SWIG

An extensible compiler for creating scriptable scientific software. Scripting languages such as Python and Tcl have become a powerful tool for the construction of flexible scientific software because they provide scientists with an interpreted problem solving environment and they form a modular framework for controlling software components written in C, C++, and Fortran. However, a common problem faced by the developers of a scripted scientific application is that of integrating compiled code with a high-level interpreter. This paper describes SWIG, an extensible compiler that automates the task of integrating compiled code with scripting language interpreters. SWIG requires no modifications to existing code and can create bindings for eight different target languages including Python, Perl, Tcl, Ruby, Guile, and Java. By automating language integration, SWIG enables scientists to use scripting languages at all stages of software development and allows existing software to be more easily integrated into a scripting environment.


References in zbMATH (referenced in 43 articles )

Showing results 1 to 20 of 43.
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  1. Siggel, Martin; Kleinert, Jan; Stollenwerk, Tobias; Maierl, Reinhold: TiGL: an open source computational geometry library for parametric aircraft design (2019)
  2. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  3. DeGroot, Christopher T: WEdiff: A Python and C++ package for automatic differentiation (2018) not zbMATH
  4. Jain, Abhinandan: An analytical workbench for system level multibody dynamics (2018)
  5. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018) not zbMATH
  6. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  7. Coelho, L.P.: Jug: Software for Parallel Reproducible Computation in Python (2017) not zbMATH
  8. Erika Tudisco; Edward Andò; Rémi Cailletaud; Stephen A.Hall: TomoWarp2: A local digital volume correlation code (2017) not zbMATH
  9. Langtangen, Hans Petter; Linge, Svein: Finite difference computing with PDEs. A modern software approach (2017)
  10. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  11. Roberto Souza, Letícia Rittner, Rubens Machado, Roberto Lotufo: iamxt: Max-tree toolbox for image processing and analysis (2017) not zbMATH
  12. König, Marcel; Radtke, Lars; Düster, Alexander: A flexible C++ framework for the partitioned solution of strongly coupled multifield problems (2016)
  13. Linaro, Daniele; Storace, Marco: \textscBAL: a library for the \textitbrute-force analysis of dynamical systems (2016)
  14. Yallop, Jeremy; Sheets, David; Madhavapeddy, Anil: Declarative foreign function binding through generic programming (2016)
  15. Pommereau, Franck: SNAKES: a flexible high-level Petri nets library (tool paper) (2015)
  16. Śmigaj, Wojciech; Betcke, Timo; Arridge, Simon; Phillips, Joel; Schweiger, Martin: Solving boundary integral problems with BEM++ (2015)
  17. Weinbub, Josef; Wastl, Matthias; Rupp, Karl; Rudolf, Florian; Selberherr, Siegfried: ViennaMaterials -- a dedicated material library for computational science and engineering (2015)
  18. Ying, Jinyong; Xie, Dexuan: A new finite element and finite difference hybrid method for computing electrostatics of ionic solvated biomolecule (2015)
  19. Krause, Dorian; Fackeldey, Konstantin; Krause, Rolf: A parallel multiscale simulation toolbox for coupling molecular dynamics and finite elements (2014)
  20. Coelho, L.P.: Mahotas: Open source software for scriptable computer vision (2013) not zbMATH

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Further publications can be found at: http://www.swig.org/doc.html