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// Copyright Ankit Daftery 2011-2012.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
/**
* @brief An example to show how to create ndarrays using arbitrary Python sequences.
*
* The Python sequence could be any object whose __array__ method returns an array, or any
* (nested) sequence. This example also shows how to create arrays using both unit and
* non-unit strides.
*/
#include <boost/python/numpy.hpp>
#include <iostream>
namespace p = boost::python;
namespace np = boost::python::numpy;
#if _MSC_VER
using boost::uint8_t;
#endif
int main(int argc, char **argv)
{
// Initialize the Python runtime.
Py_Initialize();
// Initialize NumPy
np::initialize();
// Create an ndarray from a simple tuple
p::object tu = p::make_tuple('a','b','c') ;
np::ndarray example_tuple = np::array (tu) ;
// and from a list
p::list l ;
np::ndarray example_list = np::array (l) ;
// Optionally, you can also specify a dtype
np::dtype dt = np::dtype::get_builtin<int>();
np::ndarray example_list1 = np::array (l,dt);
// You can also create an array by supplying data.First,create an integer array
int data[] = {1,2,3,4} ;
// Create a shape, and strides, needed by the function
p::tuple shape = p::make_tuple(4) ;
p::tuple stride = p::make_tuple(4) ;
// The function also needs an owner, to keep track of the data array passed. Passing none is dangerous
p::object own ;
// The from_data function takes the data array, datatype,shape,stride and owner as arguments
// and returns an ndarray
np::ndarray data_ex = np::from_data(data,dt,shape,stride,own);
// Print the ndarray we created
std::cout << "Single dimensional array ::" << std::endl << p::extract < char const * > (p::str(data_ex)) << std::endl ;
// Now lets make an 3x2 ndarray from a multi-dimensional array using non-unit strides
// First lets create a 3x4 array of 8-bit integers
uint8_t mul_data[][4] = {{1,2,3,4},{5,6,7,8},{1,3,5,7}};
// Now let's create an array of 3x2 elements, picking the first and third elements from each row
// For that, the shape will be 3x2
shape = p::make_tuple(3,2) ;
// The strides will be 4x2 i.e. 4 bytes to go to the next desired row, and 2 bytes to go to the next desired column
stride = p::make_tuple(4,2) ;
// Get the numpy dtype for the built-in 8-bit integer data type
np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
// First lets create and print out the ndarray as is
np::ndarray mul_data_ex = np::from_data(mul_data,dt1, p::make_tuple(3,4),p::make_tuple(4,1),p::object());
std::cout << "Original multi dimensional array :: " << std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ;
// Now create the new ndarray using the shape and strides
mul_data_ex = np::from_data(mul_data,dt1, shape,stride,p::object());
// Print out the array we created using non-unit strides
std::cout << "Selective multidimensional array :: "<<std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ;
}
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