If you wanted to know whether shifts in the African climate during Paleolithic times correlated with the appearance and disappearance of hominin species, how would you find the answer? It’s a tricky question because of the massive amounts of noisy, complicated data you would need to analyze.
Researchers from the Potsdam Institute for Climate Impact Research (PIK) have developed a new tool to help grapple with enormous data sets and reveal big picture trends, such as climatic tipping points and their effects on species. The researchers created a software package based on the Python programming language that unifies complex network theory and nonlinear time series analysis – two important data analysis concepts.
A complex network is just that – a social, biological or technological network with patterns of connections that are neither regular nor purely random. Nonlinear time series analyses are often used to look at complex systems, including those that unfold in a chaotic manner. Many natural phenomena, like changing weather patterns, are nonlinear in nature -- as are man-made systems, like financial markets.
“Pyunicorn works like a macroscope, if used the right way, it allows to distill the essence of information from a network or time series data,” says Jonathan Donges from PIK.
The software’s versatility fulfills a key aim of the project, which was to make the software publicly available and easy to use for researchers and practitioners in a variety of fields, ranging from complex systems science to climatology, medicine, neuroscience, economics and engineering.
“Many of the provided methods were not freely available before to the scientific community, and weren’t available in the flexible and popular Python programming language,” said PIK´s Jürgen Kurths, who supervised the work.
The pyunicorn package can be freely downloaded at: https://github.com/pik-copan/pyunicorn.
At the European Geosciences Union General Assembly 2016, the authors will host a workshop on the new tool.
Full article at AIP Publishing: Donges et al., Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, arxiv.org:1507.01571 [physics.data-an] (2015)
