On Wed, Nov 30, 2011 at 20:30, <josef.p...@gmail.com> wrote: > just a basic question (since I haven't looked at this in some time) > > I'm creating a structured array in a function. However, I want to > return the array with just a simple dtype > > uni = uni.view(dt).reshape(-1, ncols) > return uni > > the returned uni has owndata=False. Who owns the data, since the > underlying, original array went out of scope?
Every time you make a view through .view(), slicing, .T, certain restricted .reshape() calls , etc. a reference to the original object is stored on the view. Consequently, the original object does not get garbage collected until all of the views go away too. Making view of a view just adds another link in the chain. In your example, the original object that was assigned to `uni` before that last assignment statement was executed maintains ownership of the memory. The new ndarray object that gets assigned to `uni` for the return statement refers to the temporary ndarray returned by .view() which in turn refers to the original `uni` array which owns the actual memory. > 2) > uni.dtype = dt > uni.reshape(-1, ncols) > return uni > > this works and uni owns the data. uni.reshape() doesn't reshape `uni` inplace, though. It is possible that your `uni` array wasn't contiguous to begin with. In all of the cases that your first example would have owndata=False, this one should too. > I'm only worried whether assigning > to dtype directly is not a dangerous thing to do. It's no worse than .view(dt). The same kind of checking goes on in both places. -- 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion