who owns the data?
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? alternatives 1) uni = np.asarray(uni, dt).reshape(-1, ncols) return uni looks obvious but raises exception 2) uni.dtype = dt uni.reshape(-1, ncols) return uni this works and uni owns the data. I'm only worried whether assigning to dtype directly is not a dangerous thing to do.
u array([0, 0, 0, 1, 1, 0, 1, 1]) u.dtype = np.dtype("float") u array([ 0.00000000e+000, 2.12199579e-314, 4.94065646e-324, 2.12199579e-314])
adding a safety check: for t in uni.dtype.fields.values(): assert (t[0] == dt) maybe I shouldn't care if nobody owns the data. Thanks, Josef
On Wed, Nov 30, 2011 at 20:30, <josef.pktd@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
On Wed, Nov 30, 2011 at 4:00 PM, Robert Kern <robert.kern@gmail.com> wrote:
On Wed, Nov 30, 2011 at 20:30, <josef.pktd@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.
Thanks for the explanation. There where cases on the mailing list where views created problem, so I just thought of trying to own the data, but I don't think it's really relevant.
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.
this bug happened to me a few times now. I found it but only checked the flags before fixing it. Since reshape again creates a view, the next step is to assign to shape uni.shape = (uni.size//ncols, ncols) but that starts to look like too much inplace modifications just to avoid a view Thanks, Josef
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
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