Python bindings for the xtensor C++ multi-dimensional array library.

• xtensor is a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing.

• xtensor-python enables inplace use of numpy arrays with all the benefits from xtensor

  • C++ universal function and broadcasting
  • STL - compliant APIs.

The Python bindings for xtensor are based on the pybind11 C++ library, which enables seemless interoperability between C++ and Python.

xtensor-python is a header-only library. We provide a package for the conda package manager.

conda install -c conda-forge xtensor-python

xtensor-python depends on the xtensor and pybind11 libraries

These dependencies are automatically resolved when using the conda package manager.

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 bindingsdoublesum_of_sines(xt::pyarray<double> &m){auto sines = xt::sin(m); // sines does not actually hold any value, which are only computed upon accessreturnstd::accumulate(sines.begin(), sines.end(), 0.0)} PYBIND11_PLUGIN(xtensor_python_test){pybind11::modulem("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

importnumpyasnpimportxtensor_python_testasxta=np.arange(15).reshape(3, 5) s=xt.sum_of_sines(v) s

Outputs

1.2853996391883833 

C++ code

#include"pybind11/pybind11.h" #include"xtensor-python/pyvectorize.hpp" #include<numeric> #include<cmath>namespacepy= pybind11; doublescalar_func(double i, double j){returnstd::sin(i) - std::cos(j)} PYBIND11_PLUGIN(xtensor_python_test){py::modulem("xtensor_python_test", "Test module for xtensor python bindings"); m.def("vectorized_func", xt::pyvectorize(scalar_func), ""); return m.ptr()}

Python Code

importnumpyasnpimportxtensor_python_testasxtx=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]] 

We provide a package for the conda package manager.

conda install -c conda-forge xtensor-python

This will pull the dependencies to xtensor-python, that is pybind11 and xtensor.

A template for a project making use of xtensor-python is available in the form of a cookie cutter here.

This project is meant to help library authors get started with the xtensor python bindings.

It produces a project following the best practices for the packaging and distribution of Python extensions based on xtensor-python, including a setup.py file and a conda recipe.

Testing xtensor-python requires pytest

py.test .

To pick up changes in xtensor-python while rebuilding, delete the build/ directory.

xtensor-python's documentation is built with three tools

• doxygen
• sphinx
• breathe

While doxygen must be installed separately, you can install breathe by typing

pip install breathe

Breathe can also be installed with conda

conda install -c conda-forge breathe

Finally, build the documentation with

make html

from the docs subdirectory.

We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.