Metadata-Version: 2.1
Name: kinisi
Version: 0.6.2
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@ess.eu>, "Benjamin J. Morgan" <b.j.morgan@bath.ac.uk>
Maintainer-email: "Andrew R. McCluskey" <andrew.mccluskey@ess.eu>, "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: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: <3.11,>=3.8
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: scipy>=1.9.3
Requires-Dist: tqdm
Requires-Dist: uravu>=1.2.9
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Description-Content-Type: text/markdown

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

Pronunciation: *kee-nee-si*

[![Test Coverage](https://api.codeclimate.com/v1/badges/3e64239fb6cb6c837b62/test_coverage)](https://codeclimate.com/github/bjmorgan/kinisi/test_coverage)
[![Documentation Status](https://readthedocs.org/projects/kinisi/badge/?version=latest)](https://kinisi.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/kinisi.svg)](https://badge.fury.io/py/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/bjmorgan/kinisi) and install the latest development build with the commands below.

```
pip install .
```
