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From: Michael D. <md...@st...> - 2013-08-27 19:04:37
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On 08/27/2013 09:49 AM, Chris Beaumont wrote: > I've been burned by this before as well. MPL stores some intermediate > data products (for example, scaled RGB copies) at full resolution, > even though the final rendered image is downsampled depending on > screen resolution. > > I've used some hacky tricks to get around this, which mostly involve > downsampling the image on the fly based on screen resolution. One such > effort is at https://github.com/ChrisBeaumont/mpl-modest-image. It looks like this wouldn't be too hard to include in matplotlib. I don't think we'd want to change the current behavior, because sometimes its tradeoff curve makes sense, but in other cases, the "modest image" approach also makes sense. It's just a matter of coming up with an API to switch between the two behaviors. Pull request? Cheers, Mike > > If you are loading your arrays from disk, you can also use > memory-mapped arrays -- this prevents you from loading all the data > into RAM, and further cuts down on the footprint. > > cheers, > chris > > > On Tue, Aug 27, 2013 at 6:49 AM, S(te(pán Turek > <ste...@se... <mailto:ste...@se...>> wrote: > > > You could look at whether or not you actually need 64-bit > precision. Often times, 8-bit precision per color channel is > justifiable, even in grayscale. My advice is to play with the > dtype of your array or, as you mentioned, resample. > > > thanks, this helped me significantly, uint8 precision is enough. > > Also, is it needed to keep all images? It sounds to me like > your application will become very resource hungry if you're > going to be displaying several of these 2D images over each > other (and if you don't use transparency, you won't get any > benefit at all from plotting them together). > > > Yes, I need them all . > > To avoid it I am thinking about merging them into one image and > then plot it. > > > Stepan > > > ------------------------------------------------------------------------------ > Introducing Performance Central, a new site from SourceForge and > AppDynamics. Performance Central is your source for news, insights, > analysis and resources for efficient Application Performance > Management. > Visit us today! > http://pubads.g.doubleclick.net/gampad/clk?id=48897511&iu=/4140/ostg.clktrk > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > <mailto:Mat...@li...> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users > > > > > ------------------------------------------------------------------------------ > Introducing Performance Central, a new site from SourceForge and > AppDynamics. Performance Central is your source for news, insights, > analysis and resources for efficient Application Performance Management. > Visit us today! > http://pubads.g.doubleclick.net/gampad/clk?id=48897511&iu=/4140/ostg.clktrk > > > _______________________________________________ > Matplotlib-users mailing list > Mat...@li... > https://lists.sourceforge.net/lists/listinfo/matplotlib-users |