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dc.contributor.authorDonges, Jonathan F.
dc.contributor.authorHeitzig, Jobst
dc.contributor.authorBeronov, Boyan
dc.contributor.authorWiedermann, Marc
dc.contributor.authorRunge, Jakob
dc.contributor.authorFeng, Qing Yi
dc.contributor.authorTupikina, Liubov
dc.contributor.authorStolbova, Veronika
dc.contributor.authorDonner, Reik V.
dc.contributor.authorMarwan, Norbert
dc.contributor.authorDijkstra, Henk A.
dc.contributor.authorKurths, Jürgen
dc.date.accessioned2015-12-21T16:09:03Z
dc.date.available2015-12-21T16:09:03Z
dc.date.issued2015-11
dc.identifier.citationDonges , J F , Heitzig , J , Beronov , B , Wiedermann , M , Runge , J , Feng , Q Y , Tupikina , L , Stolbova , V , Donner , R V , Marwan , N , Dijkstra , H A & Kurths , J 2015 , ' Unified functional network and nonlinear time series analysis for complex systems science : The pyunicorn package ' Chaos , vol. 25 , no. 11 , 113101 . DOI: 10.1063/1.4934554en
dc.identifier.issn1054-1500
dc.identifier.otherPURE: 57967093
dc.identifier.otherPURE UUID: 14ef2e1b-f9a6-4ba9-b8a8-66a200256421
dc.identifier.otherArXiv: http://arxiv.org/abs/1507.01571v1
dc.identifier.otherScopus: 84946600450
dc.identifier.urihttp://hdl.handle.net/2164/5305
dc.description28 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.en
dc.language.isoeng
dc.relation.ispartofChaosen
dc.rightsCopyright (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/25/11/10.1063/1.4934554en
dc.subjectphysics.data-anen
dc.subjectphysics.ao-phen
dc.subjectQC Physicsen
dc.subject.lccQCen
dc.titleUnified functional network and nonlinear time series analysis for complex systems science : The pyunicorn packageen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen, Physicsen
dc.contributor.institutionUniversity of Aberdeen, Institute for Complex Systems and Mathematical Biology (ICSMB)en
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1063/1.4934554


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