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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 demonstrate use of universal functions or ufuncs
*
*
* @todo Calling the overloaded () operator is in a roundabout manner, find a simpler way
* None of the methods like np::add, np::multiply etc are supported as yet
*/
#include <boost/python/numpy.hpp>
#include <iostream>
namespace p = boost::python;
namespace np = boost::python::numpy;
// Create the structs necessary to implement the ufuncs
// The typedefs *must* be made
struct UnarySquare
{
typedef double argument_type;
typedef double result_type;
double operator()(double r) const { return r * r;}
};
struct BinarySquare
{
typedef double first_argument_type;
typedef double second_argument_type;
typedef double result_type;
double operator()(double a,double b) const { return (a*a + b*b) ; }
};
int main(int argc, char **argv)
{
// Initialize the Python runtime.
Py_Initialize();
// Initialize NumPy
np::initialize();
// Expose the struct UnarySquare to Python as a class, and let ud be the class object
p::object ud = p::class_<UnarySquare, boost::shared_ptr<UnarySquare> >("UnarySquare")
.def("__call__", np::unary_ufunc<UnarySquare>::make());
// Let inst be an instance of the class ud
p::object inst = ud();
// Use the "__call__" method to call the overloaded () operator and print the value
std::cout << "Square of unary scalar 1.0 is " << p::extract <char const * > (p::str(inst.attr("__call__")(1.0))) << std::endl ;
// Create an array in C++
int arr[] = {1,2,3,4} ;
// ..and use it to create the ndarray in Python
np::ndarray demo_array = np::from_data(arr, np::dtype::get_builtin<int>() , p::make_tuple(4), p::make_tuple(4), p::object());
// Print out the demo array
std::cout << "Demo array is " << p::extract <char const * > (p::str(demo_array)) << std::endl ;
// Call the "__call__" method to perform the operation and assign the value to result_array
p::object result_array = inst.attr("__call__")(demo_array) ;
// Print the resultant array
std::cout << "Square of demo array is " << p::extract <char const * > (p::str(result_array)) << std::endl ;
// Lets try the same with a list
p::list li ;
li.append(3);
li.append(7);
// Print out the demo list
std::cout << "Demo list is " << p::extract <char const * > (p::str(li)) << std::endl ;
// Call the ufunc for the list
result_array = inst.attr("__call__")(li) ;
// And print the list out
std::cout << "Square of demo list is " << p::extract <char const * > (p::str(result_array)) << std::endl ;
// Now lets try Binary ufuncs
// Expose the struct BinarySquare to Python as a class, and let ud be the class object
ud = p::class_<BinarySquare, boost::shared_ptr<BinarySquare> >("BinarySquare")
.def("__call__", np::binary_ufunc<BinarySquare>::make());
// Again initialise inst as an instance of the class ud
inst = ud();
// Print the two input listsPrint the two input lists
std::cout << "The two input list for binary ufunc are " << std::endl << p::extract <char const * > (p::str(demo_array)) << std::endl << p::extract <char const * > (p::str(demo_array)) << std::endl ;
// Call the binary ufunc taking demo_array as both inputs
result_array = inst.attr("__call__")(demo_array,demo_array) ;
std::cout << "Square of list with binary ufunc is " << p::extract <char const * > (p::str(result_array)) << std::endl ;
}
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