pfapack 🔢

(See my intro post)

Ever needed to calculate the square root of the determinant of a skew-symmetric matrix? Probably not, but if you’re in quantum physics, you might have! This is called the Pfaffian. Today’s project, pfapack, is a Python package that provides efficient routines for computing it. The underlying algorithms and original code implementations were developed by my PhD co-promotor Michael Wimmer. I created the pfapack package to make this powerful tool easily accessible.

📖 Origin Story

Working in quantum physics, I frequently need to compute the Pfaffian. Michael had not only developed the algorithms but also wrote highly optimized C, FORTRAN, and Python implementations, distributing them as a tar file on his website. To make it easier for researchers working in Python, I packaged his existing Python code, added wrappers for his C and FORTRAN implementations, and set up automated builds for PyPI and conda-forge to make it easily installable.

🔧 Technical Highlights

• Python Wrapper for Efficient Code: User-friendly Python interface to Wimmer’s optimized C and FORTRAN.
• Multiple Algorithms: Supports both the Parlett-Reid and Householder methods.
• Optimized for Performance: Leverages low-level optimizations for speed.
• Conda and Pip Installable: Easy installation via conda-forge and PyPI.
• Automated Wheel Builds: Uses pypa/cibuildwheel for streamlined, automated builds and releases.
• Cross-Platform Compatibility: Works on Linux, macOS, and Windows (with some caveats).

📊 Impact

• Makes specialized mathematical algorithms readily available to Python users.
• Simplifies the use of highly optimized code.
• 15 GitHub stars.
• Used in research projects that require efficient Pfaffian computations, particularly in quantum physics.

🎯 Challenges and Solutions

• Bridging Languages: Wrapping C and FORTRAN for Python integration.
• Automated Builds: Setting up automated builds for multiple platforms was complex.
• Windows Compatibility: As always Windows support is a PITA; it currently requires MSYS2 and MinGW64.

💡 Lessons Learned

• Making specialized tools accessible broadens their impact.
• Wrapping low-level code in Python can improve usability.
• Automated builds are essential for maintainability.
• pypa/cibuildwheel is awesome for cross-platform builds.

🙏 Credits

All the credits for the underlying algorithms and original code implementations go to Michael Wimmer. I simply made it easier to distribute and use in Python.

Want to compute Pfaffians efficiently in Python? Check out pfapack on GitHub or install it via pip install pfapack or conda install -c conda-forge pfapack!

#OpenSource #Python #ScientificComputing #Math #Pfaffian #FORTRAN #C