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:
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:

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.

Changed in version 1.20.0: Allow libname and loader_path to take any path-like object.

Parameters:
libnamepath-like

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

loader_pathpath-like

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:
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

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))
...
class numpy.ctypeslib.c_intp#

A ctypes signed integer type of the same size as numpy.intp.

Depending on the platform, it can be an alias for either c_int, c_long or c_longlong.

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