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

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, 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.

[1]  Harry Eugene Stanley,et al.  Catastrophic cascade of failures in interdependent networks , 2009, Nature.

[2]  R Quian Quiroga,et al.  Event synchronization: a simple and fast method to measure synchronicity and time delay patterns. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[4]  Jürgen Kurths,et al.  Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes , 2015, Physical review. E.

[5]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[6]  Michael Small,et al.  Superfamily phenomena and motifs of networks induced from time series , 2008, Proceedings of the National Academy of Sciences.

[7]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[8]  Henk A. Dijkstra,et al.  Par@Graph – a parallel toolbox for the construction and analysis of large complex climate networks , 2015 .

[9]  Andrew T. Wittenberg,et al.  Warm Pool and Cold Tongue El Nino Events as Simulated by the GFDL 2.1 Coupled GCM , 2010 .

[10]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[11]  Gerald H. Haug,et al.  Effect of the formation of the Isthmus of Panama on Atlantic Ocean thermohaline circulation , 1998, Nature.

[12]  B. Luque,et al.  Horizontal visibility graphs: exact results for random time series. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Jurgen Kurths,et al.  How complex climate networks complement eigen techniques for the statistical analysis of climatological data , 2013, Climate Dynamics.

[14]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[15]  K. Hlavácková-Schindler,et al.  Causality detection based on information-theoretic approaches in time series analysis , 2007 .

[16]  Jürgen Kurths,et al.  Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks , 2011, ArXiv.

[17]  N. Marwan,et al.  Confidence bounds of recurrence-based complexity measures , 2009 .

[18]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[19]  Jonathan F. Donges,et al.  Indications for a North Atlantic ocean circulation regime shift at the onset of the Little Ice Age , 2015, Climate Dynamics.

[20]  Jürgen Kurths,et al.  Local Difference Measures between Complex Networks for Dynamical System Model Evaluation , 2015, PloS one.

[21]  Brian E. Granger,et al.  IPython: A System for Interactive Scientific Computing , 2007, Computing in Science & Engineering.

[22]  Jürgen Kurths,et al.  Node-weighted interacting network measures improve the representation of real-world complex systems , 2013, ArXiv.

[23]  Norbert Marwan,et al.  The backbone of the climate network , 2009, 1002.2100.

[24]  Ed Hawkins,et al.  Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport , 2011 .

[25]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[26]  Paul J. Roebber,et al.  The architecture of the climate network , 2004 .

[27]  S. Havlin,et al.  Stability of Climate Networks with Time , 2011, Scientific Reports.

[28]  Timothy D. Herbert,et al.  Review of alkenone calibrations (culture, water column, and sediments) , 2001 .

[29]  Jonathan F. Donges,et al.  Geometric detection of coupling directions by means of inter-system recurrence networks , 2012, 1301.0934.

[30]  Delphine Clara Zemp,et al.  Node-weighted measures for complex networks with directed and weighted edges for studying continental moisture recycling , 2014 .

[31]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[32]  Jürgen Kurths,et al.  Complex network based techniques to identify extreme events and (sudden) transitions in spatio-temporal systems. , 2015, Chaos.

[33]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[34]  Jakob Runge,et al.  Quantifying information transfer and mediation along causal pathways in complex systems. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Jürgen Kurths,et al.  Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution , 2011, Proceedings of the National Academy of Sciences.

[36]  J. Kurths,et al.  Comparing modern and Pleistocene ENSO-like influences in NW Argentina using nonlinear time series analysis methods , 2003, nlin/0303056.

[37]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[38]  Alex Arenas,et al.  Search and Congestion in Complex Networks , 2003 .

[39]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Jurgen Kurths,et al.  Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes , 2011, The European Physical Journal B.

[41]  Jürgen Kurths,et al.  Disentangling different types of El Niño episodes by evolving climate network analysis. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[42]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[43]  H. Stanley,et al.  Networks formed from interdependent networks , 2011, Nature Physics.

[44]  S. Havlin,et al.  Climate networks around the globe are significantly affected by El Niño. , 2008, Physical review letters.

[45]  Jürgen Kurths,et al.  Networks from Flows - From Dynamics to Topology , 2014, Scientific Reports.

[46]  Potsdam,et al.  Complex networks in climate dynamics. Comparing linear and nonlinear network construction methods , 2009, 0907.4359.

[47]  Henk A. Dijkstra,et al.  Are North Atlantic multidecadal SST anomalies westward propagating? , 2014 .

[48]  George S. Burr,et al.  Chinese Loess and the East Asian Monsoon , 2014 .

[49]  J. Kurths,et al.  Interaction network based early warning indicators for the Atlantic MOC collapse , 2013 .

[50]  J. Hyttinen,et al.  Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data , 2014 .

[51]  J. C. Nuño,et al.  The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion , 2009, 0901.0888.

[52]  Jürgen Kurths,et al.  Download details: IP Address: 193.174.18.1 , 2011 .

[53]  Reinhold Kliegl,et al.  Twin surrogates to test for complex synchronisation , 2006 .

[54]  W. Collins,et al.  The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation , 2001 .

[55]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[56]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[57]  K. Jarrod Millman,et al.  Python for Scientists and Engineers , 2011, Comput. Sci. Eng..

[58]  S. Frenzel,et al.  Partial mutual information for coupling analysis of multivariate time series. , 2007, Physical review letters.

[59]  Jurgen Kurths,et al.  Testing time series irreversibility using complex network methods , 2012, 1211.1162.

[60]  Wolfgang Lucht,et al.  Tipping elements in the Earth's climate system , 2008, Proceedings of the National Academy of Sciences.

[61]  H. Stepan,et al.  Classifying healthy women and preeclamptic patients from cardiovascular data using recurrence and complex network methods , 2013, Autonomic Neuroscience.

[62]  Hui Wang,et al.  A 4-Ma record of thermal evolution in the tropical western Pacific and its implications on climate change , 2011 .

[63]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[64]  Jürgen Kurths,et al.  Estimation of the direction of the coupling by conditional probabilities of recurrence. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[65]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[66]  Norbert Marwan,et al.  The geometry of chaotic dynamics — a complex network perspective , 2011, 1102.1853.

[67]  J. Kurths,et al.  Power-laws in recurrence networks from dynamical systems , 2012, 1203.3345.

[68]  Norbert Marwan,et al.  The South American rainfall dipole: A complex network analysis of extreme events , 2014 .

[69]  Albert-László Barabási,et al.  Hierarchical organization in complex networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[70]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.

[71]  Thomas Nocke,et al.  Information Visualization in Climate Research , 2011, 2011 15th International Conference on Information Visualisation.

[72]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[73]  M. Eichler Graphical modelling of multivariate time series , 2006, math/0610654.

[74]  Gregoire Nicolis,et al.  Dynamical Aspects of Interaction Networks , 2005, Int. J. Bifurc. Chaos.

[75]  Juergen Kurths,et al.  Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate , 2015, Climate Dynamics.

[76]  Henk A. Dijkstra,et al.  Deep ocean early warning signals of an Atlantic MOC collapse , 2014, 1405.1315.

[77]  Elizabeth C. Kent,et al.  Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century , 2003 .

[78]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[79]  H. Abarbanel,et al.  Determining embedding dimension for phase-space reconstruction using a geometrical construction. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[80]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[81]  Norbert Marwan,et al.  Non-linear time series analysis of precipitation events using regional climate networks for Germany , 2015, Climate Dynamics.

[82]  M. Stuiver,et al.  Oxygen 18/16 variability in Greenland snow and ice with 10 -3- to 105-year time resolution , 1997 .

[83]  Michael Small,et al.  Recurrence-based time series analysis by means of complex network methods , 2010, Int. J. Bifurc. Chaos.

[84]  Jürgen Kurths,et al.  Recurrence networks—a novel paradigm for nonlinear time series analysis , 2009, 0908.3447.

[85]  F. Bryan,et al.  High-latitude salinity effects and interhemispheric thermohaline circulations , 1986, Nature.

[86]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[87]  Lucas Lacasa,et al.  From time series to complex networks: The visibility graph , 2008, Proceedings of the National Academy of Sciences.

[88]  Stefan Behnel,et al.  Cython: The Best of Both Worlds , 2011, Computing in Science & Engineering.

[89]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[90]  A. Tsonis,et al.  Topology and predictability of El Niño and La Niña networks. , 2008, Physical review letters.

[91]  Norbert Marwan,et al.  Characterizing the evolution of climate networks , 2014 .

[92]  Jonathan F. Donges,et al.  Visibility graph analysis of geophysical time series: Potentials and possible pitfalls , 2012, Acta Geophysica.

[93]  J. M. R. Parrondo,et al.  Time series irreversibility: a visibility graph approach , 2012 .

[94]  Timothy D. Herbert,et al.  Tropical Ocean Temperatures Over the Past 3.5 Million Years , 2010, Science.

[95]  Jürgen Kurths,et al.  Escaping the curse of dimensionality in estimating multivariate transfer entropy. , 2012, Physical review letters.

[96]  Jürgen Kurths,et al.  Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System , 2013, Entropy.

[97]  Dirk Nürnberg,et al.  Mid-Pliocene climate change amplified by a switch in Indonesian subsurface throughflow. , 2009 .

[98]  Jürgen Kurths,et al.  Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks , 2012, Climate Dynamics.

[99]  S. Havlin,et al.  Pattern of climate network blinking links follows El Niño events , 2008 .

[100]  Shilpa Chakravartula,et al.  Complex Networks: Structure and Dynamics , 2014 .

[101]  F. Chapin,et al.  A safe operating space for humanity , 2009, Nature.

[102]  Michael T. Gastner,et al.  The spatial structure of networks , 2006 .

[103]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[104]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[105]  James P. Crutchfield,et al.  Geometry from a Time Series , 1980 .

[106]  Jürgen Kurths,et al.  Identifying causal gateways and mediators in complex spatio-temporal systems , 2015, Nature Communications.

[107]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[108]  Stefan Rahmstorf,et al.  The Thermohaline Ocean Circulation: A System with Dangerous Thresholds? , 2000 .

[109]  Jonathan F. Donges,et al.  Comparing linear and nonlinear network construction methods , 2009 .

[110]  Jakob Runge,et al.  The role of the North Atlantic overturning and deep ocean for multi-decadal global-mean-temperature variability , 2014 .

[111]  Geli Wang,et al.  On the Role of Atmospheric Teleconnections in Climate , 2008 .

[112]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[113]  Mark E. J. Newman A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[114]  Jürgen Kurths,et al.  Identifying complex periodic windows in continuous-time dynamical systems using recurrence-based methods. , 2010, Chaos.

[115]  Zhi-Qiang Jiang,et al.  Degree distributions of the visibility graphs mapped from fractional Brownian motions and multifractal random walks , 2008, 0812.2099.

[116]  Norbert Marwan,et al.  A historical review of recurrence plots , 2008, 1709.09971.

[117]  J. Donges,et al.  Hierarchical structures in Northern Hemispheric extratropical winter ocean–atmosphere interactions , 2015, 1506.06634.

[118]  Jürgen Kurths,et al.  Quantifying Causal Coupling Strength: A Lag-specific Measure For Multivariate Time Series Related To Transfer Entropy , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[119]  Shlomo Havlin,et al.  Improved El Niño forecasting by cooperativity detection , 2013, Proceedings of the National Academy of Sciences.

[120]  J. Kurths,et al.  Structure–function relationship in complex brain networks expressed by hierarchical synchronization , 2007 .

[121]  Jürgen Kurths,et al.  Inferring Indirect Coupling by Means of Recurrences , 2011, Int. J. Bifurc. Chaos.

[122]  Juergen Kurths,et al.  On the influence of spatial sampling on climate networks , 2014 .

[123]  Milan Palus,et al.  Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information , 2013, Entropy.

[124]  Norbert Marwan,et al.  Geometric signature of complex synchronisation scenarios , 2013, 1301.0806.

[125]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[126]  Changsong Zhou,et al.  Hierarchical organization unveiled by functional connectivity in complex brain networks. , 2006, Physical review letters.

[127]  B. Pompe,et al.  Momentary information transfer as a coupling measure of time series. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[128]  Norbert Marwan,et al.  Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis , 2011 .

[129]  Jakob Runge,et al.  Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models , 2014 .

[130]  J. Dall,et al.  Random geometric graphs. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[131]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[132]  Jef Vandenberghe,et al.  Seven million years of wind and precipitation variability on the Chinese Loess Plateau , 2010 .

[133]  Heidrun Schumann,et al.  CGV - An interactive graph visualization system , 2009, Comput. Graph..

[134]  J. Kurths,et al.  Analytical framework for recurrence network analysis of time series. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[135]  Delphine Clara Zemp,et al.  Interactive comment on “ On the importance of cascading moisture recycling in South America ” by D . C . Zemp , 2014 .

[136]  Milan Palus,et al.  Coarse-grained entropy rates for characterization of complex time series , 1996 .

[137]  Jürgen Kurths,et al.  Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns , 2014 .

[138]  Hans-Jörg Schulz,et al.  Review: visual analytics of climate networks , 2015 .

[139]  Xintian Zhuang,et al.  A network analysis of the Chinese stock market , 2009 .

[140]  Martín Medina-Elizalde,et al.  The Mid-Pleistocene Transition in the Tropical Pacific , 2005, Science.

[141]  Shlomo Havlin,et al.  Very early warning of next El Niño , 2014, Proceedings of the National Academy of Sciences.

[142]  Ulrik Brandes,et al.  GraphML Progress Report , 2001, GD.

[143]  Milan Paluš,et al.  Discerning connectivity from dynamics in climate networks , 2011 .

[144]  J. Kurths,et al.  Complex network approach for recurrence analysis of time series , 2009, 0907.3368.

[145]  Jürgen Kurths,et al.  Late Holocene Asian summer monsoon dynamics from small but complex networks of paleoclimate data , 2013, Climate Dynamics.

[146]  Jürgen Kurths,et al.  Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka , 2014 .

[147]  Norbert Marwan,et al.  Development and Disintegration of Maya Political Systems in Response to Climate Change , 2012, Science.

[148]  Guolin Feng,et al.  Three-dimensional air–sea interactions investigated with bilayer networks , 2012, Theoretical and Applied Climatology.