def funcO2(self, val):
Ps = np.hstack((self.Data_Grid[:, :2], val.reshape(-1, 1)))
Ps = np.vstack((Ps, self.Data_Conditioning[:, [0, 1, 3]]))
vdatas = variograms.semivariogram(Ps, self.lags, self.lag_tolerance)
svs = vdatas[1] - 0.5 * (self.lags * self.trend)**2
O2 = np.sum(((svs - self.svt) / self.svt)**2)
return O2
def semivariogram(data, lags, tol, model=None):
'''
Input: (data) NumPy array with three columns, the first two
columns should be the x and y coordinates, and
third should be the measurements of the variable
of interest
(lags) the lagged distance of interest
(tol) the allowable tolerance about (lag)
(model) model function taking a distance and returning
an approximation of the semivariance
Output: empirical semivariogram
'''
# h, sv = variograms.semivariogram(data, lags, tol)
vdata = variograms.semivariogram(data, lags, tol)
h, sv = vdata[0], vdata[1]
sill = np.var(data[:, 2])
fig, ax = subplots()
if model:
ax.plot(h, model(h), 'r')
ax.plot(h, sv, 'ko-')
ax.set_ylabel('Semivariance')
ax.set_xlabel('Lag Distance')
ax.set_title('Semivariogram')
ax.text(tol * 3, sill * 1.025, str(np.round(sill, decimals=3)))
ax.axhline(sill, ls='--', color='k')
show()
def fitmodel( data, fct, lags, tol ):
'''
Input: (P) ndarray, data
(model) modeling function
- spherical
- exponential
- gaussian
(lags) lag distances
(tol) tolerance
Output: (covfct) function modeling the covariance
'''
# calculate the semivariogram
sv = variograms.semivariogram( data, lags, tol )
# calculate the sill
c = np.var( data[:,2] )
# calculate the optimal parameters
a = opt( fct, sv[0], sv[1], c )
# return a covariance function
covfct = covariance( fct, ( a, c ) )
return covfct
def fitmodel( data, fct, lags, tol ):
'''
Input: (P) ndarray, data
(model) modeling function
- spherical
- exponential
- gaussian
(lags) lag distances
(tol) tolerance
Output: (covfct) function modeling the covariance
'''
# calculate the semivariogram
sv = variograms.semivariogram( data, lags, tol )
# calculate the sill
c = np.var( data[:,2] )
# calculate the optimal parameters
a = opt( fct, sv[0], sv[1], c )
# return a covariance function
covfct = covariance( fct, ( a, c ) )
return covfct
TBs.Table()
print("----------------------------------------------------------------------")
#Variogram calculation#
#Variogram initial
lag_number = 50
svt, dist_max, lag_size, lag_tolerance, lags = fo.variogram_parameter(
Data_Grid, sill, nugget_var, a_range, lag_number, var_model=model)
svt_smooth, _, _, _, lag_smooth = fo.variogram_parameter(Data_Grid,
sill,
nugget_var,
a_range,
lag_number * 4,
var_model=model,
lag_size=lag_size / 4)
vdata = variograms.semivariogram(P, lags, lag_tolerance)
h, sv = vdata[0], vdata[1]
sv = sv - 0.5 * (h * pend)**2
graf.Experimental_Variogram(lags, [svt_smooth, sv],
sill,
a_range,
var_model=model,
variance=T_Phit.variance_value,
color_svt='red',
lags_svt=lag_smooth)
#Variogram simulated
lag_number = 10
svt, dist_max, lag_size, lag_tolerance_1, lags = fo.variogram_parameter(
Data_Grid, sill, nugget_var, a_range, lag_number, var_model=model)
svt_smooth, _, _, _, lag_smooth = fo.variogram_parameter(Data_Grid,