[Numpy-discussion] A roadmap for NumPy - longer term planning
Chris Barker
chris.barker at noaa.gov
Fri Jun 1 12:46:57 EDT 2018
On Fri, Jun 1, 2018 at 4:43 AM, Marten van Kerkwijk <
m.h.vankerkwijk at gmail.com> wrote:
> one thing that always slightly annoyed me is that numpy math is way
> slower for scalars than python math
>
numpy is also quite a bit slower than raw python for math with (very) small
arrays:
In [31]: % timeit t2 = (t[0] * 10, t[1] * 10)
162 ns ± 0.79 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
In [32]: a
Out[32]: array([ 3.4, 5.6])
In [33]: % timeit a2 = a * 10
941 ns ± 7.95 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
(I often want to so this sort of thing, not for performance, but for ease
of computation -- say you have 2 or three coordinates that represent a
point -- it's really nice to be able to scale or shift with array
operations, rather than all that indexing -- but it is pretty slo with
numpy.
I've wondered if numpy could be optimized for small 1D arrays, and maybe
even 2d arrays with a small fixed second dimension (N x 2, N x 3), by
special-casing / short-cutting those cases.
It would require some careful profiling to see if it would help, but it
sure seems possible.
And maybe scalars could be fit into the same system.
-CHB
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
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Chris.Barker at noaa.gov
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