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I am trying to make a python package distribution for some popular Fortran codes in my field. I want it to use the most standard approach with a setup.py file. The related qustion was helpful for learning how to wrap Fortran extensions.

When using this approach, I noticed some confusing behavior when mixing setuptools and numpy.distutils. Is it bad practice to mix the two? As of 2015, it seems preferable to use setuptools as much as possible.

However, I would like to build Fortran extensions in a way that is compatible with numpy. So I would like to import from numpy.distutils to get Extension and setup.

I'm using the following basic approach:

from setuptools.command.develop import develop
from numpy.distutils.core import Extension, setup

ext_modules=[Extension("my_package.fortran_mod", sources=['src/fortran_mod.f'])]

class MyDevelop(develop):
  def run(self):
    my_script()
    develop.run(self)

setup(
  ...
  ext_modules=ext_modules,
  cmdclass={'develop':MyDevelop})

This seems to work but I have questions.

  1. Is it generally good practice to mix setuptools and numpy.distribute?
  2. Does the order I import them matter? Should I always import setuptools first?
  3. Is there an official up-to-date tutorial for packaging extensions to numpy? Perhaps even one with some discussion Fortran extensions?

Some links

https://www.youtube.com/watch?v=R4yB-8tB0J0

http://www.fortran90.org/src/best-practices.html#interfacing-with-python

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  • 5
    I'm not an expert in wrapping Fortran code - but I'm pretty experience with Cython. This talk from Scipy 2015 has a really nice section on successfully wrapping Fortran code - I suggest you take a look! Commented Jul 20, 2015 at 8:51
  • 2
    What do you mean by standard Python? nogil is very important if you want to be able to delegate down to native code and make use of threading/multi-process. Your MPI example is a perfect time to release the GIL! Commented Jul 20, 2015 at 20:16
  • 2
    Compared to f2py, Cython would allow you to use iso_c_binding in the Fortran code.Because f2py is mainly build for Fortran77 code, it lacks a lot of great Fortran90+ features, such as dynamic memory allocation. With Cython you have a much greater flexibility because your Fortran and Cython code can speak (almost natively) to eachother using iso_c_binding. Commented Nov 9, 2015 at 14:26
  • 2
    Following some of the earlier comments I would like to contribute two more options to your considerations. If you use "old" Fortran, then you could directly access your Fortran functions using ctypes (just take care of the function-names which, depending on your compiler, get a trailing underscore). Recently I have become a fan of pybind11 which allows you to write the entire Python module from C++, with the bonus advantage that the ancient Fortran code gets a modern front-end. Commented Mar 31, 2017 at 7:47
  • 2
    @Will Before you undertake your Fortran conversions are you positive they aren't already implemented in the SciPy low level Fortran interfaces? github.com/scipy/scipy/blob/master/scipy/linalg/blas.py also lapack.py you'd be hard pressed to write anything faster (I've tried with MKL and can only shave maybe microseconds (I.e. not statistically significant improvements)) Also have a look there they have some prewitten Cython test programs (dgemm does have a bug you can resolve by searching on a post of mine SciPy dgemm interface not working on mx1 1xn vectors) Commented Jul 30, 2017 at 19:21

1 Answer 1

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This seems to work but I have questions.

  1. Is it generally good practice to mix setuptools and numpy.distribute?
  2. Does the order I import them matter? Should I always import setuptools first?
  3. Is there an official up-to-date tutorial for packaging extensions to numpy? Perhaps even one with some discussion Fortran extensions?

  1. You should not need to use numpy.distribute anymore.

  2. ^^ Not necessary

  3. Particularly for wrapping fortran code with numpy, there is the popular f2py. However I personally find the necessary code annotations redundant, because good fortran code contains all necessary information.

(warning personal project plug below)

Recently released is the cleaner fmodpy, which automatically generates all necessary wrapper code in an understandable and clean interface. It supports pre-Fortran90, but is best suited for Fortran90 and later. It could be used to generate a clean distribution along with the python interface of code (presuming users have gfortran installed).

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1 Comment

The personal project plug is not problem, but this answer is. Could you please reference the questions you are answering int the 1,2,3 above so that this answer can be actually read as is?

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