Related projects¶
The xtensor-python project provides the implementation of container types compatible with xtensor
‘s expression
system, pyarray
and pytensor
which effectively wrap numpy arrays, allowing operating on numpy arrays inplace.
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
#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 any value, which are only computed upon access
return std::accumulate(sines.begin(), sines.end(), 0.0);
}
PYBIND11_PLUGIN(xtensor_python_test)
{
pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");
m.def("sum_of_sines", sum_of_sines,
"Computes the sum of the sines of the values of the input array");
return m.ptr();
}
Python code:
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 universal function from a C++ scalar function¶
C++ code
#include "pybind11/pybind11.h"
#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_PLUGIN(xtensor_python_test)
{
py::module m("xtensor_python_test", "Test module for xtensor python bindings");
m.def("vectorized_func", xt::pyvectorize(scalar_func), "");
return m.ptr();
}
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]]

The xtensor-cookiecutter project helps extension authors create Python extension modules making use of xtensor.
It takes care of the initial work of generating a project skeleton with
- A complete setup.py compiling the extension module
A few examples included in the resulting project including
- A universal function defined from C++
- A function making use of an algorithm from the STL on a numpy array
- Unit tests
- The generation of the HTML documentation with sphinx