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Functions to quantify multivariate synchrony

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Important announcement - There is a new version of multiSyncPy (0.1.0) that includes a new multivariate synchronization measures as well as some visualiation functions.

# multiSyncPy

multiSyncPy is a Python package for quantifying multivariate synchrony. Our package supports the burgeoning field of research into synchrony, making accessible a set of methods for studying group-level rather than dyadic constructs of synchrony and/or coordination. We offer a range of metrics for estimating multivariate synchrony based on a collection of those used in recent literature.

The main methods of this package are functions to calculate:

  • symbolic entropy,

  • multidimensional recurrence quantification,

  • coherence (and a related ‘sum-normalized CSD’ metric),

  • the cluster-phase ‘Rho’ metric

  • the synchronization coefficient metric,

  • a statistical test based on the Kuramoto order parameter, and

  • driver-empath model with synchrony index

We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels.

Additionally, we include a set of functions to visualize the time-varying coordination metrics as well as the individual or pair-wise contributions to the multivariate measure (depending on the particular method).

multiSyncPy is freely available under the LGPL license. The source code is maintained at <>, which also includes examples of usage of the package. Documentation can be accessed through help() or accessed at <>.

Further details of the package and case studies of its use on real-world data are described in our paper.

Hudson, D., Wiltshire, T.J. & Atzmueller, M. multiSyncPy: A Python package for assessing multivariate coordination dynamics. Behav Res (2022). <>.

Please cite this paper if you use multiSyncPy in your research.

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