autofit 0.71.5

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.

• Provides advanced statistical methods such as transdimensional modeling, advanced model comparison and advanced grid-searches.

Getting Started

You can try PyAutoFit right now without installation by checking out the overview Jupyter Notebook on our Binder <https://mybinder.org/v2/gh/Jammy2211/autofit_workspace/664a86aa84ddf8fdf044e2e4e7db21876ac1de91?filepath=overview.ipynb>_.

On the PyAutoFit readthedocs <https://pyautofit.readthedocs.io/>_ you'll find the installation guide, a complete overview of PyAutoFit's features, examples scripts, and the HowToFit Jupyter notebook tutorials <https://pyautofit.readthedocs.io/en/latest/howtofit/howtofit.html>_ which introduces new users to PyAutoFit.

Installation

PyAutoFit requires Python 3.6+ and you can install it via pip or conda (see this link <https://pyautofit.readthedocs.io/en/latest/installation/conda.html>_ 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/installation/troubleshooting.html>_ 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/installation/source.html>_ 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 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 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.