This repository contains the code written to produce the figures of

Connecting Hodge and Sakaguchi-Kuramoto: a mathematical framework for coupled
oscillators on simplicial complexes
by: Alexis Arnaudon, Robert L. Peach, Giovanni Petri and Paul Expert
at: https://arxiv.org/pdf/2111.11073.pdf

It numerically solves the Kuramoto model on simplicial complexes of order 1 (a graph) and 2 (a graph with faces), with node/edge/face weights and frustration. A small suite of analysis tool is available to compute the Hodge decomposition of the solution, the simplicial order parameter or the largest Lyapunov exponent.

It also contains the models introduced in

A unified framework for Simplicial Kuramoto models
by: Marco Nurisso, Alexis Arnaudon, Maxime Lucas, Robert L. Peach, Paul Expert, Francesco Vaccarino, Giovanni Petri
at: https://arxiv.org/abs/2305.17977

and the connectome example in example/connectome_example, based on

M. Pope, M. Fukushima, R. F. Betzel, and O. Sporns,
Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics, Proc. Natl. Acad.
Sci. U.S.A. 118, e2109380118 (2021).

To install, it is on pypi.org, hence just do:

pip install simplicial-kuramoto

The documentation is available here: https://arnaudon.github.io/simplicial-kuramoto/, and the structure of the code is as follow:

• The module simplicial_complex.py extends networkx graph to include faces and computed cached values of various graph theoretical operators such as boundary operators, or Hodge Laplacians.
• The module meausures.py contains some measurements functions of the dynamics such as order parameters.
• The module graph_generator.py contains a few functions to build simplicial complexes.
• The module integrators.py solve the Kuramoto model on a given simplicial complex.
• The module plotting.py has some plotting functions of the simplicial complex and the Kuramoto solution.
• The module frustration_scan.py contains analysis tools to study frustration of simplicial Kuramoto.

In the folder examples are present scripts to generate the figures of the first paper as well as run some of the models of the second paper.