You want to make a kernel density estimate (a.k.a. a "heatmap").
Thanks for the link, i'll look into it and compare it to the suggested
hexbin().
This approach would
likely
be a bit slow if you have a very large number of points, though. It's
usually less visually messy to just plot the image
Well, that's not an option. I once tried to create a 'normal' scatterplot
of my data (it's a couple of million points), and that took a *long* time.
Plus, it made me see a 700M pdf file for the first time in my life
Cheers,
Andreas.
Andreas,
With respect to the large PDF file, while hexbin() would help in that regards, if you need further improvement in filesize, there is a kwarg for some plotting functions: rasterized=True. You might need to use a svn checkout of matplotlib for it to work though, but I am dealing with the same problem as well.
Ben Root
···
On Fri, May 21, 2010 at 3:24 PM, Andreas Hilboll <lists@…3067…> wrote:
You want to make a kernel density estimate (a.k.a. a “heatmap”).
Thanks for the link, i’ll look into it and compare it to the suggested
hexbin().
This approach would
likely
be a bit slow if you have a very large number of points, though. It’s
usually less visually messy to just plot the image
Well, that’s not an option. I once tried to create a ‘normal’ scatterplot
of my data (it’s a couple of million points), and that took a long time.
Plus, it made me see a 700M pdf file for the first time in my life
Cheers,
Andreas.
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