# Wrapping Fortran libraries for Python with PRIK. Suggestions? **URL:** \ **Category:** Uncategorized\ **Created:** [September 28, 2026, 2:48pm UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135 "2026-09-28T14:48:28Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![Said-H](https://avatars.discourse-cdn.com/v4/letter/s/51bf81/32.png) [@Said-H](https://fortran-lang.discourse.group/u/Said-H)\ **Post date:** [September 28, 2026, 2:48pm UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135/1 "2026-09-28T14:48:28Z")
Hi everyone, I’m the author of [PRIK](https://github.com/PyNumLab/prik). I have recently wrapped two large Fortran projects with PRIK. **PRIMA** (Zaikun Zhang’s modern Fortran implementation of Powell’s derivative-free solvers). All five solvers (COBYLA, UOBYQA, NEWUOA, BOBYQA, LINCOA) are generated directly from the Fortran sources.The tests check each solver against a known minimum and compare COBYLA with SciPy’s. Guide: [PRIMA Wrapper - PRIK — Bring Native Code to Python](https://pynumlab.github.io/prik/user/examples/fortran/prima-wrapper/) **Open MPI**. PRIK generates a Python extension from Open MPI’s Fortran `mpi_f08` sources. With an edited `.pyi` contract and a short Python module on top, an mpi4py-style program (send/recv, broadcast, reduce, allreduce) runs unchanged apart from its import and produces the same output. In our measurements, small Allreduce calls were on average about 16 % faster than mpi4py (28 % when calling the generated API directly). It covers one datatype for now. Tutorial: [Wrap Open MPI mpi\_f08 - PRIK — Bring Native Code to Python](https://pynumlab.github.io/prik/user/tutorials/openmpi-f08/) Besides the two I just described ( **PRIMA** and **Open MPI** ), PRIK already has maintained, numerically validated wrappers for: Fortran: **BLAS** , **LAPACK** , **FFTPACK** , **MINPACK** , **BSPLINE-FORTRAN** , **PRIMA** , and **Open MPI** mpi\_f08 C: **libm** and **TA-Lib** Do you have any suggestions for other important Fortran libraries that would be worth wrapping for Python? Documentation: [PRIK — Bring Native Code to Python](https://pynumlab.github.io/prik/) GitHub: [GitHub - PyNumLab/prik: Bring Native Code to Python with editable .pyi contracts · GitHub](https://github.com/PyNumLab/prik) ---
**Author:** ![Beliavsky](https://avatars.discourse-cdn.com/v4/letter/b/ba8739/32.png) [@Beliavsky](https://fortran-lang.discourse.group/u/Beliavsky)\ **Post date:** [September 28, 2026, 5:05pm UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135/2 "2026-09-28T17:05:29Z")
I have used ChatGPT to [translate 566 R packages to Fortran packages](https://github.com/Beliavsky/Fortran-from-R-packages). Eventually I want to have Python, Octave/Matlab, and R interfaces for all the translations. You could try PRIK on them. I use Windows on which PRIK does not yet run. ---
**Author:** ![Said-H](https://avatars.discourse-cdn.com/v4/letter/s/51bf81/32.png) [@Said-H](https://fortran-lang.discourse.group/u/Said-H)\ **Post date:** [September 29, 2026, 11:10am UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135/3 "2026-09-29T11:10:48Z")
Do you have a top 10 list of those packages? I’ll focus on those first, and then maybe, with the help of AI, we could try to wrap all of them. ---
**Author:** ![Beliavsky](https://avatars.discourse-cdn.com/v4/letter/b/ba8739/32.png) [@Beliavsky](https://fortran-lang.discourse.group/u/Beliavsky)\ **Post date:** [September 29, 2026, 11:24am UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135/4 "2026-09-29T11:24:16Z")
ChatGPT [says](https://chatgpt.com/share/6abb9f6c-b59c-83ea-b5ce-1a4414500a14) the 50 most popular of the translated packages, based on downloads of the originals, are as follows. You could start with them, although the first 3 are specific to R programming (part of the tidyverse), so providing Python interfaces to Fortran translations of them may not be useful. | Rank | R package | | --- | --- | | 1 | dplyr | | 2 | purrr | | 3 | stringr | | 4 | igraph | | 5 | Matrix | | 6 | lme4 | | 7 | survival | | 8 | mgcv | | 9 | MASS | | 10 | abind | | 11 | numDeriv | | 12 | glmnet | | 13 | cluster | | 14 | boot | | 15 | rpart | | 16 | nnet | | 17 | car | | 18 | e1071 | | 19 | psych | | 20 | forecast | | 21 | Hmisc | | 22 | mice | | 23 | sandwich | | 24 | multcomp | | 25 | lavaan | | 26 | ranger | | 27 | lmtest | | 28 | mclust | | 29 | ape | | 30 | quantreg | | 31 | survey | | 32 | fields | | 33 | fitdistrplus | | 34 | geepack | | 35 | robustbase | | 36 | deSolve | | 37 | classInt | | 38 | spdep | | 39 | coda | | 40 | tseries | | 41 | FNN | | 42 | dbscan | | 43 | fastcluster | | 44 | truncnorm | | 45 | copula | | 46 | urca | | 47 | pracma | | 48 | cubature | | 49 | DEoptim | | 50 | expm | ---
**Author:** ![rachelwilson](https://avatars.discourse-cdn.com/v4/letter/r/8edcca/32.png) [@rachelwilson](https://fortran-lang.discourse.group/u/rachelwilson)\ **Post date:** [October 9, 2026, 12:00am UTC](https://fortran-lang.discourse.group/t/wrapping-fortran-libraries-for-python-with-prik-suggestions/11135/5 "2026-10-09T00:00:29Z")
quadpack would be a great one, adaptive quadrature is something people reach for constantly and the fortran implementation is still the gold standard. odepack too, lsoda especially, having a solid stiff/non-stiff solver via python would get a lot of use. arpack for large sparse eigenvalue problems would round it out nicely.