CTypes Foreign Function Interface (numpy.ctypeslib
)¶

numpy.ctypeslib.
as_array
(obj, shape=None)[source]¶ Create a numpy array from a ctypes array or a ctypes POINTER. The numpy array shares the memory with the ctypes object.
The size parameter must be given if converting from a ctypes POINTER. The size parameter is ignored if converting from a ctypes array

numpy.ctypeslib.
as_ctypes
(obj)[source]¶ Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted.

numpy.ctypeslib.
ctypes_load_library
(*args, **kwds)[source]¶ ctypes_load_library
is deprecated, useload_library
instead!It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>]
But there are crossplatform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience.
Parameters: libname : str
Name of the library, which can have ‘lib’ as a prefix, but without an extension.
loader_path : str
Where the library can be found.
Returns: ctypes.cdll[libpath] : library object
A ctypes library object
Raises: OSError
If there is no library with the expected extension, or the library is defective and cannot be loaded.

numpy.ctypeslib.
load_library
(libname, loader_path)[source]¶ It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>]
But there are crossplatform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience.
Parameters: libname : str
Name of the library, which can have ‘lib’ as a prefix, but without an extension.
loader_path : str
Where the library can be found.
Returns: ctypes.cdll[libpath] : library object
A ctypes library object
Raises: OSError
If there is no library with the expected extension, or the library is defective and cannot be loaded.

numpy.ctypeslib.
ndpointer
(dtype=None, ndim=None, shape=None, flags=None)[source]¶ Arraychecking restype/argtypes.
An ndpointer instance is used to describe an ndarray in restypes and argtypes specifications. This approach is more flexible than using, for example,
POINTER(c_double)
, since several restrictions can be specified, which are verified upon calling the ctypes function. These include data type, number of dimensions, shape and flags. If a given array does not satisfy the specified restrictions, aTypeError
is raised.Parameters: dtype : datatype, optional
Array datatype.
ndim : int, optional
Number of array dimensions.
shape : tuple of ints, optional
Array shape.
flags : str or tuple of str
Array flags; may be one or more of:
 C_CONTIGUOUS / C / CONTIGUOUS
 F_CONTIGUOUS / F / FORTRAN
 OWNDATA / O
 WRITEABLE / W
 ALIGNED / A
 WRITEBACKIFCOPY / X
 UPDATEIFCOPY / U
Returns: klass : ndpointer type object
A type object, which is an
_ndtpr
instance containing dtype, ndim, shape and flags information.Raises: TypeError
If a given array does not satisfy the specified restrictions.
Examples
>>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64, ... ndim=1, ... flags='C_CONTIGUOUS')] ... >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64)) ...