.. Copyright (c) 2016, Johan Mabille, Sylvain Corlay and Wolf Vollprecht

   Distributed under the terms of the BSD 3-Clause License.

   The full license is in the file LICENSE, distributed with this software.

.. raw:: html

   

.. _related-projects:

Related projects
================

xtensor-python
--------------

.. image:: xtensor-python.svg
   :alt: xtensor-python

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
in-place.

Example 1: Use an algorithm of the C++ library on a NumPy array in-place
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**C++ code**

.. 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 any value
        return std::accumulate(sines.cbegin(), sines.cend(), 0.0);
    }

    PYBIND11_PLUGIN(xtensor_python_test)
    {
        xt::import_numpy();
        pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");

        m.def("sum_of_sines", sum_of_sines,
            "Sum the sines of the input values");

        return m.ptr();
    }

**Python code**

.. 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**

.. code::

    1.2853996391883833


Example 2: Create a universal function from a C++ scalar function
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**C++ code**

.. 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_PLUGIN(xtensor_python_test)
    {
        xt::import_numpy();
        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**

.. 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**

.. code::

    [[-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-python-cookiecutter
---------------------------

.. image:: xtensor-cookiecutter.svg
   :alt: xtensor-python-cookiecutter
   :width: 50%

The xtensor-python-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

xtensor-julia
-------------

.. image:: xtensor-julia.svg
   :alt: xtensor-julia

The xtensor-julia_ project provides the implementation of container types
compatible with *xtensor*'s expression system, ``jlarray`` and ``jltensor``
which effectively wrap Julia arrays, allowing operating on Julia arrays
in-place.

Example 1: Use an algorithm of the C++ library with a Julia array
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**C++ code**

.. code::

    #include <numeric>                        // Standard library import for std::accumulate
    #include <cxx_wrap.hpp>                   // CxxWrap import to define Julia bindings
    #include <xtensor-julia/jltensor.hpp>     // Import the jltensor container definition
    #include <xtensor/core/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);
    }

    JULIA_CPP_MODULE_BEGIN(registry)
        cxx_wrap::Module mod = registry.create_module("xtensor_julia_test");
        mod.method("sum_of_sines", sum_of_sines);
    JULIA_CPP_MODULE_END

**Julia code**

.. code::

    using xtensor_julia_test

    arr = [[1.0 2.0]
           [3.0 4.0]]

    s = sum_of_sines(arr)
    s

**Outputs**

.. code::

   1.2853996391883833

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

**C++ code**

.. code::

    #include <cxx_wrap.hpp>
    #include <xtensor-julia/jlvectorize.hpp>

    double scalar_func(double i, double j)
    {
        return std::sin(i) - std::cos(j);
    }

    JULIA_CPP_MODULE_BEGIN(registry)
        cxx_wrap::Module mod = registry.create_module("xtensor_julia_test");
        mod.method("vectorized_func", xt::jlvectorize(scalar_func));
    JULIA_CPP_MODULE_END

**Julia code**

.. 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 = xt.vectorized_func(x, y)
    z

**Outputs**

.. code::

    [[-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-cookiecutter
--------------------------

.. image:: xtensor-cookiecutter.svg
   :alt: xtensor-julia-cookiecutter
   :width: 50%

The xtensor-julia-cookiecutter_ project helps extension authors create Julia
extension modules making use of *xtensor*.

It takes care of the initial work of generating a project skeleton with

- A complete read-to-use Julia package

A few examples included in the resulting project including

- A NumPy-style 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

xtensor-r
---------

.. image:: xtensor-r.svg
   :alt: xtensor-r

The xtensor-r_ project provides the implementation of container types
compatible with *xtensor*'s expression system, ``rarray`` and ``rtensor``
which effectively wrap R arrays, allowing operating on R arrays in-place.

Example 1: Use an algorithm of the C++ library on a R array in-place
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

**C++ code**

.. code::

    #include <numeric>                    // Standard library import for std::accumulate
    #include <xtensor/core/xmath.hpp>     // xtensor import for the C++ universal functions
    #include <xtensor-r/rarray.hpp>       // R bindings
    #include <Rcpp.h>

    using namespace Rcpp;

    // [[Rcpp::plugins(cpp14)]]

    // [[Rcpp::export]]
    double sum_of_sines(xt::rarray<double>& m)
    {
        auto sines = xt::sin(m);  // sines does not actually hold values.
        return std::accumulate(sines.cbegin(), sines.cend(), 0.0);
    }

**R code**

.. code::

    v <- matrix(0:14, nrow=3, ncol=5)
    s <- sum_of_sines(v)
    s

**Outputs**

.. code::

    1.2853996391883833

xtensor-blas
------------

.. image:: xtensor-blas.svg
   :alt: xtensor-blas

The xtensor-blas_ project is an extension to the xtensor library, offering
bindings to BLAS and LAPACK libraries through cxxblas and cxxlapack from the
FLENS project. ``xtensor-blas`` powers the ``xt::linalg`` functionalities,
which are the counterpart to NumPy's ``linalg`` module.

xtensor-fftw
------------

.. image:: xtensor-fftw.svg
   :alt: xtensor-fftw

The xtensor-fftw_ project is an extension to the xtensor library, offering
bindings to the fftw library.  ``xtensor-fftw`` powers the ``xt::fftw``
functionalities, which are the counterpart to NumPy's ``fft`` module.

Example 1: Calculate a derivative in Fourier space
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Calculate the derivative of a (discretized) field in Fourier space, e.g. a sine shaped field ``sin``:

**C++ code**

.. code::

    #include <xtensor-fftw/basic.hpp>          // rfft, irfft
    #include <xtensor-fftw/helper.hpp>         // rfftscale
    #include <xtensor/containers/xarray.hpp>
    #include <xtensor/generators/xbuilder.hpp> // xt::arange
    #include <xtensor/core/xmath.hpp>          // xt::sin, cos
    #include <complex>
    #include <xtensor/io/xio.hpp>

    // generate a sinusoid field
    double dx = M_PI / 100;
    xt::xarray<double> x = xt::arange(0., 2 * M_PI, dx);
    xt::xarray<double> sin = xt::sin(x);

    // transform to Fourier space
    auto sin_fs = xt::fftw::rfft(sin);

    // multiply by i*k
    std::complex<double> i {0, 1};
    auto k = xt::fftw::rfftscale<double>(sin.shape()[0], dx);
    xt::xarray<std::complex<double>> sin_derivative_fs = xt::eval(i * k * sin_fs);

    // transform back to normal space
    auto sin_derivative = xt::fftw::irfft(sin_derivative_fs);

    std::cout << "x:              " << x << std::endl;
    std::cout << "sin:            " << sin << std::endl;
    std::cout << "cos:            " << xt::cos(x) << std::endl;
    std::cout << "sin_derivative: " << sin_derivative << std::endl;

**Outputs**

.. code::

    x:              { 0.      ,  0.031416,  0.062832,  0.094248, ...,  6.251769}
    sin:            { 0.000000e+00,  3.141076e-02,  6.279052e-02,  9.410831e-02, ..., -3.141076e-02}
    cos:            { 1.000000e+00,  9.995066e-01,  9.980267e-01,  9.955620e-01, ...,  9.995066e-01}
    sin_derivative: { 1.000000e+00,  9.995066e-01,  9.980267e-01,  9.955620e-01, ...,  9.995066e-01}

xtensor-io
----------

.. image:: xtensor-io.svg
   :alt: xtensor-io

The xtensor-io_ project is an extension to the xtensor library for reading and
writing image, sound and npz file formats to and from xtensor data structures.

xtensor-ros
-----------

.. image:: xtensor-ros.svg
   :alt: xtensor-ros

The xtensor-ros_ project is an extension to the xtensor library providing
helper functions to easily send and receive xtensor and xarray datastructures
as ROS messages.

xsimd
-----

.. image:: xsimd.svg
   :alt: xsimd

The xsimd_ project provides a unified API for making use of the SIMD features
of modern preprocessors for C++ library authors. It also provides accelerated
implementation of common mathematical functions operating on batches.

xsimd_ is an optional dependency to *xtensor* which enable SIMD vectorization
of xtensor operations. This feature is enabled with the ``XTENSOR_USE_XSIMD``
compilation flag, which is set to ``false`` by default.

xtl
---

.. image:: xtl.svg
   :alt: xtl

The xtl_ project, the only dependency of *xtensor* is a C++ template library
holding the implementation of basic tools used across the libraries in the ecosystem.

xframe
------

.. image:: xframe.svg
   :alt: xframe

The xframe_ project provides multi-dimensional labeled arrays and a data frame for C++,
based on *xtensor* and *xtl*.

`xframe` provides

- an extensible expression system enabling lazy broadcasting.
- an API following the idioms of the C++ standard library.
- tools to manipulate n-dimensional labeled tensor expressions.

The API of xframe is inspired by xarray_, a Python package implementing labelled multi-dimensional arrays and datasets.

z5
--

The z5_ project implements the zarr_ and n5_ storage specifications in C++.
Both specifications describe chunked nd-array storage similar to HDF5, but
use the filesystem to store chunks. This design allows for parallel write access
and efficient cloud based storage, crucial requirements in modern big data applications.
The project uses *xtensor* to represent arrays in memory
and also provides a python wrapper based on ``xtensor-python``.

.. _xtensor-python: https://github.com/xtensor-stack/xtensor-python
.. _xtensor-python-cookiecutter: https://github.com/xtensor-stack/xtensor-python-cookiecutter
.. _xtensor-julia: https://github.com/xtensor-stack/xtensor-julia
.. _xtensor-julia-cookiecutter: https://github.com/xtensor-stack/xtensor-julia-cookiecutter
.. _xtensor-r: https://github.com/xtensor-stack/xtensor-r
.. _xtensor-blas: https://github.com/xtensor-stack/xtensor-blas
.. _xtensor-io: https://github.com/xtensor-stack/xtensor-io
.. _xtensor-fftw: https://github.com/egpbos/xtensor-fftw
.. _xtensor-ros: https://github.com/wolfv/xtensor_ros
.. _xsimd: https://github.com/xtensor-stack/xsimd
.. _xtl: https://github.com/xtensor-stack/xtl
.. _xframe: https://github.com/xtensor-stack/xframe
.. _z5: https://github.com/constantinpape/z5
.. _zarr: https://github.com/zarr-developers/zarr
.. _n5: https://github.com/saalfeldlab/n5i
.. _xarray: http://xarray.pydata.org
