Metadata-Version: 2.4
Name: meeg-utils
Version: 0.1.1
Summary: A Python-based MEEG processing toolkit primarily based on MNE-Python.
Author-email: Your Name <your.email@example.com>
License: MIT
License-File: LICENSE
Keywords: EEG,MEEG,MEG,MNE-Python,neuroscience,preprocessing
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.11
Requires-Dist: loguru>=0.7.3
Requires-Dist: meegkit>=0.1.9
Requires-Dist: mne-bids>=0.18.0
Requires-Dist: mne-icalabel>=0.8.1
Requires-Dist: mne>=1.11.0
Requires-Dist: onnxruntime>=1.24.2
Requires-Dist: pyprep>=0.6.0
Description-Content-Type: text/markdown

# meeg-utils

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> A Python-based MEG/EEG processing toolkit built on MNE-Python, providing a high-level, user-friendly API for processing MEG/EEG data.

## Features
### Preprocessing
*Epoching In Progress*
- High-level `PreprocessingPipeline` class for streamlined MEG/EEG preprocessing.
![Pipeline Diagram](resources/preprocessing_pipeline.png)

### Feature Extraction
*In Progress*
- Common MEG/EEG features (e.g., power spectral density, connectivity metrics).

## 📦 Installation

```bash
pip install meeg-utils
```

## 🚀 Quick Start

```python
from meeg_utils.preprocessing import PreprocessingPipeline
from mne_bids import BIDSPath

# Create pipeline
pipeline = PreprocessingPipeline(
    input_path=BIDSPath(
        subject="01", session="01", task="rest",
        datatype="eeg", root="/data/bids"
    ),
    output_dir="/data/output"
)

# Run preprocessing
result = pipeline.run(
    filter_params={"highpass": 0.1, "lowpass": 100.0, "sfreq": 250.0},
    detect_bad_channels=True,
    remove_line_noise=True,
    apply_ica=True
)

# Save results
pipeline.save()
```

**Batch processing:**

```python
from meeg_utils.preprocessing import BatchPreprocessingPipeline

# Process multiple subjects in parallel
batch = BatchPreprocessingPipeline(
    input_paths=bids_paths,  # List of BIDSPaths
    output_dir="/data/output",
    n_jobs=4  # Use 4 parallel workers
)

batch.run(detect_bad_channels=True, remove_line_noise=True, apply_ica=True)
```

## 📚 Documentation

**Full documentation:** https://colehank.github.io/meeg-utils/

- [Installation Guide](https://colehank.github.io/meeg-utils/user_guide/installation.html)
- [Quick Start](https://colehank.github.io/meeg-utils/user_guide/quickstart.html)
- [Preprocessing Guide](https://colehank.github.io/meeg-utils/user_guide/preprocessing.html)
- [Batch Processing](https://colehank.github.io/meeg-utils/user_guide/batch_processing.html)
- [API Reference](https://colehank.github.io/meeg-utils/api/preprocessing.html)
- [Contributing Guide](https://colehank.github.io/meeg-utils/developer/contributing.html)

## 🛠️ Development

```bash
# Clone and setup
git clone https://github.com/colehank/meeg-utils.git
cd meeg-utils
uv sync --dev
uv run pre-commit install

# Run tests
uv run pytest

# Build docs
cd docs && uv run make html
```

See the [Contributing Guide](https://colehank.github.io/meeg-utils/developer/contributing.html) for detailed development instructions.

## 📄 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

Built on the excellent [MNE-Python](https://mne.tools/) ecosystem.

## 📞 Support

- 📖 [Documentation](https://colehank.github.io/meeg-utils/)
- 🐛 [Issue Tracker](https://github.com/colehank/meeg-utils/issues)
- 💬 [Discussions](https://github.com/colehank/meeg-utils/discussions)
