Jesper Larsen wrote:
Hi matplotlib users,
I have a long list of ungridded data that I would like to make a contour plot of. The data is simply a list of (longitude, latitude, datavalue) with the data value belonging the given longitude and latitude. As far as I understand contour() only accepts gridded data values.
The solution is probably to interpolate the unstructured data to a regular grid and then plot the data. Has anyone tried doing that or know where to look for an interpolation/triangulation routine?
Jesper: Since this question has come up a couple of times, I decided to cook up an example. First you'll need to download and install the natgrid python module (included in CDAT, but I've separated it out from the huge tarball and put it at ftp://ftp.cdc.noaa.gov/Public/jsw/natgrid.tar.gz). Then try this:
from RandomArray import uniform
import pylab as p
r = nat.Natgrid(y, x, yi, xi)
npts = 500
x = uniform(-2,2,npts); y = uniform(-2,2,npts)
z = x*p.exp(-x**2-y**2)
# x, y, and z are now vectors containing nonuniformly sampled data.
# Define a regular grid and grid data to it.
nx = 51; ny = 41
x1 = p.linspace(-2,2,nx)
y1 = p.linspace(-2,2,ny)
xi, yi = p.meshgrid(x1, y1)
zi = griddata(x,y,z,x1,y1)
# Contour the gridded data, plotting dots at the nonuniform data points.
CS = p.contour(xi,yi,zi,15,linewidths=0.5,colors=['k'])
CS = p.contourf(xi,yi,zi,15,cmap=p.cm.jet)
It's interesting to see what happens when you vary npts (from 50 to 1000).
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