https://doi.org/10.1140/epjs/s11734-026-02389-6
Regular Article
An optimized Python library for analyzing dynamical systems with recurrence microstates
1
Physics Department, Federal University of Paraná, 81531-990, Curitiba, Paraná, Brazil
2
Interdisciplinary Center for Science, Technology and Innovation (CICTI), Federal University of Paraná, Curitiba, Brazil
3
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14412, Potsdam, Germany
4
Institute for Geosciences, University of Potsdam, 14476, Potsdam, Germany
5
Institute for Physics and Astronomy, University of Potsdam, 14476, Potsdam, Germany
6
Humboldt Universität zu Berlin, 10099, Berlin, Germany
a
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Received:
23
October
2025
Accepted:
27
April
2026
Published online:
3
June
2026
Abstract
Recurrence analysis techniques based on recurrence plots (RPs) have become a standard tool for the study of dynamical systems, offering means to quantify nonlinear characteristics from time series and to apply machine-learning methods in their analysis. Among recent developments, recurrence microstates analysis (RMA) is emerging as a complementary approach, enabling the estimation of typical quantifiers from recurrence quantification analysis (RQA) using the microstate distributions, as well as the use of these distributions as input features for machine-learning models. In this work, we introduce a Python library designed to provide the computational efficiency required for the optimized application of RMA techniques. The proposed methodology and performance benchmarks demonstrate a substantial reduction in computation time, making the library sustainable and energy efficient, especially for large-scale data analysis, and aligned with green computing principles.
© The Author(s) 2026
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