Reblocking analysis tools for correlated data
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 and a simple tutorial can be found in the docs subdirectory and on readthedocs.
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.
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.
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.)
- Send a pull request!
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.
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