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 37 articles )

Showing results 1 to 20 of 37.
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  1. Ansmann, Gerrit: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE (2018)
  2. Jain, Abhinandan: An analytical workbench for system level multibody dynamics (2018)
  3. Kulshreshtha, K.; Narayanan, S. H. K.; Bessac, J.; MacIntyre, K.: Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C (2018)
  4. Richard Beare; Bradley Lowekamp; Ziv Yaniv: Image Segmentation, Registration and Characterization in R with SimpleITK (2018)
  5. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  6. Erika Tudisco; Edward Andò; Rémi Cailletaud; Stephen A.Hall: TomoWarp2: A local digital volume correlation code (2017)
  7. Langtangen, Hans Petter; Linge, Svein: Finite difference computing with PDEs. A modern software approach (2017)
  8. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  9. Roberto Souza, Letícia Rittner, Rubens Machado, Roberto Lotufo: iamxt: Max-tree toolbox for image processing and analysis (2017)
  10. König, Marcel; Radtke, Lars; Düster, Alexander: A flexible C++ framework for the partitioned solution of strongly coupled multifield problems (2016)
  11. Linaro, Daniele; Storace, Marco: BAL: a library for the \itbrute-force analysis of dynamical systems (2016)
  12. Yallop, Jeremy; Sheets, David; Madhavapeddy, Anil: Declarative foreign function binding through generic programming (2016)
  13. Pommereau, Franck: SNAKES: a flexible high-level Petri nets library (tool paper) (2015)
  14. Śmigaj, Wojciech; Betcke, Timo; Arridge, Simon; Phillips, Joel; Schweiger, Martin: Solving boundary integral problems with BEM++ (2015)
  15. Ying, Jinyong; Xie, Dexuan: A new finite element and finite difference hybrid method for computing electrostatics of ionic solvated biomolecule (2015)
  16. Krause, Dorian; Fackeldey, Konstantin; Krause, Rolf: A parallel multiscale simulation toolbox for coupling molecular dynamics and finite elements (2014)
  17. Andersson, Joel; Åkesson, Johan; Diehl, Moritz: CasADi: A symbolic package for automatic differentiation and optimal control (2012)
  18. Harrington, Anthony; Cahill, Vinny: Model-driven engineering of planning and optimisation algorithms for pervasive computing environments (2011) ioport
  19. Ramsey, Norman: Embedding an interpreted language using higher-order functions and types (2011)
  20. Galiano, Vicente; Migallón, Héctor; Migallón, Violeta; Penadés, Jose: PyPnetCDF: a high level framework for parallel access to netCDF files (2010)

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