Beyond Python

This is a small collection of thoughts related to the inclusion of code written in languages other than Python. Currently, the only option for languages other than Python that is explicitly documented is Cython.

Can I use a programming language other than Python to speed up my code?

Yes. The languages used in SciPy are Python, Cython, C, C++, and Fortran. All of these have their pros and cons. If Python really doesn’t offer enough performance, one of those languages can be used. Important concerns when using compiled languages are maintainability and portability. For maintainability, Cython is clearly preferred over C/C++/Fortran. Cython and C are more portable than C++/Fortran. A lot of the existing C and Fortran code in SciPy is older, battle-tested code that was only wrapped in (but not specifically written for) Python/SciPy. Therefore, the basic advice is: use Cython. If there are specific reasons why C/C++/Fortran should be preferred, please discuss those reasons first.

Can I use Numba or Pythran?

Not yet, but we’re considering it for the future.

How do I debug code written in C/C++/Fortran inside SciPy?

The easiest way to do this is to first write a Python script that invokes the C code whose execution you want to debug. For instance mytest.py:

from scipy.special import hyp2f1
print(hyp2f1(5.0, 1.0, -1.8, 0.95))

Now, you can run:

gdb --args python runtests.py -g --python mytest.py

If you didn’t compile with debug symbols enabled before, remove the build directory first. While in the debugger:

(gdb) break cephes_hyp2f1
(gdb) run

The execution will now stop at the corresponding C function and you can step through it as usual. Instead of plain gdb you can, of course, use your favorite alternative debugger; run it on the python binary with arguments runtests.py -g --python mytest.py.