ennemi 1.0.0

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
  • Conditional MI with arbitrary-dimensional conditioning variables
  • Discrete-continuous MI
• Practical data analysis:
  • Interface for evaluating multiple variable pairs and time lags with one call
  • Integrated with pandas data frames (optional)
  • Optimized and automatically parallelized estimation

This package depends only on NumPy and SciPy; Pandas is suggested for more enjoyable data analysis. Python 3.6+ on the latest macOS, Ubuntu and Windows versions is officially supported.

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

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