PyAMA

PyAMA is a modular Python application for microscopy image analysis. It provides tools for processing large time-lapse microscopy datasets, extracting quantitative cellular features, and fitting mathematical models to describe cellular dynamics.

📚 Documentation

PyAMA documentation is available at https://pyama.readthedocs.io

The documentation includes:

• Installation Guide
• Quick Start
• User Guide
• Package Documentation
• API Reference

🚀 Quick Install

# Clone the repository
git clone https://github.com/SoftmatterLMU-RaedlerGroup/pyama.git
cd pyama

# Install all dependencies
uv sync --all-extras

# Launch the Qt GUI
uv run pyama-qt

# Or start the API server (for web frontend)
uv run pyama-core serve

📦 Packages

PyAMA consists of the following packages:

• pyama-core: Core processing library with analysis workflows, CLI tools, and REST API server
• pyama-qt: Qt-based GUI for comprehensive analysis
• pyama-preact: Modern web frontend built with Preact, Tailwind CSS, and Tauri for desktop deployment
• pyama-acdc: Cell-ACDC integration plugin

🎯 Key Features

• Automated Processing: End-to-end pipeline from ND2/CZI files to CSV traces
• Multi-channel Support: Process phase contrast and multiple fluorescence channels
• Deep Learning: Spotiflow-based particle detection for fluorescence spot counting
• Quality Control: Interactive trace inspection and filtering
• Model Fitting: Built-in models for protein maturation and decay analysis
• Batch Processing: Handle large datasets with configurable parallelism
• REST API & MCP: FastAPI server with Model Context Protocol integration for programmatic access
• Modern Web UI: Preact + Tauri desktop application with task management
• Extensible: Plugin system for custom features and models

🖥️ CLI Commands

# Run processing workflow (interactive or config-based)
uv run pyama-core workflow
uv run pyama-core workflow --config config.yaml --nd2-path data.nd2

# Merge CSV outputs from multiple samples
uv run pyama-core merge

# Plot cell trajectories
uv run pyama-core trajectory traces.csv

# Plot numpy array files
uv run pyama-core plot data.npy

# Start the API server
uv run pyama-core serve --port 8000 --reload

# Connect Claude Code as an MCP client (requires server running)
export PYAMA_MCP_URL="http://localhost:8000"  # adjust host/port as needed
claude mcp add pyama --transport http "$PYAMA_MCP_URL/mcp"

🐍 Requirements

• Python 3.11 or later
• UV package manager (recommended) or pip
• Qt6 for GUI applications (pyama-qt)
• Node.js and Bun/npm for web frontend (pyama-preact)

📖 Learn More

• Visit the full documentation for comprehensive guides
• Check the User Guide for step-by-step instructions
• See the API Reference for integration details
• Read contributing guidelines to help improve PyAMA

📄 License

MIT License - see LICENSE file for details.

🤝 Contributing

We welcome contributions! See CONTRIBUTORS.md for a list of contributors and visit our contributing guide to get started.

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For the complete documentation and detailed instructions, please visit https://pyama.readthedocs.io