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Reblocking analysis tools for correlated data

Project description

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.

https://travis-ci.org/jsspencer/pyblock.svg?branch=master

Documentation

Documentation and a simple tutorial can be found in the docs subdirectory and on readthedocs.

Installation

pyblock can be used simply by adding to $PYTHONPATH. Alternatively, it can be installed using distutils:

$ pip install pyblock

or 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

Acknowledgments

Will Vigor pointed out and wrote an early implementation of the algorithm to detect the optimal reblock length. Comments and suggestions from the HANDE development team.

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