Metadata-Version: 2.1
Name: macchiato
Version: 0.1.1
Summary: Data-driven nearest neighbor models for predicting experimental results on silicon lithium-ion battery anodes.
Author-email: Francisco Fernandez <ffernandev@gmail.com>
License: MIT License
        
        Copyright (c) 2023 Francisco Fernandez
        
        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
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/fernandezfran/macchiato
Keywords: data-driven-model,nearest-neighbors,clustering
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib
Requires-Dist: mdanalysis
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pyyaml
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: importlib_metadata

# macchiato

[![macchiatos CI](https://github.com/fernandezfran/macchiato/actions/workflows/CI.yml/badge.svg)](https://github.com/fernandezfran/macchiato/actions/workflows/CI.yml)
[![documentation status](https://readthedocs.org/projects/macchiato/badge/?version=latest)](https://macchiato.readthedocs.io/en/latest/?badge=latest)
[![pypi version](https://img.shields.io/pypi/v/macchiato)](https://pypi.org/project/macchiato/)
[![python version](https://img.shields.io/badge/python-3.8%2B-4584b6)](https://www.python.org/)
[![mit license](https://img.shields.io/badge/License-MIT-ffde57)](https://github.com/fernandezfran/macchiato/blob/main/LICENSE)
[![PRB](https://img.shields.io/badge/PhysRevB-108.144201-b31033)](https://doi.org/10.1103/PhysRevB.108.144201)

Data-driven nearest neighbor models for predicting experimental results on 
silicon lithium-ion battery anodes.

## Requirements

You need Python 3.8+ to run macchiato.


## Installation

You can install the most recent stable release of macchiato with 
[pip](https://pip.pypa.io/en/latest/)

```
python -m pip install -U pip
python -m pip install -U macchiato
```


## Usage

The Jupyter Notebook pipeline in the 
[paper folder](https://github.com/fernandezfran/macchiato/tree/main/paper) 
is presented to reproduce the results of the published article.


## Citation

> Fernandez, F., Otero, M., Oviedo, M. B., Barraco, D. E., Paz, S. A., & Leiva, 
> E. P. M. (2023). NMR, x-ray, and Mössbauer results for amorphous Li-Si alloys 
> using density functional tight-binding method. Physical Review B, 108(14), 144201. 

BibTeX entry:

```bibtex
@article{fernandez2023nmr,
  title={NMR, x-ray, and M{\"o}ssbauer results for amorphous Li-Si alloys using density functional tight-binding method},
  author={Fernandez, F and Otero, M and Oviedo, MB and Barraco, DE and Paz, SA and Leiva, EPM},
  journal={Physical Review B},
  volume={108},
  number={14},
  pages={144201},
  year={2023},
  publisher={APS}
}
```


## Contact

You can contact me if you have any questions at <ffernandev@gmail.com>
