PyAutoGalaxy

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology - Published in JOSS (2023)

https://github.com/jammy2211/pyautogalaxy

Keywords from Contributors

gravitational-lenses astronomy astrophysics physics cosmology lens-modeling bayesian-inference bayesian-methods graphical-models mcmc

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Engineering Computer Science - 60% confidence

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PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

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Host: GitHub
Owner: Jammy2211
License: mit
Language: Python
Default Branch: main
Homepage: https://pyautogalaxy.readthedocs.io/
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Open Issues: 19
Releases: 17

Created about 6 years ago · Last pushed 5 months ago

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Readme Contributing License Code of conduct Citation

README.rst

PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
========================================================================

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`Installation Guide `_ |
`readthedocs `_ |
`Introduction on Binder `_ |
`HowToGalaxy `_

**PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:

.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png?raw=true
        :target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png

**PyAutoGalaxy** also fits interferometer data from observatories such as ALMA:

.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png?raw=true
        :target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png

Getting Started
---------------

The following links are useful for new starters:

- `The PyAutoGalaxy readthedocs `_, which includes `an overview of PyAutoGalaxy's core features `_, `a new user starting guide `_ and `an installation guide `_.

- `The introduction Jupyter Notebook on Binder `_, where you can try **PyAutoGalaxy** in a web browser (without installation).

- `The autogalaxy_workspace GitHub repository `_, which includes example scripts and the `HowToGalaxy Jupyter notebook lectures `_ which give new users a step-by-step introduction to **PyAutoGalaxy**.

Core Aims
---------

**PyAutoGalaxy** has three core aims:

- **Model Complexity**: Fitting complex galaxy morphology models (e.g. Multi Gaussian Expansion, Shapelets, Ellipse Fitting, Irregular Meshes) that go beyond just simple Sersic fitting (which is supported too!).

- **Data Variety**: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.

- **Big Data**: Scaling automated analysis to extremely large datasets, using tools like an SQL database to build a scalable scientific workflow.

A complete overview of the software's aims is provided in our `Journal of Open Source Software paper `_.

API Overview
------------

Galaxy morphology calculations are performed in **PyAutoGalaaxy** by building a ``Plane`` object from ``LightProfile``
and ``Galaxy`` objects. Below, we create a simple galaxy system where a redshift 0.5
``Galaxy`` with an ``Sersic`` ``LightProfile`` representing a bulge and an ``Exponential`` ``LightProfile``
representing a disk.

.. code-block:: python

    import autogalaxy as ag
    import autogalaxy.plot as aplt

    """
    To describe the galaxy emission two-dimensional grids of (y,x) Cartesian
    coordinates are used.
    """
    grid = ag.Grid2D.uniform(
        shape_native=(50, 50),
        pixel_scales=0.05,  # <- Conversion from pixel units to arc-seconds.
    )

    """
    The galaxy has an elliptical sersic light profile representing its bulge.
    """
    bulge=ag.lp.Sersic(
        centre=(0.0, 0.0),
        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.9, angle=45.0),
        intensity=1.0,
        effective_radius=0.6,
        sersic_index=3.0,
    )

    """
    The galaxy also has an elliptical exponential disk
    """
    disk = ag.lp.Exponential(
        centre=(0.0, 0.0),
        ell_comps=ag.convert.ell_comps_from(axis_ratio=0.7, angle=30.0),
        intensity=0.5,
        effective_radius=1.6,
    )

    """
    We combine the above light profiles to compose a galaxy at redshift 1.0.
    """
    galaxy = ag.Galaxy(redshift=1.0, bulge=bulge, disk=disk)

    """
    We create a Plane, which in this example has just one galaxy but can
    be extended for datasets with many galaxies.
    """
    plane = ag.Plane(
        galaxies=[galaxy],
    )

    """
    We can use the Grid2D and Plane to perform many calculations, for example
    plotting the image of the galaxyed source.
    """
    plane_plotter = aplt.GalaxiesPlotter(plane=plane, grid=grid)
    plane_plotter.figures_2d(image=True)


With **PyAutoGalaxy**, you can begin modeling a galaxy in just a couple of minutes. The example below demonstrates a
simple analysis which fits a galaxy's light.

.. code-block:: python

    import autofit as af
    import autogalaxy as ag

    import os

    """
    Load Imaging data of the strong galaxy from the dataset folder of the workspace.
    """
    dataset = ag.Imaging.from_fits(
        data_path="/path/to/dataset/image.fits",
        noise_map_path="/path/to/dataset/noise_map.fits",
        psf_path="/path/to/dataset/psf.fits",
        pixel_scales=0.1,
    )

    """
    Create a mask for the data, which we setup as a 3.0" circle.
    """
    mask = ag.Mask2D.circular(
        shape_native=dataset.shape_native,
        pixel_scales=dataset.pixel_scales,
        radius=3.0
    )

    """
    We model the galaxy using an Sersic LightProfile.
    """
    light_profile = ag.lp.Sersic

    """
    We next setup this profile as model components whose parameters are free & fitted for
    by setting up a Galaxy as a Model.
    """
    galaxy_model = af.Model(ag.Galaxy, redshift=1.0, light=light_profile)
    model = af.Collection(galaxy=galaxy_model)

    """
    We define the non-linear search used to fit the model to the data (in this case, Dynesty).
    """
    search = af.Nautilus(name="search[example]", n_live=50)
    
    """
    We next set up the `Analysis`, which contains the `log likelihood function` that the
    non-linear search calls to fit the galaxy model to the data.
    """
    analysis = ag.AnalysisImaging(dataset=masked_dataset)

    """
    To perform the model-fit we pass the model and analysis to the search's fit method. This will
    output results (e.g., dynesty samples, model parameters, visualization) to hard-disk.
    """
    result = search.fit(model=model, analysis=analysis)

    """
    The results contain information on the fit, for example the maximum likelihood
    model from the Dynesty parameter space search.
    """
    print(result.samples.max_log_likelihood())


Support
-------

Support for installation issues, help with galaxy modeling and using **PyAutoGalaxy** is available by
`raising an issue on the GitHub issues page `_.

We also offer support on the **PyAutoGalaxy** `Slack channel `_, where we also provide the
latest updates on **PyAutoGalaxy**. Slack is invitation-only, so if you'd like to join send
an `email `_ requesting an invite.

Owner

Name: James Nightingale
Login: Jammy2211
Kind: user
Location: Durham
Company: Durham University

Website: www.jamesnightingale.net
Repositories: 16
Profile: https://github.com/Jammy2211

Postdoc in Astronomy at Durham University Developer of PyAutoLens

JOSS Publication

PyAutoGalaxy: Open-Source Multiwavelength Galaxy Structure & Morphology

Published

January 27, 2023

DOI

10.21105/joss.04475

Volume 8, Issue 81, Page 4475