On Tue, May 31, 2011 at 8:36 PM, Skipper Seabold <[email protected]>wrote:

> On Tue, May 31, 2011 at 9:31 PM, Benjamin Root <[email protected]> wrote:
> >
> >
> > On Tue, May 31, 2011 at 8:18 PM, Warren Weckesser
> > <[email protected]> wrote:
> >>
> >>
> >> On Tue, May 31, 2011 at 8:08 PM, Charles R Harris
> >> <[email protected]> wrote:
> >>>
> >>> Hi All,
> >>>
> >>> I've been contemplating new functions that could be added to numpy and
> >>> thought I'd run them by folks to see if there is any interest.
> >>>
> >>> 1) Modified sort/argsort functions that return the maximum k values.
> >>>     This is easy to do with heapsort and almost as easy with mergesort.
> >>>
> >>
> >>
> >> While you're at, how about a function that finds both the max and min in
> >> one pass?  (Mentioned previously in this thread:
> >> http://mail.scipy.org/pipermail/numpy-discussion/2010-June/051072.html)
> >>
> >>
> >
> > +1 from myself and probably just about anybody in matplotlib.  If both
> the
> > maxs and mins are searched during the same run through an array, I would
> > imagine that would result in a noticeable speedup with automatic range
> > finding.
> >
>
> I don't know if it's one pass off the top of my head, but I've used
> percentile for interpercentile ranges.
>
> [docs]
> [1]: X = np.random.random(1000)
>
> [docs]
> [2]: np.percentile(X,[0,100])
> [2]: [0.00016535235312509222, 0.99961513543316571]
>
> [docs]
> [3]: X.min(),X.max()
> [3]: (0.00016535235312509222, 0.99961513543316571)
>
>

percentile() isn't one pass; using percentile like that is much slower:

In [25]: %timeit np.percentile(X,[0,100])
10000 loops, best of 3: 103 us per loop

In [26]: %timeit X.min(),X.max()
100000 loops, best of 3: 11.8 us per loop


Warren



>  Skipper
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