// 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/type_traits/is_integral.hpp>
#include <boost/python/numpy/numpy_object_mgr_traits.hpp>
#include <boost/python/numpy/dtype.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 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.
Py_intptr_t shape(int n) const { return get_shape()[n]; }
/// @brief Return the stride of the nth dimension.
Py_intptr_t strides(int n) const { return get_strides()[n]; }
/**
* @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.
*/
ndarray zeros(python::tuple const & shape, dtype const & dt);
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.
*/
ndarray empty(python::tuple const & shape, dtype const & dt);
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.
*/
ndarray array(object const & obj);
ndarray array(object const & obj, dtype const & dt);
namespace detail
{
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::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);
}
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.
*/
ndarray from_object(object const & obj, dtype const & dt,
int nd_min, int nd_max, ndarray::bitflag flags=ndarray::NONE);
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);
}
inline ndarray from_object(object const & obj, dtype const & dt, ndarray::bitflag flags=ndarray::NONE)
{
return from_object(obj, dt, 0, 0, flags);
}
ndarray from_object(object const & obj, int nd_min, int nd_max,
ndarray::bitflag flags=ndarray::NONE);
inline ndarray from_object(object const & obj, int nd, ndarray::bitflag flags=ndarray::NONE)
{
return from_object(obj, nd, nd, flags);
}
inline ndarray from_object(object const & obj, ndarray::bitflag flags=ndarray::NONE)
{
return from_object(obj, 0, 0, flags);
}
inline ndarray::bitflag operator|(ndarray::bitflag a, ndarray::bitflag b)
{
return ndarray::bitflag(int(a) | int(b));
}
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