pyoxynet 0.1.12

Python package of the Oxynet project

Pyoxynet

Automatic interpretation of cardiopulmonary exercise test (CPET) data using deep learning.

Pyoxynet is a Python package for automated CPET analysis using AI models. Part of the Oxynet project for universal access to quality healthcare.

Key Features:

• 🔬 AI-powered inference - Automatically estimate exercise intensity domains
• 🎲 Synthetic data generation - Create realistic CPET data with conditional GANs
• ⚡ Lightweight deployment - TFLite support with ~90% smaller footprint
• 📊 Model explainability - SHAP integration for understanding predictions

📚 Documentation | 🌐 Web App | 💻 GitHub

Installation

Requirements: Python 3.10+ and NumPy < 2.0

# Lite version (recommended) - Core functionality
pip install pyoxynet

# TFLite version - Adds lightweight model inference
pip install "pyoxynet[tflite]" --extra-index-url https://google-coral.github.io/py-repo/

# Full version - Complete TensorFlow support
pip install "pyoxynet[full]"

NumPy compatibility: If you encounter NumPy 2.x issues with TFLite, install: pip install "numpy<2"

Quick Start

import pyoxynet

# Load model and run inference on sample data
model = pyoxynet.load_tf_model(n_inputs=5, past_points=40, model='CNN')
pyoxynet.test_pyoxynet(model)

# Generate synthetic CPET data
generator = pyoxynet.load_tf_generator()
df = pyoxynet.generate_CPET(generator, plot=True)

Required data: VO2, VCO2, VE, PetO2, PetCO2 (sampled at 1-second intervals)

Resources

• 📖 Documentation - Complete API reference and examples
• 💻 GitHub - Source code and detailed README
• 🌐 Web App - Try it online
• 🔬 Research Papers - Scientific background

License

MIT License - See LICENSE for details.

Medical Disclaimer

This software is for informational purposes only and is not intended as a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of qualified healthcare providers.