ennemi 1.6.0

Non-linear correlation detection with mutual information

This package performs non-linear correlation analysis with mutual information (MI). MI is an information-theoretical measure of dependency between two variables. The package is designed for practical data analysis with no theoretical background required.

Features:

• Non-linear correlation detection:
  • Mutual information between two variables, continous or discrete
  • Conditional MI with arbitrary-dimensional conditioning variables
  • Quick overview of many-variable datasets with pairwise MI estimation
• Practical data analysis:
  • Interfaces for evaluating multiple variable pairs and time lags with one call
  • Integrated with pandas data frames (optional)
  • Optimized and automatically parallelized estimation
  • Algorithms verified to work, so that you can focus on your data

This package depends only on NumPy and SciPy; Pandas (2.x or newer) is suggested for more enjoyable data analysis. Recent versions of NumPy 2.x are supported. Python 3.12+ on the latest macOS, Ubuntu and Windows versions is officially supported. Older ennemi versions have generally identical behavior if you need to run on older Python/NumPy.

For more information on theoretical background and usage, please see the documentation. If you encounter any problems or have suggestions, please file an issue!

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This package was initially developed at Institute for Atmospheric and Earth System Research (INAR), University of Helsinki.