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?
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 > >
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