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Multi-unit Van Rossum spike train metric

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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.


Full documentation is hosted at


  • 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).


To install the latest release, run:

pip install pymuvr

If you prefer installing from git, use:

git clone
cd pymuvr
python install


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/ for an example of usage comparing the performance of pymuvr with the pure Python implementation of the multiunit Van Rossum distance in spykeutils.


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

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pymuvr-1.2.0.tar.gz (54.1 kB) Copy SHA256 hash SHA256 Source None Jun 2, 2014

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