autofit 0.68.0

Classy Probabilistic Programming

PyAutoFit

PyAutoFit is a Python-based probabilistic programming language which:

• Makes it straight forward 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' of model fitting, including model composition and customization, outputting results in a structured path format and model-specific visualization.
• Includes bespoke tools for big-data analysis, including massively parallel model fitting and database output structures so that large suites of results can be loaded into Jupyter notebooks post-analysis.

Advanced statistical methods in PyAutoFit include:

• Graphical Models: Combine fits to many different datasets and determine the global parameters of a model, using an Expectation Propagation Bayesian framework <https://arxiv.org/abs/1412.4869v1>_.
• Non-Linear Search Grid-Search: Simplify the fitting of highly complex parameters spaces by using a grid of non-linear searches, which can be performed simultaneously in an embarrassingly parallel fashion.
• Transdimensional Pipelines: Chain together multiple fits of different models to streamline model comparison and automate complex model-fitting tasks.

Installation

PyAutoFit requires Python 3.6+ and you can install it via pip or conda (see this link <https://pyautofit.readthedocs.io/en/latest/general/installation.html#installation-with-conda>_ for conda instructions).

.. code-block:: bash

pip install autofit

Next, clone the autofit_workspace <https://github.com/Jammy2211/autofit_workspace>_, which includes PyAutoFit configuration files, example scripts and more!

.. code-block:: bash

cd /path/on/your/computer/you/want/to/put/the/autofit_workspace git clone https://github.com/Jammy2211/autofit_workspace --depth 1 cd autofit_workspace

Finally, run welcome.py in the autofit_workspace to get started!

.. code-block:: bash

python3 welcome.py

If your installation had an error, please check the troubleshooting section <https://pyautofit.readthedocs.io/en/latest/general/installation.html#trouble-shooting>_ on our readthedocs.

If you would prefer to Fork / Clone the PyAutoFit GitHub repo, please read the cloning section <https://pyautofit.readthedocs.io/en/latest/general/installation.html#forking-cloning>_ on our readthedocs first.

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/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)

PyAutoFit recognises that this Gaussian may be treated as a model component whose parameters can be fitted for via a non-linear search 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.

Getting Started

To get started checkout our readthedocs <https://pyautofit.readthedocs.io/>, where you'll find our installation guide, a complete overview of PyAutoFit's features, examples scripts and tutorials, detailed API documentation and the HowToFit lecture series <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html> on how to integrate PyAutoFit into your modeling software.

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.