> Date: Wed, 26 Oct 2016 09:05:41 -0400
> From: Matthew Harrigan <harrigan.matt...@gmail.com>
>
> np.cumsum(np.diff(x, to_begin=x.take([0], axis=axis), axis=axis), axis=axis)
>
> That's certainly not going to win any beauty contests.  The 1d case is
> clean though:
>
> np.cumsum(np.diff(x, to_begin=x[0]))
>
> I'm not sure if this means the API should change, and if so how.  Higher
> dimensional arrays seem to just have extra complexity.
>
>>
>> I like the proposal, though I suspect that making it general has
>> obscured that the most common use-case for padding is to make the
>> inverse of np.cumsum (at least that?s what I frequently need), and now
>> in the multidimensional case you have the somewhat unwieldy:
>>
>> >>> np.diff(a, axis=axis, to_begin=np.take(a, 0, axis=axis))
>>
>> rather than
>>
>> >>> np.diff(a, axis=axis, keep_left=True)
>>
>> which of course could just be an option upon what you already have.
>>

So my suggestion was intended that you might want an additional
keyword argument (keep_left=False) to make the inverse np.cumsum
use-case easier, i.e. you would have something in your np.diff like:

if keep_left:
    if to_begin is None:
        to_begin = np.take(a, [0], axis=axis)
    else:
        raise ValueError(‘np.diff(a, keep_left=False, to_begin=None)
can be used with either keep_left or to_begin, but not both.’)

Generally I try to avoid optional keyword argument overlap, but in
this case it is probably justified.

Peter
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