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title PRIK — Python Runtime Interop Kit
description PRIK generates native Python bindings from Fortran projects, producing importable extensions and editable .pyi contracts for Pythonic APIs.
audience users
prerequisites none
related user/getting-started/index.md, user/getting-started/installation.md, user/performance.md, developer/architecture.md
status maintained
publication reviewed

PRIK — Python Runtime Interop Kit

Generate native Python bindings for Fortran, with editable .pyi contracts and Pythonic APIs.

PRIK generates native Python bindings from Fortran projects, producing importable extensions and editable .pyi contracts for Pythonic APIs.

Project status: Alpha. Core Fortran wrapper workflows are implemented and tested across supported compilers, but public APIs may still change before 1.0.

PRIK starts with Fortran-to-Python. Its semantic contract model is designed to support more native languages over time.

From Fortran to Python in one command

Install the package in a virtual environment:

python3 -m pip install prik

Create scale.f90:

real(8) function scale(value, factor) result(output)
  real(8), intent(in) :: value
  real(8), intent(in) :: factor
  output = value * factor
end function scale

Build an importable extension:

python3 -m prik scale.f90

Call the generated Python API:

import numpy as np

import scale

result = scale.scale(np.float64(3.0), np.float64(2.5))
print(result)  # 7.5

No manual binding code is required. PRIK derives the native wrapper and a readable Python signature from the Fortran source.

Why PRIK

  • Natural Python APIs: Fortran modules become namespaces and derived types become classes.
  • Editable contracts: generated .pyi files let you rename, hide, flatten, or reorganize the public API.
  • Explicit native behavior: NumPy dtypes, array layouts, ownership, and lifetimes are checked at the boundary.
  • Clear limits: unsupported contracts fail before wrapper generation with actionable diagnostics.

Proven on real Fortran libraries

The maintained examples wrap and numerically validate BLAS, LAPACK, FFTPACK, and MINPACK. The reproducible performance comparison measures PRIK and NumPy's f2py against the same Fortran kernels.

Measured against NumPy's f2py

The published benchmark compares both tools on the same Fortran sources and the same machine. The charts show the current published snapshot. Results are specific to its machine and toolchain, which are documented with the full results.

Runtime-call performance — values above 1.0× favor PRIK.

Relative runtime performance of PRIK and f2py across call, vector, and matrix workloads. Values above 1.0 mean PRIK is faster. { .prik-performance-chart }

The chart shows f2py time ÷ PRIK time: values above 1.0× favor PRIK and values below 1.0× favor f2py.

Clean end-to-end build time — lower times are better.

Clean end-to-end build time for PRIK and f2py under development and optimized compiler profiles. Lower times are better. { .prik-performance-chart }

See the benchmark machine, full results, and methodology →

Ready to wrap your Fortran project?

Install PRIK →{ .prik-primary-cta } Read Getting Started →{ .prik-primary-cta }

Working on PRIK itself?

Read Developer Documentation →{ .prik-primary-cta }