ctypes - Beginner

Question

I have the task of \"wrapping\" a c library into a python class. The docs are incredibly vague on this matter. It seems they expect only advanced python users would implem

Answer 1

The answer by Chinmay Kanchi is excellent but I wanted an example of a function which passes and returns a variables/arrays to a C++ code. I though I'd include it here in case it is useful to others.

Passing and returning an integer

The C++ code for a function which takes an integer and adds one to the returned value,

extern "C" int add_one(int i)
{
    return i+1;
}

Saved as file test.cpp, note the required extern "C" (this can be removed for C code). This is compiled using g++, with arguments similar to Chinmay Kanchi answer,

g++ -shared -o testlib.so -fPIC test.cpp

The Python code uses load_library from the numpy.ctypeslib assuming the path to the shared library in the same directory as the Python script,

import numpy.ctypeslib as ctl
import ctypes

libname = 'testlib.so'
libdir = './'
lib=ctl.load_library(libname, libdir)

py_add_one = lib.add_one
py_add_one.argtypes = [ctypes.c_int]
value = 5
results = py_add_one(value)
print(results)

This prints 6 as expected.

Passing and printing an array

You can also pass arrays as follows, for a C code to print the element of an array,

extern "C" void print_array(double* array, int N)
{
    for (int i=0; i<N; i++) 
        cout << i << " " << array[i] << endl;
}

which is compiled as before and the imported in the same way. The extra Python code to use this function would then be,

import numpy as np

py_print_array = lib.print_array
py_print_array.argtypes = [ctl.ndpointer(np.float64, 
                                         flags='aligned, c_contiguous'), 
                           ctypes.c_int]
A = np.array([1.4,2.6,3.0], dtype=np.float64)
py_print_array(A, 3)

where we specify the array, the first argument to print_array, as a pointer to a Numpy array of aligned, c_contiguous 64 bit floats and the second argument as an integer which tells the C code the number of elements in the Numpy array. This then printed by the C code as follows,

1.4
2.6
3.0

Answer 2

Here's a quick and dirty ctypes tutorial.

First, write your C library. Here's a simple Hello world example:

testlib.c

#include <stdio.h>

void myprint(void);

void myprint()
{
    printf("hello world\n");
}

Now compile it as a shared library (mac fix found here):

$ gcc -shared -Wl,-soname,testlib -o testlib.so -fPIC testlib.c

# or... for Mac OS X 
$ gcc -shared -Wl,-install_name,testlib.so -o testlib.so -fPIC testlib.c

Then, write a wrapper using ctypes:

testlibwrapper.py

import ctypes

testlib = ctypes.CDLL('/full/path/to/testlib.so')
testlib.myprint()

Now execute it:

$ python testlibwrapper.py

And you should see the output

Hello world
$

If you already have a library in mind, you can skip the non-python part of the tutorial. Make sure ctypes can find the library by putting it in /usr/lib or another standard directory. If you do this, you don't need to specify the full path when writing the wrapper. If you choose not to do this, you must provide the full path of the library when calling ctypes.CDLL().

This isn't the place for a more comprehensive tutorial, but if you ask for help with specific problems on this site, I'm sure the community would help you out.

PS: I'm assuming you're on Linux because you've used ctypes.CDLL('libc.so.6'). If you're on another OS, things might change a little bit (or quite a lot).

Answer 3

Firstly: The >>> code you see in python examples is a way to indicate that it is Python code. It's used to separate Python code from output. Like this:

>>> 4+5
9

Here we see that the line that starts with >>> is the Python code, and 9 is what it results in. This is exactly how it looks if you start a Python interpreter, which is why it's done like that.

You never enter the >>> part into a .py file.

That takes care of your syntax error.

Secondly, ctypes is just one of several ways of wrapping Python libraries. Other ways are SWIG, which will look at your Python library and generate a Python C extension module that exposes the C API. Another way is to use Cython.

They all have benefits and drawbacks.

SWIG will only expose your C API to Python. That means you don't get any objects or anything, you'll have to make a separate Python file doing that. It is however common to have a module called say "wowza" and a SWIG module called "_wowza" that is the wrapper around the C API. This is a nice and easy way of doing things.

Cython generates a C-Extension file. It has the benefit that all of the Python code you write is made into C, so the objects you write are also in C, which can be a performance improvement. But you'll have to learn how it interfaces with C so it's a little bit extra work to learn how to use it.

ctypes have the benefit that there is no C-code to compile, so it's very nice to use for wrapping standard libraries written by someone else, and already exists in binary versions for Windows and OS X.