> On Wednesday, Nov 14, 2018 at 11:46 PM, Stephan Hoyer <sho...@gmail.com > (mailto:sho...@gmail.com)> wrote: > > > On Wed, Nov 14, 2018 at 2:35 PM Sebastian Berg <sebast...@sipsolutions.net > (mailto:sebast...@sipsolutions.net)> wrote: > > On Wed, 2018-11-14 at 14:32 -0500, Marten van Kerkwijk wrote: > > > Code being better than words: see > > > https://github.com/numpy/numpy/pull/12388 for an implementation. The > > > change in the code proper is very small, though it is worrying that > > > it causes two rather unrelated tests too fail (even if arguably both > > > tests were wrong). > > > > > > Note that this does not give flexibility to put the axis where one > > > wants; as written, the code made putting it at the start the obvious > > > solution, as it avoids doing anything with the shapes of start and > > > stop. > > > > Hehe, my first gut feeling was the last axis to be the obvious one ;). > > This has been discussed before (but what hasn't) I believe, probably > > some old issue or even PR somewhere. > > I am mildly in favor, just because there is probably not much reason > > against an easy vectorization. Doesn't need to be advertised much in > > the docs anyway. > > Although it might be good to settle the "obvious" part in case I am not > > alone in first thinking of -1 being the obvious default. I would > > probably skip the axis argument for now, unless someone actually has a > > use case. > > Indeed -- I think the best argument for adding an "axis" argument is that it > allows people to be explicit about where the axis ends up, e.g., both > np.linspace(start, stop, num=5, axis=0) and np.linspace(start, stop, num=5, > axis=-1) make their intent quite clear. > > To me, axis=0 feels like the right default, matching np.concatenate and > np.stack. But NumPy already has split conventions for this sort of thing > (e.g., gufuncs add axes at the end), so I like the explicit option.
I’d like to have another vote for axis=-1 by default. Stack and concatenate are different because we are concatenating/stacking complete arrays, so it makes sense to “compose” them along the first axis to maintain C-contiguous-ness. I actually think of this as the reverse, we are “composing/combining” lots of 1D arrays over all the other dimensions, so to preserve C-contiguous-ness, it’s better to have axis=-1. > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion Best Regards, Hameer Abbasi
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