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790 lines (666 loc) · 23.8 KB
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/***************************************************************************
* Copyright (c) 2016, Johan Mabille and Sylvain Corlay *
* *
* 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 <algorithm>
#include <cstddef>
#include <vector>
#include "xtensor/xbuffer_adaptor.hpp"
#include "xtensor/xiterator.hpp"
#include "xtensor/xsemantic.hpp"
#include "pycontainer.hpp"
#include "pystrides_adaptor.hpp"
#include "xtensor_type_caster_base.hpp"
namespace xt
{
template <class T, layout_type L = layout_type::dynamic>
class pyarray;
}
namespace pybind11
{
namespace detail
{
template <class T, xt::layout_type L>
struct handle_type_name<xt::pyarray<T, L>>
{
static PYBIND11_DESCR name()
{
return _("numpy.ndarray[") + npy_format_descriptor<T>::name() + _("]");
}
};
template <typename T, xt::layout_type L>
struct pyobject_caster<xt::pyarray<T, L>>
{
using type = xt::pyarray<T, L>;
bool load(handle src, bool convert)
{
if (!convert)
{
if (!xt::detail::check_array<T>(src))
{
return false;
}
}
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());
};
// Type caster for casting ndarray to xexpression<pyarray>
template <typename T, xt::layout_type L>
struct type_caster<xt::xexpression<xt::pyarray<T, L>>> : pyobject_caster<xt::pyarray<T, L>>
{
using Type = xt::xexpression<xt::pyarray<T, L>>;
operator Type&()
{
return this->value;
}
operator const Type&()
{
return this->value;
}
};
// Type caster for casting xarray to ndarray
template <class T, xt::layout_type L>
struct type_caster<xt::xarray<T, L>> : xtensor_type_caster_base<xt::xarray<T, L>>
{
};
}
}
namespace xt
{
/**************************
* pybackstrides_iterator *
**************************/
template <class B>
class pybackstrides_iterator
{
public:
using self_type = pybackstrides_iterator<B>;
using value_type = typename B::value_type;
using pointer = const value_type*;
using reference = value_type;
using difference_type = std::ptrdiff_t;
using iterator_category = std::random_access_iterator_tag;
inline pybackstrides_iterator(const B* b, std::size_t offset)
: p_b(b), m_offset(offset)
{
}
inline reference operator*() const
{
return p_b->operator[](m_offset);
}
inline pointer operator->() const
{
// Returning the address of a temporary
value_type res = p_b->operator[](m_offset);
return &res;
}
inline reference operator[](difference_type n) const
{
return p_b->operator[](m_offset + n);
}
inline self_type& operator++()
{
++m_offset;
return *this;
}
inline self_type& operator--()
{
--m_offset;
return *this;
}
inline self_type operator++(int)
{
self_type tmp(*this);
++m_offset;
return tmp;
}
inline self_type operator--(int)
{
self_type tmp(*this);
--m_offset;
return tmp;
}
inline self_type& operator+=(difference_type n)
{
m_offset += n;
return *this;
}
inline self_type& operator-=(difference_type n)
{
m_offset -= n;
return *this;
}
inline self_type operator+(difference_type n) const
{
return self_type(p_b, m_offset + n);
}
inline self_type operator-(difference_type n) const
{
return self_type(p_b, m_offset - n);
}
inline self_type operator-(const self_type& rhs) const
{
self_type tmp(*this);
tmp -= (m_offset - rhs.m_offset);
return tmp;
}
inline std::size_t offset() const
{
return m_offset;
}
private:
const B* p_b;
std::size_t m_offset;
};
template <class B>
inline bool operator==(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return lhs.offset() == rhs.offset();
}
template <class B>
inline bool operator!=(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return !(lhs == rhs);
}
template <class B>
inline bool operator<(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return lhs.offset() < rhs.offset();
}
template <class B>
inline bool operator<=(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return (lhs < rhs) || (lhs == rhs);
}
template <class B>
inline bool operator>(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return !(lhs <= rhs);
}
template <class B>
inline bool operator>=(const pybackstrides_iterator<B>& lhs,
const pybackstrides_iterator<B>& rhs)
{
return !(lhs < rhs);
}
template <class A>
class pyarray_backstrides
{
public:
using self_type = pyarray_backstrides<A>;
using array_type = A;
using value_type = typename array_type::size_type;
using const_reference = value_type;
using const_pointer = const value_type*;
using size_type = typename array_type::size_type;
using difference_type = typename array_type::difference_type;
using const_iterator = pybackstrides_iterator<self_type>;
pyarray_backstrides() = default;
pyarray_backstrides(const array_type& a);
bool empty() const;
size_type size() const;
value_type operator[](size_type i) const;
const_reference front() const;
const_reference back() const;
const_iterator begin() const;
const_iterator end() const;
const_iterator cbegin() const;
const_iterator cend() const;
private:
const array_type* p_a;
};
template <class T, layout_type L>
struct xiterable_inner_types<pyarray<T, L>>
: xcontainer_iterable_types<pyarray<T, L>>
{
};
template <class T, layout_type L>
struct xcontainer_inner_types<pyarray<T, L>>
{
using storage_type = xbuffer_adaptor<T*>;
using shape_type = std::vector<typename storage_type::size_type>;
using strides_type = std::vector<typename storage_type::difference_type>;
using backstrides_type = pyarray_backstrides<pyarray<T, L>>;
using inner_shape_type = xbuffer_adaptor<std::size_t*>;
using inner_strides_type = pystrides_adaptor<sizeof(T)>;
using inner_backstrides_type = backstrides_type;
using temporary_type = pyarray<T, L>;
static constexpr layout_type layout = L;
};
/**
* @class pyarray
* @brief Multidimensional container providing the xtensor container semantics to a numpy array.
*
* pyarray is similar to the xarray container in that it has a dynamic dimensionality. Reshapes of
* a pyarray container are reflected in the underlying numpy array.
*
* @tparam T The type of the element stored in the pyarray.
* @sa pytensor
*/
template <class T, layout_type L>
class pyarray : public pycontainer<pyarray<T, L>>,
public xcontainer_semantic<pyarray<T, L>>
{
public:
using self_type = pyarray<T, L>;
using semantic_base = xcontainer_semantic<self_type>;
using base_type = pycontainer<self_type>;
using storage_type = typename base_type::storage_type;
using value_type = typename base_type::value_type;
using reference = typename base_type::reference;
using const_reference = typename base_type::const_reference;
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();
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_type l = layout_type::row_major);
explicit pyarray(const shape_type& shape, const_reference value, layout_type l = layout_type::row_major);
explicit pyarray(const shape_type& shape, const strides_type& strides, const_reference value);
explicit pyarray(const shape_type& shape, const strides_type& strides);
template <class S = shape_type>
static pyarray from_shape(S&& s);
pyarray(const self_type& rhs);
self_type& operator=(const self_type& rhs);
pyarray(self_type&&) = default;
self_type& operator=(self_type&& e) = default;
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;
storage_type m_storage;
void init_array(const shape_type& shape, const strides_type& strides);
void init_from_python();
const inner_shape_type& shape_impl() const noexcept;
const inner_strides_type& strides_impl() const noexcept;
const inner_backstrides_type& backstrides_impl() const noexcept;
storage_type& storage_impl() noexcept;
const storage_type& storage_impl() const noexcept;
friend class xcontainer<pyarray<T, L>>;
friend class pycontainer<pyarray<T, L>>;
};
/**************************************
* pyarray_backstrides implementation *
**************************************/
template <class A>
inline pyarray_backstrides<A>::pyarray_backstrides(const array_type& a)
: p_a(&a)
{
}
template <class A>
inline bool pyarray_backstrides<A>::empty() const
{
return p_a->dimension() == 0;
}
template <class A>
inline auto pyarray_backstrides<A>::size() const -> size_type
{
return p_a->dimension();
}
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;
}
template <class A>
inline auto pyarray_backstrides<A>::front() const -> const_reference
{
value_type sh = p_a->shape()[0];
value_type res = sh == 1 ? 0 : (sh - 1) * p_a->strides()[0];
return res;
}
template <class A>
inline auto pyarray_backstrides<A>::back() const -> const_reference
{
auto index = p_a->size() - 1;
value_type sh = p_a->shape()[index];
value_type res = sh == 1 ? 0 : (sh - 1) * p_a->strides()[index];
return res;
}
template <class A>
inline auto pyarray_backstrides<A>::begin() const -> const_iterator
{
return cbegin();
}
template <class A>
inline auto pyarray_backstrides<A>::end() const -> const_iterator
{
return cend();
}
template <class A>
inline auto pyarray_backstrides<A>::cbegin() const -> const_iterator
{
return const_iterator(this, 0);
}
template <class A>
inline auto pyarray_backstrides<A>::cend() const -> const_iterator
{
return const_iterator(this, size());
}
/**************************
* pyarray implementation *
**************************/
/**
* @name Constructors
*/
//@{
template <class T, layout_type L>
inline pyarray<T, L>::pyarray()
: base_type()
{
// TODO: avoid allocation
shape_type shape = xtl::make_sequence<shape_type>(0, size_type(1));
strides_type strides = xtl::make_sequence<strides_type>(0, size_type(0));
init_array(shape, strides);
detail::default_initialize(m_storage);
}
/**
* Allocates a pyarray with nested initializer lists.
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const value_type& t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(nested_initializer_list_t<T, 1> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(nested_initializer_list_t<T, 2> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(nested_initializer_list_t<T, 3> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(nested_initializer_list_t<T, 4> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(nested_initializer_list_t<T, 5> t)
: base_type()
{
base_type::resize(xt::shape<shape_type>(t), layout_type::row_major);
nested_copy(m_storage.begin(), t);
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(pybind11::handle h, pybind11::object::borrowed_t b)
: base_type(h, b)
{
init_from_python();
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(pybind11::handle h, pybind11::object::stolen_t s)
: base_type(h, s)
{
init_from_python();
}
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const pybind11::object& o)
: base_type(o)
{
init_from_python();
}
/**
* Allocates an uninitialized pyarray with the specified shape and
* layout.
* @param shape the shape of the pyarray
* @param l the layout of the pyarray
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const shape_type& shape, layout_type l)
: base_type()
{
strides_type strides(shape.size());
compute_strides(shape, l, strides);
init_array(shape, strides);
}
/**
* Allocates a pyarray with the specified shape and layout. Elements
* are initialized to the specified value.
* @param shape the shape of the pyarray
* @param value the value of the elements
* @param l the layout of the pyarray
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const shape_type& shape, const_reference value, layout_type l)
: base_type()
{
strides_type strides(shape.size());
compute_strides(shape, l, strides);
init_array(shape, strides);
std::fill(m_storage.begin(), m_storage.end(), value);
}
/**
* Allocates an uninitialized pyarray with the specified shape and strides.
* Elements are initialized to the specified value.
* @param shape the shape of the pyarray
* @param strides the strides of the pyarray
* @param value the value of the elements
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const shape_type& shape, const strides_type& strides, const_reference value)
: base_type()
{
init_array(shape, strides);
std::fill(m_storage.begin(), m_storage.end(), value);
}
/**
* Allocates an uninitialized pyarray with the specified shape and strides.
* @param shape the shape of the pyarray
* @param strides the strides of the pyarray
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const shape_type& shape, const strides_type& strides)
: base_type()
{
init_array(shape, strides);
}
/**
* Allocates and returns an pyarray with the specified shape.
* @param shape the shape of the pyarray
*/
template <class T, layout_type L>
template <class S>
inline pyarray<T, L> pyarray<T, L>::from_shape(S&& shape)
{
auto shp = xtl::forward_sequence<shape_type>(shape);
return self_type(shp);
}
//@}
/**
* @name Copy semantic
*/
//@{
/**
* The copy constructor.
*/
template <class T, layout_type L>
inline pyarray<T, L>::pyarray(const self_type& rhs)
: base_type(), semantic_base(rhs)
{
auto tmp = pybind11::reinterpret_steal<pybind11::object>(
PyArray_NewLikeArray(rhs.python_array(), NPY_KEEPORDER, nullptr, 1));
if (!tmp)
{
throw std::runtime_error("NumPy: unable to create ndarray");
}
this->m_ptr = tmp.release().ptr();
init_from_python();
std::copy(rhs.storage().cbegin(), rhs.storage().cend(), this->storage().begin());
}
/**
* The assignment operator.
*/
template <class T, layout_type L>
inline auto pyarray<T, L>::operator=(const self_type& rhs) -> self_type&
{
self_type tmp(rhs);
*this = std::move(tmp);
return *this;
}
//@}
/**
* @name Extended copy semantic
*/
//@{
/**
* The extended copy constructor.
*/
template <class T, layout_type L>
template <class E>
inline pyarray<T, L>::pyarray(const xexpression<E>& e)
: base_type()
{
// TODO: prevent intermediary shape allocation
shape_type shape = xtl::forward_sequence<shape_type>(e.derived_cast().shape());
strides_type strides = xtl::make_sequence<strides_type>(shape.size(), size_type(0));
compute_strides(shape, layout_type::row_major, strides);
init_array(shape, strides);
semantic_base::assign(e);
}
/**
* The extended assignment operator.
*/
template <class T, layout_type L>
template <class E>
inline auto pyarray<T, L>::operator=(const xexpression<E>& e) -> self_type&
{
return semantic_base::operator=(e);
}
//@}
template <class T, layout_type L>
inline auto pyarray<T, L>::ensure(pybind11::handle h) -> self_type
{
return base_type::ensure(h);
}
template <class T, layout_type L>
inline bool pyarray<T, L>::check_(pybind11::handle h)
{
return base_type::check_(h);
}
template <class T, layout_type L>
inline void pyarray<T, L>::init_array(const shape_type& shape, const strides_type& strides)
{
strides_type adapted_strides(strides);
std::transform(strides.begin(), strides.end(), adapted_strides.begin(),
[](auto v) { return sizeof(T) * v; });
int flags = NPY_ARRAY_ALIGNED;
if (!std::is_const<T>::value)
{
flags |= NPY_ARRAY_WRITEABLE;
}
auto dtype = pybind11::detail::npy_format_descriptor<T>::dtype();
npy_intp* shape_data = reinterpret_cast<npy_intp*>(const_cast<size_type*>(shape.data()));
npy_intp* strides_data = reinterpret_cast<npy_intp*>(adapted_strides.data());
auto tmp = pybind11::reinterpret_steal<pybind11::object>(
PyArray_NewFromDescr(&PyArray_Type, (PyArray_Descr*) dtype.release().ptr(), static_cast<int>(shape.size()), shape_data, strides_data,
nullptr, flags, nullptr));
if (!tmp)
{
throw std::runtime_error("NumPy: unable to create ndarray");
}
this->m_ptr = tmp.release().ptr();
init_from_python();
}
template <class T, layout_type L>
inline void pyarray<T, L>::init_from_python()
{
m_shape = inner_shape_type(reinterpret_cast<size_type*>(PyArray_SHAPE(this->python_array())),
static_cast<size_type>(PyArray_NDIM(this->python_array())));
m_strides = inner_strides_type(reinterpret_cast<size_type*>(PyArray_STRIDES(this->python_array())),
static_cast<size_type>(PyArray_NDIM(this->python_array())));
if (L != layout_type::dynamic && !do_strides_match(m_shape, m_strides, L))
{
throw std::runtime_error("NumPy: passing container with bad strides for layout (is it a view?).");
}
m_backstrides = backstrides_type(*this);
m_storage = storage_type(reinterpret_cast<pointer>(PyArray_DATA(this->python_array())),
this->get_min_stride() * static_cast<size_type>(PyArray_SIZE(this->python_array())));
}
template <class T, layout_type L>
inline auto pyarray<T, L>::shape_impl() const noexcept -> const inner_shape_type&
{
return m_shape;
}
template <class T, layout_type L>
inline auto pyarray<T, L>::strides_impl() const noexcept -> const inner_strides_type&
{
return m_strides;
}
template <class T, layout_type L>
inline auto pyarray<T, L>::backstrides_impl() const noexcept -> const inner_backstrides_type&
{
// m_backstrides wraps the numpy array backstrides, which is a raw pointer.
// The address of the raw pointer stored in the wrapper would be invalidated when the pyarray is copied.
// Hence, we build a new backstrides object (cheap wrapper around the underlying pointer) upon access.
m_backstrides = backstrides_type(*this);
return m_backstrides;
}
template <class T, layout_type L>
inline auto pyarray<T, L>::storage_impl() noexcept -> storage_type&
{
return m_storage;
}
template <class T, layout_type L>
inline auto pyarray<T, L>::storage_impl() const noexcept -> const storage_type&
{
return m_storage;
}
}
#endif