Krige of 2d Gaussian model returns a 2d tuple

[whyzjuhit]

Hi,
I'm trying this example and want to get the value of ff2. But I found the ff2 is a 2d tuple, I guess the first ff2[0] is the value simulated. What is the second one ff2[1]?

import numpy as np
from matplotlib import pyplot as plt
import gstools as gs

cond_x = [0.3, 1.9, 1.1, 3.3, 4.7]
cond_y = [1.2, 0.6, 3.2, 4.4, 3.8]
cond_val = [0.47, 0.56, 0.74, 1.47, 1.74]
gridx = np.arange(0.0, 5.5, 0.1)
gridy = np.arange(0.0, 6.5, 0.1)
model2 = gs.Gaussian(
dim=2, len_scale=1, anis=0.2, angles=-0.5, var=0.5, nugget=0.1
)

OK2 = gs.krige.Ordinary(model2, [cond_x, cond_y], cond_val, exact=True)
ff2 = OK2.structured([gridx, gridy])

[MuellerSeb]

The return values of the kriging routines are:

1. the interpolated field
2. the kriging error variance

If you only want the kriging field, you can turn of the variance calculation by:

field = OK2.structured([gridx, gridy], return_var=False)

Cheers,
Sebastian

[whyzjuhit]

Thanks a lot Sebastian