Ah, yes. I forgot that my issues with trying to do rendering in the browser shared some similarities here.
When I did the last major backend refactoring (which is coming on 6 years ago now), I considered exactly this, which is why draw_path takes both a path object (which is unlikely to change) and a transform. It turned out to not be terribly useful for most the backends we have, since PDF, PS, SVG (at least in the versions of the specs we're using) all scale the stroke width along with the vertices, so you couldn't, for example, zoom in on a line plot without the line width exploding. The Agg backend is able to perform the transform itself since we wrote it to not scale the stroke using the path's transform, but there's little advantage since it's all software anyway. But the API still works this way even if nothing takes advantage of it. Though technically paths are mutable, since they store Numpy arrays which are mutable, in practice they are rarely if never mutated (I'll have to verify this, but certainly in the course of panning and zooming, they wouldn't be), so it should be possible to know whether a path is already on the GPU by checking its id against a table of what's on the GPU. The other wrinkle is that the transform is a combination of affine transforms (which I'm sure OpenGL handles just fine) with arbitrary transforms such as log scaling or polar transformations. I assume this would all have to be implemented as a vertex shader to get the performance benefits of OpenGL. We could probably implement all of the transforms built in to matplotlib (which cover a lot of cases), and then just provide some slow "escape route" to handle arbitrary transforms written in Numpy/Python, and for bonus points provide a hook for users to add their own OpenGL transforms (just as it would have been necessary to do with Javascript in the case of web browser rendering). But I bet a lot of people would be happy as a start to just have the built-in transforms working. The last thing to raise is the case of very large data. When I was investigating rendering in a web browser, there were pretty low limits to the number of vertices I could keep hold of in the browser. matplotlib has a "path simplification" algorithm to reduce the number of vertices without changing the appearance of the plot, but of course that's dependant on the scale, so it has to be rerun every time the scale changes. While path simplification may not be as necessary for speed on the GPU, it may be necessary due to memory constraints. Mike On 03/07/2013 09:50 AM, Nicolas Rougier wrote: > > Indeed, you analyzed/explained the overall situation very well in your blog > post: http://mdboom.github.com/blog/2012/08/06/matplotlib-client-side/ > > > OpenGL can make things very fast as long as there is not too many transfer > between CPU/GPU memory. Once the data is within the GPU, most transformations > (offset/translate/scale/rotate/interpolation/etc) can be made entirely on the > GPU side. In the template backend however, the "draw_path" function (for > example) receives a path to be rendered and I would need to ensure it is > build only once and only applying transforms for subsequent calls. > > At least it is my understanding of the matplotlib machinery but I may be > wrong. > > > Nicolas > > > > > > > On Mar 7, 2013, at 15:36 , Michael Droettboom wrote: > >> I'm not aware of any discussion about participating in GSoC this year, >> though I am open to the idea. I was involved as a mentor a few years >> ago, but I wasn't terribly involved in the administrative side, so I >> don't know what's involved. I think then we did it under the umbrella >> of the PSF. >> >> I'd be interested in exploring a GL backend further. What, from 10,000 >> m, is the main impedence mismatch between the current matplotlib backend >> design and OpenGL rendering? >> >> Mike >> >> On 03/07/2013 03:39 AM, Nicolas Rougier wrote: >>> Hi all, >>> >>> Are there any ongoing project for GSOC 2013 ? I would like to propose >>> something around a GL backend but I'm not still sure OpenGL "philosophy" is >>> compatible with current matplotlib design and any project would require >>> co-mentoring with a matplotlib devel guru. There is a lot of experienced >>> people around (Luke Campagnola, Cyrille Rossant, Almar Klein to name a few) >>> who can help on the GL part and we're also currently trying to design >>> together a common low-level API that may definitely help for the GL >>> backend. What do you think ? Do we need first to make sure a GL backend >>> make any sense at all before going further ? >>> >>> >>> Here is a small set of GL experiments I did recently that make me thinks it >>> should be possible to come up with something nice and fast: >>> https://github.com/rougier/gl-agg >>> >>> >>> Some movies for those who don't want to test: >>> >>> http://www.youtube.com/watch?v=T010zMtorAk >>> http://www.youtube.com/watch?v=iFwEzV9Pw-4 >>> >>> >>> >>> >>> Nicolas >>> ------------------------------------------------------------------------------ >>> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester >>> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the >>> endpoint security space. For insight on selecting the right partner to >>> tackle endpoint security challenges, access the full report. >>> http://p.sf.net/sfu/symantec-dev2dev >>> _______________________________________________ >>> Matplotlib-devel mailing list >>> [email protected] >>> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel >> >> ------------------------------------------------------------------------------ >> Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester >> Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the >> endpoint security space. For insight on selecting the right partner to >> tackle endpoint security challenges, access the full report. >> http://p.sf.net/sfu/symantec-dev2dev >> _______________________________________________ >> Matplotlib-devel mailing list >> [email protected] >> https://lists.sourceforge.net/lists/listinfo/matplotlib-devel ------------------------------------------------------------------------------ Symantec Endpoint Protection 12 positioned as A LEADER in The Forrester Wave(TM): Endpoint Security, Q1 2013 and "remains a good choice" in the endpoint security space. For insight on selecting the right partner to tackle endpoint security challenges, access the full report. http://p.sf.net/sfu/symantec-dev2dev _______________________________________________ Matplotlib-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
