Metadata-Version: 1.1
Name: pymuvr
Version: 1.2.0
Summary: Multi-unit Van Rossum spike train metric
Home-page: https://github.com/epiasini/pymuvr
Author: Eugenio Piasini
Author-email: e.piasini@ucl.ac.uk
License: GPLv3+
Description: pymuvr
        ======
        
        Overview
        --------
        .. image:: https://travis-ci.org/epiasini/pymuvr.svg?branch=master
            :target: https://travis-ci.org/epiasini/pymuvr
            :alt: build status
        
        A Python package for the fast calculation of Multi-unit Van Rossum
        neural spike train metrics, with the kernel-based algorithm described
        in Houghton and Kreuz, *On the efficient calculation of Van Rossum
        distances* (Network: Computation in Neural Systems, 2012, 23,
        48-58). This package started out as a Python wrapping of the original
        C++ implementation given by the authors of the paper, and evolved from
        there with bugfixes and improvements.
        
        Documentation
        -------------
        Full documentation is hosted at http://pymuvr.readthedocs.org/.
        
        Requirements
        ------------
        - Python 2.7 or 3.x.
        - NumPy>=1.7.
        - C++ development tools and Standard Library (package `build-essential` on Debian).
        - Python development tools (package `python-dev` on Debian).
        
        Installation
        ------------
        To install the latest release, run::
        
          pip install pymuvr
        
        If you prefer installing from git, use::
        
          git clone https://github.com/epiasini/pymuvr.git
          cd pymuvr
          python setup.py install
        
        Usage
        -----
        The module exposes two functions::
        
          pymuvr.distance_matrix(observations1, observations2, cos, tau)
        
        ::
        
           pymuvr.square_distance_matrix(observations, cos, tau)
        
        `distance_matrix` calculates the 'bipartite' (rectangular)
        dissimilarity matrix between the multi-unit trains in `observations1`
        and those in `observations2`.
        
        `square_distance_matrix` calculates the 'all-to-all' dissimilarity
        matrix between each pair of trains in parallel_trains. It's an
        optimised form of `distance_matrix(observations, observations, cos,
        tau)`.
        
        They both return their results as a 2D numpy array.
        
        The `observations` arguments must be thrice-nested lists of
        spiketimes, in such a way that `observations[i][j][k]` represents
        the time of the kth spike of the jth cell of the ith observation.  `cos` and
        `tau` are the usual parameters for the multiunit Van Rossum metric.
        
        See `examples/benchmark_versus_spykeutils.py` for an example of usage
        comparing the performance of pymuvr with the pure Python
        implementation of the multiunit Van Rossum distance in
        `spykeutils <https://github.com/rproepp/spykeutils>`_.
        
        License
        -------
        This package is licensed under version 3 of the GPL or any later
        version. See COPYING for details.
        
        Getting the source
        ------------------
        Source code for pymuvr is hosted at https://github.com/epiasini/pymuvr.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
