On Tue, Jul 26, 2016 at 12:06 PM, <ursch...@gmail.com> wrote:
> I'm using ctypes to interface with a binary which returns a void pointer
> (ctypes c_void_p) to a nested 64-bit float array:
If this comes from a function result, are you certain that its restype
is ctypes.c_void_p? I commonly see typos here such as setting
"restypes" instead of "restype".
> [[1.0, 2.0], [3.0, 4.0], … ]
> then return the pointer so it can be freed
>
> I'm using the following code to de-reference it:
>
> # a 10-element array
> shape = (10, 2)
> array_size = np.prod(shape)
> mem_size = 8 * array_size
> array_str = ctypes.string_at(ptr, mem_size)
> # convert to NumPy array,and copy to a list
> ls = np.frombuffer(array_str, dtype="float64",
> count=array_size).reshape(shape).tolist()
> # return pointer so it can be freed
> drop_array(ptr)
> return ls
>
> This works correctly and consistently on Linux and OSX using NumPy 1.11.0,
> but fails on
> Windows 32 bit and 64-bit about 50% of the time, returning nonsense values.
> Am I doing
> something wrong? Is there a better way to do this?
numpy.ctypeslib facilitates working with ctypes functions, pointers
and arrays via the factory functions as_array, as_ctypes, and
ndpointer.
ndpointer creates a c_void_p subclass that overrides the default
from_param method to allow passing arrays as arguments to ctypes
functions and also implements the _check_retval_ hook to automatically
convert a pointer result to a numpy array.
The from_param method validates an array argument to ensure it has the
proper data type, shape, and memory layout. For example:
g = ctypes.CDLL(None) # Unix only
Base = np.ctypeslib.ndpointer(dtype='B', shape=(4,))
# strchr example
g.strchr.argtypes = (Base, ctypes.c_char)
g.strchr.restype = ctypes.c_char_p
d = np.array(list(b'012\0'), dtype='B')
e = np.array(list(b'0123\0'), dtype='B') # wrong shape
>>> g.strchr(d, b'0'[0])
b'012'
>>> g.strchr(e, b'0'[0])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ctypes.ArgumentError: argument 1: <class 'TypeError'>:
array must have shape (4,)
The _check_retval_ hook of an ndpointer calls numpy.array on the
result of a function. Its __array_interface__ property is used to
create a copy with the defined data type and shape. For example:
g.strchr.restype = Base
>>> d.ctypes._as_parameter_ # source address
c_void_p(24657952)
>>> a = g.strchr(d, b'0'[0])
>>> a
array([48, 49, 50, 0], dtype=uint8)
>>> a.ctypes._as_parameter_ # it's a copy
c_void_p(19303504)
As a copy, the array owns its data:
>>> a.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
You can subclass the ndpointer type to have _check_retval_ instead
return a view of the result (i.e. copy=False), which may be desirable
for a large result array but probably isn't worth it for small arrays.
For example:
class Result(Base):
@classmethod
def _check_retval_(cls, result):
return np.array(result, copy=False)
g.strchr.restype = Result
>>> a = g.strchr(d, b'0'[0])
>>> a.ctypes._as_parameter_ # it's NOT a copy
c_void_p(24657952)
Because it's not a copy, the array view doesn't own the data, but note
that it's not a read-only view:
>>> a.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False
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