Extensions

Contents

Extensions#

Python code can be slow, so we sometimes turn to extension modules to get performance in critical parts of our algorithms There are a number of ways to write extension modules in python – these can be in another language like C or Fortran or use a library that converts python into compiled code (with some restrictions) like Cython or Numba.

We’ll look at some examples of these and talk about their strengths and weaknesses.

Methods#

  • C++

    • pybind11 : this is a header-only library that allows you to call C++ functions directly from python.

  • C

    • C-API : the standard python interpreter (cpython) is written in C, so it is natural that we can write C code to interact with our python code. This is the python C-API. Since NumPy is also written in C, we can work with NumPy arrays in C code as well.

      This will give us the performance of C compiled code, but the downside is that we lose a lot of what makes python great. We need to pass data into C as pointers and cast them into types that represent the arrays we use. This means writing a lot of boilerplate code just to deal with some simple operations.

      This underlies most of the techniques that we’ll see here.

      These days, there are better methods for most applications.

    • ctypes : this is a module that allows you to call functions in shared libraries. This is part of standard python.

      With ctypes, you don’t need to modify your C code – you just need to define an interface to the C function in python. However, the calling mechanism can be slow.

      There is support for NumPy through numpy.ctypeslib.

  • Fortran

    • f2py : this is part of NumPy. It allows for easy calling of Fortran from python.

      You essentially just need to add some comments to your Fortran code to allow f2py to build an interface. f2py understands the different orderings of indices between C and Fortran arrays.

  • python

    • Cython : this is a superset of python that can convert python into compiled C code.

      The advantage here is that the code looks like python, with some declarations of the variable types with cdef. Performance can be really great when you need to explicitly write out loops over NumPy array indices.

    • Numba : this is a just-in-time compiler. It just requires a simple decorator and then it will compile a python function the first time it is encountered.

Contents