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#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Dimension of the input model
# Size of the TimeGrid
# dimension parameter
dimension = 1
# Amplitude values
amplitude = NumericalPoint(dimension, 1.00)
# Scale values
scale = NumericalPoint(dimension, 1.0)
size = 128
timeGrid = RegularGrid(0., 0.1, size)
# Cauchy model
model = ExponentialCauchy(amplitude, scale)
myProcess = SpectralNormalProcess(model, timeGrid)
# Create a sample of size N = 1000
sample = myProcess.getSample(1000)
# StationaryCovarianceModelFactory using default parameter - Factory
# initiate
myFactory = 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 = model.computeCovariance(t)[0, 0]
print("Covariance C( %.6g" % t, ") : ", " evaluation = %.6g" %
estimatedValue, " model = %.6g" % modelValue)
except:
import sys
print("t_StationaryCovarianceModelFactory_std.py",
sys.exc_info()[0], sys.exc_info()[1])
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