used to fit the model to ``data`` and compute a likelihood.
                """
        
                def profile_from_xvalues(self, xvalues):
        
                    transformed_xvalues = xvalues - self.centre
        
                    return (self.intensity / (self.sigma * (2.0 * np.pi) ** 0.5)) * \
                            np.exp(-0.5 * (transformed_xvalues / self.sigma) ** 2.0)
        
        **PyAutoFit** recognises that this Gaussian may be treated as a model component whose parameters can be fitted for via
        a ``NonLinearSearch`` like `emcee <https://github.com/dfm/emcee>`_.
        
        To fit this Gaussian to the ``data`` we create an Analysis object, which gives **PyAutoFit** the ``data`` and a
        ``log_likelihood_function`` describing how to fit the ``data`` with the model:
        
        .. code-block:: python
        
            class Analysis(af.Analysis):
        
                def __init__(self, data, noise_map):
        
                    self.data = data
                    self.noise_map = noise_map
        
                def log_likelihood_function(self, instance):
        
                    """
                    The 'instance' that comes into this method is an instance of the Gaussian class
                    above, with the parameters set to values chosen by the non-linear search.
                    """
        
                    print("Gaussian Instance:")
                    print("Centre = ", instance.centre)
                    print("Intensity = ", instance.intensity)
                    print("Sigma = ", instance.sigma)
        
                    """
                    We fit the ``data`` with the Gaussian instance, using its
                    "profile_from_xvalues" function to create the model data.
                    """
        
                    xvalues = np.arange(self.data.shape[0])
        
                    model_data = instance.profile_from_xvalues(xvalues=xvalues)
                    residual_map = self.data - model_data
                    chi_squared_map = (residual_map / self.noise_map) ** 2.0
                    log_likelihood = -0.5 * sum(chi_squared_map)
        
                    return log_likelihood
        
        We can now fit our model to the ``data`` using a ``NonLinearSearch``:
        
        .. code-block:: python
        
            model = af.PriorModel(Gaussian)
        
            analysis = Analysis(data=data, noise_map=noise_map)
        
            emcee = af.Emcee(nwalkers=50, nsteps=2000)
        
            result = emcee.fit(model=model, analysis=analysis)
        
        The ``result`` contains information on the model-fit, for example the parameter samples, maximum log likelihood
        model and marginalized probability density functions.
        
        Support
        -------
        
        Support for installation issues, help with Fit modeling and using **PyAutoFit** is available by
        `raising an issue on the GitHub issues page <https://github.com/rhayes777/PyAutoFit/issues>`_.
        
        We also offer support on 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.
Keywords: cli
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
