from geostatsmodels import utilities, variograms, model, kriging, geoplot
import numpy as np
import matplotlib.pyplot as plt
from math import *
z = utilities.readGeoEAS('ZoneA.dat')
P = z[:,[0,1,3]]
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])
# <markdowncell>
# This notebook is the reproduction of an exercise found at http://people.ku.edu/~gbohling/cpe940/Kriging.pdf
# <codecell>
from geostatsmodels import utilities, variograms, model, kriging, geoplot
import pandas
# <markdowncell>
# We'll read the data from `ZoneA.dat`.
# <codecell>
z = utilities.readGeoEAS('../data/ZoneA.dat')
# <markdowncell>
# We want the first, second and fourth columns of the data set, representing the x and y spatial coordinates, and the porosity.
# <codecell>
P = z[:, [0, 1, 3]]
# <markdowncell>
# We'll be interested in determining the porosity at a point (2000,4700).
# <codecell>
#!/usr/bin/env python
import geostatsmodels.utilities as u
import geostatsmodels.kriging as k
import geostatsmodels.simulation as s
from pylab import *
import numpy as np
# read cluster.dat
d = u.readGeoEAS("cluster.dat")
# take the first three columns
d = d[:,:3]
# define the lags and tolerance
# for the semivariogram modeling
lags, tol = np.linspace( 10, 50, 10 ), 5
# if the data is not normally distributed,
# perfrom a z-score transformation
d, inv = u.to_norm( d )
# perform sequential Gaussian simulation using
# a spherical model, on a 5x5 grid
m = s.sgs( d, k.spherical, lags, tol, 5, 5 )
# use the [:,::-1].T to a) reverse the order of the columns
# and then b) transpose the data, this takes it from Python
# conventions, back to the way we normally think of spatial data
print m[:,::-1].T
# <markdowncell>
# This notebook is the reproduction of an exercise found at http://people.ku.edu/~gbohling/cpe940/Kriging.pdf
# <codecell>
from geostatsmodels import utilities, variograms, model, kriging, geoplot
import pandas
# <markdowncell>
# We'll read the data from `ZoneA.dat`.
# <codecell>
z = utilities.readGeoEAS('../data/ZoneA.dat')
# <markdowncell>
# We want the first, second and fourth columns of the data set, representing the x and y spatial coordinates, and the porosity.
# <codecell>
P = z[:,[0,1,3]]
# <markdowncell>
# We'll be interested in determining the porosity at a point (2000,4700).
# <codecell>
