Unified functional network and nonlinear time series analysis for complex systems science

The pyunicorn package

Jonathan F. Donges, Jobst Heitzig, Boyan Beronov, Marc Wiedermann, Jakob Runge, Qing Yi Feng, Liubov Tupikina, Veronika Stolbova, Reik V. Donner, Norbert Marwan, Henk A. Dijkstra, Jürgen Kurths

Research output: Contribution to journalArticle

30 Citations (Scopus)
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Abstract

We introduce the 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. 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, 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.
Original languageEnglish
Article number113101
JournalChaos
Volume25
Issue number11
DOIs
Publication statusPublished - Nov 2015

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Nonlinear Time Series Analysis
time series analysis
Systems science
Time series analysis
Complex networks
complex systems
Climatology
Large scale systems
Time series
Complex Systems
Circuit theory
Complex Networks
Electric network analysis
Visibility
Software packages
Data structures
network analysis
Brain
Recurrence
climatology

Keywords

  • physics.data-an
  • physics.ao-ph

Cite this

Donges, J. F., Heitzig, J., Beronov, B., Wiedermann, M., Runge, J., Feng, Q. Y., ... Kurths, J. (2015). Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package. Chaos, 25(11), [113101]. https://doi.org/10.1063/1.4934554

Unified functional network and nonlinear time series analysis for complex systems science : The pyunicorn package. / Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen.

In: Chaos, Vol. 25, No. 11, 113101, 11.2015.

Research output: Contribution to journalArticle

Donges, JF, Heitzig, J, Beronov, B, Wiedermann, M, Runge, J, Feng, QY, Tupikina, L, Stolbova, V, Donner, RV, Marwan, N, Dijkstra, HA & Kurths, J 2015, 'Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package', Chaos, vol. 25, no. 11, 113101. https://doi.org/10.1063/1.4934554
Donges JF, Heitzig J, Beronov B, Wiedermann M, Runge J, Feng QY et al. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package. Chaos. 2015 Nov;25(11). 113101. https://doi.org/10.1063/1.4934554
Donges, Jonathan F. ; Heitzig, Jobst ; Beronov, Boyan ; Wiedermann, Marc ; Runge, Jakob ; Feng, Qing Yi ; Tupikina, Liubov ; Stolbova, Veronika ; Donner, Reik V. ; Marwan, Norbert ; Dijkstra, Henk A. ; Kurths, Jürgen. / Unified functional network and nonlinear time series analysis for complex systems science : The pyunicorn package. In: Chaos. 2015 ; Vol. 25, No. 11.
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abstract = "We introduce the 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. 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, 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.",
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note = "28 pages, 16 figures ACKNOWLEDGMENTS This work has been financially supported by the Leibniz association (project ECONS), the German National Academic Foundation, the Federal Ministry for Education and Research (BMBF) via the Potsdam Research Cluster for Georisk Analysis, Environmental Change and Sustainability (PROGRESS), the BMBF Young Investigators Group CoSy-CC2 (grant no. 01LN1306A), BMBF project GLUES, the Stordalen Foundation, IRTG 1740 (DFG) and Marie-Curie ITN LINC (P7-PEOPLE-2011-ITN, grant No. 289447). We thank Kira Rehfeld and Nora Molkenthin for helpful discussions. Hanna C.H. Schultz, Alraune Zech, Jan H. Feldhoff, Aljoscha Rheinwalt, Hannes Kutza, Alexander Radebach, Alexej Gluschkow, Paul Schultz, and Stefan Schinkel are acknowledged for contributing to the development of pyunicorn at different stages. We thank all those people who have helped improving the software by testing, using, and commenting on it. pyunicorn is available at https://github.com/pik-copan/pyunicorn as a part of PIK’s TOCSY toolbox. The distribution includes an extensive online documentation system with the detailed API documentation also being available in the PDF format (Supplementary Material). The software description in this article as well as in the Supplementary Material are based on the pyunicorn release version 0.5.0.",
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AU - Dijkstra, Henk A.

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