Pyunicorn Tutorials

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

pyunicorn (Unified Complex Network and RecurreNce analysis toolbox) is a fully object-oriented Python package for the advanced analysis and modeling of complex networks. Above the standard measures of complex network theory such as degree, betweenness and clustering coefficient it provides some uncommon but interesting statistics like Newman’s random walk betweenness. pyunicorn features novel node-weighted (node splitting invariant) network statistics as well as measures designed for analyzing networks of interacting/interdependent networks.

Moreover, pyunicorn allows to easily construct networks from uni- and multivariate time series data (functional (climate) networks and recurrence networks). This involves linear and nonlinear measures of time series analysis for constructing functional networks from multivariate data as well as modern techniques of nonlinear analysis of single time series like recurrence quantification analysis (RQA) and recurrence network analysis. Other introductory information about pyunicorn can be found at:  http://www.pik-potsdam.de/~donges/pyunicorn/index.html .

Tutorials for pyunicorn are designed to be self-explanatory.  Besides being online, the tutorials are also available as ipython notebooks.  For further details about the used classes and methods please refer to the API at:  http://www.pik-potsdam.de/~donges/pyunicorn/api_doc.html.

Authoring Person(s) Name: 
J.F. Donges
J. Heitzig
B. Beronov
M. Wiedermann
J. Runge
Q.-Y. Feng
L. Tupikina
V. Stolbova
R.V. Donner
N. Marwan
H.A. Dijkstra
J. Kurths
Authoring Organization(s) Name: 
PIK
License - link to legal statement specifying the copyright status of the learning resource: 
BSD-3-Clause
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
German
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data modeling
Network analysis
Python
Published / Broadcast: 
Thursday, January 1, 2015
Created: 
Tuesday, January 1, 2008
Publisher - organization credited with publishing or broadcasting the learning resource: 
PIK
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Contributor Name: 
Name: 
Jakob Runge
Type: 
Collaborator
Name: 
Alexander Radebach
Type: 
Collaborator
Name: 
Hanna Schultz
Type: 
Collaborator
Name: 
Marc Wiedermann
Type: 
Collaborator
Name: 
Alraune Zech
Type: 
Collaborator
Name: 
Jan Feldhoff
Type: 
Collaborator
Name: 
Aljoscha Rheinwalt
Type: 
Collaborator
Name: 
Boyan Beronov
Type: 
Collaborator
Name: 
Paul Schultz
Type: 
Collaborator
Name: 
Stefan Schinkel
Type: 
Collaborator
Name: 
Wolfram Barfuss
Type: 
Collaborator
Contact Person(s): 
J.F. Donges
J. Heitzig
B. Beronov
M. Wiedermann
J. Runge
Q.-Y. Feng
L. Tupikina
V. Stolbova
R.V. Donner
N. Marwan
H.A. Dijkstra
J. Kurths
Contact Organization(s): 
PIK
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Data professional
Early-career research scientist
Graduate student
Mid-career research scientist
Research faculty
Research scientist
Software engineer
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)