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Description:

astroML 1.0.2.post1

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 >= 0.18
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.

Authors

Package Author

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

Maintainer

Brigitta Sipocz https://github.com/bsipocz

Code Contribution

Morgan Fouesneau https://github.com/mfouesneau
Julian Taylor http://github.com/juliantaylor