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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Default dimension parameter to evaluate the model
defaultDimension = 1
# Amplitude values
amplitude = ot.Point(defaultDimension, 1.0)
# Scale values
scale = ot.Point(defaultDimension, 1.0)
# Default constructor
myDefaultModel = ot.CauchyModel()
print("myDefaultModel = ", myDefaultModel)
# Second order model with parameters
myModel = ot.CauchyModel(scale, amplitude)
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 = ot.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 = ot.SpectralModel(
ot.CauchyModel(scale, amplitude, spatialCorrelation)
)
print("mySecondOrderModel = ", mySecondOrderModel)
# checking the cast
# Second order model - dimension 10
myHighModel = ot.CauchyModel(scale, amplitude, spatialCorrelation)
print("myHighModel = ", myHighModel)
print("spectral density matrix at f = ", frequencyValue, " : ", myModel(frequencyValue))
print(
"spectral density matrix at f = ",
frequencyValueHigh,
" : ",
myModel(frequencyValueHigh),
)
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