```{tip} Dive deeper into our features and usage with the official [documentation](https://nsquaredlab.github.io/MyoVerse/). ``` # MyoVerse - The AI toolkit for myocontrol research ## What is MyoVerse? MyoVerse is your cutting-edge **research** companion for unlocking the secrets hidden within biomechanical data! It's specifically designed for exploring the complex interplay between **electromyography (EMG)** signals, **kinematics** (movement), and **kinetics** (forces). Leveraging the power of **PyTorch** and **PyTorch Lightning**, MyoVerse provides a comprehensive suite of tools, including: * **Data loaders** and **preprocessing filters** tailored for biomechanical signals. * Peer-reviewed **AI models** and components for analysis and prediction tasks. * Essential **utilities** to streamline the research workflow. Whether you're predicting movement from muscle activity, analyzing forces during motion, or developing novel AI approaches for biomechanical challenges, MyoVerse aims to accelerate your research journey. ```{important} MyoVerse is built for **research**. While powerful, it's evolving and may not have the same level of stability as foundational libraries like NumPy. We appreciate your understanding and contributions! ``` ## Installation MyoVerse automatically installs with the correct PyTorch version for your platform. ### Basic installation: ```bash # Install from PyPI pip install myoverse ``` This will automatically: - On Linux: Install PyTorch and TorchVision from PyPI (with CUDA support) - On Windows: Install PyTorch and TorchVision with CUDA 12.4 support ## Development For development, install the dev dependencies: 1. **Clone the Repository:** ```bash git clone https://github.com/NsquaredLab/MyoVerse.git # Replace with your actual repo URL if different cd MyoVerse ``` 2. **Install uv:** If you don't have it yet, install `uv`. Follow the instructions on the [uv GitHub page](https://github.com/astral-sh/uv). 3. **Set up Virtual Environment & Install Dependencies:** Simply run: ```bash uv sync --group dev ``` ```{note} The project is configured to automatically install: - On Linux: Standard PyTorch with CUDA from PyPI - On Windows: PyTorch with CUDA 12.4 support from the PyTorch custom index ``` ## What is what? This project uses the following structure: - `myoverse`: This is the main package. It contains: - `datasets`: Contains data loaders, dataset creators, and a wide array of filters to preprocess your biomechanical data (e.g., EMG, kinematics). - `models`: Contains all AI models and their components, ready for training and evaluation. - `utils`: Various utilities to support data handling, model training, and analysis. - `docs`: Contains the source files for the documentation. - `examples`: Contains practical examples demonstrating how to use the package, including tutorials (`01_tutorials`) and specific use cases like applying filters (`02_filters`). - `tests`: Contains tests to ensure package integrity and correctness.