PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology
========================================================================
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`Installation Guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_ |
`readthedocs <https://pyautogalaxy.readthedocs.io/en/latest/index.html>`_ |
`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_ |
`HowToGalaxy <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_
**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 <https://pyautogalaxy.readthedocs.io/en/latest>`_, which includes `an overview of PyAutoGalaxy's core features <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_.
- `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_, where you can try **PyAutoGalaxy** in a web browser (without installation).
- `The autogalaxy_workspace GitHub repository <https://github.com/Jammy2211/autogalaxy_workspace>`_, which includes example scripts and the `HowToGalaxy Jupyter notebook lectures <https://github.com/Jammy2211/autogalaxy_workspace/tree/main/notebooks/howtogalaxy>`_ 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 <https://joss.theoj.org/papers/10.21105/joss.04475>`_.
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 <https://github.com/Jammy2211/PyAutoGalaxy/issues>`_.
We also offer support on the **PyAutoGalaxy** `Slack channel <https://pyautogalaxy.slack.com/>`_, where we also provide the
latest updates on **PyAutoGalaxy**. Slack is invitation-only, so if you'd like to join send
an `email <https://github.com/Jammy2211>`_ requesting an invite.
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"description": "PyAutoGalaxy: Open-Source Multi Wavelength Galaxy Structure & Morphology\n========================================================================\n\n.. image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/HEAD\n\n.. image:: https://readthedocs.org/projects/pyautogalaxy/badge/?version=latest\n :target: https://pyautogalaxy.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://github.com/Jammy2211/PyAutoGalaxy/actions/workflows/main.yml/badge.svg\n :target: https://github.com/Jammy2211/PyAutoGalaxy/actions\n\n.. image:: https://github.com/Jammy2211/PyAutoBuild/actions/workflows/release.yml/badge.svg\n :target: https://github.com/Jammy2211/PyAutoBuild/actions\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n :target: https://github.com/psf/black\n\n.. image:: https://joss.theoj.org/papers/10.21105/joss.04475/status.svg\n :target: https://doi.org/10.21105/joss.04475\n\n`Installation Guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_ |\n`readthedocs <https://pyautogalaxy.readthedocs.io/en/latest/index.html>`_ |\n`Introduction on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_ |\n`HowToGalaxy <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>`_\n\n**PyAutoGalaxy** is software for analysing the morphologies and structures of galaxies:\n\n.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png?raw=true\n :target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/hstcombined.png\n\n**PyAutoGalaxy** also fits interferometer data from observatories such as ALMA:\n\n.. image:: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png?raw=true\n :target: https://github.com/Jammy2211/PyAutoGalaxy/blob/main/paper/almacombined.png\n\nGetting Started\n---------------\n\nThe following links are useful for new starters:\n\n- `The PyAutoGalaxy readthedocs <https://pyautogalaxy.readthedocs.io/en/latest>`_, which includes `an overview of PyAutoGalaxy's core features <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_start_here.html>`_, `a new user starting guide <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_2_new_user_guide.html>`_ and `an installation guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html>`_.\n\n- `The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=start_here.ipynb>`_, where you can try **PyAutoGalaxy** in a web browser (without installation).\n\n- `The autogalaxy_workspace GitHub repository <https://github.com/Jammy2211/autogalaxy_workspace>`_, which includes example scripts and the `HowToGalaxy Jupyter notebook lectures <https://github.com/Jammy2211/autogalaxy_workspace/tree/main/notebooks/howtogalaxy>`_ which give new users a step-by-step introduction to **PyAutoGalaxy**.\n\nCore Aims\n---------\n\n**PyAutoGalaxy** has three core aims:\n\n- **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!).\n\n- **Data Variety**: Support for many data types (e.g. CCD imaging, interferometry, multi-band imaging) which can be fitted independently or simultaneously.\n\n- **Big Data**: Scaling automated analysis to extremely large datasets, using tools like an SQL database to build a scalable scientific workflow.\n\nA complete overview of the software's aims is provided in our `Journal of Open Source Software paper <https://joss.theoj.org/papers/10.21105/joss.04475>`_.\n\nAPI Overview\n------------\n\nGalaxy morphology calculations are performed in **PyAutoGalaaxy** by building a ``Plane`` object from ``LightProfile``\nand ``Galaxy`` objects. Below, we create a simple galaxy system where a redshift 0.5\n``Galaxy`` with an ``Sersic`` ``LightProfile`` representing a bulge and an ``Exponential`` ``LightProfile``\nrepresenting a disk.\n\n.. code-block:: python\n\n import autogalaxy as ag\n import autogalaxy.plot as aplt\n\n \"\"\"\n To describe the galaxy emission two-dimensional grids of (y,x) Cartesian\n coordinates are used.\n \"\"\"\n grid = ag.Grid2D.uniform(\n shape_native=(50, 50),\n pixel_scales=0.05, # <- Conversion from pixel units to arc-seconds.\n )\n\n \"\"\"\n The galaxy has an elliptical sersic light profile representing its bulge.\n \"\"\"\n bulge=ag.lp.Sersic(\n centre=(0.0, 0.0),\n ell_comps=ag.convert.ell_comps_from(axis_ratio=0.9, angle=45.0),\n intensity=1.0,\n effective_radius=0.6,\n sersic_index=3.0,\n )\n\n \"\"\"\n The galaxy also has an elliptical exponential disk\n \"\"\"\n disk = ag.lp.Exponential(\n centre=(0.0, 0.0),\n ell_comps=ag.convert.ell_comps_from(axis_ratio=0.7, angle=30.0),\n intensity=0.5,\n effective_radius=1.6,\n )\n\n \"\"\"\n We combine the above light profiles to compose a galaxy at redshift 1.0.\n \"\"\"\n galaxy = ag.Galaxy(redshift=1.0, bulge=bulge, disk=disk)\n\n \"\"\"\n We create a Plane, which in this example has just one galaxy but can\n be extended for datasets with many galaxies.\n \"\"\"\n plane = ag.Plane(\n galaxies=[galaxy],\n )\n\n \"\"\"\n We can use the Grid2D and Plane to perform many calculations, for example\n plotting the image of the galaxyed source.\n \"\"\"\n plane_plotter = aplt.GalaxiesPlotter(plane=plane, grid=grid)\n plane_plotter.figures_2d(image=True)\n\n\nWith **PyAutoGalaxy**, you can begin modeling a galaxy in just a couple of minutes. The example below demonstrates a\nsimple analysis which fits a galaxy's light.\n\n.. code-block:: python\n\n import autofit as af\n import autogalaxy as ag\n\n import os\n\n \"\"\"\n Load Imaging data of the strong galaxy from the dataset folder of the workspace.\n \"\"\"\n dataset = ag.Imaging.from_fits(\n data_path=\"/path/to/dataset/image.fits\",\n noise_map_path=\"/path/to/dataset/noise_map.fits\",\n psf_path=\"/path/to/dataset/psf.fits\",\n pixel_scales=0.1,\n )\n\n \"\"\"\n Create a mask for the data, which we setup as a 3.0\" circle.\n \"\"\"\n mask = ag.Mask2D.circular(\n shape_native=dataset.shape_native,\n pixel_scales=dataset.pixel_scales,\n radius=3.0\n )\n\n \"\"\"\n We model the galaxy using an Sersic LightProfile.\n \"\"\"\n light_profile = ag.lp.Sersic\n\n \"\"\"\n We next setup this profile as model components whose parameters are free & fitted for\n by setting up a Galaxy as a Model.\n \"\"\"\n galaxy_model = af.Model(ag.Galaxy, redshift=1.0, light=light_profile)\n model = af.Collection(galaxy=galaxy_model)\n\n \"\"\"\n We define the non-linear search used to fit the model to the data (in this case, Dynesty).\n \"\"\"\n search = af.Nautilus(name=\"search[example]\", n_live=50)\n \n \"\"\"\n We next set up the `Analysis`, which contains the `log likelihood function` that the\n non-linear search calls to fit the galaxy model to the data.\n \"\"\"\n analysis = ag.AnalysisImaging(dataset=masked_dataset)\n\n \"\"\"\n To perform the model-fit we pass the model and analysis to the search's fit method. This will\n output results (e.g., dynesty samples, model parameters, visualization) to hard-disk.\n \"\"\"\n result = search.fit(model=model, analysis=analysis)\n\n \"\"\"\n The results contain information on the fit, for example the maximum likelihood\n model from the Dynesty parameter space search.\n \"\"\"\n print(result.samples.max_log_likelihood())\n\n\nSupport\n-------\n\nSupport for installation issues, help with galaxy modeling and using **PyAutoGalaxy** is available by\n`raising an issue on the GitHub issues page <https://github.com/Jammy2211/PyAutoGalaxy/issues>`_.\n\nWe also offer support on the **PyAutoGalaxy** `Slack channel <https://pyautogalaxy.slack.com/>`_, where we also provide the\nlatest updates on **PyAutoGalaxy**. Slack is invitation-only, so if you'd like to join send\nan `email <https://github.com/Jammy2211>`_ requesting an invite.\n",
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