Current File : //proc/self/root/opt/alt/python37/lib64/python3.7/site-packages/numpy/ctypeslib.py
"""
============================
``ctypes`` Utility Functions
============================
See Also
---------
load_library : Load a C library.
ndpointer : Array restype/argtype with verification.
as_ctypes : Create a ctypes array from an ndarray.
as_array : Create an ndarray from a ctypes array.
References
----------
.. [1] "SciPy Cookbook: ctypes", http://www.scipy.org/Cookbook/Ctypes
Examples
--------
Load the C library:
>>> _lib = np.ctypeslib.load_library('libmystuff', '.') #doctest: +SKIP
Our result type, an ndarray that must be of type double, be 1-dimensional
and is C-contiguous in memory:
>>> array_1d_double = np.ctypeslib.ndpointer(
... dtype=np.double,
... ndim=1, flags='CONTIGUOUS') #doctest: +SKIP
Our C-function typically takes an array and updates its values
in-place. For example::
void foo_func(double* x, int length)
{
int i;
for (i = 0; i < length; i++) {
x[i] = i*i;
}
}
We wrap it using:
>>> _lib.foo_func.restype = None #doctest: +SKIP
>>> _lib.foo_func.argtypes = [array_1d_double, c_int] #doctest: +SKIP
Then, we're ready to call ``foo_func``:
>>> out = np.empty(15, dtype=np.double)
>>> _lib.foo_func(out, len(out)) #doctest: +SKIP
"""
from __future__ import division, absolute_import, print_function
__all__ = ['load_library', 'ndpointer', 'test', 'ctypes_load_library',
'c_intp', 'as_ctypes', 'as_array']
import sys, os
from numpy import integer, ndarray, dtype as _dtype, deprecate, array
from numpy.core.multiarray import _flagdict, flagsobj
try:
import ctypes
except ImportError:
ctypes = None
if ctypes is None:
def _dummy(*args, **kwds):
"""
Dummy object that raises an ImportError if ctypes is not available.
Raises
------
ImportError
If ctypes is not available.
"""
raise ImportError("ctypes is not available.")
ctypes_load_library = _dummy
load_library = _dummy
as_ctypes = _dummy
as_array = _dummy
from numpy import intp as c_intp
_ndptr_base = object
else:
import numpy.core._internal as nic
c_intp = nic._getintp_ctype()
del nic
_ndptr_base = ctypes.c_void_p
# Adapted from Albert Strasheim
def 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.
"""
if ctypes.__version__ < '1.0.1':
import warnings
warnings.warn("All features of ctypes interface may not work " \
"with ctypes < 1.0.1", stacklevel=2)
ext = os.path.splitext(libname)[1]
if not ext:
# Try to load library with platform-specific name, otherwise
# default to libname.[so|pyd]. Sometimes, these files are built
# erroneously on non-linux platforms.
from numpy.distutils.misc_util import get_shared_lib_extension
so_ext = get_shared_lib_extension()
libname_ext = [libname + so_ext]
# mac, windows and linux >= py3.2 shared library and loadable
# module have different extensions so try both
so_ext2 = get_shared_lib_extension(is_python_ext=True)
if not so_ext2 == so_ext:
libname_ext.insert(0, libname + so_ext2)
else:
libname_ext = [libname]
loader_path = os.path.abspath(loader_path)
if not os.path.isdir(loader_path):
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
libpath = os.path.join(libdir, ln)
if os.path.exists(libpath):
try:
return ctypes.cdll[libpath]
except OSError:
## defective lib file
raise
## if no successful return in the libname_ext loop:
raise OSError("no file with expected extension")
ctypes_load_library = deprecate(load_library, 'ctypes_load_library',
'load_library')
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = ['C_CONTIGUOUS', 'F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE',
'OWNDATA', 'UPDATEIFCOPY']
def _flags_fromnum(num):
res = []
for key in _flagnames:
value = _flagdict[key]
if (num & value):
res.append(key)
return res
class _ndptr(_ndptr_base):
def _check_retval_(self):
"""This method is called when this class is used as the .restype
attribute for a shared-library function. It constructs a numpy
array from a void pointer."""
return array(self)
@property
def __array_interface__(self):
return {'descr': self._dtype_.descr,
'__ref': self,
'strides': None,
'shape': self._shape_,
'version': 3,
'typestr': self._dtype_.descr[0][1],
'data': (self.value, False),
}
@classmethod
def from_param(cls, obj):
if not isinstance(obj, ndarray):
raise TypeError("argument must be an ndarray")
if cls._dtype_ is not None \
and obj.dtype != cls._dtype_:
raise TypeError("array must have data type %s" % cls._dtype_)
if cls._ndim_ is not None \
and obj.ndim != cls._ndim_:
raise TypeError("array must have %d dimension(s)" % cls._ndim_)
if cls._shape_ is not None \
and obj.shape != cls._shape_:
raise TypeError("array must have shape %s" % str(cls._shape_))
if cls._flags_ is not None \
and ((obj.flags.num & cls._flags_) != cls._flags_):
raise TypeError("array must have flags %s" %
_flags_fromnum(cls._flags_))
return obj.ctypes
# Factory for an array-checking class with from_param defined for
# use with ctypes argtypes mechanism
_pointer_type_cache = {}
def 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
- 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
"""
if dtype is not None:
dtype = _dtype(dtype)
num = None
if flags is not None:
if isinstance(flags, str):
flags = flags.split(',')
elif isinstance(flags, (int, integer)):
num = flags
flags = _flags_fromnum(num)
elif isinstance(flags, flagsobj):
num = flags.num
flags = _flags_fromnum(num)
if num is None:
try:
flags = [x.strip().upper() for x in flags]
except:
raise TypeError("invalid flags specification")
num = _num_fromflags(flags)
try:
return _pointer_type_cache[(dtype, ndim, shape, num)]
except KeyError:
pass
if dtype is None:
name = 'any'
elif dtype.names:
name = str(id(dtype))
else:
name = dtype.str
if ndim is not None:
name += "_%dd" % ndim
if shape is not None:
try:
strshape = [str(x) for x in shape]
except TypeError:
strshape = [str(shape)]
shape = (shape,)
shape = tuple(shape)
name += "_"+"x".join(strshape)
if flags is not None:
name += "_"+"_".join(flags)
else:
flags = []
klass = type("ndpointer_%s"%name, (_ndptr,),
{"_dtype_": dtype,
"_shape_" : shape,
"_ndim_" : ndim,
"_flags_" : num})
_pointer_type_cache[(dtype, shape, ndim, num)] = klass
return klass
if ctypes is not None:
ct = ctypes
################################################################
# simple types
# maps the numpy typecodes like '<f8' to simple ctypes types like
# c_double. Filled in by prep_simple.
_typecodes = {}
def prep_simple(simple_type, dtype):
"""Given a ctypes simple type, construct and attach an
__array_interface__ property to it if it does not yet have one.
"""
try: simple_type.__array_interface__
except AttributeError: pass
else: return
typestr = _dtype(dtype).str
_typecodes[typestr] = simple_type
def __array_interface__(self):
return {'descr': [('', typestr)],
'__ref': self,
'strides': None,
'shape': (),
'version': 3,
'typestr': typestr,
'data': (ct.addressof(self), False),
}
simple_type.__array_interface__ = property(__array_interface__)
simple_types = [
((ct.c_byte, ct.c_short, ct.c_int, ct.c_long, ct.c_longlong), "i"),
((ct.c_ubyte, ct.c_ushort, ct.c_uint, ct.c_ulong, ct.c_ulonglong), "u"),
((ct.c_float, ct.c_double), "f"),
]
# Prep that numerical ctypes types:
for types, code in simple_types:
for tp in types:
prep_simple(tp, "%c%d" % (code, ct.sizeof(tp)))
################################################################
# array types
_ARRAY_TYPE = type(ct.c_int * 1)
def prep_array(array_type):
"""Given a ctypes array type, construct and attach an
__array_interface__ property to it if it does not yet have one.
"""
try: array_type.__array_interface__
except AttributeError: pass
else: return
shape = []
ob = array_type
while type(ob) is _ARRAY_TYPE:
shape.append(ob._length_)
ob = ob._type_
shape = tuple(shape)
ai = ob().__array_interface__
descr = ai['descr']
typestr = ai['typestr']
def __array_interface__(self):
return {'descr': descr,
'__ref': self,
'strides': None,
'shape': shape,
'version': 3,
'typestr': typestr,
'data': (ct.addressof(self), False),
}
array_type.__array_interface__ = property(__array_interface__)
def prep_pointer(pointer_obj, shape):
"""Given a ctypes pointer object, construct and
attach an __array_interface__ property to it if it does not
yet have one.
"""
try: pointer_obj.__array_interface__
except AttributeError: pass
else: return
contents = pointer_obj.contents
dtype = _dtype(type(contents))
inter = {'version': 3,
'typestr': dtype.str,
'data': (ct.addressof(contents), False),
'shape': shape}
pointer_obj.__array_interface__ = inter
################################################################
# public functions
def as_array(obj, shape=None):
"""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
"""
tp = type(obj)
try: tp.__array_interface__
except AttributeError:
if hasattr(obj, 'contents'):
prep_pointer(obj, shape)
else:
prep_array(tp)
return array(obj, copy=False)
def as_ctypes(obj):
"""Create and return a ctypes object from a numpy array. Actually
anything that exposes the __array_interface__ is accepted."""
ai = obj.__array_interface__
if ai["strides"]:
raise TypeError("strided arrays not supported")
if ai["version"] != 3:
raise TypeError("only __array_interface__ version 3 supported")
addr, readonly = ai["data"]
if readonly:
raise TypeError("readonly arrays unsupported")
tp = _typecodes[ai["typestr"]]
for dim in ai["shape"][::-1]:
tp = tp * dim
result = tp.from_address(addr)
result.__keep = ai
return result
Mini Shell