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Michael Droettboom wrote:
> Jesper Larsen wrote:
>> Hi Matplotlib users,
>>
>> 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.
> 
>> 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.

This seems to contradict your previous assertion that the bottleneck is likely
to be disk I/O - if you're saving to a Python file-like object, there's no disk
I/O.  Why is this slower?

thanks,
Dave
[snip]
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