Metadata-Version: 2.4
Name: baderkit
Version: 0.2.2
Summary: BaderKit is a python reimplementaion of the Henkelman Group's Bader code built on numba, numpy, and pymatgen.
Author-email: "Sam M. Weaver" <sammweaver@gmail.com>
License: BSD 3-Clause License
Project-URL: repository, https://github.com/SWeav02/baderkit
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy>=1.26.0
Requires-Dist: pandas>=2.1.0
Requires-Dist: pymatgen>=2022.1.9
Requires-Dist: scipy>=1.7.2
Requires-Dist: numba>=0.59.0
Requires-Dist: typer>=0.6.1
Requires-Dist: rich>=11.0
Provides-Extra: webapp
Requires-Dist: streamlit>=1.40.0; extra == "webapp"
Requires-Dist: pyvista[jupyter]>=0.44.0; extra == "webapp"
Provides-Extra: dev
Requires-Dist: streamlit>=1.40.0; extra == "dev"
Requires-Dist: pyvista[jupyter]>=0.44.0; extra == "dev"
Requires-Dist: pytest; extra == "dev"
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Requires-Dist: mkdocs; extra == "dev"
Requires-Dist: mkdocs-material; extra == "dev"
Requires-Dist: mkdocstrings-python; extra == "dev"
Dynamic: license-file

# BaderKit

## About

BaderKit is a python implementation of Bader's Atomic Theory of Atoms in Molecules. It is largely based on the algorithms of [Henkelman et. al.](https://theory.cm.utexas.edu/henkelman/code/bader/) at UT Austin. The app is partially part of my PhD at UNC Chapel Hill in the [Warren Lab](https://materials-lab.io/) with funding from the [NSF's GRFP](https://nsfgrfp.org/), but is largely my own passion project aimed at making my life easier when developing other packages.

For information on installation and use, see the [docs](https://sweav02.github.io/baderkit/)


## Contributing

If you are interested in this project and have suggestions, please use this repositories Issues or Discussions tab. Any suggestions or discussion would be deeply appreciated!
