Abstract
The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to estimate the following measures: 1) For network inference: multivariate transfer entropy (TE)/Granger causality (GC), multivariate mutual information (MI), bivariate TE/GC, bivariate MI 2) For analysis of node dynamics: active information storage (AIS), partial information decomposition (PID) IDTxl implements estimators for discrete and continuous data with parallel computing engines for both GPU and CPU platforms. Written for Python3.4.3+.
Highlights
Information theory (Cover & Thomas, 2006; MacKay, 2003; Shannon, 1948) is the mathematical theory of information and its transmission over communication channels
We present IDTxl, a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub
Information theory provides quantitative measures of the information content of a single random variable and of the information shared between two variables
Summary
Information theory (Cover & Thomas, 2006; MacKay, 2003; Shannon, 1948) is the mathematical theory of information and its transmission over communication channels. We present IDTxl (the Information Dynamics Toolkit xl), a new open source Python toolbox for effective network inference from multivariate time series using information theory, available from GitHub (https://github.com/pwollstadt/IDTxl). Transfer entropy (TE) (Schreiber, 2000) is an extension of mutual information that measures the directed information transfer between time series of a source and a target variable.
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