xtensor-python/include/xtensor-python/pyarray.hpp at master · BenJamesbabala/xtensor-python · GitHub

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/***************************************************************************

* *

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

* *

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

****************************************************************************/

#ifndef PY_ARRAY_HPP

#define PY_ARRAY_HPP

#include <cstddef>

#include <vector>

#include <algorithm>

#include "xtensor/xsemantic.hpp"

#include "xtensor/xiterator.hpp"

#include "pycontainer.hpp"

#include "pybuffer_adaptor.hpp"

namespace xt

{

template <class T>

class pyarray;

}

namespace pybind11

{

namespace detail

{

template <class T>

struct handle_type_name<xt::pyarray<T>>

{

static PYBIND11_DESCR name()

{

return _("numpy.ndarray[") + make_caster<T>::name() + _("]");

}

};

template <typename T>

struct pyobject_caster<xt::pyarray<T>>

{

using type = xt::pyarray<T>;

bool load(handle src, bool)

{

value = type::ensure(src);

return static_cast<bool>(value);

}

static handle cast(const handle &src, return_value_policy, handle)

{

return src.inc_ref();

}

PYBIND11_TYPE_CASTER(type, handle_type_name<type>::name());

};

}

namespace xt

{

template <class A>

class pyarray_backstrides

{

public:

using array_type = A;

using value_type = typename array_type::size_type;

using size_type = typename array_type::size_type;

pyarray_backstrides() = default;

pyarray_backstrides(const array_type& a);

value_type operator[](size_type i) const;

private:

const array_type* p_a;

};

template <class T>

struct xiterable_inner_types<pyarray<T>>

: xcontainer_iterable_types<pyarray<T>>

{

};

template <class T>

struct xcontainer_inner_types<pyarray<T>>

{

using container_type = pybuffer_adaptor<T>;

using shape_type = std::vector<typename container_type::size_type>;

using strides_type = shape_type;

using backstrides_type = pyarray_backstrides<pyarray<T>>;

using inner_shape_type = pybuffer_adaptor<std::size_t>;

using inner_strides_type = pystrides_adaptor<sizeof(T)>;

using inner_backstrides_type = backstrides_type;

using temporary_type = pyarray<T>;

};

/**

* @class pyarray

* @brief Wrapper on the Python buffer protocol.

template <class T>

class pyarray : public pycontainer<pyarray<T>>,

public xcontainer_semantic<pyarray<T>>

{

public:

using self_type = pyarray<T>;

using semantic_base = xcontainer_semantic<self_type>;

using base_type = pycontainer<self_type>;

using container_type = typename base_type::container_type;

using value_type = typename base_type::value_type;

using pointer = typename base_type::pointer;

using size_type = typename base_type::size_type;

using shape_type = typename base_type::shape_type;

using strides_type = typename base_type::strides_type;

using backstrides_type = typename base_type::backstrides_type;

using inner_shape_type = typename base_type::inner_shape_type;

using inner_strides_type = typename base_type::inner_strides_type;

using inner_backstrides_type = typename base_type::inner_backstrides_type;

pyarray() = default;

pyarray(const value_type& t);

pyarray(nested_initializer_list_t<T, 1> t);

pyarray(nested_initializer_list_t<T, 2> t);

pyarray(nested_initializer_list_t<T, 3> t);

pyarray(nested_initializer_list_t<T, 4> t);

pyarray(nested_initializer_list_t<T, 5> t);

pyarray(pybind11::handle h, pybind11::object::borrowed_t);

pyarray(pybind11::handle h, pybind11::object::stolen_t);

pyarray(const pybind11::object &o);

explicit pyarray(const shape_type& shape, layout l = layout::row_major);

pyarray(const shape_type& shape, const strides_type& strides);

template <class E>

pyarray(const xexpression<E>& e);

template <class E>

self_type& operator=(const xexpression<E>& e);

using base_type::begin;

using base_type::end;

static self_type ensure(pybind11::handle h);

static bool check_(pybind11::handle h);

private:

inner_shape_type m_shape;

inner_strides_type m_strides;

mutable inner_backstrides_type m_backstrides;

container_type m_data;

void init_array(const shape_type& shape, const strides_type& strides);

void init_from_python();

const inner_shape_type& shape_impl() const;

const inner_strides_type& strides_impl() const;

const inner_backstrides_type& backstrides_impl() const;

container_type& data_impl();

const container_type& data_impl() const;

friend class xcontainer<pyarray<T>>;

};

/**************************************

* pyarray_backstrides implementation *

**************************************/

template <class A>

inline pyarray_backstrides<A>::pyarray_backstrides(const array_type& a)

: p_a(&a)

{

}

template <class A>

inline auto pyarray_backstrides<A>::operator[](size_type i) const -> value_type

{

value_type sh = p_a->shape()[i];

value_type res = sh == 1 ? 0 : (sh - 1) * p_a->strides()[i];

return res;

}

/**************************

* pyarray implementation *

**************************/

template <class T>

inline pyarray<T>::pyarray(const value_type& t)

{