Fit Variogram with automatic binning

import numpy as np
import gstools as gs

Generate a synthetic field with an exponential model.

x = np.random.RandomState(19970221).rand(1000) * 100.0
y = np.random.RandomState(20011012).rand(1000) * 100.0
model = gs.Exponential(dim=2, var=2, len_scale=8)
srf = gs.SRF(model, mean=0, seed=19970221)
field = srf((x, y))
print(field.var())

Out:

1.6791948750716688

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))

Out:

estimated bin number: 21
maximal bin distance: 45.88516574202333

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)

Out:

Stable(dim=2, var=1.85, len_scale=7.42, nugget=0.0, alpha=1.09)

Plot the fitting result.

ax = fit_model.plot(x_max=max(bin_center))
ax.scatter(bin_center, gamma)
05 auto fit variogram

Total running time of the script: ( 0 minutes 0.323 seconds)

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