On Di, 2016-02-23 at 21:06 +0100, Sebastian Berg wrote: > 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. >
Another thought. Maybe you can figure out the -1 correctly, if there is no *other* 0 involved. If there is any other 0, I could imagine problems. > - 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 > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion
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