Python - Kriging (Gaussian Process) in scikit_learn

Question

I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data

Answer 1

In the 2d case, something like this should work:

import numpy as np
from sklearn.gaussian_process import GaussianProcess

x = np.arange(1,51)
y = np.arange(1,51)
X, Y = np.meshgrid(lons, lats)

points = zip(obs_x,  obs_y)
values = obs_data    # Replace with your observed data

gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1., nugget=0.001)
gp.fit(points, values)
XY_pairs = np.column_stack([X.flatten(), Y.flatten()])
predicted = gp.predict(XY_pairs).reshape(X.shape)