Metadata-Version: 2.3
Name: unite-toolbox
Version: 0.2.1
Summary: A toolbox for practical applications of information theory.
Author: Manuel Álvarez Chaves, Uwe Ehret
Author-email: Manuel Álvarez Chaves <manuel.alvarezchaves@simtech.uni-stuttgart.de>, Uwe Ehret <uwe.ehret@kit.edu>
License: MIT License
         
         Copyright (c) 2023 Manuel Álvarez Chaves
         
         Permission is hereby granted, free of charge, to any person obtaining a copy
         of this software and associated documentation files (the "Software"), to deal
         in the Software without restriction, including without limitation the rights
         to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
         copies of the Software, and to permit persons to whom the Software is
         furnished to do so, subject to the following conditions:
         
         The above copyright notice and this permission notice shall be included in all
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         THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Requires-Dist: coverage>=7.10.3
Requires-Dist: numpy>=1.25.2
Requires-Dist: scipy>=1.12.0
Requires-Dist: tqdm>=4.66.6
Maintainer: Manuel Álvarez Chaves
Maintainer-email: Manuel Álvarez Chaves <manuel.alvarezchaves@simtech.uni-stuttgart.de>
Requires-Python: >=3.10
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/
Description-Content-Type: text/markdown

# 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) [![Identifier](<https://img.shields.io/badge/DOI-10.18419/darus--4188-blue>)](https://doi.org/10.18419/darus-4188)

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
```

Or `uv`.

```
uv add 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.
