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Linux build and bug fix · pelson/xtensor-python@a331f6d · GitHub

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  2. xtensor-python

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JohanMabille
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Linux build and bug fix
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‎docs/source/array_tensor.rst‎

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.. Copyright (c) 2016, Johan Mabille and Sylvain Corlay
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Distributed under the terms of the BSD 3-Clause License.
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The full license is in the file LICENSE, distributed with this software.
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Arrays and tensors
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==================
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``xtensor-python`` provides two container types wrapping numpy arrays: ``pyarray`` and ``pytensor``. They are the counterparts
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to ``xarray`` and ``xtensor`` containers.
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pyarray
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-------
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Like ``xarray``, ``pyarray`` has a dynamic shape. This means that you can reshape the numpy array on the C++ side and see this
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change reflected on the python side. ``pyarray`` doesn't make a copy of the shape or the strides, but reads them each time it
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is needed. Therefore, if a reference on a ``pyarray`` is kept in the C++ code and the corresponding numpy array is then reshaped
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in the python code, this modification will reflect in the ``pyarray``.
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pytensor
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--------
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Like ``xtensor``, ``pytensor`` has a static stack-allocated shape. This means that the shape of the numpy array is copied into
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the shape of the ``pytensor`` upon creation. As a consequence, reshapes are not reflected across languages. However, this drawback
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is offset by a more effective computation of shape and broadcast.
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docs/source/usage.rst renamed to docs/source/basic_usage.rst

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The full license is in the file LICENSE, distributed with this software.
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Usage
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=====
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Basic Usage
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-----------
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===========
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Example 1: Use an algorithm of the C++ library on a numpy array inplace
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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-----------------------------------------------------------------------
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**C++ code**
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Example 2: Create a universal function from a C++ scalar function
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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-----------------------------------------------------------------
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**C++ code**
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[-1.499227, 0.136731, 1.646979, 1.643002, 0.128456],
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[-1.084323, -0.583843, 0.45342 , 1.073811, 0.706945]]
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Getting started with xtensor-cookiecutter
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-----------------------------------------
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`xtensor-cookiecutter`_ helps extension authors create Python extension modules making use of xtensor.
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It takes care of the initial work of generating a project skeleton with
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- A complete ``setup.py`` compiling the extension module
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- A few examples included in the resulting project including
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- A universal function defined from C++
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- A function making use of an algorithm from the STL on a numpy array
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- Unit tests
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- The generation of the HTML documentation with sphinx
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Usage
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^^^^^
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Install cookiecutter_
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.. code::
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pip install cookiecutter
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After installing cookiecutter, use the xtensor-cookiecutter_:
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.. code::
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cookiecutter https://github.com/QuantStack/xtensor-cookiecutter.git
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As xtensor-cookiecutter runs, you will be asked for basic information about
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your custom extension project. You will be prompted for the following
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information:
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- ``author_name``: your name or the name of your organization,
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- ``author_email`` : your project's contact email,
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- ``github_project_name``: name of the GitHub repository for your project,
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- ``github_organization_name``: name of the GithHub organization for your project,
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- ``python_package_name``: name of the Python package created by your extension,
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- ``cpp_namespace``: name for the cpp namespace holding the implementation of your extension,
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- ``project_short_description``: a short description for your project.
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This will produce a directory containing all the required content for a minimal extension
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project making use of xtensor with all the required boilerplate for package management,
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together with a few basic examples.
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.. _xtensor-cookicutter: https://github.com/QuantStack/xtensor-cookiecutter
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.. _cookiecutter: https://github.com/audreyr/cookiecutter

‎docs/source/cookie_cutter.rst‎

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.. Copyright (c) 2016, Johan Mabille and Sylvain Corlay
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Distributed under the terms of the BSD 3-Clause License.
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The full license is in the file LICENSE, distributed with this software.
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Getting started with xtensor-cookiecutter
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=========================================
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`xtensor-cookiecutter`_ helps extension authors create Python extension modules making use of xtensor.
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It takes care of the initial work of generating a project skeleton with
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- A complete ``setup.py`` compiling the extension module
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- A few examples included in the resulting project including
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- A universal function defined from C++
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- A function making use of an algorithm from the STL on a numpy array
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- Unit tests
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- The generation of the HTML documentation with sphinx
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Usage
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-----
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Install cookiecutter_
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.. code::
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pip install cookiecutter
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After installing cookiecutter, use the xtensor-cookiecutter_:
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.. code::
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cookiecutter https://github.com/QuantStack/xtensor-cookiecutter.git
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As xtensor-cookiecutter runs, you will be asked for basic information about
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your custom extension project. You will be prompted for the following
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information:
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- ``author_name``: your name or the name of your organization,
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- ``author_email`` : your project's contact email,
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- ``github_project_name``: name of the GitHub repository for your project,
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- ``github_organization_name``: name of the GithHub organization for your project,
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- ``python_package_name``: name of the Python package created by your extension,
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- ``cpp_namespace``: name for the cpp namespace holding the implementation of your extension,
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- ``project_short_description``: a short description for your project.
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This will produce a directory containing all the required content for a minimal extension
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project making use of xtensor with all the required boilerplate for package management,
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together with a few basic examples.
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.. _xtensor-cookicutter: https://github.com/QuantStack/xtensor-cookiecutter
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.. _cookiecutter: https://github.com/audreyr/cookiecutter

‎docs/source/index.rst‎

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:caption: USAGE
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:maxdepth: 2
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usage
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basic_usage
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array_tensor
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cookie_cutter
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.. toctree::
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:caption: API REFERENCE

‎include/xtensor-python/pyarray.hpp‎

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template <class T>
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inline void pyarray<T>::init_from_python()
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{
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m_shape = inner_shape_type(reinterpret_cast<size_type*>(PyArray_SHAPE(python_array())),
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static_cast<size_type>(PyArray_NDIM(python_array())));
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m_strides = inner_strides_type(reinterpret_cast<size_type*>(PyArray_STRIDES(python_array())),
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static_cast<size_type>(PyArray_NDIM(python_array())));
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m_shape = inner_shape_type(reinterpret_cast<size_type*>(PyArray_SHAPE(this->python_array())),
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static_cast<size_type>(PyArray_NDIM(this->python_array())));
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m_strides = inner_strides_type(reinterpret_cast<size_type*>(PyArray_STRIDES(this->python_array())),
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static_cast<size_type>(PyArray_NDIM(this->python_array())));
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m_backstrides = backstrides_type(*this);
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(python_array())),
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static_cast<size_type>(PyArray_SIZE(python_array())));
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
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static_cast<size_type>(PyArray_SIZE(this->python_array())));
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}
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template <class T>

‎include/xtensor-python/pycontainer.hpp‎

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template <class D>
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inline void pycontainer<D>::fill_default_strides(const shape_type& shape, strides_type& strides)
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{
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strides_type::value_type data_size = 1;
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typename strides_type::value_type data_size = 1;
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for(size_type i = strides.size(); i != 0; --i)
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{
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strides[i - 1] = data_size;
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template <class D>
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inline bool pycontainer<D>::check_(pybind11::handle h)
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{
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int type_num = detail::numpy_traits<T>::type_num;
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return PyArray_Check(h.ptr()) && PyArray_EquivTypenums(PyArray_TYPE(h.ptr()), type_num);
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int type_num = detail::numpy_traits<value_type>::type_num;
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return PyArray_Check(h.ptr()) && PyArray_EquivTypenums(PyArray_TYPE(python_array()), type_num);
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}
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template <class D>
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return nullptr;
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int type_num = detail::numpy_traits<value_type>::type_num;
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auto res = PyArray_FromAny(ptr, PyArray_DescrFromType(type_num), 0, 0, NPY_ARRAY_ENSUREARRAY, nullptr);
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auto res = PyArray_FromAny(ptr, PyArray_DescrFromType(type_num), 0, 0,
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NPY_ARRAY_ENSUREARRAY | NPY_ARRAY_FORCECAST, nullptr);
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return res;
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}
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‎include/xtensor-python/pytensor.hpp‎

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m_shape = shape;
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m_strides = strides;
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compute_backstrides();
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(python_array())),
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static_cast<size_type>(PyArray_SIZE(python_array())));
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
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static_cast<size_type>(PyArray_SIZE(this->python_array())));
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}
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template <class T, std::size_t N>
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inline void pytensor<T, N>::init_from_python()
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{
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if(PyArray_NDIM(python_array()) != N)
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if(PyArray_NDIM(this->python_array()) != N)
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throw std::runtime_error("NumPy: ndarray has incorrect number of dimensions");
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std::copy(PyArray_DIMS(python_array()), PyArray_DIMS(python_array()) + N, m_shape.begin());
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std::transform(PyArray_STRIDES(python_array()), PyArray_STRIDES(python_array()) + N, m_strides.begin(),
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std::copy(PyArray_DIMS(this->python_array()), PyArray_DIMS(this->python_array()) + N, m_shape.begin());
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std::transform(PyArray_STRIDES(this->python_array()), PyArray_STRIDES(this->python_array()) + N, m_strides.begin(),
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[](auto v) { return v / sizeof(T); });
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compute_backstrides();
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(python_array())),
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static_cast<size_type>(PyArray_SIZE(python_array())));
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m_data = container_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
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static_cast<size_type>(PyArray_SIZE(this->python_array())));
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}
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template <class T, std::size_t N>

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