%matplotlib widget
import matplotlib.pyplot as plt
plt.ioff()
# turn of warnings
import warnings
warnings.filterwarnings('ignore')You can give a polynomial order or a list of self defined functions representing the internal drift of the given values. This drift will be fitted internally during the kriging interpolation.
In the following we are creating artificial data, where a linear drift was added. The resulting samples are then used as input for Universal kriging.
The "linear" drift is then estimated during the interpolation.
To access only the estimated mean/drift, we provide a switch only_mean
in the call routine.
import numpy as np
import gstools as gs
# synthetic condtions with a drift
drift_model = gs.Gaussian(dim=1, var=0.1, len_scale=2)
drift = gs.SRF(drift_model, seed=101)
cond_pos = np.linspace(0.1, 8, 10)
# adding a drift
cond_val = drift(cond_pos) + cond_pos * 0.1 + 1
# resulting grid
gridx = np.linspace(0.0, 15.0, 151)
drift_field = drift(gridx) + gridx * 0.1 + 1# kriging
model = gs.Gaussian(dim=1, var=0.1, len_scale=2)
krig = gs.krige.Universal(model, cond_pos, cond_val, drift_functions="linear")
krig(gridx)
ax = krig.plot()
ax.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions")
ax.plot(gridx, gridx * 0.1 + 1, ":", label="linear drift")
ax.plot(gridx, drift_field, "--", label="original field")
mean = krig(gridx, only_mean=True)
ax.plot(gridx, mean, label="estimated drift")
ax.legend()Loading...