Using via cmake

In terms of complexity, cmake falls between make and meson. The learning curve is steeper since CMake syntax is not pythonic and is closer to make with environment variables.

However, the trade-off is enhanced flexibility and support for most architectures and compilers. An introduction to the syntax is out of scope for this document, but this extensive CMake collection of resources is great.

Fibonacci walkthrough (F77)

Returning to the fib example from f2py-getting-started section.

We do not need to explicitly generate the python -m numpy.f2py fib1.f output, which is fib1module.c, which is beneficial. With this; we can now initialize a CMakeLists.txt file as follows:

A key element of the CMakeLists.txt file defined above is that the add_custom_command is used to generate the wrapper C files and then added as a dependency of the actual shared library target via a add_custom_target directive which prevents the command from running every time. Additionally, the method used for obtaining the fortranobject.c file can also be used to grab the numpy headers on older cmake versions.

This then works in the same manner as the other modules, although the naming conventions are different and the output library is not automatically prefixed with the cython information.

ls .
# CMakeLists.txt fib1.f
cmake -S . -B build
cmake --build build
cd build
python -c "import numpy as np; import fibby; a = np.zeros(9); fibby.fib(a); print (a)"
# [ 0.  1.  1.  2.  3.  5.  8. 13. 21.]

This is particularly useful where an existing toolchain already exists and scikit-build or other additional python dependencies are discouraged.