Metadata-Version: 2.4
Name: kinisi
Version: 2.0.5
Summary: Efficient estimation of diffusion processes from molecular dynamics.
Project-URL: homepage, https://github.com/bjmorgan/kinisi
Project-URL: documentation, https://kinisi.rtfd.io
Author-email: "Andrew R. McCluskey" <andrew.mccluskey@bristol.ac.uk>, "Benjamin J. Morgan" <b.j.morgan@bath.ac.uk>
Maintainer-email: "Andrew R. McCluskey" <andrew.mccluskey@bristol.ac.uk>, "Benjamin J. Morgan" <b.j.morgan@bath.ac.uk>
License-Expression: MIT
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
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Description-Content-Type: text/markdown

<picture>
  <source media="(prefers-color-scheme: light)" srcset="https://github.com/kinisi-dev/kinisi/blob/main/docs/source/_static/logo_light.png?raw=true">
  <source media="(prefers-color-scheme: dark)" srcset="https://github.com/kinisi-dev/kinisi/blob/main/docs/source/_static/logo_dark.png?raw=true">
  <img alt="The kinisi logo" src="https://github.com/kinisi-dev/kinisi/blob/main/docs/source/_static/logo_light.png?raw=true">
</picture>

Pronunciation: *kee-nee-see*

[![JCTC Status](https://img.shields.io/badge/JCTC-10.1021/acs.jctc.4c01249-blue.svg)](https://doi.org/10.1021/acs.jctc.4c01249)
[![JOSS Status](https://joss.theoj.org/papers/1ae102ffb6b3c63b04c002976440815d/status.svg)](https://joss.theoj.org/papers/1ae102ffb6b3c63b04c002976440815d)
[![PyPI version](https://badge.fury.io/py/kinisi.svg)](https://badge.fury.io/py/kinisi)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/kinisi-dev/kinisi)

`kinisi` is an open-source package focussed on accurately quantifying the uncertainty in atomic and molecular displacements, and using this to more completely understand diffusion in materials.

## Installation

`kinisi` is available from the [PyPI](https://pypi.org/project/kinisi/) repository so can be installed using `pip` or alternatively `clone` [this repository](https://github.com/kinisi-dev/kinisi) and install the latest development build with the commands below.

```
pip install .
```

## Contributing

If you would like to contribute to `kinisi`, have a look at the [CONTRIBUTING.md](https://github.com/kinisi-dev/kinisi/blob/master/CONTRIBUTING.md) that outlines the different ways to help out.
