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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Default dimension parameter to evaluate the model
defaultDimension = 1
# Amplitude values
amplitude = NumericalPoint(defaultDimension, 1.0)
# Scale values
scale = NumericalPoint(defaultDimension, 1.0)
# Default constructor
myDefautModel = CauchyModel()
print("myDefautModel = ", myDefautModel)
# Second order model with parameters
myModel = CauchyModel(amplitude, scale)
print("myModel = ", myModel)
frequencyValue = 1.0
print("spectral density matrix at f = ",
frequencyValue, " : ", myModel(frequencyValue))
# Evaluation at time higher to check the decrease of the cauchy values
frequencyValueHigh = 10.0
print("spectral density matrix at f = ", frequencyValueHigh,
" : ", myModel(frequencyValueHigh))
# Default dimension parameter to evaluate the model
highDimension = 3
# Reallocation of adequate sizes
amplitude.resize(highDimension)
scale.resize(highDimension)
spatialCorrelation = CorrelationMatrix(highDimension)
for index in range(highDimension):
amplitude[index] = 1.0
scale[index] = (index + 1.0) / (defaultDimension * defaultDimension)
if index > 0:
spatialCorrelation[index, index - 1] = 1.0 / (index * index)
# check the cast
mySecondOrderModel = SpectralModel(
CauchyModel(amplitude, scale, spatialCorrelation))
print("mySecondOrderModel = ", mySecondOrderModel)
# checking the cast
# Second order model - dimension 10
myHighModel = CauchyModel(amplitude, scale, spatialCorrelation)
print("myHighModel = ", myHighModel)
print("spectral density matrix at f = ",
frequencyValue, " : ", myModel(frequencyValue))
print("spectral density matrix at f = ", frequencyValueHigh,
" : ", myModel(frequencyValueHigh))
except:
import sys
print("t_CauchyModel_std.py", sys.exc_info()[0], sys.exc_info()[1])
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