Astro modelling
Project description
PyAutoGalaxy
The study of a galaxy's light, structure and dynamics is at the heart of modern day Astrophysical research. PyAutoGalaxy makes it simple to model galaxies, like this oidkne:
Missing for now :(
Example
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
# In this example, we'll fit an image of a single galaxy .
dataset_path = '{}/../data/'.format(os.path.dirname(os.path.realpath(__file__)))
galaxy_name = 'example_galaxy'
# Use the relative path to the dataset to load the imaging data.
imaging = ag.Imaging.from_fits(
image_path=dataset_path + galaxy_name + '/image.fits',
psf_path=dataset_path+galaxy_name+'/psf.fits',
noise_map_path=dataset_path+galaxy_name+'/noise_map.fits',
pixel_scales=0.1)
# Create a mask for the data, which we setup as a 3.0" circle.
mask = ag.Mask.circular(shape_2d=imaging.shape_2d, pixel_scales=imaging.pixel_scales, radius=3.0)
# We model our galaxy using a light profile (an elliptical Sersic).
light_profile = ag.lp.EllipticalSersic
# To setup our model galaxy, we use the GalaxyModel class, which represents a galaxy whose parameters
# are free & fitted for by PyAutoGalaxy. The galaxy is also assigned a redshift.
galaxy_model = ag.GalaxyModel(redshift=1.0, light=light_profile)
# To perform the analysis we set up a phase, which takes our galaxy model & fits its parameters using a non-linear
# search (in this case, MultiNest).
phase = ag.PhaseImaging(
galaxies=dict(galaxy=galaxy_model),
phase_name='example/phase_example',
non_linear_class=af.MultiNest
)
# We pass the imaging data and mask to the phase, thereby fitting it with the galaxy model & plot the resulting fit.
result = phase.run(data=imaging, mask=mask)
ag.plot.FitImaging.subplot_fit_imaging(fit=result.max_log_likelihood_fit)
Features
PyAutoGalaxy's advanced modeling features include:
- Profiles - Use light & mass profiles to make galaxies & perform studies of galaxy light, structure and dynamics.
- Pipelines - Write automated analysis pipelines to fit complex models to large samples of galaxies.
- Pixelizations - Reconstruct complex galaxy morphologies on a variety of pixel-grids.
- Adaption - Adapt the galaxy analysis to the features of the observed galaxy imaging.
- Interferometry - Model radio / sub-mm interferometer data directly in the uv-plane.
- Visualization - Custom visualization libraries for plotting physical galaxying quantities & modeling results.
- PyAutoFit - Perform fits using many non-linear searches (MCMC, Nested Sampling) and manipulate large result outputs
via the probablistic programming language
PyAutoFit <https://github.com/rhayes777/PyAutoFit>_
HowToGalaxy
Included with PyAutoGalaxy is the HowToGalaxy lecture series, which provides an introduction to galaxy modeling with PyAutoGalaxy. It can be found in the workspace & consists of 5 chapters:
- Introduction - An introduction to galaxy fitting & PyAutoGalaxy.
- Galaxy Modeling - How to perform model fits of galaxies, including a primer on Bayesian non-linear analysis.
- Pipelines - How to build model-fitting pipelines & tailor them to your own science case.
- Inversions - How to perform pixelized reconstructions of a galaxy.
- Hyper-Mode - How to use PyAutoGalaxy advanced modeling features that adapt the model to the galaxy being analysed.
Workspace
PyAutoGalaxy comes with a workspace, which can be found here <https://github.com/Jammy2211/autogalaxy_workspace>_ & which includes:
- Aggregator - Manipulate large suites of modeling results via Jupyter notebooks, using PyAutoFit's in-built results database.
- API - Illustrative scripts of the PyAutoGalaxy interface, for examples on how to make plots, perform galaxy calculations, etc.
- Config - Configuration files which customize PyAutoGalaxy's behaviour.
- Dataset - Where data is stored, including example datasets distributed with PyAutoGalaxy.
- HowToGalaxy - The HowToGalaxy lecture series.
- Output - Where the PyAutoGalaxy analysis and visualization are output.
- Pipelines - Example pipelines for modeling galaxies.
- Preprocess - Tools to preprocess data before an analysis (e.g. convert units, create masks).
- Quick Start - A quick start guide, so you can begin modeling galaxies within hours.
- Runners - Scripts for running PyAutoGalaxy pipelines.
- Simulators - Scripts for simulating galaxy datasets with PyAutoGalaxy.
Slack
We're building a PyAutoGalaxy community on Slack, so you should contact us on our
Slack channel <https://pyautogalaxy.slack.com/>_ before getting started. Here, I will give you the latest updates on
the software & discuss how best to use PyAutoGalaxy for your science case.
Unfortunately, Slack is invitation-only, so first send me an email <https://github.com/Jammy2211>_ requesting an
invite.
Documentation & Installation
The PyAutoGalaxy documentation can be found at our readthedocs <https://pyautogalaxy.readthedocs.io/en/master>,
including instructions on installation <https://pyautogalaxy.readthedocs.io/en/master/installation.html>.
Contributing
If you have any suggestions or would like to contribute please get in touch.
Papers
A list of published articles using PyAutoGalaxy can be found
here <https://pyautogalaxy.readthedocs.io/en/master/papers.html>_ .
Credits
Developers:
James Nightingale <https://github.com/Jammy2211>_ - Lead developer & PyAutoGalaxy guru.
Richard Hayes <https://github.com/rhayes777>_ - Lead developer &
PyAutoFit <https://github.com/rhayes777/PyAutoFit>_ guru.
Ashley Kelly <https://github.com/AshKelly>_ - Developer of pyquad <https://github.com/AshKelly/pyquad>_ for fast
numerical integration.
Amy Etherington <https://github.com/amyetherington>_ - Mass profile calcluation methods.
Xiaoyue Cao <https://github.com/caoxiaoyue>_ - Analytic Ellipitcal Power-Law Calculations.
Qiuhan He - NFW Profile Calculations.
Nan Li <https://github.com/linan7788626>_ - Docker integration & support.
Code Donors:
Mattia Negrello - Visibility models in the uv-plane via direct Fourier transforms.
Andrea Enia <https://github.com/AndreaEnia>_ - Voronoi source-plane plotting tools.
Aristeidis Amvrosiadis <https://github.com/Sketos>_ - ALMA imaging data loading.
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