Software and models developed in the COPAN project
Click here for an overview of model codes and data analysis software developed in the COPAN project including the COPAN:CORE World-Earth modelling framework and the COPAN:EXPLOIT and COPAN:BEHAVE models.
Python modules for complex network and nonlinear time series analysis
In our group we are currently developing the high performance, object oriented package pyunicorn for analyzing general (spatially embedded) networks, climate networks, recurrence plots and recurrence networks using the scripting language Python. Particularly, the libraries implement the algorithms and measures described in our publications on climate and recurrence networks.
- pyunicorn website (documentation, download)
- pyunicorn development version (github)
- pyunicorn description paper (Donges et al., Chaos 25, 113101, 2015; Preprint: arxiv.org:1507.01571 [physics.data-an])
- Modules, Tutorials und installation guide in the PIK intranet
Event coincidence analysis
We have developed the method of event coincidence analysis (ECA) for quantifying the strength, lag and possible directionality of statistical interrelationships between event time series (considered as realizations of unmarked point processes). A package in the statistical scripting language R is available (developed by Jonatan F. Siegmund) that implements ECA:
- R package CoinCalc on github
- CoinCalc description paper (Siegmund et al., Computers & Geosciences 98, 64-72, 2017; Preprint arxiv.org:1603.05038)
- Methodological paper on event coincidence analysis (Donges et al., European Physical Journal Special Topics, 225(3), 471-487, 2016; Preprint: arxiv.org:1508.03534 [physics.soc-ph])
EvoMine algorithm
Complex networks like social networks are ever-changing. New links are formed, existing ties are broken, individuals change their attitudes. In this project, we aim at microscopic descriptions of the processes that govern network evolution by mining frequently occurring graph evolution rules. These rules formally characterize the evolution of a dynamic network, help domain experts analyze the underlying processes, and allow to build data-driven models for friendship and opinion dynamics. In collaboration with Erik Scharwächter and the Knowledge Discovery and Data Mining group of Prof. Dr. Emmanuel Müller at Hasso-Plattner-Institute Potsdam.
- Erik Scharwächter, Emmanuel Müller, Jonathan Donges, Marwan Hassani, Thomas Seidl. Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), 2016. (pdf)
- Corresponding author: Erik Scharwächter <erik.scharwaechter(at)hpi.de>
- More information on EvoMine and software download
pyregimeshifts: Python scripts for detecting regime shifts in paleoclimate time series
Scripts for reproducing the analysis reported in:
- Donges et al., Nonlinear regime shifts in Holocene Asian monsoon variability: Potential impacts on cultural change and migratory patterns, Climate of the Past 11, 709-741 (2015).
The analysis scripts provided in this package provide a general toolkit for detecting regime shifts in multiple (paleo-) climate time series. They should, hence, prove useful for diverse studies on Earth system dynamics beyond the work reported in the original paper. First, the methodology developed in the original paper can be applied to a broad range of data sets of interest. Second, the methodology can be easily generalized by making full use of the capabilities of the pyunicorn package. For example, other measures for detecting regime shifts from recurrence analysis such determinism or laminarity could be used or visibility graph analysis could be applied instead of recurrence networks.
Development version and download of pyregimeshifts:
- https://github.com/pik-copan/pyregimeshifts
- Original software release along with paper publication:
Mathematica Demonstrations
During her internship in our group in fall 2009, Alraune Zech has created a few Mathematica Demonstrations illustrating time series analysis using recurrence plots and recurrence networks:
- Frequency Distribution of the Logistic Map
- Power Spectrum of the Logistic Map
- Visualization of recurrence plots and networks for the logistic map
- Recurrence network measures for the logistic map
Additional mathematica notebooks created by Wolfram Research:
Python resources
A great collection of resources on learning Python compiled by Ciaron Linstead.
Python for climate- and geoscientists at KlimaCampus Hamburg:
