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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 #include #include #include #include #include #include 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(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 const & shape, std::vector const & strides, object const & owner, bool writeable); template 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 >::type * enabled = NULL) { std::vector shape_(shape.begin(),shape.end()); std::vector 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 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 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(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