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C-Types Foreign Function Interface (numpy.ctypeslib)

numpy.ctypeslib.as_array(obj, shape=None)

Create a numpy array from a ctypes array or POINTER.

The numpy array shares the memory with the ctypes object.

The shape parameter must be given if converting from a ctypes POINTER. The shape parameter is ignored if converting from a ctypes array

numpy.ctypeslib.as_ctypes(obj)

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)

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)

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)

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
• 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))
...