297 lines
10 KiB
C++
297 lines
10 KiB
C++
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// Copyright Jim Bosch 2010-2012.
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// Copyright Stefan Seefeld 2016.
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// Distributed under the Boost Software License, Version 1.0.
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// (See accompanying file LICENSE_1_0.txt or copy at
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// http://www.boost.org/LICENSE_1_0.txt)
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#ifndef boost_python_numpy_ndarray_hpp_
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#define boost_python_numpy_ndarray_hpp_
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/**
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* @brief Object manager and various utilities for numpy.ndarray.
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*/
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#include <boost/python.hpp>
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#include <boost/utility/enable_if.hpp>
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#include <boost/type_traits/is_integral.hpp>
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#include <boost/python/numpy/numpy_object_mgr_traits.hpp>
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#include <boost/python/numpy/dtype.hpp>
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#include <vector>
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namespace boost { namespace python { namespace numpy {
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/**
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* @brief A boost.python "object manager" (subclass of object) for numpy.ndarray.
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*
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* @todo This could have a lot more functionality (like boost::python::numeric::array).
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* Right now all that exists is what was needed to move raw data between C++ and Python.
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*/
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class ndarray : public object
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{
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/**
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* @brief An internal struct that's byte-compatible with PyArrayObject.
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*
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* This is just a hack to allow inline access to this stuff while hiding numpy/arrayobject.h
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* from the user.
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*/
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struct array_struct
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{
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PyObject_HEAD
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char * data;
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int nd;
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Py_intptr_t * shape;
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Py_intptr_t * strides;
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PyObject * base;
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PyObject * descr;
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int flags;
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PyObject * weakreflist;
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};
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/// @brief Return the held Python object as an array_struct.
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array_struct * get_struct() const { return reinterpret_cast<array_struct*>(this->ptr()); }
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public:
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/**
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* @brief Enum to represent (some) of Numpy's internal flags.
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*
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* These don't match the actual Numpy flag values; we can't get those without including
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* numpy/arrayobject.h or copying them directly. That's very unfortunate.
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*
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* @todo I'm torn about whether this should be an enum. It's very convenient to not
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* make these simple integer values for overloading purposes, but the need to
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* define every possible combination and custom bitwise operators is ugly.
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*/
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enum bitflag
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{
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NONE=0x0, C_CONTIGUOUS=0x1, F_CONTIGUOUS=0x2, V_CONTIGUOUS=0x1|0x2,
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ALIGNED=0x4, WRITEABLE=0x8, BEHAVED=0x4|0x8,
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CARRAY_RO=0x1|0x4, CARRAY=0x1|0x4|0x8, CARRAY_MIS=0x1|0x8,
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FARRAY_RO=0x2|0x4, FARRAY=0x2|0x4|0x8, FARRAY_MIS=0x2|0x8,
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UPDATE_ALL=0x1|0x2|0x4, VARRAY=0x1|0x2|0x8, ALL=0x1|0x2|0x4|0x8
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};
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BOOST_PYTHON_FORWARD_OBJECT_CONSTRUCTORS(ndarray, object);
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/// @brief Return a view of the scalar with the given dtype.
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ndarray view(dtype const & dt) const;
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/// @brief Copy the array, cast to a specified type.
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ndarray astype(dtype const & dt) const;
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/// @brief Copy the scalar (deep for all non-object fields).
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ndarray copy() const;
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/// @brief Return the size of the nth dimension.
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Py_intptr_t shape(int n) const { return get_shape()[n]; }
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/// @brief Return the stride of the nth dimension.
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Py_intptr_t strides(int n) const { return get_strides()[n]; }
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/**
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* @brief Return the array's raw data pointer.
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*
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* This returns char so stride math works properly on it. It's pretty much
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* expected that the user will have to reinterpret_cast it.
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*/
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char * get_data() const { return get_struct()->data; }
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/// @brief Return the array's data-type descriptor object.
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dtype get_dtype() const;
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/// @brief Return the object that owns the array's data, or None if the array owns its own data.
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object get_base() const;
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/// @brief Set the object that owns the array's data. Use with care.
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void set_base(object const & base);
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/// @brief Return the shape of the array as an array of integers (length == get_nd()).
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Py_intptr_t const * get_shape() const { return get_struct()->shape; }
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/// @brief Return the stride of the array as an array of integers (length == get_nd()).
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Py_intptr_t const * get_strides() const { return get_struct()->strides; }
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/// @brief Return the number of array dimensions.
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int get_nd() const { return get_struct()->nd; }
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/// @brief Return the array flags.
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bitflag get_flags() const;
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/// @brief Reverse the dimensions of the array.
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ndarray transpose() const;
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/// @brief Eliminate any unit-sized dimensions.
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ndarray squeeze() const;
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/// @brief Equivalent to self.reshape(*shape) in Python.
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ndarray reshape(python::tuple const & shape) const;
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/**
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* @brief If the array contains only a single element, return it as an array scalar; otherwise return
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* the array.
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*
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* @internal This is simply a call to PyArray_Return();
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*/
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object scalarize() const;
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};
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/**
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* @brief Construct a new array with the given shape and data type, with data initialized to zero.
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*/
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ndarray zeros(python::tuple const & shape, dtype const & dt);
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ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt);
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/**
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* @brief Construct a new array with the given shape and data type, with data left uninitialized.
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*/
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ndarray empty(python::tuple const & shape, dtype const & dt);
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ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt);
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/**
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* @brief Construct a new array from an arbitrary Python sequence.
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*
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* @todo This does't seem to handle ndarray subtypes the same way that "numpy.array" does in Python.
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*/
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ndarray array(object const & obj);
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ndarray array(object const & obj, dtype const & dt);
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namespace detail
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{
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ndarray from_data_impl(void * data,
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dtype const & dt,
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std::vector<Py_intptr_t> const & shape,
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std::vector<Py_intptr_t> const & strides,
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object const & owner,
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bool writeable);
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template <typename Container>
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ndarray from_data_impl(void * data,
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dtype const & dt,
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Container shape,
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Container strides,
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object const & owner,
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bool writeable,
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typename boost::enable_if< boost::is_integral<typename Container::value_type> >::type * enabled = NULL)
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{
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std::vector<Py_intptr_t> shape_(shape.begin(),shape.end());
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std::vector<Py_intptr_t> strides_(strides.begin(), strides.end());
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return from_data_impl(data, dt, shape_, strides_, owner, writeable);
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}
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ndarray from_data_impl(void * data,
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dtype const & dt,
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object const & shape,
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object const & strides,
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object const & owner,
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bool writeable);
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} // namespace boost::python::numpy::detail
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/**
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* @brief Construct a new ndarray object from a raw pointer.
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*
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* @param[in] data Raw pointer to the first element of the array.
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* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().
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* @param[in] shape Shape of the array as STL container of integers; must have begin() and end().
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* @param[in] strides Shape of the array as STL container of integers; must have begin() and end().
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* @param[in] owner An arbitray Python object that owns that data pointer. The array object will
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* keep a reference to the object, and decrement it's reference count when the
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* array goes out of scope. Pass None at your own peril.
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*
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* @todo Should probably take ranges of iterators rather than actual container objects.
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*/
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template <typename Container>
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inline ndarray from_data(void * data,
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dtype const & dt,
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Container shape,
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Container strides,
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python::object const & owner)
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{
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return numpy::detail::from_data_impl(data, dt, shape, strides, owner, true);
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}
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/**
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* @brief Construct a new ndarray object from a raw pointer.
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*
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* @param[in] data Raw pointer to the first element of the array.
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* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().
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* @param[in] shape Shape of the array as STL container of integers; must have begin() and end().
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* @param[in] strides Shape of the array as STL container of integers; must have begin() and end().
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* @param[in] owner An arbitray Python object that owns that data pointer. The array object will
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* keep a reference to the object, and decrement it's reference count when the
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* array goes out of scope. Pass None at your own peril.
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*
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* This overload takes a const void pointer and sets the "writeable" flag of the array to false.
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*
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* @todo Should probably take ranges of iterators rather than actual container objects.
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*/
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template <typename Container>
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inline ndarray from_data(void const * data,
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dtype const & dt,
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Container shape,
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Container strides,
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python::object const & owner)
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{
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return numpy::detail::from_data_impl(const_cast<void*>(data), dt, shape, strides, owner, false);
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}
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/**
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* @brief Transform an arbitrary object into a numpy array with the given requirements.
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*
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* @param[in] obj An arbitrary python object to convert. Arrays that meet the requirements
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* will be passed through directly.
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* @param[in] dt Data type descriptor. Often retrieved with dtype::get_builtin().
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* @param[in] nd_min Minimum number of dimensions.
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* @param[in] nd_max Maximum number of dimensions.
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* @param[in] flags Bitwise OR of flags specifying additional requirements.
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*/
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ndarray from_object(object const & obj, dtype const & dt,
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int nd_min, int nd_max, ndarray::bitflag flags=ndarray::NONE);
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inline ndarray from_object(object const & obj, dtype const & dt,
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int nd, ndarray::bitflag flags=ndarray::NONE)
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{
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return from_object(obj, dt, nd, nd, flags);
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}
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inline ndarray from_object(object const & obj, dtype const & dt, ndarray::bitflag flags=ndarray::NONE)
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{
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return from_object(obj, dt, 0, 0, flags);
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}
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ndarray from_object(object const & obj, int nd_min, int nd_max,
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ndarray::bitflag flags=ndarray::NONE);
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inline ndarray from_object(object const & obj, int nd, ndarray::bitflag flags=ndarray::NONE)
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{
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return from_object(obj, nd, nd, flags);
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}
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inline ndarray from_object(object const & obj, ndarray::bitflag flags=ndarray::NONE)
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{
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return from_object(obj, 0, 0, flags);
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}
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inline ndarray::bitflag operator|(ndarray::bitflag a, ndarray::bitflag b)
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{
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return ndarray::bitflag(int(a) | int(b));
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}
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inline ndarray::bitflag operator&(ndarray::bitflag a, ndarray::bitflag b)
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{
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return ndarray::bitflag(int(a) & int(b));
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}
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} // namespace boost::python::numpy
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namespace converter
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{
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NUMPY_OBJECT_MANAGER_TRAITS(numpy::ndarray);
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}}} // namespace boost::python::converter
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#endif
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