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.as_ctypes_type(dtype)

Convert a dtype into a ctypes type.

Parameters

dtypedtype

The dtype to convert

Returns

ctype

A ctype scalar, union, array, or struct

Raises

NotImplementedError

If the conversion is not possible

Notes

This function does not losslessly round-trip in either direction.

np.dtype(as_ctypes_type(dt)) will:

• insert padding fields

• reorder fields to be sorted by offset

• discard field titles

as_ctypes_type(np.dtype(ctype)) will:

• discard the class names of ctypes.Structures and ctypes.Unions

• convert single-element ctypes.Unions into single-element ctypes.Structures

• insert padding fields

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>] # doctest: +SKIP

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

libnamestr

Name of the library, which can have ‘lib’ as a prefix, but without an extension.

loader_pathstr

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>] # doctest: +SKIP

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

libnamestr

Name of the library, which can have ‘lib’ as a prefix, but without an extension.

loader_pathstr

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

dtypedata-type, optional

Array data-type.

ndimint, optional

Number of array dimensions.

shapetuple of ints, optional

Array shape.

flagsstr 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

klassndpointer 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))
...