A small template project that acts as a working tutorial on how to wrap C code into python using cython, and integrate the extensions into an installable module. Developed around python 2.7, but written for compatibility with python 3.x.
A cython pyx file is included that implements a few C functions which can accept and return standard python data types and numpy ndarrays. These are compiled, wrapped, and integrated into the module using a standard setup.py script.
In addition to concrete examples of cython syntax, this repo also illustrates a working project structure with a working setup.py configuration (using setuptools) for Python projects with C extension modules, along with basic unit tests (together with a working Travis-CI config).
First, install numpy and cython (using pip or from a package manager) if you don't already have them.
Then just run python setup.py develop to build the project in-place.
The module (with its wrapped C functions c_hello, factorial, array_sum,
and tessellation) will then be importable in python:
>>> from cython_example_proj import c_hello, factorial, array_sum, tessellation4 examples functions are defined in wrapped.pyx:
- A direct wrapping of a simple C "hello world" function, implemented in cfunc.c
- A C function to compute the factorial of a python integer, built using Cython syntax
- A C function to compute the sum of a numpy ndarray
- A C function to compute and return the tessellation structure (ndarray) of the pixels of an inputted digital image (ndarray). This is a re-implementation of first half of the main method used in the STL Tools library.
For a quick benchmark of the two ndarray functions listed, run
python timings.py. This will show timings of the wrapped C functions
vs. numpy+python and/or pure python implementations of the same functions.
-----------------------------------------------------------
Initialized array for sum; starting comparison:
-----------------------------------------------------------
cython finished : 0.0239799022675 s
numpy finished : 0.024649143219 s
python finished : 14.2366518974 s
-----------------------------------------------------------
Initialized array for tessellate; starting comparison:
-----------------------------------------------------------
cython finished : 0.3423628807067871 s
python+numpy finished : 48.1245310307 s
So the cython-generated C implementation of array_sum is on parity with numpy.sum, and are
are each much faster than the pure python implementation.
The C implementation of tessellate is much faster than the python+numpy implementation. Using this information,
I've written a complete c-extension for STL Tools.
If you're interested in possibily rewriting some or part of a module as a compiled extension, you should profile the execution of your module first. The Python standard library module cProfile can help you with this.
Unfortunately, sorting the output of cProfile using their API leaves a few options to be desired. Namely: sorting by percall time (cumtime divided by ncalls).
profile.py shows an example of a work-around to this. Re-define the f() function to execute functions from your library,
and a profile will be printed, broken down by function, that is sorted by percall.