Metadata-Version: 2.1
Name: multiSyncPy
Version: 0.1.0
Summary: Functions to quantify multivariate synchrony
Home-page: https://github.com/cslab-hub/multiSyncPy
Author: Dan Hudson
Author-email: daniel.dominic.hudson@uni-osnabrueck.de
License: GNU LGPL
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt

**Important announcement** - *There is an issue with the calculation of determinism in version 0.0.3 and below; update to version 0.0.4 or above to receive the fix (pip install --upgrade multiSyncPy). Thanks to @mrrezaie for spotting the issue. Please inform anyone you know who might be using multiSyncPy.*

# 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 mulivariate 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, and 
 * a statistical test based on the Kuramoto order parameter

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

multiSyncPy is freely available under the LGPL license. The source code is maintained at <https://github.com/cslab-hub/multiSyncPy>, which also includes examples of usage of the package. Documentation can be accessed through `help()` or accessed at <https://cslab-hub.github.io/multiSyncPy/>. 

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). <https://doi.org/10.3758/s13428-022-01855-y>. 

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