autofit 0.44.2

Classy non-linear optimisation

PyAutoFit

PyAutoFit is a Python software framework that enables contemporary Bayesian inference techniques to be applied to large data-sets in a fully automated way. Acting as an interface between Python classes and non-linear sampling packages such as PyMultiNest, PyAutoFit allows transdimensional model fitting pipelines to be built for general modeling problems.

[The key thing about PyAutoFit is that it uses multiplle NLOs.]

Python Example

In this example, we perform galaxy photometry on two merging galaxies by fitting each with an elliptial Sersic light profile:

Traditional methods would fit the light of both galaxies simultaneously, making parameter space more complex and slower to sample. With PyAutoFit we can break the analysis down into three phases:

1. Fit the light of the left galaxy.
2. Fit the light of the right galaxy, using the model of the left galaxy from phase 1 to better deblend their light.
3. Fit the light of both galaxies, using the results of phase 1 & 2 to initialize where the non-linear optimizer searches parameter space.

PyAutoFit determines the components of a model by interacting with Python classes. For this example we use the EllipticalSersic class:

class EllipticalSersic(object):

    def __init__(
            self,
            centre: tuple = (0.0, 0.0),     # <- PyAutoFit recognises these
            axis_ratio: float = 1.0,        #    constructor inputs as the
            phi: float = 0.0,               #    model parameters of the
            intensity: float = 0.1,         #    EllipticalSersic model.
            effective_radius: float = 0.6,
            sersic_index: float = 4.0,
    ):

        self.centre = centre
        self.axis_ratio = axis_ratio
        self.phi = phi
        self.intensity = intensity
        self.effective_radius = effective_radius
        self.sersic_index = sersic_index

This model, and its model parameters, are then used by PyAutoFit to build our 3 phase model-fitting pipeline:

import autofit as af

def make_pipeline():

    pipeline_name = "pipeline_example__fitting_multiple_galaxies"

    # In phase 1, we fit the main galaxy with a bulge + disk model, assuming that
    # not fitting the satellite won't significantly degrade the overall fit.

    phase1 = af.Phase(phase_name="phase_1__main_galaxy_fit",
            main_galaxy=af.PriorModel(
                bulge=af.toy.light_profiles.EllipticalSersic,
                disk=af.toy.light_profiles.EllipticalSersic),
        optimizer_class=af.MultiNest)

    # In phase 2, we fit the satellite galaxy's light. The best-fit model of the
    # main galaxy in phase 1 is used to subtract its light and cleanly reveal the
    # satellite for the fit. This information is passed using 'instance' term below.

    phase2 = af.Phase(phase_name="phase_2__satellite",
            main_galaxy=af.PriorModel(
                bulge=phase1.result.instance.main_galaxy.bulge,
                disk=phase1.result.instance.main_galaxy.disk),
            satellite_galaxy=af.PriorModel(
                light=af.toy.EllipticalSersic),
        optimizer_class=af.MultiNest)

    # In phase 3, we fit the light of both galaxies simultaneously using priors
    # derived from the results of phases 1 & 2 to begin sampling in the maximum
    # likelihood regions of parameter space. This information is passed using
    # the 'model' term below.

    phase3 = af.Phase(phase_name="phase_3__all_galaxies",
            main_galaxy=af.PriorModel(
                bulge=phase1.result.model.main_galaxy.bulge,
                disk=phase1.result.model.main_galaxy.disk),
            left_satellite=af.PriorModel(
                light=phase2.result.model.left_satellite.light),
        optimizer_class=af.MultiNest)

    return af.Pipeline(pipeline_name, phase1, phase2, phase3)

Status

PyAutoFit is ready for adoption by model-fitting packages, and is currently used by PyAutoLens for modeling strong gravitational lens galaxies.PyAutoFit

However, PyAutoFit is still currently in alpha and lacking many keey materials to help new user adoption (documentation, templates, etc.). Projects interested in using PyAutoFit should therefore contact us directly first about the best way to get started.

Slack

We're building a PyAutoFit community on Slack, so you should contact us on our Slack channel before getting started. Here, I will give you the latest updates on the software & discuss how best to use PyAutoFit for your science case.

Unfortunately, Slack is invitation-only, so first send me an email requesting an invite.

Features

PyAutoFit's advanced modeling features include:

• Non-linear Optimizers - Fit models to data using a variety of non-linear search techniques and algorithms.
• Model Mapping - Interface with Python classes to define a model and map non-linear optimizer samples to a model parameterization.
• Pipelines - Write transdimensional analysis pipelines to fit complex models to large data-sets in a fully automated way.

The following features are planned for the next year:

• Hierarchical modeling - Fit for global trends of a model within individual fits to a data-set, permitting more general inferences to be made.
• Time series modelling - Fit temporally varying models using bespoke model-fits which marginalize over the fit as a function of time.
• Transdimensional Sampling - Sample non-linear parameter spaces with model numbers of model components and parameters.

Depedencies

PyAutoFit requires PyMultiNest.

Installation with conda

We recommend installation using a conda environment as this circumvents a number of compatibility issues when installing PyMultiNest.

First, install conda.

Create a conda environment:

conda create -n autofit python=3.7 anaconda

Activate the conda environment:

conda activate autofit

Install multinest:

conda install -c conda-forge multinest

Install autofit:

pip install autofit

Installation with pip

Installation is also available via pip, however there are reported issues with installing PyMultiNest that can make installation difficult, see the file INSTALL.notes

$ pip install autofit

Support & Discussion

If you're having difficulty with installation, lens modeling, or just want a chat, feel free to message us on our Slack channel.

Contributing

If you have any suggestions or would like to contribute please get in touch.

Credits

Developers

Richard Hayes - Lead developer

James Nightingale - Lead developer