Calling C/C++ from Python? [closed]

Answer 1

For modern C++, use cppyy: http://cppyy.readthedocs.io/en/latest/

It's based on Cling, the C++ interpreter for Clang/LLVM. Bindings are at run-time and no additional intermediate language is necessary. Thanks to Clang, it supports C++17.

Install it using pip:

    $ pip install cppyy

For small projects, simply load the relevant library and the headers that you are interested in. E.g. take the code from the ctypes example is this thread, but split in header and code sections:

    $ cat foo.h
    class Foo {
    public:
        void bar();
    };

    $ cat foo.cpp
    #include "foo.h"
    #include <iostream>

    void Foo::bar() { std::cout << "Hello" << std::endl; }

Compile it:

    $ g++ -c -fPIC foo.cpp -o foo.o
    $ g++ -shared -Wl,-soname,libfoo.so -o libfoo.so  foo.o

and use it:

    $ python
    >>> import cppyy
    >>> cppyy.include("foo.h")
    >>> cppyy.load_library("foo")
    >>> from cppyy.gbl import Foo
    >>> f = Foo()
    >>> f.bar()
    Hello
    >>>

Large projects are supported with auto-loading of prepared reflection information and the cmake fragments to create them, so that users of installed packages can simply run:

    $ python
    >>> import cppyy
    >>> f = cppyy.gbl.Foo()
    >>> f.bar()
    Hello
    >>>

Thanks to LLVM, advanced features are possible, such as automatic template instantiation. To continue the example:

    >>> v = cppyy.gbl.std.vector[cppyy.gbl.Foo]()
    >>> v.push_back(f)
    >>> len(v)
    1
    >>> v[0].bar()
    Hello
    >>>

Note: I'm the author of cppyy.

Answer 2

There is also pybind11, which is like a lightweight version of Boost.Python and compatible with all modern C++ compilers:

https://pybind11.readthedocs.io/en/latest/

Answer 3

You should have a look at Boost.Python. Here is the short introduction taken from their website:

The Boost Python Library is a framework for interfacing Python and C++. It allows you to quickly and seamlessly expose C++ classes functions and objects to Python, and vice-versa, using no special tools -- just your C++ compiler. It is designed to wrap C++ interfaces non-intrusively, so that you should not have to change the C++ code at all in order to wrap it, making Boost.Python ideal for exposing 3rd-party libraries to Python. The library's use of advanced metaprogramming techniques simplifies its syntax for users, so that wrapping code takes on the look of a kind of declarative interface definition language (IDL).

Answer 4

I started my journey in the Python <-> C++ binding from this page, with the objective of linking high level data types (multidimensional STL vectors with Python lists) :-)

Having tried the solutions based on both ctypes and boost.python (and not being a software engineer) I have found them complex when high level datatypes binding is required, while I have found SWIG much more simple for such cases.

This example uses therefore SWIG, and it has been tested in Linux (but SWIG is available and is widely used in Windows too).

The objective is to make a C++ function available to Python that takes a matrix in form of a 2D STL vector and returns an average of each row (as a 1D STL vector).

The code in C++ ("code.cpp") is as follow:

#include <vector>
#include "code.h"

using namespace std;

vector<double> average (vector< vector<double> > i_matrix) {

  // Compute average of each row..
  vector <double> averages;
  for (int r = 0; r < i_matrix.size(); r++){
    double rsum = 0.0;
    double ncols= i_matrix[r].size();
    for (int c = 0; c< i_matrix[r].size(); c++){
      rsum += i_matrix[r][c];
    }
    averages.push_back(rsum/ncols);
  }
  return averages;
}

The equivalent header ("code.h") is:

#ifndef _code
#define _code

#include <vector>

std::vector<double> average (std::vector< std::vector<double> > i_matrix);

#endif

We first compile the C++ code to create an object file:

g++ -c -fPIC code.cpp

We then define a SWIG interface definition file ("code.i") for our C++ functions.

%module code
%{
#include "code.h"
%}
%include "std_vector.i"
namespace std {

  /* On a side note, the names VecDouble and VecVecdouble can be changed, but the order of first the inner vector matters! */
  %template(VecDouble) vector<double>;
  %template(VecVecdouble) vector< vector<double> >;
}

%include "code.h"

Using SWIG, we generate a C++ interface source code from the SWIG interface definition file..

swig -c++ -python code.i

We finally compile the generated C++ interface source file and link everything together to generate a shared library that is directly importable by Python (the "_" matters):

g++ -c -fPIC code_wrap.cxx  -I/usr/include/python2.7 -I/usr/lib/python2.7
g++ -shared -Wl,-soname,_code.so -o _code.so code.o code_wrap.o

We can now use the function in Python scripts:

#!/usr/bin/env python

import code
a= [[3,5,7],[8,10,12]]
print a
b = code.average(a)
print "Assignment done"
print a
print b

Answer 5

I think cffi for python can be an option.

The goal is to call C code from Python. You should be able to do so without learning a 3rd language: every alternative requires you to learn their own language (Cython, SWIG) or API (ctypes). So we tried to assume that you know Python and C and minimize the extra bits of API that you need to learn.

http://cffi.readthedocs.org/en/release-0.7/

Answer 6

I love cppyy, it makes it very easy to extend Python with C++ code, dramatically increasing performance when needed.

It is powerful and frankly very simple to use,

here it is an example of how you can create a numpy array and pass it to a class member function in C++.

cppyy_test.py

import cppyy
import numpy as np
cppyy.include('Buffer.h')


s = cppyy.gbl.Buffer()
numpy_array = np.empty(32000, np.float64)
s.get_numpy_array(numpy_array.data, numpy_array.size)
print(numpy_array[:20])

Buffer.h

struct Buffer {
  void get_numpy_array(double *ad, int size) {
    for( long i=0; i < size; i++)
        ad[i]=i;
  }
};

You can also create a Python module very easily (with CMake), this way you will avoid recompile the C++ code all the times.