Metadata-Version: 2.1
Name: emgdecompy
Version: 0.3.0
Summary: A package for decomposing raw EMG signals into individual motor unit activity.
License: MIT
Author: Daniel King
Requires-Python: >=3.9,<3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: altair (>=4.2.0,<5.0.0)
Requires-Dist: altair-data-server (>=0.4.1,<0.5.0)
Requires-Dist: numpy (>=1.22.3,<2.0.0)
Requires-Dist: pandas (>=1.4.2,<2.0.0)
Requires-Dist: scipy (>=1.8.0,<2.0.0)
Requires-Dist: sklearn (>=0.0,<0.1)
Description-Content-Type: text/markdown

# EMGdecomPy

[![ci-cd](https://github.com/UBC-SPL-MDS/emg-decomPy/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/UBC-SPL-MDS/emg-decomPy/actions/workflows/ci-cd.yml)
[![codecov](https://codecov.io/gh/UBC-SPL-MDS/EMGdecomPy/branch/main/graph/badge.svg?token=78ZU40UEOE)](https://codecov.io/gh/UBC-SPL-MDS/EMGdecomPy)

A package for decomposing raw EMG signals into individual motor unit activity.

## Proposal

Our project proposal can be found [here](https://github.com/UBC-SPL-MDS/emg-decomPy/blob/main/docs/proposal/proposal.pdf).

To generate the proposal locally, run the following command from the root directory:

```Rscript -e "rmarkdown::render('docs/proposal/proposal.Rmd')"```

## Installation

```bash
pip install emgdecompy
```

## Usage

- TODO

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`emgdecompy` was created by Daniel King, Jasmine Ortega, Rada Rudyak, and Rowan Sivanandam. It is licensed under the terms of the MIT license.

## Credits

`emgdecompy` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).

The data used for validation was obtained from [`Hug et al. (2021)`](https://figshare.com/articles/dataset/Analysis_of_motor_unit_spike_trains_estimated_from_high-density_surface_electromyography_is_highly_reliable_across_operators/13695937).

