Metadata-Version: 1.1
Name: pyblock
Version: 0.6
Summary: Reblocking analysis tools for correlated data
Home-page: http://github.com/jsspencer/pyblock
Author: James Spencer
Author-email: UNKNOWN
License: Modified BSD license
Description: pyblock
        =======
        
        `pyblock` is a python module for performing a reblocking analysis on
        serially-correlated data.
        
        The algorithms implemented in `pyblock` are not new; please see the documentation for
        references.
        
        pyblock is compatible with (and tested on!) python 2.7 and python 3.3-3.4 and should work
        on any other version supported by `pandas`.
        
        .. image:: https://travis-ci.org/jsspencer/pyblock.svg?branch=master
            :target: https://travis-ci.org/jsspencer/pyblock
        
        Documentation
        -------------
        
        Documentation and a simple tutorial can be found in the docs subdirectory and on
        `readthedocs <http://pyblock.readthedocs.org>`_.
        
        Installation
        ------------
        
        `pyblock` can be used simply by adding to `$PYTHONPATH`.  Alternatively, it can be
        installed using distutils by running:
        
        ::
        
            $ pip install /path/to/pyblock
        
        where `/path/to/` is the (relative or absolute) path to the directory containing
        `pyblock`.  To install an editable version (useful for development work) do:
        
        ::
        
            $ pip install -e /path/to/pyblock
        
        `pyblock` can also be installed from PyPI:
        
        ::
        
            $ pip install pyblock
        
        `pyblock` requires numpy and (optionally) pandas and matplotlib.  Please see the
        documentation for more details.
        
        License
        -------
        
        Modified BSD license; see LICENSE for more details.
        
        Please cite ``pyblock, James Spencer, http://github.com/jsspencer/pyblock`` if used to
        analyse data for an academic publication.
        
        Author
        ------
        
        James Spencer, Imperial College London
        
        Contributing
        ------------
        
        Contributions are extremely welcome, either by raising an issue or contributing code.
        For code contributions, please try to follow the following points:
        
        #. Divide commits into logical units (e.g. don't mix feature development with
           refactoring).
        #. Ensure all existing tests pass.
        #. Create tests for new functionality.  I aim for complete test coverage.
           (Currently the only function not tested is one that creates plots.)
        #. Write nice git commit messages (see `Tim Pope's advice <http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html>`_.)
        #. Send a pull request!
        
        Acknowledgments
        ---------------
        
        Will Vigor (Imperial College London) pointed out and wrote an early implementation of
        the algorithm to detect the optimal reblock length.
        
        Tom Poole (Imperial College London) contributed code to handle weighted averages.
        
        The HANDE FCIQMC/CCMC development team made several helpful comments and suggestions.
        
Platform: UNKNOWN
Requires: numpy
