Sending a C++ array to Python and back (Extending

问题:

I am going to send a c++ array to a python function as numpy array and get back another numpy array. After consulting with numpy documentation and some other threads and tweaking the code, finally the code is working but I would like to know if this code is written optimally considering the:

• Unnecessary copying of the array between c++ and numpy (python).
• Correct dereferencing of the variables.
• Easy straight-forward approach.

C++ code:

// python_embed.cpp : Defines the entry point for the console application.
//

#include "stdafx.h"

#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#include "Python.h"
#include "numpy/arrayobject.h"
#include<iostream>

using namespace std;

int _tmain(int argc, _TCHAR* argv[])
{
    Py_SetProgramName(argv[0]);
    Py_Initialize();
    import_array()

    // Build the 2D array
    PyObject *pArgs, *pReturn, *pModule, *pFunc;
    PyArrayObject *np_ret, *np_arg;
    const int SIZE{ 10 };
    npy_intp dims[2]{SIZE, SIZE};
    const int ND{ 2 };
    long double(*c_arr)[SIZE]{ new long double[SIZE][SIZE] };
    long double* c_out;
    for (int i{}; i < SIZE; i++)
        for (int j{}; j < SIZE; j++)
            c_arr[i][j] = i * SIZE + j;

    np_arg = reinterpret_cast<PyArrayObject*>(PyArray_SimpleNewFromData(ND, dims, NPY_LONGDOUBLE, 
        reinterpret_cast<void*>(c_arr)));

    // Calling array_tutorial from mymodule
    PyObject *pName = PyUnicode_FromString("mymodule");
    pModule = PyImport_Import(pName);
    Py_DECREF(pName);
    if (!pModule){
        cout << "mymodule can not be imported" << endl;
        Py_DECREF(np_arg);
        delete[] c_arr;
        return 1;
    }
    pFunc = PyObject_GetAttrString(pModule, "array_tutorial");
    if (!pFunc || !PyCallable_Check(pFunc)){
        Py_DECREF(pModule);
        Py_XDECREF(pFunc);
        Py_DECREF(np_arg);
        delete[] c_arr;
        cout << "array_tutorial is null or not callable" << endl;
        return 1;
    }
    pArgs = PyTuple_New(1);
    PyTuple_SetItem(pArgs, 0, reinterpret_cast<PyObject*>(np_arg));
    pReturn = PyObject_CallObject(pFunc, pArgs);
    np_ret = reinterpret_cast<PyArrayObject*>(pReturn);
    if (PyArray_NDIM(np_ret) != ND - 1){ // row[0] is returned
        cout << "Function returned with wrong dimension" << endl;
        Py_DECREF(pFunc);
        Py_DECREF(pModule);
        Py_DECREF(np_arg);
        Py_DECREF(np_ret);
        delete[] c_arr;
        return 1;
    }
    int len{ PyArray_SHAPE(np_ret)[0] };
    c_out = reinterpret_cast<long double*>(PyArray_DATA(np_ret));
    cout << "Printing output array" << endl;
    for (int i{}; i < len; i++)
        cout << c_out[i] << ' ';
    cout << endl;

    // Finalizing
    Py_DECREF(pFunc);
    Py_DECREF(pModule);
    Py_DECREF(np_arg);
    Py_DECREF(np_ret);
    delete[] c_arr;
    Py_Finalize();
    return 0;
}

In CodeReview, there is a fantastic answer: Link...

回答1:

Try out xtensor and the xtensor-python python bindings.

xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.

xtensor provides

• an extensible expression system enabling numpy-style broadcasting (see the numpy to xtensor cheat sheet).
• an API following the idioms of the C++ standard library.
• tools to manipulate array expressions and build upon xtensor.
• bindings for Python, but also R and Julia.

Example of usage

Initialize a 2-D array and compute the sum of one of its rows and a 1-D array.

#include <iostream>
#include "xtensor/xarray.hpp"
#include "xtensor/xio.hpp"

xt::xarray<double> arr1
  {{1.0, 2.0, 3.0},
   {2.0, 5.0, 7.0},
   {2.0, 5.0, 7.0}};

xt::xarray<double> arr2
  {5.0, 6.0, 7.0};

xt::xarray<double> res = xt::view(arr1, 1) + arr2;

std::cout << res;

Outputs

{7, 11, 14}

Creating a Numpy-style universal function in C++.

#include "pybind11/pybind11.h"
#include "xtensor-python/pyvectorize.hpp"
#include <numeric>
#include <cmath>

namespace py = pybind11;

double scalar_func(double i, double j)
{
    return std::sin(i) - std::cos(j);
}

PYBIND11_PLUGIN(xtensor_python_test)
{
    py::module m("xtensor_python_test", "Test module for xtensor python bindings");

    m.def("vectorized_func", xt::pyvectorize(scalar_func), "");

    return m.ptr();
}

Python code:

import numpy as np
import xtensor_python_test as xt

x = np.arange(15).reshape(3, 5)
y = [1, 2, 3, 4, 5]
z = xt.vectorized_func(x, y)
z

Outputs

[[-0.540302,  1.257618,  1.89929 ,  0.794764, -1.040465],
 [-1.499227,  0.136731,  1.646979,  1.643002,  0.128456],
 [-1.084323, -0.583843,  0.45342 ,  1.073811,  0.706945]]

回答2:

From my experience that seems to be pretty efficient. To get even more efficiency out of it try this : http://ubuntuforums.org/showthread.php?t=1266059

Using weave you can inline C/C++ code in Python so that could be useful.

http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.weave.inline.html

Here's a link on how Python can be used to interface between many different languages along with examples.

http://docs.scipy.org/doc/numpy/user/c-info.python-as-glue.html

This is a quick and easy example of how to pass numpy arrays to c++ using Cython:

http://www.birving.com/blog/2014/05/13/passing-numpy-arrays-between-python-and/

回答3:

As an additional way, without touching directly to the Python C API, it is possible to use pybind11 ( header-only library) :

CPP :

#include <pybind11/embed.h> // everything needed for embedding
#include <iostream>
#include <Eigen/Dense>  
#include<pybind11/eigen.h>
using Eigen::MatrixXd;
namespace py = pybind11;

int main() 
{    
  try 
  {          
        Py_SetProgramName("PYTHON");
        py::scoped_interpreter guard{}; 

        py::module py_test = py::module::import("py_test");

        MatrixXd m(2,2);
        m(0,0) = 1;
        m(1,0) = 2;
        m(0,1) = 3;
        m(1,1) = 4;

        py::object result = py_test.attr("test_mat")(m);

        MatrixXd res = result.cast<MatrixXd>();
        std::cout << "In c++ \n" << res << std::endl;
  }
  catch (std::exception ex)
  {
      std::cout << "ERROR   : " << ex.what() << std::endl;
  }
  return 1;
}

In py_test.py :

def test_mat(m):
    print ("Inside python m = \n ",m )
    m[0,0] = 10
    m[1,1] = 99 
    return m

Output :

Inside python m =
  [[ 1.  3.]
  [ 2.  4.]]
In c++
10  3
 2 99

See the official documentation.

ps: I'm using Eigen for the C++ Matrix.