Metadata-Version: 2.1
Name: kinisi
Version: 0.2.1
Summary: Uncertainty analysis and model comparison for atomistic molecular dynamics.
Home-page: UNKNOWN
Author: Andrew R. McCluskey and Benjamin J. Morgan
Author-email: andrew.mccluskey@ess.eu, b.j.morgan@bath.ac.uk
License: MIT
Platform: UNKNOWN
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 :: 3.7
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
Description-Content-Type: text/markdown
License-File: LICENSE

[![kinisi Logo](https://github.com/bjmorgan/kinisi/raw/master/docs/source/_static/kinisi_logo.png)](https://kinisi.readthedocs.io)

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 -r requirements.txt
pip install .
pytest
```


