F2PY

The purpose of the F2PY --Fortran to Python interface generator-- project is to provide connection between Python and Fortran languages. F2PY is a Python extension tool for creating Python C/API modules from (handwritten or F2PY generated) signature files (or directly from Fortran sources). The generated extension modules facilitate: Calling Fortran 77/90/95, Fortran 90/95 module, and C functions from Python. Accessing Fortran 77 COMMON blocks and Fortran 90/95 module data (including allocatable arrays) from Python. Calling Python functions from Fortran or C (call-backs). Automatically handling the difference in the data storage order of multi-dimensional Fortran and Numerical Python (i.e. C) arrays. In addition, F2PY can build the generated extension modules to shared libraries with one command. F2PY uses the scipy_distutils module from SciPy that supports number of major Fortran compilers. F2PY generated extension modules depend on NumPy package that provides fast multi-dimensional array language facility to Python.


References in zbMATH (referenced in 20 articles )

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  1. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  2. Römer, Ulrich; Narayanamurthi, Mahesh; Sandu, Adrian: Solving parameter estimation problems with discrete adjoint exponential integrators (2018)
  3. Ahmed Attia, Adrian Sandu: DATeS: A Highly-Extensible Data Assimilation Testing Suite (2017) arXiv
  4. Pierre Fernique, Christophe Pradal: AutoWIG: Automatic Generation of Python Bindings for C++ Libraries (2017) arXiv
  5. Andersson, C., Führer, C., Åkesson, J.: Assimulo: A unified framework for ODE solvers (2015)
  6. Izaac, Josh A.; Wang, Jingbo B.: \itpyCTQW: a continuous-time quantum walk simulator on distributed memory computers (2015)
  7. Ying, Jinyong; Xie, Dexuan: A new finite element and finite difference hybrid method for computing electrostatics of ionic solvated biomolecule (2015)
  8. Belson, Brandt A.; Tu, Jonathan H.; Rowley, Clarence W.: Algorithm 945: modred -- a parallelized model reduction library (2014)
  9. Christopher Strickland; Robert Burdett; Kerrie Mengersen; Robert Denham: PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models (2014)
  10. Xie, Dexuan: New solution decomposition and minimization schemes for Poisson-Boltzmann equation in calculation of biomolecular electrostatics (2014)
  11. Xie, Dexuan; Jiang, Yi; Scott, L. Ridgway: Efficient algorithms for a nonlocal dielectric model for protein in ionic solvent (2013)
  12. Corrigan, Andrew; Camelli, Fernando; Löhner, Rainald; Mut, Fernando: Semi-automatic porting of a large-scale Fortran CFD code to GPUs (2012)
  13. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  14. Tamm, Kert; Salupere, Andrus: On the propagation of 1D solitary waves in Mindlin-type microstructured solids (2012)
  15. Yuffa, Alex J.; Scales, John A.: Object-oriented electrodynamic S-matrix code with modern applications (2012)
  16. Rasch, Arno; Bücker, H. Martin: EFCOSS: an interactive environment facilitating optimal experimental design (2010)
  17. Drummond, L. Anthony; Galiano, Vicente; Migallón, Violeta; Penadés, Jose: PyACTS: A Python based interface to ACTS tools and parallel scientific applications (2009)
  18. Ilison, Lauri; Salupere, Andrus: Propagation of $sech^2$-type solitary waves in hierarchical KdV-type systems (2009)
  19. Cai, Xing; Langtangen, Hans Petter: Parallelizing PDE solvers using the Python programming language (2006)
  20. Rickett, Christopher D.; Choi, Sung-Eun; Rasmussen, Craig E.; Sottile, Matthew J.: Rapid prototyping frameworks for developing scientific applications: A case study (2006) ioport