Basic Usage
Example 1: Use an algorithm of the C++ library on a numpy array inplace
C++ code
#include <numeric> // Standard library import for std::accumulate
#include "pybind11/pybind11.h" // Pybind11 import to define Python bindings
#include "xtensor/xmath.hpp" // xtensor import for the C++ universal functions
#define FORCE_IMPORT_ARRAY // numpy C api loading
#include "xtensor-python/pyarray.hpp" // Numpy bindings
double sum_of_sines(xt::pyarray<double>& m)
{
auto sines = xt::sin(m); // sines does not actually hold values.
return std::accumulate(sines.cbegin(), sines.cend(), 0.0);
}
PYBIND11_MODULE(xtensor_python_test, m)
{
xt::import_numpy();
m.doc() = "Test module for xtensor python bindings";
m.def("sum_of_sines", sum_of_sines, "Sum the sines of the input values");
}
Python code:
import numpy as np
import xtensor_python_test as xt
a = np.arange(15).reshape(3, 5)
s = xt.sum_of_sines(v)
s
Outputs
1.2853996391883833
Example 2: Create a numpy-style universal function from a C++ scalar function
C++ code
#include "pybind11/pybind11.h"
#define FORCE_IMPORT_ARRAY
#include "xtensor-python/pyvectorize.hpp"
#include <numeric>
#include <cmath>
namespace py = pybind11;
double scalar_func(double i, double j)
{
return std::sin(i) - std::cos(j);
}
PYBIND11_MODULE(xtensor_python_test, m)
{
xt::import_numpy();
m.doc() = "Test module for xtensor python bindings";
m.def("vectorized_func", xt::pyvectorize(scalar_func), "");
}
Python code:
import numpy as np
import xtensor_python_test as xt
x = np.arange(15).reshape(3, 5)
y = [1, 2, 3, 4, 5]
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]]