On Mon, 2008-03-03 at 08:16 -0500, Michael Droettboom wrote: > > I have an application which produces PNG files using the AGG backend. > > When I profile the application I can see that much of the cpu time is > > spent in the method write_png called by print_figure in backend_agg.py. > > I have seen this myself. Keep in mind that timing includes a lot of > disk I/O, so if your images are particularly large, or you're saving to > a network or external disk, or if another process steps in at that > moment and wants to read/write to the disk, that could be the > bottleneck, more so than just the CPU time spent doing the PNG > compression. On any reasonably modern PC, I suspect that's the case. > > > Does anyone know which backend is the best for producing fast good > > quality PNG files (with fast being as important as good quality)? > > They should all be approximately the same wrt actually writing out the > file -- they're all using libpng either directly or indirectly. It also > means there's not much that matplotlib can do to improve its > performance, short of submitting patches to libpng -- but I suspect > there isn't a lot of long-hanging fruit left to improve in such a > widely-used library.
My application is web based. I am therefore considering serving the png files directly from memory in a future release as outlined here: http://www.scipy.org/Cookbook/Matplotlib/Matplotlib_and_Zope Although I am still considering what impacts that will have on my caching of the plots. I am currently saving the png files and reusing them if the same plot is requested again - but I am considering pickling individual elements of the plots instead since there are a lot of plots in which the only differences is some text (I am already caching parts of the plot in memory). But I don't know the performance of such a solution yet. I will give you a heads up when I know (which won't be in the immediate future since I have other things that are higher up on my to do list). > > In another thread I read that antialiasing could be disabled for better > > performance. I tried doing that in each call to contourf and it resulted > > in a performance improvement. Does anyone have other performance tips > > with regard to PNG files? > > Saving to a Python file-like object (if you're doing that) is slower > than saving directly to a file path. > > See the recent thread on "Matplotlib performance" for a discussion of > decimation of data (if your data set is really large). I am writing directly to a file path and I have already decimated my data sets - so that won't help me. Cheers, Jesper ------------------------------------------------------------------------- This SF.net email is sponsored by: Microsoft Defy all challenges. Microsoft(R) Visual Studio 2008. http://clk.atdmt.com/MRT/go/vse0120000070mrt/direct/01/ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
