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https://github.com/astroML/astroML

Machine learning, statistics, and data mining for astronomy and astrophysics
https://github.com/astroML/astroML

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Machine learning, statistics, and data mining for astronomy and astrophysics

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README 

        
.. -*- mode: rst -*-



=======================================

AstroML: Machine Learning for Astronomy

=======================================



.. image:: https://img.shields.io/badge/arXiv-1411.5039-orange.svg?style=flat

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        :alt: Reference proceedings

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        :alt: Github Actions CI Status

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        :alt: Latest PyPI version

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        :alt: PyPI download stat

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AstroML is a Python module for machine learning and data mining

built on numpy, scipy, scikit-learn, and matplotlib,

and distributed under the BSD license.

It contains a growing library of statistical and machine learning

routines for analyzing astronomical data in python, loaders for several open

astronomical datasets, and a large suite of examples of analyzing and

visualizing astronomical datasets.



This project was started in 2012 by Jake VanderPlas to accompany the book

*Statistics, Data Mining, and Machine Learning in Astronomy* by

Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.



Important Links

===============

- HTML documentation: https://www.astroML.org

- Core source-code repository: https://github.com/astroML/astroML

- Figure source-code repository: https://github.com/astroML/astroML-figures

- Issue Tracker: https://github.com/astroML/astroML/issues

- Mailing List: https://groups.google.com/forum/#!forum/astroml-general



Installation

============



**Before installation, make sure your system meets the prerequisites

listed in Dependencies, listed below.**



Core

----

To install the core ``astroML`` package in your home directory, use::



  pip install astroML



A conda package for astroML is also available either on the conda-forge or

on the astropy conda channels::



  conda install -c astropy astroML



The core package is pure python, so installation should be straightforward

on most systems.  To install from source, use::



  python setup.py install



You can specify an arbitrary directory for installation using::



  python setup.py install --prefix='/some/path'



To install system-wide on Linux/Unix systems::



  python setup.py build

  sudo python setup.py install



Dependencies

============

There are two levels of dependencies in astroML.  *Core* dependencies are

required for the core ``astroML`` package. *Optional* dependencies are required

to run some (but not all) of the example scripts.  Individual example scripts

will list their optional dependencies at the top of the file.



Core Dependencies

-----------------

The core ``astroML`` package requires the following (some of the

functionality might work with older versions):



- Python_ version 3.6+

- Numpy_ >= 1.13

- Scipy_ >= 0.19

- Scikit-learn_ >= 0.18

- Matplotlib_ >= 3.0

- AstroPy_ >= 3.0



Optional Dependencies

---------------------

Several of the example scripts require specialized or upgraded packages.

These requirements are listed at the top of the particular scripts



- HEALPy_ provides an interface to

  the HEALPix pixelization scheme, as well as fast spherical harmonic

  transforms.



Development

===========

This package is designed to be a repository for well-written astronomy code,

and submissions of new routines are encouraged.  After installing the

version-control system Git_, you can check out

the latest sources from GitHub_ using::



  git clone git://github.com/astroML/astroML.git



or if you have write privileges::



  git clone [email protected]:astroML/astroML.git



Contribution

------------

We strongly encourage contributions of useful astronomy-related code:

for `astroML` to be a relevant tool for the python/astronomy community,

it will need to grow with the field of research.  There are a few

guidelines for contribution:



General

~~~~~~~

Any contribution should be done through the github pull request system (for

more information, see the

`help page `_

Code submitted to ``astroML`` should conform to a BSD-style license,

and follow the `PEP8 style guide `_.



Documentation and Examples

~~~~~~~~~~~~~~~~~~~~~~~~~~

All submitted code should be documented following the

`Numpy Documentation Guide`_.  This is a unified documentation style used

by many packages in the scipy universe.



In addition, it is highly recommended to create example scripts that show the

usefulness of the method on an astronomical dataset (preferably making use

of the loaders in ``astroML.datasets``).  These example scripts are in the

``examples`` subdirectory of the main source repository.



.. _Numpy Documentation Guide: https://numpydoc.readthedocs.io/en/latest/format.html



Authors

=======



Package Author

--------------

* Jake Vanderplas https://github.com/jakevdp

  http://jakevdp.github.com



Maintainer

----------

* Brigitta Sipocz https://github.com/bsipocz



Contributors

------------



* Alex Conley

* Andreas Kopecky

* Andrew Connolly

* Asif Imran

* Benjamin Alan Weaver

* Brigitta Sipőcz

* Chris Desira

* Daniel Andreasen

* Dino Bektešević

* Edward Betts

* Hans Moritz Günther

* Hugo van Kemenade

* Jake Vanderplas

* Jeremy Blow

* Jonathan Sick

* Joris van Vugt

* Juanjo Bazán

* Julian Taylor

* Lars Buitinck

* Michael Radigan

* Morgan Fouesneau

* Nicholas Hunt-Walker

* Ole Streicher

* Pey Lian Lim

* Rodrigo Nemmen

* Ross Fadely

* Vlad Skripniuk

* Zlatan Vasović

* Engineero

* stonebig



.. _Python: https://www.python.org

.. _Numpy: https://www.numpy.org

.. _Scipy: https://www.scipy.org

.. _Scikit-learn: https://scikit-learn.org

.. _Matplotlib: https://matplotlib.org

.. _AstroPy: http://www.astropy.org/

.. _HEALPy: https://github.com/healpy/healpy

.. _Git: https://git-scm.com/

.. _GitHub: https://www.github.com