Metadata-Version: 1.1
Name: astroML
Version: 0.4.post1
Summary: tools for machine learning and data mining in Astronomy
Home-page: http://astroML.github.com
Author: Jake VanderPlas
Author-email: vanderplas@astro.washington.edu
License: BSD
Download-URL: http://github.com/astroML/astroML
Description: .. -*- mode: rst -*-
        
        =======================================
        AstroML: Machine Learning for Astronomy
        =======================================
        
        .. image:: http://img.shields.io/badge/arXiv-1411.5039-orange.svg?style=flat
                :target: http://arxiv.org/abs/1411.5039
        .. image:: http://img.shields.io/travis/astroML/astroML/master.svg?style=flat
                :target: https://travis-ci.org/astroML/astroML/
        .. image:: http://img.shields.io/pypi/v/astroML.svg?style=flat
                :target: https://pypi.python.org/pypi/astroML
        .. image:: http://img.shields.io/pypi/dm/astroML.svg?style=flat
                :target: https://pypi.python.org/pypi/astroML
        .. image:: http://img.shields.io/badge/license-BSD-blue.svg?style=flat
                :target: https://github.com/astroml/astroml/blob/master/LICENSE
        
        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: http://www.astroML.org
        - Core source-code repository: http://github.com/astroML/astroML
        - Issue Tracker: http://github.com/astroML/astroML/issues
        - Mailing List: https://groups.google.com/forum/#!forum/astroml-general
        
        
        Installation
        ============
        
        This package uses distutils, which is the default way of installing python
        modules.  **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
        
        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:
        
        - Python_ version 2.7 and 3.3+
        - Numpy_ >= 1.4
        - Scipy_ >= 0.11
        - Scikit-learn_ >= 0.18
        - Matplotlib_ >= 0.99
        - AstroPy_ >= 1.1
          AstroPy is required to read Flexible Image Transport
          System (FITS) files, which are used by several datasets.
        
        This configuration matches the Ubuntu 10.04 LTS release from April 2010,
        with the addition of scikit-learn.
        
        To run unit tests, you will also need nose >= 0.10
        
        Optional Dependencies
        ---------------------
        Several of the example scripts require specialized or upgraded packages.
        These requirements are listed at the top of the particular scripts
        
        - PyMC_ provides a nice interface for Markov-Chain Monte Carlo. Several astroML
          examples use pyMC for exploration of high-dimensional spaces. The examples
          were written with pymc version 2.2
        
        - 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 <https://help.github.com/articles/using-pull-requests>`_
        Code submitted to ``astroML`` should conform to a BSD-style license,
        and follow the `PEP8 style guide <http://www.python.org/dev/peps/pep-0008/>`_.
        
        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://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
        
        Authors
        =======
        
        Package Author
        --------------
        * Jake Vanderplas <vanderplas@astro.washington.edu> http://jakevdp.github.com
        
        Code Contribution
        -----------------
        * Morgan Fouesneau https://github.com/mfouesneau
        * Julian Taylor http://github.com/juliantaylor
        
        
        .. _Python: http://www.python.org
        .. _Numpy: http://www.numpy.org
        .. _Scipy: http://www.scipy.org
        .. _Scikit-learn: http://scikit-learn.org
        .. _Matplotlib: http://matplotlib.org
        .. _AstroPy: http://www.astropy.org/
        .. _PyMC: http://pymc-devs.github.com/pymc/
        .. _HEALPy: https://github.com/healpy/healpy>
        .. _Git: http://git-scm.com/
        .. _GitHub: http://www.github.com
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Astronomy
