a writes: > Michael Droettboom <md...@...> writes: > > > > Thanks for the pointers. > > > > The original simplification code was written by John Hunter (I believe), > > and I don't know if it was designed by him also or is a replication of > > something published elsewhere. So I take no credit for and have little > > knowledge of its original goals. > > I'm not sure on everything it does, but it seems to do clipping and removes > line segments where the change in slope is less than some limit. There are > probably better algorithms out there, but this one works surprisingly well > and is fast and simple. I think it should be a requirement that it returns > points which are a subset of the original points- with the change you've > made it does this, right?
Oh Hey! I'm the one who originally wrote the path simplification code. I'd have thought it would be gone by now, but I am very happy it turned out to be useful. I made it up in order to plot a very large set of noisy data I had. The goal was to simplify two types of plots at once: Smooth curves, as well as very noisy data where many lines are 'on top' of each other. (eg plot(rand(100000)) ). I noticed both could be taken care of by checking for changes in slope. An important goal (for me) was making sure that the min/max span of the points plotted was preserved. (so that eg plot(rand(1000)) spans from the lowest to highest point in the data (ie ~ 0 to 1) for any zoom factor). I'm not sure if this property survived...: If you do plot(rand(1000)) with the latest matplotlib and gradually zoom out on the x axis, you can see the top/bottom tips of the plotted line flickering in height, which is what I was trying to avoid. I forget whether I actually got it as I wanted it though, maybe I gave up. Allan ------------------------------------------------------------------------------ Open Source Business Conference (OSBC), March 24-25, 2009, San Francisco, CA -OSBC tackles the biggest issue in open source: Open Sourcing the Enterprise -Strategies to boost innovation and cut costs with open source participation -Receive a $600 discount off the registration fee with the source code: SFAD http://p.sf.net/sfu/XcvMzF8H _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
