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Area of Science:

  • Scientific Computing
  • Software Engineering

Background:

  • Scientific packages in Python and R often use compiled libraries for performance.
  • Wrapping these libraries manually is time-consuming and complex.

Purpose of the Study:

  • Introduce AutoWIG, a novel Python package for automatic library wrapping.
  • Enable seamless integration of compiled scientific libraries into high-level languages.

Main Methods:

  • Utilizes LLVM/Clang technologies for code analysis and transformation.
  • Employs the Mako templating engine for generating wrapper code.
  • Designed to handle complex C++ libraries with numerous classes and meta-programming.

Main Results:

  • AutoWIG provides an automatic and extensible solution for wrapping compiled libraries.
  • Successfully wraps complex C++ libraries, including those with thousands of classes.
  • Facilitates the reuse of legacy and high-performance compiled code.

Conclusions:

  • AutoWIG significantly reduces the effort required to integrate compiled libraries.
  • Enhances the accessibility and usability of scientific software by bridging compiled and high-level languages.