A Julia library for analyzing dynamical systems with recurrence microstates
Recurrence Microstates Analysis (RMA) is an advanced approach that generalizes the analysis of recurrence structures by capturing the statistical properties of recurrence motifs. RecurrenceMicrostatesAnalysis.jl is an efficient Julia package for performing RMA, offering support for a wide range of motif shapes, flexible sampling strategies, and comprehensive distribution computation capabilities. Furthermore, the library features an optimized pipeline for estimating standard RQA quantifiers, with significantly reduced memory and computational requirements, making it particularly well-suited for large-scale datasets.
Installation
Download Julia 1.8 or later, preferably the current stable release.
You can add RecurrenceMicrostatesAnalysis.jl using Julia's package manager. In the Julia prompt, you can use the following code snippets:
using Pkg
Pkg.add("RecurrenceMicrostatesAnalysis")or, in Pkg REPL mode write:
] add RecurrenceMicrostatesAnalysisRecurrenceMicrostatesAnalysis.jl is an open-source library available at GitHub repository DynamicsUFPR/RMA.jl. If you have found this library useful, please consider starring it on GitHub 😉.
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Learning RMA
If you have worked with recurrence microstates analysis before, the Quick Start page offers a brief guide on how to apply the RecurrenceMicrostatesAnalysis.jl to time series data and dynamical systems.
If you haven't, then you might prefer the Theoretical Overview page, which provides a quick and simple introduction about the recurrence microstates field. The rest of the guide explains how to use the library to compute recurrence motifs probability distributions and calculate common recurrence quantifiers, along with descriptions of all available configuration options. We also include the Utils page, which covers utility functions to simplify the use of RecurrenceMicrostatesAnalysis.jl, and the Performance Tips page, where we discuss how to improve the library’s usage performance.