1How to access data using raw pointers 2===================================== 3 4One of the advantages of the ndarray wrapper is that the same data can be used in both Python and C++ and changes can be made to reflect at both ends. 5The from_data method makes this possible. 6 7Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module:: 8 9 #include <boost/python/numpy.hpp> 10 #include <iostream> 11 12 namespace p = boost::python; 13 namespace np = boost::python::numpy; 14 15 int main(int argc, char **argv) 16 { 17 Py_Initialize(); 18 np::initialize(); 19 20Create an array in C++ , and pass the pointer to it to the from_data method to create an ndarray:: 21 22 int arr[] = {1,2,3,4,5}; 23 np::ndarray py_array = np::from_data(arr, np::dtype::get_builtin<int>(), 24 p::make_tuple(5), 25 p::make_tuple(sizeof(int)), 26 p::object()); 27 28Print the source C++ array, as well as the ndarray, to check if they are the same:: 29 30 std::cout << "C++ array :" << std::endl; 31 for (int j=0;j<4;j++) 32 { 33 std::cout << arr[j] << ' '; 34 } 35 std::cout << std::endl 36 << "Python ndarray :" << p::extract<char const *>(p::str(py_array)) << std::endl; 37 38Now, change an element in the Python ndarray, and check if the value changed correspondingly in the source C++ array:: 39 40 py_array[1] = 5 ; 41 std::cout << "Is the change reflected in the C++ array used to create the ndarray ? " << std::endl; 42 for (int j = 0; j < 5; j++) 43 { 44 std::cout << arr[j] << ' '; 45 } 46 47Next, change an element of the source C++ array and see if it is reflected in the Python ndarray:: 48 49 arr[2] = 8; 50 std::cout << std::endl 51 << "Is the change reflected in the Python ndarray ?" << std::endl 52 << p::extract<char const *>(p::str(py_array)) << std::endl; 53 } 54 55As we can see, the changes are reflected across the ends. This happens because the from_data method passes the C++ array by reference to create the ndarray, and thus uses the same locations for storing data. 56 57