Would it be preferable to have to_begin='first' as an option under the existing kwarg to avoid overlapping? On Wed, Oct 26, 2016 at 3:35 PM, Peter Creasey < p.e.creasey.00@googlemail.com> wrote:

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Date: Wed, 26 Oct 2016 09:05:41 -0400 From: Matthew Harrigan <harrigan.matthew@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.

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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:

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np.diff(a, axis=axis, to_begin=np.take(a, 0, axis=axis))

rather than

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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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Would it be preferable to have to_begin='first' as an option under the existing kwarg to avoid overlapping? On Wed, Oct 26, 2016 at 3:35 PM, Peter Creasey < p.e.creasey.00@googlemail.com> wrote:

...

Date: Wed, 26 Oct 2016 09:05:41 -0400 From: Matthew Harrigan <harrigan.matthew@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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion