Hello Jeff,

>> - Points stored in the above descripbed format (lat, lon, value)?

This one I solved using a m.scatter() function

>> - Interpolate a grid of data points by using different interpolation

methods like inverse distance wheighting, natural neighbor

interpolation, etc. to get a contour map?

> For interpolation of irregular, randomly distributed data points see

> http://www.scipy.org/Cookbook/Matplotlib/

Gridding_irregularly_spaced_data.

>

> However, if there is some structure to the data grid then it's probably

> better not to use these approaches.

The problem is that although regular spaced the grid is still to large to

countour to a nice map. I will play a bit more with contour and other

interpolation functions.

I tried griddata:

2) using the griddata package

Here I was nearly without orientation how to call griddata correctly.

I tried again.

Here is what I got:

x = data[:,1]

y = data[:,0]

z = data[:,2]

X, Y = mlab.meshgrid(x, y)

X, Z = mlab.meshgrid(x, y)

# zi = griddata(x,y,z,xi,yi,**kwargs)

Z = grid.griddata(x,y,z, X, Y)

plt.contour(X,Y, Z)

=> ValueError: output grid defined by xi,yi must be monotone increasing

The coordinates are stored in a way that first longitude (x) increases and

then the latitude (y) increases.

10 6.0 4

10 6.25 3

10 6.50 2

10 6.75 1

10 6.0 6

11 6.25 7

11 6.50 6

11 6.75 9

12 6.0 4

What how do I need to arrange my data to get it monotone increasing for

griddata?

Thanks for your help. One settled I will send you another example for the

examples package.

Kind regards,

Timmie