[Numpy-discussion] reshaping empty array bug?

[Benjamin Root]

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.weckesser at gmail.com> wrote:

>
>
> On Tue, Feb 23, 2016 at 11:32 AM, Benjamin Root <ben.v.root at 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
>>
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>>
>>
>
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