John Hunter wrote: > On Fri, Mar 12, 2010 at 8:30 AM, Tornes, Ivan E <torn...@battelle.org> wrote: >> I’m working on a project that handles large data sets. Up to this point I >> had not had any issues using matplotlib, but I tried yesterday to have it >> plot a file that had 8 million float,float pairs in it and dies with the >> following message: > > There have been some enhancements in path simplification in svn, so > you may want to try this but no guarantees this will help > > http://matplotlib.sourceforge.net/faq/installing_faq.html#install-from-svn > > Alternatively, you could use something like the "clippedline" example. > The basic idea is that most screen devices have order of a couple > million pixels, so there is no way to resolve more points than that. > But you may want to be able to zoom into a certain region will full > detail, which is not possible if you decimate your data before hand. > What the clippedline demo does is just pass the points that are in the > current viewport to mpl. > > http://matplotlib.sourceforge.net/examples/pylab_examples/clippedline.html
John, This example is mostly obsolete--the clipping procedure is built-in. (The value added by the example is the change in marker style.) Path simplification does a more thorough and general job of clipping. The reason for having the pre-clipping in the special but common case of monotonic x is that it can take advantage of a binary search, which is faster than the path-simplification's linear search for large datasets. Eric > > While this may not solve your case, because it looks like you may be > exceeding the rendering complexity with data in the viewport, the > design pattern may help inspire to you to write a custom class to > handle adaptively decimating your data so you can still see a sketch > of your data when panned out, but nonethless get the full detail when > zoomed in. > > This is in part what what the path simplification algorithm tries to > achieve, so do take a look if things work better on svn HEAD. > > Hope this helps, > JDH > > ------------------------------------------------------------------------------ > Download Intel® Parallel Studio Eval > Try the new software tools for yourself. Speed compiling, find bugs > proactively, and fine-tune applications for parallel performance. > See why Intel Parallel Studio got high marks during beta. > http://p.sf.net/sfu/intel-sw-dev > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ Download Intel® Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
