[Numpy-discussion] slow numpy.clip ?

[Eric Firing]

David Cournapeau wrote:
> Hi,
> 
>     When trying to speed up some matplotlib routines with the matplotlib 
> dev team, I noticed that numpy.clip is pretty slow: clip(data, m, M) is 
> slower than a direct numpy implementation (that is data[data<m] = m; 
> data[data>M] = M; return data.copy()). My understanding is that the code 
> does the same thing, right ?
> 
>     Below, a small script which shows the difference (twice slower for a 
> 8000x256 array on my workstation):

I think there was a bug in your clip2_bench that was making it 
artificially fast.  Attached is a script that I think gives a more fair 
comparison, in which clip1 and clip2 are nearly identical, and includes 
a third version using putmask which is faster than either of the others:

          15 function calls in 6.450 CPU seconds

    Ordered by: cumulative time

    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
         1    0.004    0.004    6.450    6.450 cliptest.py:10(bench_clip)
         1    2.302    2.302    2.302    2.302 cliptest.py:19(clip2_bench)
         1    0.013    0.013    2.280    2.280 cliptest.py:15(clip1_bench)
        10    2.267    0.227    2.267    0.227 
/usr/local/lib/python2.4/site-packages/numpy/core/fromnumeric.py:357(clip)
         1    1.498    1.498    1.498    1.498 cliptest.py:25(clip3_bench)
         1    0.366    0.366    0.366    0.366 
cliptest.py:6(generate_data_2d)
         0    0.000             0.000          profile:0(profiler)

Eric

> 
> import numpy as N
> 
> #==========================
> # To benchmark imshow alone
> #==========================
> def generate_data_2d(fr, nwin, hop, len):
>     nframes = 1.0 * fr / hop * len
>     return N.random.randn(nframes, nwin)
> 
> def bench_clip():
>     m   = -1.
>     M   = 1.
>     # 2 minutes (120 sec) of sounds @ 8 kHz with 256 samples with 50 % 
> overlap
>     data    = generate_data_2d(8000, 256, 128, 120)
> 
>     def clip1_bench(data, niter):
>         for i in range(niter):
>             blop    = N.clip(data, m, M)
>     def clip2_bench(data, niter):
>         for i in range(niter):
>             data[data<m]    = m
>             data[data<M]    = M
>             blop    = data.copy()
> 
>     clip1_bench(data, 10)
>     clip2_bench(data, 10)
> 
> if __name__ == '__main__':
>     # test clip
>     import hotshot, hotshot.stats
>     profile_file    = 'clip.prof'
>     prof    = hotshot.Profile(profile_file, lineevents=1)
>     prof.runcall(bench_clip)
>     p = hotshot.stats.load(profile_file)
>     print p.sort_stats('cumulative').print_stats(20)
>     prof.close()
> 
>     cheers,
> 
>     David
> _______________________________________________
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> Numpy-discussion at scipy.org
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