ordpy: A Python package for data analysis with permutation entropy and ordinal network methods

TL;DR

ordpy is an open-source Python package that unifies various permutation entropy and ordinal network methods for analyzing one- and two-dimensional data, facilitating comprehensive time series and image analysis.

Contribution

This work introduces ordpy, the first unified software package implementing permutation entropy and ordinal network methods for time series and image data analysis.

Findings

Successfully replicates several literature results

Provides a comprehensive toolkit for permutation-based analysis

Facilitates multiscale data analysis

Abstract

Since Bandt and Pompe's seminal work, permutation entropy has been used in several applications and is now an essential tool for time series analysis. Beyond becoming a popular and successful technique, permutation entropy inspired a framework for mapping time series into symbolic sequences that triggered the development of many other tools, including an approach for creating networks from time series known as ordinal networks. Despite the increasing popularity, the computational development of these methods is fragmented, and there were still no efforts focusing on creating a unified software package. Here we present ordpy, a simple and open-source Python module that implements permutation entropy and several of the principal methods related to Bandt and Pompe's framework to analyze time series and two-dimensional data. In particular, ordpy implements permutation entropy, Tsallis and R\'enyi permutation entropies, complexity-entropy plane, complexity-entropy curves, missing ordinal patterns, ordinal networks, and missing ordinal transitions for one-dimensional (time series) and two-dimensional (images) data as well as their multiscale generalizations. We review some theoretical aspects of these tools and illustrate the use of ordpy by replicating several literature results.