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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Dimension of the input model
# Size of the TimeGrid
# dimension parameter
dimension = 1
# Amplitude values
amplitude = ot.Point(dimension, 1.00)
# Scale values
scale = ot.Point(dimension, 1.0)
size = 128
timeGrid = ot.RegularGrid(0.0, 0.1, size)
# Cauchy model
model = ot.CauchyModel(scale, amplitude)
covModel = ot.AbsoluteExponential(scale, amplitude)
myProcess = ot.SpectralGaussianProcess(model, timeGrid)
# Create a sample of size N = 1000
sample = myProcess.getSample(1000)
# StationaryCovarianceModelFactory using default parameter - Factory
# initiate
myFactory = ot.StationaryCovarianceModelFactory()
# Build a UserDefinedCovarianceModel using the Wellch method
myCovarianceModel = myFactory.buildAsUserDefinedStationaryCovarianceModel(sample)
tg = myCovarianceModel.getTimeGrid()
# Get the time grid of the model
for i in range(tg.getN()):
t = tg.getValue(i)
estimatedValue = myCovarianceModel(t)[0, 0]
modelValue = covModel(t)[0, 0]
print(
"Covariance C( %.6g" % t,
") : ",
" evaluation = %.6g" % estimatedValue,
" model = %.6g" % modelValue,
)
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