Metadata-Version: 2.4
Name: python-oephys
Version: 0.1.0
Summary: Python interface and utilities for working with Open Ephys devices and electrophysiology data
Author-email: Jonathan Shulgach <jshulgac@andrew.cmu.edu>
License: MIT
Keywords: open-ephys,emg,neurotech,signal processing,biomedical
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.26.0
Requires-Dist: scipy==1.14.1
Requires-Dist: matplotlib
Requires-Dist: tqdm
Requires-Dist: torch
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: pyyaml
Requires-Dist: PyQt5
Requires-Dist: pyqtgraph
Requires-Dist: pyzmq
Requires-Dist: pyserial
Requires-Dist: open-ephys-python-tools
Requires-Dist: pylsl
Provides-Extra: docs
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx-autodoc-typehints; extra == "docs"
Requires-Dist: myst-parser; extra == "docs"
Provides-Extra: gui
Requires-Dist: PyQt5; extra == "gui"
Requires-Dist: pyqtgraph; extra == "gui"
Provides-Extra: ml
Requires-Dist: torch; extra == "ml"
Requires-Dist: scikit-learn; extra == "ml"
Requires-Dist: joblib; extra == "ml"
Dynamic: license-file

# Python OEphys

[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://badge.fury.io/py/python-oephys.svg)](https://badge.fury.io/py/python-oephys)

**python-oephys** is a comprehensive Python toolkit for working with Open Ephys devices and electrophysiology data. From file loading to real-time ZMQ streaming, signal processing to machine learning, and visualization tools—everything you need for high-density neural data analysis in one package.

---

## ✨ Key Features

- 📁 **File I/O**: Robust support for Open Ephys Binary (`.oebin`) and `.npz` formats
- 🔴 **Real-time Streaming**: Seamless integration with the Open Ephys GUI via ZMQ
- 🎛️ **Signal Processing**: Filtering (Bandpass, Notch), Channel QC, and synchronization
- 🤖 **Machine Learning**: Hybrid CNN-LSTM models for real-time gesture recognition
- 📊 **Visualization**: Real-time EMG viewer, offline analysis, and trial segmentation tools
- 🚀 **Performance**: Optimized for low-latency real-time applications

---

## 📦 Installation

### From TestPyPI (Current Development Release)

```bash
pip install --index-url https://test.pypi.org/simple/ --no-deps python-oephys
```

### From Source

```bash
git clone https://github.com/Neuro-Mechatronics-Interfaces/python-open-ephys.git
cd python-open-ephys
pip install -e .
```

### Optional Extras

- **GUI**: `pip install 'python-oephys[gui]'` (PyQt5, pyqtgraph)
- **ML**: `pip install 'python-oephys[ml]'` (PyTorch, scikit-learn)
- **Docs**: `pip install 'python-oephys[docs]'` (Sphinx)

---

## 🚀 Getting Started

### Load and Filter Data

```python
from pyoephys.io import load_open_ephys_session
from pyoephys.processing import filter_emg

# Load session
sess = load_open_ephys_session('path/to/recording.oebin')
data = sess['amplifier_data']
fs = sess['sample_rate']

# Apply filters
filtered = filter_emg(data, filter_type='bandpass', lowcut=10, highcut=500, fs=fs)
```

### Real-time ZMQ Streaming

```bash
# Launch the live viewer (ensure ZMQ Interface plugin is active in GUI)
python -m pyoephys.applications._realtime_viewer --host 127.0.0.1 --channels 0:8
```

---

## 🗂️ Package Structure

```text
pyoephys/
├── applications/     # GUI applications (Real-time & Offline viewers)
├── interface/        # ZMQ, LSL, and playback clients
├── io/               # Unified file loaders (.oebin, .npz)
├── ml/               # Gesture classification (CNN-LSTM)
├── plotting/         # Visualization utilities
└── processing/       # Signal filters, QC, and synchronization

examples/
├── benchmarks/       # Throughput and latency tests
├── interface/        # LSL, ZMQ, and hardware control
│   ├── hardware/     # Serial/UDP Pico integration
│   ├── imu/          # Sleeve IMU client & monitor
│   ├── lsl/          # LSL streaming & capture
│   └── zmq/          # ZMQ clients & plotters
└── machine_learning/ # Model training and evaluation
```

---

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

<p align="center">
  Made with ❤️ by the Neuromechatronics Lab
</p>
