David Cournapeau wrote: > But the show case is more interesting: > > ncalls tottime percall cumtime percall filename:lineno(function) > 1 0.002 0.002 3.886 3.886 > slowmatplotlib.py:177(bench_imshow_show) > 1 0.000 0.000 3.884 3.884 > slowmatplotlib.py:163(bench_imshow) > 1 0.698 0.698 3.003 3.003 > /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:70(show) > > > 2 0.000 0.000 2.266 1.133 > /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtk.py:275(expose_event) > > > 1 0.009 0.009 2.266 2.266 > /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_gtkagg.py:71(_render_figure) > > > 1 0.000 0.000 2.256 2.256 > /home/david/local/lib/python2.4/site-packages/matplotlib/backends/backend_agg.py:385(draw) > > > 1 0.000 0.000 2.253 2.253 > /home/david/local/lib/python2.4/site-packages/matplotlib/figure.py:510(draw) > > 1 0.000 0.000 2.251 2.251 > /home/david/local/lib/python2.4/site-packages/matplotlib/axes.py:994(draw) > > 1 0.005 0.005 1.951 1.951 > /home/david/local/lib/python2.4/site-packages/matplotlib/image.py:173(draw) > > 1 0.096 0.096 1.946 1.946 > /home/david/local/lib/python2.4/site-packages/matplotlib/image.py:109(make_image) > > > 1 0.002 0.002 1.850 1.850 > /home/david/local/lib/python2.4/site-packages/matplotlib/cm.py:50(to_rgba) > > 1 0.001 0.001 0.949 0.949 > /home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:735(__call__) > > > 1 0.097 0.097 0.899 0.899 > /home/david/local/lib/python2.4/site-packages/matplotlib/colors.py:568(__call__) > > > 325 0.050 0.000 0.671 0.002 > /home/david/local/lib/python2.4/site-packages/numpy/core/ma.py:533(__init__) > > 1 0.600 0.600 0.600 0.600 > /home/david/local/lib/python2.4/site-packages/numpy/core/fromnumeric.py:282(resize) > > > 1 0.000 0.000 0.596 0.596 > /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:1894(imshow) > > > 10 0.570 0.057 0.570 0.057 > /home/david/local/lib/python2.4/site-packages/numpy/oldnumeric/functions.py:117(where) > > > 3 0.000 0.000 0.513 0.171 > /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:883(gca) > > 1 0.000 0.000 0.513 0.513 > /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:950(ishold) > > 4 0.000 0.000 0.408 0.102 > /home/david/local/lib/python2.4/site-packages/matplotlib/pylab.py:903(gcf) > > > For more details, see the .kc files which are the in the tbz2 archive, > with the script for generating profiles for kcachegrind, Here is some stuff I tried:
- first, we can see that in expose_event (one is expensive, the other negligeable, from my understanding), two calls are pretty expensive: the __call__ at line 735 (for normalize functor) and one for __call__ at line 568 (for colormap functor). - for normalize functor, one line is expensive: val = ma.array(clip(val.filled(vmax), vmin, vmax), mask=mask). If I put a test on mask when mask is None (which it is in my case), then the function becomes negligeable. - for colormap functor, the 3 where calls are expensive. I am not sure to understand in which case they are useful; if I understand correctly, one tries to avoid values out of range (0, N), and force out of range values to be clipped. Isn't there an easier way than using where ? If I remove the where in the colormap functor, I have a 4x speed increase for the to_rgba function. After that, it becomes a bit more tricky to change things for someone like me who have no knowledge about matplotlib internals. Cheers, David ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys - and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
