astroml

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

https://github.com/astroml/astroml

Keywords from Contributors

closember wx tk qt gtk astropy astronomy mcmc astrophysics distributed

Last synced: 10 months ago · JSON representation ·

Repository

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

Basic Info

Host: GitHub
Owner: astroML
License: bsd-2-clause
Language: Python
Default Branch: main
Homepage: https://www.astroml.org/
Size: 9.38 MB

Statistics

Stars: 1,113
Watchers: 95
Forks: 315
Open Issues: 68
Releases: 0

Created over 13 years ago · Last pushed about 2 years ago

Metadata Files

Readme Changelog License Citation

README.rst

.. -*- mode: rst -*-

=======================================
AstroML: Machine Learning for Astronomy
=======================================

.. image:: https://img.shields.io/badge/arXiv-1411.5039-orange.svg?style=flat
        :target: https://arxiv.org/abs/1411.5039
        :alt: Reference proceedings
.. image:: https://github.com/astroML/astroML/workflows/CI/badge.svg
        :target: https://github.com/astroML/astroML/actions?query=workflow%3ACI
        :alt: Github Actions CI Status
.. image:: https://img.shields.io/pypi/v/astroML.svg?style=flat
        :target: https://pypi.python.org/pypi/astroML
        :alt: Latest PyPI version
.. image:: https://img.shields.io/pypi/dm/astroML.svg?style=flat
        :target: https://pypi.python.org/pypi/astroML
        :alt: PyPI download stat
.. image:: https://img.shields.io/badge/license-BSD-blue.svg?style=flat
        :target: https://github.com/astroml/astroml/blob/main/LICENSE.rst
        :alt: License badge

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 git@github.com: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

Owner

Name: astroML
Login: astroML
Kind: organization

Website: http://www.astroML.org
Repositories: 11
Profile: https://github.com/astroML

Citation (CITATION)

If you make use of any of these datasets, tools, or examples in a scientific
publication, please consider citing astroML.  You may reference the following
paper:

- Introduction to astroML: Machine learning for astrophysics

  http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6382200&tag=1

Bibtex entry:

@INPROCEEDINGS{astroML,
  author={{Vanderplas}, J.T. and {Connolly}, A.J.
          and {Ivezi{\'c}}, {\v Z}. and {Gray}, A.},
  booktitle={Conference on Intelligent Data Understanding (CIDU)},
  title={Introduction to astroML: Machine learning for astrophysics},
  month={oct.},
  pages={47 -54},
  doi={10.1109/CIDU.2012.6382200},
  year={2012}
}

You may also reference the accompanying textbook:


Statistics, Data Mining, and Machine Learning for Astronomy
http://press.princeton.edu/titles/10159.html

Bibtex entry:

@BOOK{astroMLText,
  title={Statistics, Data Mining and Machine Learning in Astronomy},
  author={{Ivezi{\'c}}, {\v Z}. and {Connolly}, A.J.
          and {Vanderplas}, J.T. and {Gray}, A.},
  publisher={Princeton University Press},
  location={Princeton, NJ},
  year={2014}
}

GitHub Events

Total

Issues event: 2
Watch event: 60
Issue comment event: 2
Pull request event: 1
Fork event: 9

Last Year

Issues event: 2
Watch event: 60
Issue comment event: 2
Pull request event: 1
Fork event: 9

Committers

Last synced: over 1 year ago

All Time

Total Commits: 481
Total Committers: 31
Avg Commits per committer: 15.516
Development Distribution Score (DDS): 0.53

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Brigitta Sipőcz	b**z@g**m	226
Jake Vanderplas	j**p@g**m	188
Dino Bektešević	d**b@u**u	16
Hugo van Kemenade	h****k	7
Nicholas Hunt-Walker	n**r@g**m	6
Morgan Fouesneau	m**n@a**u	5
Lars Buitinck	l**s@g**m	3
Jonathan Sick	j**k@m**m	3
Alex Conley	a**y@c**u	2
Benjamin Alan Weaver	b**r@n**u	2
Asif Imran	c**y@g**m	2
Vlad Skripniuk	s**v@y**u	2
Chris Desira	c**3@s**k	1
Daniel Andreasen	d**n@a**t	1
Andreas Kopecky	a**y@g**m	1
Andrew Connolly	a**k@g**m	1
Edward Betts	e**d@4**m	1
Engineero	e**s@g**m	1
Hans Moritz Günther	m**r@g**e	1
Jeremy Blow	j**y@i**m	1
Joris van Vugt	j**t@l**l	1
Juanjo Bazán	j**n@g**m	1
Julian Taylor	j**n@g**m	1
Michael Radigan	m**l@r**k	1
Ole Streicher	o**e@d**g	1
Pey Lian Lim	2****m	1
Rodrigo Nemmen	r**n@i**r	1
Ross Fadely	r**y@g**m	1
Sergio Pascual	s**r@f**s	1
Zlatan Vasović	z**c@g**m	1
and 1 more...

Committer Domains (Top 20 + Academic)

fis.ucm.es: 1 iag.usp.br: 1 debian.org: 1 radigan.co.uk: 1 live.nl: 1 incerasolutions.com: 1 gmx.de: 1 4angle.com: 1 astro.up.pt: 1 soton.ac.uk: 1 yandex.ru: 1 nyu.edu: 1 colorado.edu: 1 mac.com: 1 astro.washington.edu: 1 uw.edu: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 57
Total pull requests: 53
Average time to close issues: 12 months
Average time to close pull requests: 2 months
Total issue authors: 22
Total pull request authors: 15
Average comments per issue: 1.26
Average comments per pull request: 0.92
Merged pull requests: 41
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 1
Average comments per issue: 1.0
Average comments per pull request: 0.0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

bsipocz (32)
zlatanvasovic (2)
crhea93 (2)
olebole (2)
gtrichards (2)
arjunsavel (1)
ismaelharunid (1)
nicomeza (1)
jiwoncpark (1)
lxlofpku (1)
OrionStark (1)
kelle (1)
DinoBektesevic (1)
blazar716 (1)
Lace-t (1)

Pull Request Authors

bsipocz (35)
sergiopasra (4)
DinoBektesevic (2)
jotavemonte (2)
pllim (2)
nhuntwalker (2)
zlatanvasovic (1)
VladSkripniuk (1)
michaelRadigan (1)
jakevdp (1)
stonebig (1)
jeremyblow (1)
olebole (1)
arjunsavel (1)
JuLieAlgebra (1)

Top Labels

Issue Labels

docs (12) infrastructure (11) bug (10) incompatibility (7) testing (6) data (6) enhancement (3) critical (2) feature request (2) upstream (2) plotting (2) question (2) figure (1) refactoring (1) time_series (1) duplicate (1)

Pull Request Labels

no-changelog-entry-needed (34) docs (9) infrastructure (8) testing (7) incompatibility (6) data (3) bug (3) refactoring (3) enhancement (2) merge-when-ci-passes (2) time_series (1) affect-dev (1)

Packages

Total packages: 3
Total downloads:
- pypi 1,750 last-month

Total dependent packages: 1
(may contain duplicates)
Total dependent repositories: 15
(may contain duplicates)
Total versions: 23
Total maintainers: 2

pypi.org: astroml

Tools for machine learning and data mining in Astronomy

Homepage: http://astroML.github.com
Documentation: https://astroml.readthedocs.io/
License: BSD 3-Clause License
Latest release: 1.0.2
published over 4 years ago

Versions: 13
Dependent Packages: 1
Dependent Repositories: 12
Downloads: 1,675 Last month

Rankings

Dependent repos count: 4.2%

Dependent packages count: 4.8%

Average: 5.5%

Downloads: 7.4%

Maintainers (2)

Jake.Vanderplas bsipocz

Last synced: 10 months ago

pypi.org: astroml_addons

Performance add-ons for the astroML package

Homepage: http://astroML.github.com
Documentation: https://astroml_addons.readthedocs.io/
License: BSD
Latest release: 0.2.2
published almost 11 years ago

Versions: 4
Dependent Packages: 0
Dependent Repositories: 2
Downloads: 75 Last month

Rankings

Dependent packages count: 10.1%

Dependent repos count: 11.6%

Average: 14.4%

Downloads: 21.7%

Maintainers (1)

Jake.Vanderplas

Last synced: 10 months ago

conda-forge.org: astroml

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.

Homepage: http://www.astroML.org/
License: BSD-2-Clause
Latest release: 1.0.2
published over 4 years ago

Versions: 6
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Forks count: 10.2%

Stargazers count: 13.5%

Dependent repos count: 24.4%

Average: 24.9%

Dependent packages count: 51.6%

Last synced: 10 months ago

astroml

Science Score: 51.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.rst

Owner

Citation (CITATION)

GitHub Events

Total

Last Year

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pypi.org: astroml

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pypi.org: astroml_addons

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conda-forge.org: astroml

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