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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=introduction.ipynb> |
HowToGalaxy <https://pyautogalaxy.readthedocs.io/en/latest/howtogalaxy/howtogalaxy.html>
The study of a galaxy's structure and morphology is at the heart of modern day Astrophysical research.
PyAutoGalaxy makes it simple to model galaxies, for example this Hubble Space Telescope imaging of a spiral galaxy:
.. 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
The following links are useful for new starters:
The PyAutoGalaxy readthedocs <https://pyautogalaxy.readthedocs.io/en/latest>, which includes an installation guide <https://pyautogalaxy.readthedocs.io/en/latest/installation/overview.html> and an overview of PyAutoGalaxy's core features <https://pyautogalaxy.readthedocs.io/en/latest/overview/overview_1_galaxies.html>_.
The introduction Jupyter Notebook on Binder <https://mybinder.org/v2/gh/Jammy2211/autogalaxy_workspace/release?filepath=introduction.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/master/notebooks/howtogalaxy>_ which give new users a step-by-step introduction to PyAutoGalaxy.
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 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.