On Di, 2016-02-23 at 14:57 -0500, Benjamin Root wrote: > I'd be more than happy to write up the patch. I don't think it would > be quite like make zeros be ones, but it would be along those lines. > One case I need to wrap my head around is to make sure that a 0 would > happen if the following was true: > > >>> a = np.ones((0, 5*64)) > >>> a.shape = (-1, 5, 64) > > EDIT: Just tried the above, and it works as expected (zero in the > first dim)! > > Just tried out a couple of other combos: > >>> a.shape = (-1,) > >>> a.shape > (0,) > >>> a.shape = (-1, 5, 64) > >>> a.shape > (0, 5, 64) >
Seems right to me on first sight :). (I don't like shape assignments though, who cares for one extra view). Well, maybe 1 instead of 0 (ignore 0s), but if the result for -1 is to use 1 and the shape is 0 convert the 1 back to 0. But it is starting to sound a bit tricky, though I think it might be straight forward (i.e. no real traps and when it works it always is what you expect). The main point is, whether you can design cases where the conversion back to 0 hides bugs by not failing when it should. And whether that would be a tradeoff we are willing to accept. - Sebastian > > This is looking more and more like a bug to me. > > Ben Root > > > On Tue, Feb 23, 2016 at 1:58 PM, Sebastian Berg < > sebast...@sipsolutions.net> wrote: > > On Di, 2016-02-23 at 11:45 -0500, Benjamin Root wrote: > > > but, it isn't really ambiguous, is it? The -1 can only refer to a > > > single dimension, and if you ignore the zeros in the original and > > new > > > shape, the -1 is easily solvable, right? > > > > I think if there is a simple logic (like using 1 for all zeros in > > both > > input and output shape for the -1 calculation), maybe we could do > > it. I > > would like someone to think about it carefully that it would not > > also > > allow some unexpected generalizations. And at least I am getting a > > BrainOutOfResourcesError right now trying to figure that out :). > > > > - Sebastian > > > > > > > Ben Root > > > > > > On Tue, Feb 23, 2016 at 11:41 AM, Warren Weckesser < > > > warren.weckes...@gmail.com> wrote: > > > > > > > > > > > > On Tue, Feb 23, 2016 at 11:32 AM, Benjamin Root < > > > > ben.v.r...@gmail.com> wrote: > > > > > Not exactly sure if this should be a bug or not. This came up > > in > > > > > a fairly general function of mine to process satellite data. > > > > > Unexpectedly, one of the satellite files had no scans in it, > > > > > triggering an exception when I tried to reshape the data from > > it. > > > > > > > > > > >>> import numpy as np > > > > > >>> a = np.zeros((0, 5*64)) > > > > > >>> a.shape > > > > > (0, 320) > > > > > >>> a.shape = (0, 5, 64) > > > > > >>> a.shape > > > > > (0, 5, 64) > > > > > >>> a.shape = (0, 5*64) > > > > > >>> a.shape = (0, 5, -1) > > > > > Traceback (most recent call last): > > > > > File "<stdin>", line 1, in <module> > > > > > ValueError: total size of new array must be unchanged > > > > > > > > > > So, if I know all of the dimensions, I can reshape just fine. > > But > > > > > if I wanted to use the nifty -1 semantic, it completely falls > > > > > apart. I can see arguments going either way for whether this > > is a > > > > > bug or not. > > > > > > > > > > > > > When you try `a.shape = (0, 5, -1)`, the size of the third > > > > dimension is ambiguous. From the Zen of Python: "In the face > > of > > > > ambiguity, refuse the temptation to guess." > > > > > > > > Warren > > > > > > > > > > > > > > > > > > > > > > Thoughts? > > > > > > > > > > Ben Root > > > > > > > > > > _______________________________________________ > > > > > NumPy-Discussion mailing list > > > > > NumPy-Discussion@scipy.org > > > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > > > > > > _______________________________________________ > > > > NumPy-Discussion mailing list > > > > NumPy-Discussion@scipy.org > > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > _______________________________________________ > > > NumPy-Discussion mailing list > > > NumPy-Discussion@scipy.org > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion
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