/usr/lib/python3/dist-packages/numpy/ctypeslib.py is in python3-numpy 1:1.12.1-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 | """
============================
``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)
try:
import sysconfig
so_ext3 = '.%s-%s.so' % (sysconfig.get_config_var('SOABI'),
sysconfig.get_config_var('MULTIARCH'))
libname_ext.insert(0, libname + so_ext3)
except (KeyError, ImportError):
pass
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
|