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.
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.
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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