Hello, I'm using matplotlib for various tasks beautifully...but on some occasions, I have to visualize large datasets (in the range of 10M data points) (using imshow or regular plots)...system start to choke a bit at that point...
I would like to be consistent somehow and not use different tools for basically similar tasks... so I'd like some pointers regarding rendering performance...as I would be interested to be involved in dev is there is something to be done.... To active developers, what's the general feel does matplotlib have room to spare in its rendering performance?... or is it pretty tied down to the speed of Agg right now? Is there something to gain from using the multiprocessing module now included by default in 2.6? or even go as far as using something like pyGPU for fast vectorized computations...? I've seen around previous discussions about OpenGL being a backend in some future... would it really stand up compared to the current backends? is there clues about that right now? thanks for any inputs! :D bye -- View this message in context: http://www.nabble.com/Large-datasets-performance....-tp24074329p24074329.html Sent from the matplotlib - devel mailing list archive at Nabble.com. ------------------------------------------------------------------------------ Crystal Reports - New Free Runtime and 30 Day Trial Check out the new simplified licensing option that enables unlimited royalty-free distribution of the report engine for externally facing server and web deployment. http://p.sf.net/sfu/businessobjects _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
