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)
print "Processing file " + str(i) + " of " + str(len(sat_data))
# Load the SECS grid and convert to ECEF
sec_grid = np.loadtxt(SEC_file)
grid_xyz = lla2ecef(sec_grid)
# Load the Satellite position and convert it to ECEF
sat_latlon = np.zeros((1, 3))
sat_latlon[:, (0, 1)] = sat_data[i, (6, 7)]
sat_xyz = lla2ecef(sat_latlon)
# Determine the sill for the semivariance model of the grid
sill = np.var(grid_xyz[:, 2])
# Define the type of semivariacne model to be used for kriging
covfct = model.covariance(model.exponential, (900000, sill))
# Krig the value for the satellite position using simple kriging and the defined semivariance model and 10
# neighbouring points
ptz = kriging.simple(grid_xyz, covfct, sat_xyz[:, :2], N=10)
# Add the krigged value to the different variables
sat_latlon[0, 2] = ptz[0]
sat_xyz[0, 2] = ptz[0]
sat_data[i, 8] = ptz[0]
timestamp = sat_ymd + sat_hms
plot_grid(sec_grid, sat_latlon, timestamp)
prev_min = sat_mins
# Save the updated satellite data with the krigged values
#np.savetxt('sat_data_april_krigged.txt',sat_data,delimiter='\t')
print "Processing file "+str(i)+" of "+str(len(sat_data))
# Load the SECS grid and convert to ECEF
sec_grid = np.loadtxt(SEC_file)
grid_xyz = lla2ecef(sec_grid)
# Load the Satellite position and convert it to ECEF
sat_latlon = np.zeros((1,3))
sat_latlon[:,(0,1)] = sat_data[i,(6,7)]
sat_xyz = lla2ecef(sat_latlon)
# Determine the sill for the semivariance model of the grid
sill = np.var(grid_xyz[:,2])
# Define the type of semivariacne model to be used for kriging
covfct = model.covariance(model.exponential,(900000, sill))
# Krig the value for the satellite position using simple kriging and the defined semivariance model and 10
# neighbouring points
ptz = kriging.simple(grid_xyz,covfct,sat_xyz[:,:2],N=10)
# Add the krigged value to the different variables
sat_latlon[0,2] = ptz[0]
sat_xyz[0,2] = ptz[0]
sat_data[i,8] = ptz[0]
timestamp = sat_ymd+sat_hms
plot_grid(sec_grid, sat_latlon, timestamp)
prev_min = sat_mins
# Save the updated satellite data with the krigged values
#np.savetxt('sat_data_april_krigged.txt',sat_data,delimiter='\t')
#The EICS grid contains 2 values for the horizontal components have to separate them and krig it separately
eic_xyu = lla2ecef(eic_u)
eic_xyv = lla2ecef(eic_v)
# Load the Satellite position and convert it to ECEF
sat_latlon = np.zeros((1, 3))
sat_latlon[:, (0, 1)] = sat_data[i, (6, 7)]
sat_latlon[:, 1] = sat_latlon[:, 1] - 360
sat_xyz = lla2ecef(sat_latlon)
# Determine the sill for the semivariance model of the grid
sill_u = np.var(eic_xyu[:, 2])
sill_v = np.var(eic_xyv[:, 2])
# Define the type of semivariance model to be used for kriging
covfct_u = model.covariance(model.exponential, (900000, sill_u))
covfct_v = model.covariance(model.exponential, (900000, sill_v))
# Krig the value for the satellite position using simple kriging and the defined semivariance model and 10
# neighbouring points
ptz_u = kriging.simple(eic_xyu, covfct_u, sat_xyz[:, :2], N=10)
ptz_v = kriging.simple(eic_xyv, covfct_v, sat_xyz[:, :2], N=10)
# Add the krigged value to the different variables
sat_data[i, 8] = ptz_u[0]
sat_data[i, 9] = ptz_v[0]
timestamp = sat_ymd + sat_hms
#Call the plotting function to plot the grid and krigged values
plot_grid(EIC_grid, sat_latlon, ptz_u, ptz_v, timestamp)
prev_min = sat_mins
#The EICS grid contains 2 values for the horizontal components have to separate them and krig it separately
eic_xyu = lla2ecef(eic_u)
eic_xyv = lla2ecef(eic_v)
# Load the Satellite position and convert it to ECEF
sat_latlon = np.zeros((1,3))
sat_latlon[:,(0,1)] = sat_data[i,(6,7)]
sat_latlon[:,1] = sat_latlon[:,1]-360
sat_xyz = lla2ecef(sat_latlon)
# Determine the sill for the semivariance model of the grid
sill_u = np.var(eic_xyu[:,2])
sill_v = np.var(eic_xyv[:,2])
# Define the type of semivariance model to be used for kriging
covfct_u = model.covariance(model.exponential,(900000, sill_u))
covfct_v = model.covariance(model.exponential,(900000, sill_v))
# Krig the value for the satellite position using simple kriging and the defined semivariance model and 10
# neighbouring points
ptz_u = kriging.simple(eic_xyu,covfct_u,sat_xyz[:,:2],N=10)
ptz_v = kriging.simple(eic_xyv,covfct_v,sat_xyz[:,:2],N=10)
# Add the krigged value to the different variables
sat_data[i,8] = ptz_u[0]
sat_data[i,9] = ptz_v[0]
timestamp = sat_ymd+sat_hms
#Call the plotting function to plot the grid and krigged values
plot_grid(EIC_grid,sat_latlon,ptz_u,ptz_v,timestamp)
prev_min = sat_mins
