[NumPy-Tickets] [NumPy] #2079: calling multiple times numpy.ctypeslib.as_array(pointer, shape) always uses the same shape

[NumPy Trac]

#2079: calling multiple times numpy.ctypeslib.as_array(pointer, shape) always uses
the same shape
----------------------------+-----------------------------------------------
 Reporter:  pieleric        |       Owner:  somebody   
     Type:  defect          |      Status:  new        
 Priority:  normal          |   Milestone:  Unscheduled
Component:  numpy.core      |     Version:  1.6.1      
 Keywords:  ctypes pointer  |  
----------------------------+-----------------------------------------------
 The latest version of numpy supports numpy.ctypeslib.as_array() for a
 ctypes.POINTER. However, when using numpy.ctypeslib.as_array() on a
 pointer multiple times in a row with different shapes, it's always the
 shape of the first call which is used.

 Example:
 {{{
 import ctypes
 import numpy
 >>> buffer = ctypes.pointer((ctypes.c_uint16 * 100)())
 >>> p = ctypes.cast(buffer, ctypes.POINTER(ctypes.c_uint16))
 >>> a = numpy.ctypeslib.as_array(p, shape=(100,))
 >>> a.shape
 (100,)
 >>> b = numpy.ctypeslib.as_array(p, shape=(10,10))
 >>> b.shape
 (100,)
 }}}

 We would expect b.shape == (10,10) .

 The bug is in as_array() and prep_pointer(). They are too much a copy-
 paste of the case of an ndarray: only create the __array_interface__ if it
 is not yet there. For an ndarray this works fine as the
 __array_interface__ is saved on the type. For a pointer, it should be
 updated every time.

 So, I propose something like this:

 {{{
     def prep_pointer(pointer_obj, shape):
         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

     def as_array(obj, shape=None):
         if hasattr(obj, 'contents'):
             prep_pointer(obj, shape)
         else:
             tp = type(obj)
             try: tp.__array_interface__
             except AttributeError: prep_array(tp)
         return array(obj, copy=False)
 }}}


 BTW, the description of as_array() should be updated to mention the
 "shape" parameter instead of the "size" parameter.

-- 
Ticket URL: <http://projects.scipy.org/numpy/ticket/2079>
NumPy <http://projects.scipy.org/numpy>
My example project