Tutorials
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Fit Variogram with automatic binning

%matplotlib widget
import matplotlib.pyplot as plt
plt.ioff()
# turn of warnings
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import gstools as gs

Generate a synthetic field with an exponential model.

x = np.random.RandomState(20220425).rand(1000) * 100.0
y = np.random.RandomState(20220426).rand(1000) * 100.0
model = gs.Exponential(dim=2, var=2, len_scale=8)
srf = gs.SRF(model, mean=0, seed=20220425)
field = srf((x, y))
srf.plot(contour_plot=False)
print(field.var())
Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …
1.9111014285597958

Estimate the variogram of the field with automatic binning.

bin_center, gamma = gs.vario_estimate((x, y), field)
print("estimated bin number:", len(bin_center))
print("maximal bin distance:", max(bin_center))
estimated bin number: 21
maximal bin distance: 45.88467983385915

Fit the variogram with a stable model (no nugget fitted).

fit_model = gs.Stable(dim=2)
fit_model.fit_variogram(bin_center, gamma, nugget=False)
print(fit_model)
Stable(dim=2, var=1.91, len_scale=7.95, nugget=0.0, alpha=1.02)

Plot the fitting result.

ax = fit_model.plot(x_max=max(bin_center))
ax.scatter(bin_center, gamma)