Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

Abstract

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

28 pages, 17 figures

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Cited by in corpus (28)

• Social physics
• Complex network approaches to nonlinear time series analysis
• Event coincidence analysis for quantifying statistical interrelationships between event time series: on the role of flood events as possible triggers of epidemic outbreaks
• Statistical physics approaches to the complex Earth system
• ordpy: A Python package for data analysis with permutation entropy and ordinal network methods
• Trends in recurrence analysis of dynamical systems
• A climate network-based index to discriminate different types of El Niño and La Niña
• Early Detection of Thermoacoustic Instabilities in a Cryogenic Rocket Thrust Chamber using Combustion Noise Features and Machine Learning
• Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes
• Early warning signals for critical transitions in complex systems
• Hierarchical structures in Northern Hemispheric extratropical winter ocean-atmosphere interactions
• Recurrence based quantification of dynamical complexity in the Earth's magnetosphere at geospace storm timescales
• A Climate Network Based Stability Index for El Niño Variability
• Phase space reconstruction for non-uniformly sampled noisy time series
• Disentangling synchrony from serial dependency in paired event time series
• Stickiness and recurrence plots: an entropy-based approach
• Early Warning Signals Indicate a Critical Transition in Betelgeuse
• Radius selection using kernel density estimation for the computation of nonlinear measures
• Edge anisotropy and the geometric perspective on flow networks
• Tipping detection using climate networks
• Recurrence Network Analysis of Exoplanetary Observables
• Investigating Non-linear and Stochastic Hard X-ray Variability of Active Galactic Nuclei using Recurrence Analysis
• Impact of lag information on network inference
• Dynamical anomalies in terrestrial proxies of North Atlantic climate variability during the last 2 ka
• Recurrence analysis of meteorological data from climate zones in India
• Exploring localization in nonlinear oscillator systems through network-based predictions
• Stability of exoplanetary systems retrieved from scalar time series
• Advancing multivariate time series similarity assessment: an integrated computational approach