🦎 Gecko IoT Client

A modern, asynchronous Python client library for Gecko IoT devices with AWS IoT integration. Control and monitor your Gecko-powered spas, hot tubs, and pool equipment with ease.

✨ Features

• 🌐 AWS IoT Integration: Secure MQTT communication via AWS IoT Core
• 🏊 Multi-Zone Control: Temperature, lighting, and flow zone management
• 📡 Real-time Updates: Event-driven state synchronization
• 🔄 Async/Await Support: Modern Python async programming patterns
• 🛡️ Type Safety: Full type hints and mypy compatibility
• 📊 Comprehensive Logging: Detailed debug and monitoring capabilities
• 🧪 Well Tested: Extensive test suite with code coverage
• 📖 Rich Documentation: Auto-generated API docs and examples

🚀 Quick Start

Installation

pip install gecko-iot-client

Basic Usage

import asyncio
from gecko_iot_client import GeckoIotClient
from gecko_iot_client.transporters.mqtt import MqttTransporter

async def main():
    # Create MQTT transporter with your AWS IoT endpoint
    transporter = MqttTransporter(
        endpoint="wss://your-endpoint.iot.region.amazonaws.com/mqtt",
        device_id="your-device-id"
    )

    # Initialize client
    client = GeckoIotClient(idd="your-device-id", transporter=transporter)

    async with client:
        # Get all temperature control zones
        temp_zones = client.get_zones_by_type(ZoneType.TEMPERATURE_CONTROL_ZONE)

        for zone in temp_zones:
            print(f"Zone {zone.name}: {zone.temperature}°C (target: {zone.target_temperature}°C)")

        # Control lighting
        lighting_zones = client.get_zones_by_type(ZoneType.LIGHTING_ZONE)
        if lighting_zones:
            light = lighting_zones[0]
            await light.activate()  # Turn on
            await light.set_color(255, 0, 0)  # Set to red

if __name__ == "__main__":
    asyncio.run(main())

🏗️ Architecture

Zone Types

The client supports three main zone types:

• 🌡️ Temperature Control Zones: Heater and temperature management
• 💡 Lighting Zones: LED lighting control with color support
• 🌊 Flow Zones: Pump and circulation control

Event System

def on_temperature_change(zone, old_temp, new_temp):
    print(f"Temperature changed from {old_temp}°C to {new_temp}°C")

client.register_zone_callbacks(lambda zone_name: on_temperature_change)

📁 Project Structure

gecko-iot-client/
├── .github/workflows/     # CI/CD pipelines
├── gecko_iot_client/      # Main package
│   ├── src/              # Source code
│   │   └── gecko_iot_client/
│   │       ├── models/   # Data models and zone types
│   │       └── transporters/  # Communication layers
│   ├── tests/            # Test suite
│   ├── docs/             # Sphinx documentation
│   └── examples/         # Usage examples
└── README.md             # This file

🛠️ Development

Prerequisites

• Python 3.13+
• Poetry (recommended) or pip

Setup

# Clone the repository
git clone https://github.com/geckoal/gecko-iot-client.git
cd gecko-iot-client

# Install dependencies
cd gecko_iot_client
pip install -e ".[dev,docs]"

# Run tests
pytest tests/ --cov=gecko_iot_client

# Format code
black src/ tests/
isort src/ tests/

# Build documentation
sphinx-build docs/source docs/build/html

# Generate changelog from git history
./scripts/generate-changelog.sh
# or using the Python entry point
gecko-build-changelog

Running Examples

# Run the demo script
python gecko_iot_client/examples/demo.py

📚 Documentation

• 📖 Full Documentation - Complete API reference and guides
• 🎯 Quick Start Guide - Get up and running fast
• 🔧 Configuration - Setup and configuration options
• 💡 Examples - Code examples and use cases

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Ensure all tests pass (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📊 Testing & Quality

• ✅ Automated Testing: GitHub Actions run tests on every push and PR
• 📈 Code Coverage: Monitored via SonarCloud with detailed quality reports
• 🔍 Code Quality: SonarCloud analysis with Black, isort, and flake8 for consistent style
• 📝 Documentation: Auto-generated and deployed on every release

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

🏷️ Versioning Strategy

This project follows Semantic Versioning (SemVer) combined with PEP 440 for Python compatibility.

Version Format

MAJOR.MINOR.PATCH[-PRERELEASE]

• MAJOR: Breaking changes (incompatible API changes)
• MINOR: New features (backward-compatible functionality)
• PATCH: Bug fixes (backward-compatible fixes)

Git Tag Format

All releases use the v prefix in git tags:

# Release versions
v1.0.0          # Major release
v1.1.0          # Minor release
v1.1.1          # Patch release

# Pre-release versions
v1.0.0-alpha.1  # Alpha release
v1.0.0-beta.1   # Beta release
v1.0.0-rc.1     # Release candidate

Release Lifecycle

Version Detection

Versions are automatically determined from git tags using setuptools-scm:

import gecko_iot_client
print(gecko_iot_client.__version__)  # e.g., "0.1.0a2.post1"

Version Components:

• 0.1.0a2 - Alpha version 2 of release 0.1.0
• .post1 - 1 commit after the tagged release
• +dirty - Local uncommitted changes (development builds)

Release Process

1. Development happens on the develop branch
2. Tagging creates releases: git tag v0.1.0-alpha.3 && git push origin v0.1.0-alpha.3
3. GitHub Actions automatically builds and publishes to PyPI
4. Documentation is automatically updated and deployed

🏷️ Version History

See CHANGELOG.md for a detailed version history.

Made with ❤️ by the Gecko Team Powering the future of spa control