pt = [2000, 4700]
plt.scatter(P[:,0], P[:,1], c=P[:,2], cmap=geoplot.YPcmap)
plt.title('Zone A Subset % Porosity')
plt.colorbar()
xmin, xmax = 0, 4250
ymin, ymax = 3200, 6250
plt.xlim(xmin,xmax)
plt.ylim(ymin,ymax)
for i in range( len( P[:,2] ) ):
x, y, por = P[i]
if( x < xmax )&( y > ymin )&( y < ymax ):
plt.text( x+100, y, '{:4.2f}'.format( por ) )
plt.scatter( pt[0], pt[1], marker='x', c='k' )
plt.text( pt[0]+100 , pt[1], '?')
plt.xlabel('Easting (m)')
plt.ylabel('Northing (m)')
plt.show()
tolerance = 250
lags = np.arange(tolerance, 10000, tolerance*2)
sill = np.var(P[:,2])
geoplot.semivariogram(P, lags, tolerance)
svm = model.semivariance(model.spherical, (4000, sill))
geoplot.semivariogram(P, lags, tolerance, model=svm)
covfct = model.covariance(model.spherical, (4000, sill))
print kriging.simple(P, covfct, pt, N=6)
# <codecell> tolerance = 250 lags = np.arange( tolerance, 10000, tolerance*2 ) sill = np.var(P[:,2]) geoplot.semivariogram( P, lags, tolerance ) # <markdowncell> # Looking at the figure above, we can say that the semivariogram levels off around 4000, so we can set the range, $a$, to that value and model the covariance function. # <codecell> svm = model.semivariance( model.spherical, [ 4000, sill ] ) geoplot.semivariogram( P, lags, tolerance, model=svm ) # <markdowncell> # We can visualize the distribution of the lagged distances with the `laghistogram()` function. # <codecell> geoplot.laghistogram( P, pw, lags, tolerance ) # <markdowncell> # If we want to perform anisotropic kriging, we can visualize the distribution of the anisotropic lags using the `anisotropiclags()` function. Note that we use the bearing, which is measured in degrees, clockwise from North. # <codecell>
