Re: [Matplotlib-users] Contouring a large set of ungridded data

[Andrew S. <str...@as...>]

Hi Jeff,

This looks great but unfortunately I get the following error:

astraw@aspiring:~/other-peoples-src/natgrid/example$ python example.py
Traceback (most recent call last):
  File "example.py", line 3, in ?
    import nat
  File "/usr/lib/python2.3/site-packages/nat.py", line 362, in ?
    import string, math, sys, Numeric, cdms, MA, natgridmodule
ImportError: No module named cdms

So I modified nat.py in the following way, and it now works.  And there
was much rejoicing!

-import string, math, sys, Numeric, cdms, MA, natgridmodule
+import string, math, sys, Numeric, MA, natgridmodule

Jeff Whitaker wrote:
> 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?
>>
>> Cheers,
>> Jesper
>>
>>
>>
>
> 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
> import nat
>
> def griddata(x,y,z,xi,yi):
>    r = nat.Natgrid(y, x, yi, xi)
>    return r.rgrd(z)
>
> 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)
> p.scatter(x,y,marker='o',c='b',s=5)
> p.xlim(-2,2)
> p.ylim(-2,2)
> p.show()
>
>
>
> It's interesting to see what happens when you vary npts (from 50 to 1000).
>
> HTH,
>
> -Jeff
>