On Tuesday, 26 July 2016 19:10:46 UTC+1, eryk sun wrote: > On Tue, Jul 26, 2016 at 12:06 PM, sth 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
The restype is a ctypes Structure instance with a single __fields__ entry (coords), which is a Structure with two fields (len and data) which are the FFI array's length and the void pointer to its memory: https://github.com/urschrei/pypolyline/blob/master/pypolyline/util.py#L109-L117 I'm only half-following your explanation of how ctypeslib works, but it seems clear that I'm doing something wrong. -- https://mail.python.org/mailman/listinfo/python-list
