Metadata-Version: 2.1
Name: unite_toolbox
Version: 0.1.9
Summary: A toolbox for practical applications of information theory.
Author-email: Manuel Álvarez Chaves <manuel.alvarezchaves@simtech.uni-stuttgart.de>, Anneli Guthke <anneli.guthke@simtech.uni-stuttgart.de>, Uwe Ehret <uwe.ehret@kit.edu>, Hoshin Gupta <hoshin@arizona.edu>
Maintainer-email: Manuel Álvarez Chaves <manuel.alvarezchaves@simtech.uni-stuttgart.de>
License: MIT License
Project-URL: Documentation, https://unite-toolbox.readthedocs.io/
Project-URL: Repository, https://github.com/manuel-alvarez-chaves/unite_toolbox
Project-URL: Team, https://www.simtech.uni-stuttgart.de/exc/research/junior-research-groups/statistical-model-data-integration/
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.25.0
Requires-Dist: scipy>=1.12.0
Requires-Dist: tqdm>=4.66.2
Provides-Extra: dev
Requires-Dist: jupyterlab>=4.1.2; extra == "dev"
Requires-Dist: matplotlib>=3.8.3; extra == "dev"
Requires-Dist: nbsphinx>=0.9.4; extra == "dev"
Requires-Dist: pytest>=8.2.0; extra == "dev"
Requires-Dist: ruff>=0.4.3; extra == "dev"
Requires-Dist: Sphinx>=7.3.7; extra == "dev"
Requires-Dist: sphinx_rtd_theme>=2.0.0; extra == "dev"

# UNITE Toolbox

### Unified diagnostic evaluation of scientific models based on information theory

![PyPI - Version](https://img.shields.io/pypi/v/unite_toolbox) ![Tests Badge](https://github.com/manuel-alvarez-chaves/unite_toolbox/actions/workflows/run-tests.yml/badge.svg) [![Coverage](https://codecov.io/gh/manuel-alvarez-chaves/unite_toolbox/graph/badge.svg?token=MWNDWXLZ9B)](https://codecov.io/gh/manuel-alvarez-chaves/unite_toolbox)

The **UNITE Toolbox** is a Python library for incorporating _Information Theory_
into data analysis and modeling workflows.
The toolbox collects different methods of estimating information-theoretic quantities
in one easy-to-use Python package.
Currently, UNITE includes functions to calculate entropy $H(X)$,
Kullback-Leibler divergence $D_{KL}(p||q)$, and mutual information $I(X; Y)$,
using three methods:

- Kernel density-based estimation (KDE)
- Binning using histograms
- _k_-nearest neighbor-based estimation (_k_-NN)

## Installation

Although the code is still highly experimental and in very active development,
a release version is available on PyPI and can be installed using `pip`.

```
pip install unite_toolbox
```

Alternatively, the latest updates can be installed directly from this repository

```
pip install git+https://github.com/manuel-alvarez-chaves/unite_toolbox
```

Check the `pyproject.toml` for requirements.

## How-to

In the [documentation](https://unite-toolbox.readthedocs.io/) please find
[tutorials](https://unite-toolbox.readthedocs.io/en/latest/tutorials.html) on
the general usage of the toolbox and some applications.
