Metadata-Version: 2.4
Name: hdsemg-pipe
Version: 0.1.1
Summary: hdsemg-pipe package
Home-page: https://github.com/johanneskasser/hdsemg-pipe
Author: Johannes Kasser
Author-email: johanneskasser@outlook.de
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: PyQt5>=5.15.0
Requires-Dist: pyqt5-tools>=5.15.0; sys_platform == "win32"
Requires-Dist: matplotlib>=3.4.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: pyinstaller>=6.11.0
Requires-Dist: pandas>=1.3.5
Requires-Dist: torch>=1.10.0
Requires-Dist: requests>=2.26.0
Requires-Dist: hdsemg-shared>=0.11.1
Requires-Dist: hdsemg-select>=0.1.0
Requires-Dist: pefile>=2023.2.7; sys_platform != "win32"
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
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<div align="center">
<br>
  <img src="hdsemg_pipe/resources/icon.png" alt="App Icon" width="100" height="100"><br>
    <h2 align="center">🧠 hdsemg-pipe</h2>
    <h3 align="center">HDsEMG Workflow Manager</h3>
</div>

hdsemg-pipe is a Python-based application for processing HD-sEMG (High-Density Surface Electromyography) data. It provides a user-friendly interface for managing and analyzing HD-sEMG recordings.

## Documentation

The full documentation for hdsemg-pipe is available at:

📚 **[Usage Documentation](https://johanneskasser.github.io/hdsemg-pipe/)**

This includes detailed guides on installation, usage, and contributing to the project.

## Installation

In order to install hdsemg-pipe, follow the instructions in the [installation guide](docs/installation.md).

### Quick Installation

1. hdsemg-pipe is available as a Python package and can be installed via pip:

```bash
  pip install hdsemg-pipe
```

2. After the installation, you can run the application using from the command line:

```bash
  hdsemg-pipe
```


## Features

- 📁 File management and preprocessing
- 🔗 Grid association and ROI definition
- 🎚️ Channel selection and decomposition result visualization
- ⚡️ Integration with external tools like OpenHD-EMG
