My source data consists of a set of (x,y,z,e) samples. It can be visualized like this, where the dots are the (x,y,z) samples in 3D space and the color reflects the e value. The (x,y,z) samples compose a surface.
I want a kind of interpolation method, that I can feed with random (x,y,z) coordinates that are close to the surface and that can output an interpolated e value.
I tried the scipy LinearNDInterpolator. It works fine, but only for input (x,y,z) points that lie inside the convex hull of the surface. When the input is only slightly outside, the interpolator returns 'nan'.
I'm a bit out of ideas how to solve this.
I can only think of iterating the each line in the grid to find the points closest to the random (x,y,z) input and do linear interpolations from these points. But if I could somehow reconstruct the surface, that would be more accurate.