57 lines
2.0 KiB
ReStructuredText
57 lines
2.0 KiB
ReStructuredText
How to access data using raw pointers
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=====================================
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One 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.
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The from_data method makes this possible.
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Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module::
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#include <boost/python/numpy.hpp>
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#include <iostream>
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namespace p = boost::python;
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namespace np = boost::python::numpy;
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int main(int argc, char **argv)
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{
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Py_Initialize();
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np::initialize();
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Create an array in C++ , and pass the pointer to it to the from_data method to create an ndarray::
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int arr[] = {1,2,3,4,5};
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np::ndarray py_array = np::from_data(arr, np::dtype::get_builtin<int>(),
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p::make_tuple(5),
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p::make_tuple(sizeof(int)),
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p::object());
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Print the source C++ array, as well as the ndarray, to check if they are the same::
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std::cout << "C++ array :" << std::endl;
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for (int j=0;j<4;j++)
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{
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std::cout << arr[j] << ' ';
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}
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std::cout << std::endl
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<< "Python ndarray :" << p::extract<char const *>(p::str(py_array)) << std::endl;
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Now, change an element in the Python ndarray, and check if the value changed correspondingly in the source C++ array::
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py_array[1] = 5 ;
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std::cout << "Is the change reflected in the C++ array used to create the ndarray ? " << std::endl;
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for (int j = 0; j < 5; j++)
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{
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std::cout << arr[j] << ' ';
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}
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Next, change an element of the source C++ array and see if it is reflected in the Python ndarray::
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arr[2] = 8;
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std::cout << std::endl
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<< "Is the change reflected in the Python ndarray ?" << std::endl
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<< p::extract<char const *>(p::str(py_array)) << std::endl;
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}
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As 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.
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