Ennemi

[](https://doi.org/10.5281/zenodo.14135362)

`ennemi`: _easy nearest neighbor estimation of mutual information_. Mutual information (MI) can be used to find non-linear correlations between variables, and this Python 3 package is designed to fit into your data analysis workflow.

This release only contains internal infrastructure changes. This version should produce same results as ennemi 1.4.0 (up to possible differences in random number generation). This minor release drops support for `pandas` 1.x. If you cannot upgrade `pandas`, you should stay in ennemi 1.4.0.

The documentation for this release is available at https://polsys.github.io/ennemi. This release requires at least
- Python 3.11
- NumPy 1.24 (NumPy 2.0 is supported)
- SciPy 1.10
- (Optional: pandas 2.0+)

[](https://doi.org/10.5281/zenodo.10930816)

`ennemi`: _easy nearest neighbor estimation of mutual information_. Mutual information (MI) can be used to find non-linear correlations between variables, and this Python 3 package is designed to fit into your data analysis workflow.

This minor release adds support for NumPy 2.0, pandas 2.0, and Python 3.12. There are no user-visible changes or differences in the algorithms compared to 1.3.0. (However, results may be slightly different due to changes in e.g. SciPy random number generation.)

The documentation for this release is available at https://polsys.github.io/ennemi. This release requires at least
- Python 3.10
- NumPy 1.23 (NumPy 2.0 is supported)
- SciPy 1.9
- (Optional: pandas 1.5+ or 2.x)

[](https://doi.org/10.5281/zenodo.7528072)

`ennemi`: _easy nearest neighbor estimation of mutual information_. Mutual information (MI) can be used to find non-linear correlations between variables, and this Python 3 package is designed to fit into your data analysis workflow.

This minor release adds official support for NumPy 1.24 and Python 3.11. There are no user-visible changes or differences in the algorithms compared to 1.2.0. (However, results may be slightly different due to changes in e.g. SciPy random number generation.)

The documentation for this release is available at https://polsys.github.io/ennemi. This release requires at least
- Python 3.8
- NumPy 1.21
- SciPy 1.7
- (Optional: pandas 1.0)

[](https://doi.org/10.5281/zenodo.7073594)

`ennemi`: _easy nearest neighbor estimation of mutual information_. Mutual information (MI) can be used to find non-linear correlations between variables, and this Python 3 package is designed to fit into your data analysis workflow.

This patch release adds official support for NumPy 1.23. There are no user-visible changes or differences in the algorithms compared to 1.2.0 or 1.1.1.

_Note: This version was re-released because of a typo in the PyPI publish script. Apologies to those who get release notifications via GitHub._

The documentation for this release is available at https://polsys.github.io/ennemi. This release requires at least
- Python 3.7
- NumPy 1.19
- SciPy 1.5
- (Optional: pandas 1.0.0)

This release is tested with NumPy 1.24. Support for Python 3.7 and certain older NumPy and SciPy versions is dropped.

There are some internal cleanups enabled by the NumPy upgrade, but user-visible behavior should be identical.

The [documentation pages](https://polsys.github.io/ennemi) received some improvements in this release.


Installation
This package is available on PyPI. To install/update it, execute

pip install --upgrade ennemi

on your Python installation.

Contributing
Your feedback is very valuable! If you encounter any problems, please [file an issue](https://github.com/polsys/ennemi/issues). Code contributions are welcomed as well.

[](https://doi.org/10.5281/zenodo.6418344)

`ennemi`: _easy nearest neighbor estimation of mutual information_. Mutual information (MI) can be used to find non-linear correlations between variables, and this Python 3 package is designed to fit into your data analysis workflow.

This is a minor release that contains only small but significant "quality-of-life" changes. It also drops support for older NumPy and SciPy versions. There is no difference in the algorithms compared to 1.1.0.

The documentation for this release is available at https://polsys.github.io/ennemi. This release requires at least
- Python 3.7
- NumPy 1.19
- SciPy 1.5
- (Optional: pandas 1.0.0)