Package: 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.This project was started in 2012 by Jake VanderPlas to accompany the bookStatistics, Data Mining, and Machine Learning in Astronomyby
Zeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.Important LinksHTML documentation:https://www.astroML.orgCore source-code repository:https://github.com/astroML/astroMLFigure source-code repository:https://github.com/astroML/astroML-figuresIssue Tracker:https://github.com/astroML/astroML/issuesMailing List:https://groups.google.com/forum/#!forum/astroml-generalInstallationBefore installation, make sure your system meets the prerequisites
listed in Dependencies, listed below.CoreTo install the coreastroMLpackage in your home directory, use:pip install astroMLA conda package for astroML is also available either on the conda-forge or
on the astropy conda channels:conda install -c astropy astroMLThe core package is pure python, so installation should be straightforward
on most systems.  To install from source, use:python setup.py installYou 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 installDependenciesThere are two levels of dependencies in astroML.Coredependencies are
required for the coreastroMLpackage.Optionaldependencies 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 DependenciesThe coreastroMLpackage requires the following (some of the
functionality might work with older versions):Pythonversion 3.6+Numpy>= 1.13Scipy>= 0.18Scikit-learn>= 0.18Matplotlib>= 3.0AstroPy>= 3.0Optional DependenciesSeveral of the example scripts require specialized or upgraded packages.
These requirements are listed at the top of the particular scriptsHEALPyprovides an interface to
the HEALPix pixelization scheme, as well as fast spherical harmonic
transforms.DevelopmentThis package is designed to be a repository for well-written astronomy code,
and submissions of new routines are encouraged.  After installing the
version-control systemGit, you can check out
the latest sources fromGitHubusing:git clone git://github.com/astroML/astroML.gitor if you have write privileges:git clone git@github.com:astroML/astroML.gitContributionWe strongly encourage contributions of useful astronomy-related code:
forastroMLto 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:GeneralAny contribution should be done through the github pull request system (for
more information, see thehelp pageCode submitted toastroMLshould conform to a BSD-style license,
and follow thePEP8 style guide.Documentation and ExamplesAll submitted code should be documented following theNumpy 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 inastroML.datasets).  These example scripts are in theexamplessubdirectory of the main source repository.AuthorsPackage AuthorJake Vanderplashttps://github.com/jakevdphttp://jakevdp.github.comMaintainerBrigitta Sipoczhttps://github.com/bsipoczCode ContributionMorgan Fouesneauhttps://github.com/mfouesneauJulian Taylorhttp://github.com/juliantaylor