Ordinary Kriging

With the help of a variogram that describes spatial properties of a sample, the sample can be interpolated.

from time import time
import plotly
import hydrobox
from hydrobox.data import pancake
from hydrobox.plotting import plotting_backend
plotting_backend('plotly')

Load sample data from the data sub-module

df = pancake()

Estimate a exponential variogram again. More details are given in the Estimate a Variogram example.

vario = hydrobox.geostat.variogram(
    coordinates=df[['x', 'y']].values,
    values=df.z.values,
    model='exponential',
    bin_func='kmeans',
    n_lags=25,
    return_type='object'
)

Run ordinary kriging on a 100x100 grid. In this run, the result is directly plotted. Other return types are 'grid', to return the resulting interpolated grid and kriging error grid, or 'object' to return the Krige class. This class is already parameterized, but the interpolation was not yet performed. This is most helpful if other grid should be constructed.

t1 = time()
fig = hydrobox.geostat.ordinary_kriging(
    variogram=vario,
    grid_resolution=100,
    exact=True,
    cond_err='nugget',
    return_type='plot'
)
t2 = time()

print('Took: %2f sec' % (t2 - t1))

# show the plot
plotly.io.show(fig)

Out:

Took: 2.842178 sec

Hydrobox Examples Estimate a Variogram