python/include/boost/python/numpy/ndarray.hpp at python313 · boostorg/python · GitHub

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// Copyright Jim Bosch 2010-2012.

// Copyright Stefan Seefeld 2016.

// Distributed under the Boost Software License, Version 1.0.

// (See accompanying file LICENSE_1_0.txt or copy at

// http://www.boost.org/LICENSE_1_0.txt)

#ifndef boost_python_numpy_ndarray_hpp_

#define boost_python_numpy_ndarray_hpp_

/**

* @brief Object manager and various utilities for numpy.ndarray.

#include <boost/python.hpp>

#include <boost/utility/enable_if.hpp>

#include <boost/python/detail/type_traits.hpp>

#include <boost/python/numpy/numpy_object_mgr_traits.hpp>

#include <boost/python/numpy/dtype.hpp>

#include <boost/python/numpy/config.hpp>

#include <vector>

namespace boost { namespace python { namespace numpy {

/**

* @brief A boost.python "object manager" (subclass of object) for numpy.ndarray.

* @todo This could have a lot more functionality (like boost::python::numeric::array).

* Right now all that exists is what was needed to move raw data between C++ and Python.

class BOOST_NUMPY_DECL ndarray : public object

{

/**

* @brief An internal struct that's byte-compatible with PyArrayObject.

* This is just a hack to allow inline access to this stuff while hiding numpy/arrayobject.h

* from the user.

struct array_struct

{

PyObject_HEAD

char * data;

int nd;

Py_intptr_t * shape;

Py_intptr_t * strides;

PyObject * base;

PyObject * descr;

int flags;

PyObject * weakreflist;

};

/// @brief Return the held Python object as an array_struct.

array_struct * get_struct() const { return reinterpret_cast<array_struct*>(this->ptr()); }

public:

/**

* @brief Enum to represent (some) of Numpy's internal flags.

* These don't match the actual Numpy flag values; we can't get those without including

* numpy/arrayobject.h or copying them directly. That's very unfortunate.

* @todo I'm torn about whether this should be an enum. It's very convenient to not

* make these simple integer values for overloading purposes, but the need to

* define every possible combination and custom bitwise operators is ugly.

enum bitflag

{

NONE=0x0, C_CONTIGUOUS=0x1, F_CONTIGUOUS=0x2, V_CONTIGUOUS=0x1|0x2,

ALIGNED=0x4, WRITEABLE=0x8, BEHAVED=0x4|0x8,

CARRAY_RO=0x1|0x4, CARRAY=0x1|0x4|0x8, CARRAY_MIS=0x1|0x8,

FARRAY_RO=0x2|0x4, FARRAY=0x2|0x4|0x8, FARRAY_MIS=0x2|0x8,

UPDATE_ALL=0x1|0x2|0x4, VARRAY=0x1|0x2|0x8, ALL=0x1|0x2|0x4|0x8

};

BOOST_PYTHON_FORWARD_OBJECT_CONSTRUCTORS(ndarray, object);

/// @brief Return a view of the scalar with the given dtype.

ndarray view(dtype const & dt) const;

/// @brief Copy the array, cast to a specified type.

ndarray astype(dtype const & dt) const;

/// @brief Copy the scalar (deep for all non-object fields).

ndarray copy() const;

/// @brief Return the size of the nth dimension. raises IndexError if k not in [-get_nd() : get_nd()-1 ]

Py_intptr_t shape(int n) const;

/// @brief Return the stride of the nth dimension. raises IndexError if k not in [-get_nd() : get_nd()-1]

Py_intptr_t strides(int n) const;

/**

* @brief Return the array's raw data pointer.

* This returns char so stride math works properly on it. It's pretty much

* expected that the user will have to reinterpret_cast it.

char * get_data() const { return get_struct()->data; }

/// @brief Return the array's data-type descriptor object.

dtype get_dtype() const;

/// @brief Return the object that owns the array's data, or None if the array owns its own data.

object get_base() const;

/// @brief Set the object that owns the array's data. Use with care.

void set_base(object const & base);

/// @brief Return the shape of the array as an array of integers (length == get_nd()).

Py_intptr_t const * get_shape() const { return get_struct()->shape; }

/// @brief Return the stride of the array as an array of integers (length == get_nd()).

Py_intptr_t const * get_strides() const { return get_struct()->strides; }

/// @brief Return the number of array dimensions.

int get_nd() const { return get_struct()->nd; }

/// @brief Return the array flags.

bitflag get_flags() const;

/// @brief Reverse the dimensions of the array.

ndarray transpose() const;

/// @brief Eliminate any unit-sized dimensions.

ndarray squeeze() const;

/// @brief Equivalent to self.reshape(*shape) in Python.

ndarray reshape(python::tuple const & shape) const;

/**

* @brief If the array contains only a single element, return it as an array scalar; otherwise return

* the array.

* @internal This is simply a call to PyArray_Return();

object scalarize() const;

};

/**

* @brief Construct a new array with the given shape and data type, with data initialized to zero.

BOOST_NUMPY_DECL ndarray zeros(python::tuple const & shape, dtype const & dt);

BOOST_NUMPY_DECL ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt);

/**

* @brief Construct a new array with the given shape and data type, with data left uninitialized.

BOOST_NUMPY_DECL ndarray empty(python::tuple const & shape, dtype const & dt);

BOOST_NUMPY_DECL ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt);

/**

* @brief Construct a new array from an arbitrary Python sequence.

* @todo This does't seem to handle ndarray subtypes the same way that "numpy.array" does in Python.

BOOST_NUMPY_DECL ndarray array(object const & obj);

BOOST_NUMPY_DECL ndarray array(object const & obj, dtype const & dt);

namespace detail

{

BOOST_NUMPY_DECL ndarray from_data_impl(void * data,

dtype const & dt,

std::vector<Py_intptr_t> const & shape,

std::vector<Py_intptr_t> const & strides,

object const & owner,

bool writeable);

template <typename Container>

ndarray from_data_impl(void * data,

dtype const & dt,

Container shape,

Container strides,

object const & owner,

bool writeable,

typename boost::enable_if< boost::python::detail::is_integral<typename Container::value_type> >::type * enabled = NULL)

{

std::vector<Py_intptr_t> shape_(shape.begin(),shape.end());

std::vector<Py_intptr_t> strides_(strides.begin(), strides.end());

return from_data_impl(data, dt, shape_, strides_, owner, writeable);

}

BOOST_NUMPY_DECL ndarray from_data_impl(void * data,

dtype const & dt,

object const & shape,

object const & strides,

object const & owner,

bool writeable);

} // namespace boost::python::numpy::detail

/**

* @brief Construct a new ndarray object from a raw pointer.

* @param[in] data Raw pointer to the first element of the array.

* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().

* @param[in] shape Shape of the array as STL container of integers; must have begin() and end().

* @param[in] strides Shape of the array as STL container of integers; must have begin() and end().

* @param[in] owner An arbitray Python object that owns that data pointer. The array object will

* keep a reference to the object, and decrement it's reference count when the

* array goes out of scope. Pass None at your own peril.

* @todo Should probably take ranges of iterators rather than actual container objects.

template <typename Container>

inline ndarray from_data(void * data,

dtype const & dt,

Container shape,

Container strides,

python::object const & owner)

{

return numpy::detail::from_data_impl(data, dt, shape, strides, owner, true);

}

/**

* @brief Construct a new ndarray object from a raw pointer.

* @param[in] data Raw pointer to the first element of the array.

* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().

* @param[in] shape Shape of the array as STL container of integers; must have begin() and end().

* @param[in] strides Shape of the array as STL container of integers; must have begin() and end().

* @param[in] owner An arbitray Python object that owns that data pointer. The array object will

* keep a reference to the object, and decrement it's reference count when the

* array goes out of scope. Pass None at your own peril.

* This overload takes a const void pointer and sets the "writeable" flag of the array to false.

* @todo Should probably take ranges of iterators rather than actual container objects.

template <typename Container>

inline ndarray from_data(void const * data,

dtype const & dt,

Container shape,

Container strides,

python::object const & owner)

{

return numpy::detail::from_data_impl(const_cast<void*>(data), dt, shape, strides, owner, false);

}

/**

* @brief Transform an arbitrary object into a numpy array with the given requirements.

* @param[in] obj An arbitrary python object to convert. Arrays that meet the requirements

* will be passed through directly.

* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().

* @param[in] nd_min Minimum number of dimensions.

* @param[in] nd_max Maximum number of dimensions.

* @param[in] flags Bitwise OR of flags specifying additional requirements.

BOOST_NUMPY_DECL ndarray from_object(object const & obj,

dtype const & dt,

int nd_min,

int nd_max,

ndarray::bitflag flags=ndarray::NONE);

BOOST_NUMPY_DECL inline ndarray from_object(object const & obj,

dtype const & dt,

int nd,

ndarray::bitflag flags=ndarray::NONE)

{

return from_object(obj, dt, nd, nd, flags);

}

BOOST_NUMPY_DECL inline ndarray from_object(object const & obj,

dtype const & dt,

ndarray::bitflag flags=ndarray::NONE)

{

return from_object(obj, dt, 0, 0, flags);

}

BOOST_NUMPY_DECL ndarray from_object(object const & obj,

int nd_min,

int nd_max,

ndarray::bitflag flags=ndarray::NONE);

BOOST_NUMPY_DECL inline ndarray from_object(object const & obj,

int nd,

ndarray::bitflag flags=ndarray::NONE)

{

return from_object(obj, nd, nd, flags);

}

BOOST_NUMPY_DECL inline ndarray from_object(object const & obj,

ndarray::bitflag flags=ndarray::NONE)

{

return from_object(obj, 0, 0, flags);

}

BOOST_NUMPY_DECL inline ndarray::bitflag operator|(ndarray::bitflag a,

ndarray::bitflag b)

{

return ndarray::bitflag(int(a) | int(b));

}

BOOST_NUMPY_DECL inline ndarray::bitflag operator&(ndarray::bitflag a,

ndarray::bitflag b)

{

return ndarray::bitflag(int(a) & int(b));

}

} // namespace boost::python::numpy

namespace converter

{

NUMPY_OBJECT_MANAGER_TRAITS(numpy::ndarray);

}}} // namespace boost::python::converter

#endif

python/include/boost/python/numpy/ndarray.hpp at python313 · boostorg/python · GitHub

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