[Numpy-discussion] padding options for diff

Matthew Harrigan harrigan.matthew at gmail.com
Wed Oct 26 09:05:41 EDT 2016


The inverse of cumsum is actually a little more unweildy since you can't
drop a dimension with take.  This returns the original array (numerical
caveats aside):

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.

On Tue, Oct 25, 2016 at 1:26 PM, Peter Creasey <
p.e.creasey.00 at googlemail.com> wrote:

> > Date: Mon, 24 Oct 2016 08:44:46 -0400
> > From: Matthew Harrigan <harrigan.matthew at gmail.com>
> >
> > I posted a pull request <https://github.com/numpy/numpy/pull/8206> which
> > adds optional padding kwargs "to_begin" and "to_end" to diff.  Those
> > options are based on what's available in ediff1d.  It closes this issue
> > <https://github.com/numpy/numpy/issues/8132>
>
> 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.
>
> Best,
> Peter
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