xtensor-python/include/xtensor-python/pycontainer.hpp at master · Mbakaya/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_CONTAINER_HPP

#define PY_CONTAINER_HPP

#include <cmath>

#include <functional>

#include <numeric>

#include "pybind11/complex.h"

#include "pybind11/pybind11.h"

#ifndef FORCE_IMPORT_ARRAY

#define NO_IMPORT_ARRAY

#endif

#ifndef PY_ARRAY_UNIQUE_SYMBOL

#define PY_ARRAY_UNIQUE_SYMBOL xtensor_python_ARRAY_API

#endif

#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION

#include "numpy/arrayobject.h"

// Required because pyconfig.hpp defines copysign to _copysign

#undef copysign

#include <cmath>

#include "xtensor/xcontainer.hpp"

#include "xtl/xsequence.hpp"

namespace xt

{

inline void import_numpy();

/**

* @class pycontainer

* @brief Base class for xtensor containers wrapping numpy arryays.

* The pycontainer class should not be instantiated directly. Instead, used should

* use pytensor and pyarray instancs.

* @tparam D The derived type, i.e. the inheriting class for which pycontainer

* provides the interface.

template <class D>

class pycontainer : public pybind11::object,

public xcontainer<D>

{

public:

using derived_type = D;

using base_type = xcontainer<D>;

using inner_types = xcontainer_inner_types<D>;

using container_type = typename inner_types::container_type;

using value_type = typename container_type::value_type;

using reference = typename container_type::reference;

using const_reference = typename container_type::const_reference;

using pointer = typename container_type::pointer;

using const_pointer = typename container_type::const_pointer;

using size_type = typename container_type::size_type;

using difference_type = typename container_type::difference_type;

using shape_type = typename inner_types::shape_type;

using strides_type = typename inner_types::strides_type;

using backstrides_type = typename inner_types::backstrides_type;

using inner_shape_type = typename inner_types::inner_shape_type;

using inner_strides_type = typename inner_types::inner_strides_type;

using iterable_base = xiterable<D>;

using iterator = typename iterable_base::iterator;

using const_iterator = typename iterable_base::const_iterator;

using stepper = typename iterable_base::stepper;

using const_stepper = typename iterable_base::const_stepper;

static constexpr layout_type static_layout = layout_type::dynamic;

static constexpr bool contiguous_layout = false;

template <class S = shape_type>

void reshape(const S& shape);

template <class S = shape_type>

void reshape(const S& shape, layout_type l);

template <class S = shape_type>

void reshape(const S& shape, const strides_type& strides);

layout_type layout() const;

using base_type::operator();

using base_type::operator[];

using base_type::begin;

using base_type::end;

protected:

pycontainer();

~pycontainer() = default;

pycontainer(pybind11::handle h, borrowed_t);

pycontainer(pybind11::handle h, stolen_t);

pycontainer(const pybind11::object& o);

pycontainer(const pycontainer&) = default;

pycontainer& operator=(const pycontainer&) = default;

pycontainer(pycontainer&&) = default;

pycontainer& operator=(pycontainer&&) = default;

static derived_type ensure(pybind11::handle h);

static bool check_(pybind11::handle h);

static PyObject* raw_array_t(PyObject* ptr);

PyArrayObject* python_array() const;

size_type get_min_stride() const;

};

namespace detail

{

template <class T>

struct numpy_traits

{

private:

// On Windows 64 bits, NPY_INT != NPY_INT32 and NPY_UINT != NPY_UINT32

// We use the NPY_INT32 and NPY_UINT32 which are consistent with the values

// of NPY_LONG and NPY_ULONG

// On Linux x64, NPY_INT64 != NPY_LONGLONG and NPY_UINT64 != NPY_ULONGLONG,

// we use the values of NPY_INT64 and NPY_UINT64 which are consistent with the

// values of NPY_LONG and NPY_ULONG.

constexpr static const int value_list[15] = {

NPY_BOOL,

NPY_BYTE, NPY_UBYTE, NPY_SHORT, NPY_USHORT,

NPY_INT32, NPY_UINT32, NPY_INT64, NPY_UINT64,

NPY_FLOAT, NPY_DOUBLE, NPY_LONGDOUBLE,

NPY_CFLOAT, NPY_CDOUBLE, NPY_CLONGDOUBLE};

public:

using value_type = std::remove_const_t<T>;

static constexpr int type_num = value_list[pybind11::detail::is_fmt_numeric<value_type>::index];

};

// On Linux x64, NPY_INT64 != NPY_LONGLONG and NPY_UINT64 != NPY_ULONGLONG

// NPY_LONGLONG and NPY_ULONGLONG must be adjusted so the right type is

// selected

template <bool>

struct numpy_enum_adjuster

{

static inline int pyarray_type(PyArrayObject* obj)

{

return PyArray_TYPE(obj);

}

};

template <>

struct numpy_enum_adjuster<true>

{

static inline int pyarray_type(PyArrayObject* obj)

{

int res = PyArray_TYPE(obj);

if(res == NPY_LONGLONG || res == NPY_ULONGLONG)

{

res -= 2;

}

return res;

}

};

inline int pyarray_type(PyArrayObject* obj)

{

return numpy_enum_adjuster<NPY_LONGLONG != NPY_INT64>::pyarray_type(obj);

}

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

* pycontainer implementation *

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

template <class D>

inline pycontainer<D>::pycontainer()

: pybind11::object()

{

}

template <class D>

inline pycontainer<D>::pycontainer(pybind11::handle h, borrowed_t b)

: pybind11::object(h, b)

{

}

template <class D>

inline pycontainer<D>::pycontainer(pybind11::handle h, stolen_t s)

: pybind11::object(h, s)

{

}

template <class D>

inline pycontainer<D>::pycontainer(const pybind11::object& o)

: pybind11::object(raw_array_t(o.ptr()), pybind11::object::stolen_t{})

{

if (!this->m_ptr)

{

throw pybind11::error_already_set();

}

template <class D>

inline auto pycontainer<D>::ensure(pybind11::handle h) -> derived_type

{

auto result = pybind11::reinterpret_steal<derived_type>(raw_array_t(h.ptr()));

if (result.ptr() == nullptr)

{

PyErr_Clear();

}

return result;

}

template <class D>

inline bool pycontainer<D>::check_(pybind11::handle h)

{

int type_num = detail::numpy_traits<value_type>::type_num;

return PyArray_Check(h.ptr()) &&

PyArray_EquivTypenums(PyArray_TYPE(reinterpret_cast<PyArrayObject*>(h.ptr())), type_num);

}

template <class D>

inline PyObject* pycontainer<D>::raw_array_t(PyObject* ptr)

{

if (ptr == nullptr)

{

return nullptr;

}

int type_num = detail::numpy_traits<value_type>::type_num;

auto res = PyArray_FromAny(ptr, PyArray_DescrFromType(type_num), 0, 0,

NPY_ARRAY_ENSUREARRAY | NPY_ARRAY_FORCECAST, nullptr);

return res;

}

template <class D>

inline PyArrayObject* pycontainer<D>::python_array() const

{

return reinterpret_cast<PyArrayObject*>(this->m_ptr);

}

template <class D>

inline auto pycontainer<D>::get_min_stride() const -> size_type

{

const size_type& (*min)(const size_type&, const size_type&) = std::min<size_type>;

return std::max(size_type(1), std::accumulate(this->strides().cbegin(), this->strides().cend(), std::numeric_limits<size_type>::max(), min));

}

/**

* Reshapes the container.

* @param shape the new shape

template <class D>

template <class S>

inline void pycontainer<D>::reshape(const S& shape)

{

if (shape.size() != this->dimension() || !std::equal(std::begin(shape), std::end(shape), std::begin(this->shape())))

{

reshape(shape, layout_type::row_major);

}

/**

* Reshapes the container.

* @param shape the new shape

* @param l the new layout

template <class D>

template <class S>

inline void pycontainer<D>::reshape(const S& shape, layout_type l)

{

strides_type strides = xtl::make_sequence<strides_type>(shape.size(), size_type(1));

compute_strides(shape, l, strides);

reshape(shape, strides);

}

/**

* Reshapes the container.

* @param shape the new shape

* @param strides the new strides

template <class D>

template <class S>

inline void pycontainer<D>::reshape(const S& shape, const strides_type& strides)

{

derived_type tmp(xtl::forward_sequence<shape_type>(shape), strides);

*static_cast<derived_type*>(this) = std::move(tmp);

}

/**

* Return the layout_type of the container

* @return layout_type of the container

template <class D>

inline layout_type pycontainer<D>::layout() const

{

if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_C_CONTIGUOUS))

return layout_type::row_major;

else if (PyArray_CHKFLAGS(python_array(), NPY_ARRAY_F_CONTIGUOUS))

return layout_type::column_major;

else

return layout_type::dynamic;

}

/**

* Import the numpy Python module.

inline void import_numpy()

{

#ifdef FORCE_IMPORT_ARRAY

if (_import_array() < 0)

{

PyErr_Print();

PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import");

}

#endif

}

#endif

xtensor-python/include/xtensor-python/pycontainer.hpp at master · Mbakaya/xtensor-python · GitHub

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