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

doi:10.1063/1.4934554

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

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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 language	English
Article number	113101
Journal	Chaos
Volume	25
Issue number	11
DOIs	https://doi.org/10.1063/1.4934554
Publication status	Published - Nov 2015

Bibliographical 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.

Keywords

physics.data-an
physics.ao-ph

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10.1063/1.4934554Licence: Unspecified

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
Copyright (2015) AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The following article appeared in Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package Donges, Jonathan F. and Heitzig, Jobst and Beronov, Boyan and Wiedermann, Marc and Runge, Jakob and Feng, Qing Yi and Tupikina, Liubov and Stolbova, Veronika and Donner, Reik V. and Marwan, Norbert and Dijkstra, Henk A. and Kurths, Jürgen, Chaos, 25, 113101 (2015), and may be found at http://scitation.aip.org/content/aip/journal/chaos/25/11/10.1063/1.4934554
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Cite this

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

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{\textquoteright}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.",

year = "2015",

month = nov,

doi = "10.1063/1.4934554",

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AU - Beronov, Boyan

AU - Wiedermann, Marc

AU - Runge, Jakob

AU - Feng, Qing Yi

AU - Tupikina, Liubov

AU - Stolbova, Veronika

AU - Donner, Reik V.

AU - Marwan, Norbert

AU - Dijkstra, Henk A.

AU - Kurths, Jürgen

N1 - 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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AB - 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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SN - 1054-1500

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JO - Chaos

JF - Chaos

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ER -

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

Abstract

Bibliographical note

Keywords

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