scipy.interpolate.interp2d to create an interpolation function for a surface. I then have two arrays of real data that I want to calculate interpolated points for. If I pass the two arrays to the
interp2d function I get an array of all the points, not just the pairs of points.
My solution to this is to zip the two arrays into a list of coordinate pairs and pass this to the interpolation function in a loop:
f_interp = interpolate.interp2d(X_table, Y_table,Z_table, kind='cubic') co_ords = zip(X,Y) out =  for i in range(len(co_ords)): X = co_ords[i] Y = co_ords[i] value = f_interp(X,Y) out.append(float(value))
My question is, is there a better (more elegant, Pythonic?) way of achieving the same result?
For one, you can do
for Xtmp,Ytmp in zip(X,Y): ...
in your loop. Or even better, just
out = [float(f_interp(XX,YY)) for XX,YY in zip(X,Y)]
replacing the loop.
On a different note, I suggest using
interpolate.griddata instead. It tends to behave much better than
interp2d, and it accepts arbitrary-shaped points as input. As you've seen,
interp2d interpolators will only return you values on a mesh.