def simple_Krig(SiteInfo, XYTargets, DataRecord):
'''
Wrapper for Kriging interpolation of all data, and save in PKL format \n
Parameters
----------
**SiteInfo** -- Path to file with gauge location \n
**XYTargets** -- Path to file with interpolation target locations \n
**DataRecord** -- Path to file with variable registries \n
Returns
-------
**VarField** -- file with pickled variable field \n
**VarUnc** -- file with pickled variance of estimated variable \n
**AvgVar** -- file with pickled average of estimated field
'''
stations, targets, records = data_load.lcsv(SiteInfo, XYTargets,
DataRecord)
experimental_sv, record_covariance_matrix = exp_semivariogram(records,
stations)
xopt, ModOpt, candidate_sv = theor_variogram(experimental_sv)
Z, SP, ZAvg = krig(xopt[0]/3.0, targets, stations, records,
record_covariance_matrix, ModOpt, xopt,
candidate_sv, tmin=0, tmax='def', MinNumSt=3,
krig_type='Sim')
return Z, SP, ZAvg
def simple_Krig(SiteInfo, XYTargets, DataRecord):
'''
Wrapper for Kriging interpolation of all data, and save in PKL format \n
Parameters
----------
**SiteInfo** -- Path to file with gauge location \n
**XYTargets** -- Path to file with interpolation target locations \n
**DataRecord** -- Path to file with variable registries \n
Returns
-------
**VarField** -- file with pickled variable field \n
**VarUnc** -- file with pickled variance of estimated variable \n
**AvgVar** -- file with pickled average of estimated field
'''
stations, targets, records = data_load.lcsv(SiteInfo, XYTargets,
DataRecord)
experimental_sv, record_covariance_matrix = exp_semivariogram(
records, stations)
xopt, ModOpt, candidate_sv = theor_variogram(experimental_sv)
Z, SP, ZAvg = krig(xopt[0] / 3.0,
targets,
stations,
records,
record_covariance_matrix,
ModOpt,
xopt,
candidate_sv,
tmin=0,
tmax='def',
MinNumSt=3,
krig_type='Sim')
return Z, SP, ZAvg
ssp[t, tarcoun] = sp_zeros
zz[t, tarcoun] = 0
if verbose:
print ('Finished step {0}/{1} at {2}'.format(n, len(data),
ctime()))
return zz, ssp
if __name__ == '__main__':
'''
Module testing function
'''
stations, targets, records = data_load.lcsv('TestData\GaugeLoc.csv',
'TestData\InterpPts.csv',
'TestData\Dataset.csv')
stations = np.array(stations)/1000.0
targets = np.array(targets)/1000.0
experimental_sv, record_covariance_matrix = exp_semivariogram(records,
stations)
xopt, ModOpt, candidate_sv = theor_variogram(experimental_sv)
meas_err_mat = np.random.rand(len(records), len(stations))
Z, SP, ZAvg = krig(10.0, targets, stations, records,
record_covariance_matrix, ModOpt, xopt, candidate_sv,
10 ,20, MinNumSt=3, krig_type='Ord')
print 'Ordinary Kriging working fine'
Z, SP, ZAvg = krig(10.0, targets, stations, records,
ssp[t, tarcoun] = sp_zeros
zz[t, tarcoun] = 0
if verbose:
print('Finished step {0}/{1} at {2}'.format(
n, len(data), ctime()))
return zz, ssp
if __name__ == '__main__':
'''
Module testing function
'''
stations, targets, records = data_load.lcsv('TestData\GaugeLoc.csv',
'TestData\InterpPts.csv',
'TestData\Dataset.csv')
stations = np.array(stations) / 1000.0
targets = np.array(targets) / 1000.0
experimental_sv, record_covariance_matrix = exp_semivariogram(
records, stations)
xopt, ModOpt, candidate_sv = theor_variogram(experimental_sv)
meas_err_mat = np.random.rand(len(records), len(stations))
Z, SP, ZAvg = krig(10.0,
targets,
stations,
records,
record_covariance_matrix,
ModOpt,