analogy to scipy.interpolate.griddata?

Question

I want to interpolate a given 3D point cloud:

I had a look at scipy.interpolate.griddata and the result is exactly what I need, but as I understand, I need to input "griddata" which means something like x = [[0,0,0],[1,1,1],[2,2,2]].

But my given 3D point cloud don't has this grid-look - The x,y-values don't behave like a grid - anyway there is only a single z-value for each x,y-value.*

So is there an alternative to scipy.interpolate.griddata for my not-in-a-grid-point-cloud?

*edit: "no grid look" means my input looks like this:

x = [0,4,17]
y = [-7,25,116]
z = [50,112,47]

Answer 1

This is a function I use for this kind of stuff:

from numpy import linspace, meshgrid

def grid(x, y, z, resX=100, resY=100):
    "Convert 3 column data to matplotlib grid"
    xi = linspace(min(x), max(x), resX)
    yi = linspace(min(y), max(y), resY)
    Z = griddata(x, y, z, xi, yi)
    X, Y = meshgrid(xi, yi)
    return X, Y, Z

Then use it like this:

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