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
The Julia bindings for xtensor are based on the CxxWrap.jl C++ library.
using Pkg; Pkg.add("Xtensor");
To get started with using Xtensor.jl and xtensor-julia, check out the full documentation
http://xtensor-julia.readthedocs.io/
xtensor-julia offers two container types wrapping julia arrays inplace to provide an xtensor semantics
jltensorjlarray.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.
C++ code
#include <numeric> // 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 functionC++ 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]]
xtensor-julia's documentation is built with three tools
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.
xtensor, xtensor-julia, and CxxWrap
Xtensor.jl depends on xtensor-julia, xtensor and CxxWrap libraries
Xtensor.jl xtensor xtensor-julia CxxWrap master >=0.24.2,<0.25 0.10.2 >=0.12.0,<0.13 0.9.1 >=0.24.2,<0.25 0.10.2 >=0.12.0,<0.13 0.9.0 >=0.24.0,<0.25 0.10.1 >=0.11.2,<0.12
These dependencies are automatically resolved when using the Julia package manager.
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.
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