GitHub - xtensor-stack/Xtensor.jl: Julia package for xtensor-julia (original) (raw)

Julia package for the xtensor-julia library, the Julia bindings for xtensor.

• xtensor is a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing.
• xtensor-julia enables inplace use of julia arrays in C++ with all the benefits from xtensor
  • C++ universal function and broadcasting
  • STL - compliant APIs.
  • A broad coverage of numpy APIs (see the numpy to xtensor cheat sheet).

The Julia bindings for xtensor are based on the CxxWrap.jl C++ library.

Installation

using Pkg; Pkg.add("Xtensor");

Documentation

To get started with using Xtensor.jl and xtensor-julia, check out the full documentation

http://xtensor-julia.readthedocs.io/

Usage

xtensor-julia offers two container types wrapping julia arrays inplace to provide an xtensor semantics

• jltensor
• jlarray.

Both containers enable the numpy-style APIs of xtensor (see the numpy to xtensor cheat sheet).

• On the one hand, jlarray has a dynamic number of dimensions. It can be reshaped dynamically and the new shape is reflected on the Julia side.
• On the other hand jltensor has a compile time number of dimensions, specified with a template parameter. Shapes of jltensor instances are stack allocated, making jltensor a significantly faster expression than jlarray.

Example 1: Use an algorithm of the C++ standard library with Julia array.

C++ code

#include // Standard library import for std::accumulate #include <cxx_wrap.hpp> // libcxxwrap 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

Example 2: Create a numpy-style universal function from a C++ scalar function

C++ code

#include <cxx_wrap.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 = 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]]

Building the HTML Documentation

xtensor-julia's documentation is built with three tools

• doxygen
• sphinx
• breathe

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

Breathe can also be installed with mamba (or conda)

mamba install -c conda-forge breathe

Finally, build the documentation with

from the docs subdirectory.

Dependencies on xtensor, xtensor-julia, and CxxWrap

Xtensor.jl depends on xtensor-julia, xtensor and CxxWrap libraries

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

License

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.