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        "author": "Jake VanderPlas",
        "author_email": "vanderplas@astro.washington.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2.6",
            "Topic :: Scientific/Engineering :: Astronomy"
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        "description": ".. -*- mode: rst -*-\n\n==============\nAstroML addons\n==============\nThis package contains addon code for the astroML package, available at\nhttp://github.com/astroML/astroML.\n\nAstroML is a Python module for machine learning and data mining\nbuilt on numpy, scipy, scikit-learn, and matplotlib,\nand distributed under the 3-Clause BSD license.\nIt contains a growing library of statistical and machine learning\nroutines for analyzing astronomical data in python, loaders for several open\nastronomical datasets, and a large suite of examples of analyzing and\nvisualizing astronomical datasets.\n\nThis project was started in 2012 by Jake VanderPlas to accompany the book\n*Statistics, Data Mining, and Machine Learning in Astronomy* by\nZeljko Ivezic, Andrew Connolly, Jacob VanderPlas, and Alex Gray.\n\nCore and Addons\n===============\nThe project is split into two components.  The core ``astroML`` library is\nwritten in python only, and is designed to be very easy to install for\nany users, even those who don't have a working C or fortran compiler.\nA companion library, ``astroML_addons``, can be optionally installed for\nincreased performance on certain algorithms.  Every algorithm\nin ``astroML_addons`` has a pure python counterpart in the\ncore ``astroML`` implementation, but the ``astroML_addons`` library\ncontains faster and more efficient implementations in compiled code.\nFurthermore, if ``astroML_addons`` is installed on your system, the core\n``astroML`` library will import and use the faster routines by default.\n\nThe reason for this split is the ease of use for newcomers to Python.  If the\nprerequisites are already installed on your system, the core ``astroML``\nlibrary can be installed and used on any system with little trouble.  The\n``astroML_addons`` library requires a C compiler, but is also designed to be\neasy to install for more advanced users.  See further discussion in\n\"Development\", below.\n\n\nImportant Links\n===============\n- HTML documentation: http://astroML.github.com\n- Source-code repository: http://github.com/astroML/astroML\n- Issue Tracker: http://github.com/astroML/astroML/issues\n- Mailing List: https://groups.google.com/forum/#!forum/astroml-general\n\n\nInstallation\n============\n\nThis package uses distutils, which is the default way of installing python\nmodules.  **Before installation, make sure your system meets the prerequisites\nlisted in Dependencies, listed below.**\n\nCore\n----\nTo install the core ``astroML`` package in your home directory, use::\n\n  pip install astroML\n\nThe core package is pure python, so installation should be straightforward\non most systems.  To install from source, refer to http://github.com/astroML/\n\nAddons\n------\nThe ``astroML_addons`` package requires a working C/C++ compiler for\ninstallation.  It can be installed using::\n\n  pip install astroML_addons\n\nTo install from source, use::\n\n  python setup_addons.py install\n\nYou can specify an arbitrary directory for installation using::\n\n  python setup.py install --prefix='/some/path'\n\nTo install system-wide on Linux/Unix systems::\n\n  python setup.py build\n  sudo python setup.py install\n\n\nDependencies\n============\nThere are three levels of dependencies in astroML.  *Core* dependencies are\nrequired for the core ``astroML`` package.  *Add-on* dependencies are required\nfor the performance ``astroML_addons``.  *Optional* dependencies are required\nto run some (but not all) of the example scripts.  Individual example scripts\nwill list their optional dependencies at the top of the file.\n\nCore Dependencies\n-----------------\nThe core ``astroML`` package requires the following:\n\n- Python_ version 2.6.x - 2.7.x\n  (astroML does not yet support python 3.x)\n- Numpy_ >= 1.4\n- Scipy_ >= 0.7\n- Scikit-learn_ >= 0.10\n- Matplotlib_ >= 0.99\n- PyFITS_ >= 3.0.\n  PyFITS is a python reader for Flexible Image Transport\n  System (FITS) files, based on cfitsio.  Several of the dataset loaders\n  require pyfits.\n\nThis configuration matches the Ubuntu 10.04 LTS release from April 2010,\nwith the addition of scikit-learn.\n\nTo run unit tests, you will also need nose >= 0.10\n\nAdd-on Dependencies\n-------------------\nThe fast code in ``astroML_addons`` requires a working C/C++ compiler.\n\nOptional Dependencies\n---------------------\nSeveral of the example scripts require specialized or upgraded packages.\nThese requirements are listed at the top of the particular scripts\n\n- Scipy_ version 0.11 added a sparse graph submodule.\n  The minimum spanning tree example requires scipy >= 0.11\n\n- PyMC_ provides a nice interface for Markov-Chain Monte Carlo. Several astroML\n  examples use pyMC for exploration of high-dimensional spaces. The examples\n  were written with pymc version 2.2\n\n- HEALPy_ provides an interface to\n  the HEALPix pixelization scheme, as well as fast spherical harmonic\n  transforms.\n\nDevelopment\n===========\nThis package is designed to be a repository for well-written astronomy code,\nand submissions of new routines are encouraged.  After installing the\nversion-control system Git_, you can check out\nthe latest sources from GitHub_ using::\n\n  git clone git://github.com/astroML/astroML.git\n\nor if you have write privileges::\n\n  git clone git@github.com:astroML/astroML.git\n\nContribution\n------------\nWe strongly encourage contributions of useful astronomy-related code:\nfor `astroML` to be a relevant tool for the python/astronomy community,\nit will need to grow with the field of research.  There are a few\nguidelines for contribution:\n\nGeneral\n~~~~~~~\nAny contribution should be done through the github pull request system (for\nmore information, see the\n`help page <https://help.github.com/articles/using-pull-requests>`_\nCode submitted to ``astroML`` should conform to a BSD-style license,\nand follow the `PEP8 style guide <http://www.python.org/dev/peps/pep-0008/>`_.\n\nDocumentation and Examples\n~~~~~~~~~~~~~~~~~~~~~~~~~~\nAll submitted code should be documented following the\n`Numpy Documentation Guide`_.  This is a unified documentation style used\nby many packages in the scipy universe.\n\nIn addition, it is highly recommended to create example scripts that show the\nusefulness of the method on an astronomical dataset (preferably making use\nof the loaders in ``astroML.datasets``).  These example scripts are in the\n``examples`` subdirectory of the main source repository.\n\nAdd-on code\n~~~~~~~~~~~\nWe made the decision early-on to separate the core routines from\nhigh-performance compiled routines.\nThis is to make sure that installation of the core\npackage is as straightforward as possible (i.e. not requiring a C compiler).\n\nContributions of efficient compiled code to ``astroML_addons`` is encouraged:\nthe availability of efficient implementations of common algorithms in python\nis one of the strongest features of the python universe.  The preferred\nmethod of wrapping compiled libraries is to use\n`cython <http://www.cython.org>`_; other options (weave, SWIG, etc.) are\nharder to build and maintain.\n\nCurrently, the policy is that any efficient algorithm included in\n``astroML_addons`` should have a duplicate python-only implementation in\n``astroML``, with code that selects the faster routine if it's available.\n(For an example of how this works, see the definition of the ``lomb_scargle``\nfunction in ``astroML/periodogram.py``).\nThis policy exists for two reasons:\n\n 1. it allows novice users to have all the functionality of ``astroML`` without\n    requiring the headache of complicated installation steps.\n 2. it serves a didactic purpose: python-only implementations are often easier\n    to read and understand than equivalent implementations in C or cython.\n 3. it enforces the good coding practice of avoiding premature optimization.\n    First make sure the code works (i.e. write it in simple python).  Then\n    create an optimized version in the addons.\n\nIf this policy proves especially burdensome in the future, it may be revisited.\n\n.. _Numpy Documentation Guide: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt\n\nAuthors\n=======\n\nPackage Author\n--------------\n* Jake Vanderplas <vanderplas@astro.washington.edu> http://jakevdp.github.com\n\nCode Contribution\n-----------------\n* Morgan Fouesneau https://github.com/mfouesneau\n* Julian Taylor http://github.com/juliantaylor\n\n\n.. _Python: http://www.python.org\n.. _Numpy: http://www.numpy.org\n.. _Scipy: http://www.scipy.org\n.. _Scikit-learn: http://scikit-learn.org\n.. _Matplotlib: http://matplotlib.org\n.. _PyFITS: http://www.stsci.edu/institute/software_hardware/pyfits\n.. _PyMC: http://pymc-devs.github.com/pymc/\n.. _HEALPy: https://github.com/healpy/healpy>\n.. _Git: http://git-scm.com/\n.. _GitHub: http://www.github.com",
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