pyTree : a collection of python codes for tree-based neighbor search

author : Jake Vanderplas <vanderplas@astro.washington.edu>

This is mainly a test-bed for codes to be included in scikit-learn.
As such, it's not set up to be a fully-fledged package, but to be installed
in-place.

Compiling
---------
This is set-up for in-place compilation.  Compile with the command

    python setup.py build_ext --inplace

Contents
--------

cpp_balltree :
    This is the C++ BallTree code which I submitted to scikit-learn.  The
    cython wrapper and a few tweaks to the C++ source are due to some of
    the scikit-learn contributers

npy_balltree :
    This is an attempt to speed-up and streamline the ball tree implementation
    Its approach is to use no dynamic memory allocation, but instead store
    all the info for the built ball tree in a collection of numpy arrays.
    I'm also making the ball tree more flexible (any minkowski p-distance
    can be used).
    This is a work in progress. Currently initializing the tree is
    approximately 8-10 times faster than in the C++ version.  The
    query() function is about 50% faster.  The query_radius()
    function has not yet been implemented.