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