David Cournapeau wrote:
> Eric Firing wrote:
>> There is a clip function in all three numeric packages, so a native
>> clip is being used.
>>
>> If numpy.clip is actually slower than your version, that sounds like a
>> problem with the implementation in numpy. By all logic a single clip
>> function should either be the same (if it is implemented like yours)
>> or faster (if it is a single loop in C-code, as I would expect). This
>> warrants a little more investigation before changing the mpl code.
>> The best thing would be if you could make a simple standalone numpy
>> test case profiling both versions and post the results as a question
>> to the numpy-discussion list. Many such questions in the past have
>> resulted in big speedups in numpy.
> I am much more familiar with internal numpy code than matplotlib's, so
> this is much easier for me, too :)
>> One more thought: it is possible that the difference is because myclip
>> operates on the array in place while clip generates a new array. If
>> this is the cause of the difference then changing your last line to
>> "return a.copy()" probably would slow it down to the numpy clip speed
>> or slower.
> It would be scary if a copy of a 8008x256 array of double took 100 ms...
> Fortunately, it does not, this does not seem to be the problem.
>
> cheers,
>
> David
Ok, so now, with my clip function, still for a 8000x256 double array: we
have show() after imshow which takes around 760 ms. 3/5 are in
make_image, 2/5 in the function blop, which is just an alias I put to
measure the difference between axes.py:1043(draw) and image.py:173(draw)
in the function Axis.draw (file axes.py):
def blop(dsu):
for zorder, i, a in dsu:
a.draw(renderer)
blop(dsu)
In make_image, most of the time is taken into to_rgba: almost half of it
is taken in by the take call in the Colormap.__call__. Almost 200 ms to
get colors from the indexes seems quite a lot (this means 280 cycles /
pixel on average !). I can reproduce this number by using a small numpy
test.
On my laptop (pentium M, 1.2 Ghz), make_image takes almost 85 % of the
time, which seems to imply that this is where one should focus if one
wants to improve the speed,
cheers,
David
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