Basic Usage¶
Example 1: Use an algorithm of the C++ library with a Julia array¶
C++ code
#include <numeric> // Standard library import for std::accumulate
#include "jlcxx/jlcxx.hpp¨ // CxxWrap import to define Julia bindings
#include "xtensor-julia/jltensor.hpp" // Import the jltensor container definition
#include "xtensor/xmath.hpp" // xtensor import for the C++ universal functions
double sum_of_sines(xt::jltensor<double, 2> m)
{
auto sines = xt::sin(m); // sines does not actually hold values.
return std::accumulate(sines.cbegin(), sines.cend(), 0.0);
}
JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
{
mod.method("sum_of_sines", sum_of_sines);
}
Julia code:
using xtensor_julia_test
arr = [[1.0 2.0]
[3.0 4.0]]
s = sum_of_sines(arr)
s
Outputs
1.2853996391883833
Example 2: Create a numpy-style universal function from a C++ scalar function¶
C++ code
#include "jlcxx/jlcxx.hpp"
#include "xtensor-julia/jlvectorize.hpp"
double scalar_func(double i, double j)
{
return std::sin(i) - std::cos(j);
}
JLCXX_MODULE define_julia_module(jlcxx::Module& mod)
{
mod.method("vectorized_func", xt::jlvectorize(scalar_func));
}
Julia code:
using xtensor_julia_test
x = [[ 0.0 1.0 2.0 3.0 4.0]
[ 5.0 6.0 7.0 8.0 9.0]
[10.0 11.0 12.0 13.0 14.0]]
y = [1.0, 2.0, 3.0, 4.0, 5.0]
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]]