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, use load_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, a TypeError
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:


Returns: 
klass : ndpointer type object A type object, which is an 
Raises: 
TypeError If a given array does not satisfy the specified restrictions. 
>>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64, ... ndim=1, ... flags='C_CONTIGUOUS')] ... >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64)) ...
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy1.13.0/reference/routines.ctypeslib.html