clifpy 0.0.3

A Python package for working with CLIF EHR data

clifpy - Python Client for CLIF

⚠️ Status: This project is currently in active development

clifpy is a Python package for working with CLIF (Common Longitudinal ICU data Format) data. It provides a standardized interface for loading, validating, and analyzing critical care data across different healthcare systems.

🚧 Project Status

✅ Completed Features

• Core CLIF-2.0.0 class implementation
• All 9 CLIF-2.0.0 beta table implementations (patient, vitals, labs, etc.)
• Data validation against mCIDE schemas
• Timezone handling and conversion
• Advanced filtering and querying capabilities
• Comprehensive test suite
• CLIF Demo Dataset created using MIMIC-IV Clinical Database Demo
• Example notebooks demonstrating usage

🔄 In Progress

• Package distribution setup (PyPI)
• Additional clinical calculation functions
• Performance optimizations for large datasets
• Enhanced documentation
• Integration with additional data sources

📋 Planned Features

• SOFA score calculations
• Additional clinical severity scores
• Data visualization utilities
• Export functionality to other formats

📦 Installation

User Installation

For most users, simply install from PyPI using pip:

pip install clifpy

This is all you need to start using clifpy in your projects.

📋 Requirements

• Python 3.8+
• pandas >= 2.0.0
• duckdb >= 0.9.0
• pyarrow >= 10.0.0
• pytz
• pydantic >= 2.0

See pyproject.toml for complete dependencies.

🤝 Contributing

We welcome contributions! Please see our contributing guidelines (coming soon).

Prerequisites

First, install uv if you haven't already:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
# Or on Windows: powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Development Setup

1. Fork the repository
2. Clone your fork and set up the development environment:

   # Clone the repository
   git clone https://github.com/<your github username>/clifpy.git
   cd clifpy
   
   # Install dependencies and create virtual environment automatically
   uv sync
   

3. Create a feature branch (git checkout -b feature/amazing-feature)
4. Make your changes
5. Add new dependencies with uv add <package> (for permanent dependencies)
6. Run tests (uv run pytest)
7. Commit your changes (git commit -m 'Add amazing feature')
8. Push to the branch (git push origin feature/amazing-feature)
9. Open a Pull Request

Development Commands

# Run tests
uv run pytest

# Add a new dependency (updates pyproject.toml)
uv add <package>

# Add a development dependency
uv add --dev <package>

# Install temporary/experimental package (not committed to pyproject.toml)
uv pip install <package>

# Run any Python script
uv run python your_script.py

# Sync dependencies after pulling changes
uv sync

📄 License

This project is licensed under the [LICENSE] file in the repository.

🔗 Links

• CLIF Specification
• Issue Tracker

📧 Contact

For questions or support, please open an issue on GitHub.

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Note: This project is under active development. APIs may change between versions until the 1.0 release.