[Numpy-discussion] Unexpected behavior with numpy array

[Robert Kern]

Travis E. Oliphant wrote:
> Damian Eads wrote:
>> This is good to know because there have been a few situations when this 
>> would have been very useful.
>>
>> Suppose I do something like (in Python):
>>
>>    import ctypes
>>    mylib = ctypes.CDLL('libmylib.so')
>>    y = mylib.get_float_array_from_c_function()
>>
>> which returns a float* as a Python int, and then I do
>>
>>    nelems = mylib.get_float_array_num_elems()
>>    x = numpy.frombuffer(ctypes.c_buffer(y), 'float', nelems)
>>
>> This gives me an ndarray x with its (.data) buffer pointing to the 
>> memory address give by y. When the ndarray x is no longer referenced 
>> (even as another array's base), does numpy attempt to free the memory 
>> pointed to by y? In other words, does numpy always deallocate the 
>> (.data) buffer in the __del__ method? Or, does fromarray set a flag 
>> telling it not to?
> 
> NumPy won't free the memory unless the OWNDATA flag is set.  Look at the 
> flags attribute.   Frombuffer creates arrays that don't own there own 
> data, so you are safe.  A reference is kept in the NumPy array to the 
> buffer object so it won't be deleted. 
> 
> The ctypes.c_buffer is a new one for me. 

Unfortunately, it doesn't work the way it is used in Damian's example. It is a 
deprecated alias for create_string_buffer(), which creates a ctypes array of 
c_char from a string or a size. It does not make a buffer object from a ctypes 
pointer object or an address.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco