Using scatter, it seems less probably (numerous) points show just as much as
more probable points. Can anyone suggest a good way to emphasize the more
probable points?
I was thinking maybe the easy way is just scale down the markers. Drawback may
be too many points plotted.
Colors would be nice, but I guess that would be more work?
Using scatter, it seems less probably (numerous) points show just as much as
more probable points. Can anyone suggest a good way to emphasize the more
probable points?
This is what hexbin is for, although it takes the additional step of showing points on a grid, not at their original precise locations.
Eric
On 05/18/2011 09:01 AM, Neal Becker wrote:
I was thinking maybe the easy way is just scale down the markers. Drawback may
be too many points plotted.Colors would be nice, but I guess that would be more work?
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You could try something like the scatter+histograms shown here:
http://matplotlib.sourceforge.net/examples/pylab_examples/scatter_hist.html
Takes a bit more space, but the colors aren’t as important.
On Wed, May 18, 2011 at 3:01 PM, Neal Becker <ndbecker2@…985…> wrote:
Using scatter, it seems less probably (numerous) points show just as much as
more probable points. Can anyone suggest a good way to emphasize the more
probable points?
I was thinking maybe the easy way is just scale down the markers. Drawback may
be too many points plotted.
Colors would be nice, but I guess that would be more work?
Using scatter, it seems less probably (numerous) points show just as much as
more probable points. Can anyone suggest a good way to emphasize the more
probable points?
Another idea: set alpha to something less than 1, maybe something like 0.3; then superimposed markers will show up darker than lone markers.
Eric
On 05/18/2011 09:01 AM, Neal Becker wrote:
I was thinking maybe the easy way is just scale down the markers. Drawback may
be too many points plotted.Colors would be nice, but I guess that would be more work?
------------------------------------------------------------------------------
What Every C/C++ and Fortran developer Should Know!
Read this article and learn how Intel has extended the reach of its
next-generation tools to help Windows* and Linux* C/C++ and Fortran
developers boost performance applications - including clusters.
http://p.sf.net/sfu/intel-dev2devmay
_______________________________________________
Matplotlib-users mailing list
Matplotlib-users@lists.sourceforge.net
matplotlib-users List Signup and Options