basic_usage.rst

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/core/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]]