C-Types 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, use load_library instead!
It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>]
But there are cross-platform 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 cross-platform 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]¶
Array-checking 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 : data-type, optional
Array data-type.
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
- 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)) ...