CTypes Foreign Function Interface (numpy.ctypeslib
)¶

numpy.ctypeslib.
as_array
(obj, shape=None)[source]¶ 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)[source]¶ 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)[source]¶ Convert a dtype into a ctypes type.
Parameters:  dtype : dtype
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 roundtrip 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.Structure
s andctypes.Union
s  convert singleelement
ctypes.Union
s into singleelementctypes.Structure
s  insert padding fields

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>] # doctest: +SKIP
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>] # doctest: +SKIP
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')] ... #doctest: +SKIP >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64)) ... #doctest: +SKIP