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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Dimension of the input model
# Size of the TimeGrid
size = 64
dimension = 1
timeGrid = ot.RegularGrid(0.0, 0.1, size)
amplitude = ot.Point(dimension, 2.0)
scale = ot.Point(dimension, 1.0)
model = ot.CauchyModel(scale, amplitude)
myProcess = ot.SpectralGaussianProcess(model, timeGrid)
# Create a Process sample
N = 100
sample = ot.ProcessSample(myProcess.getSample(N))
# Filtering Windows
myFactory = ot.WelchFactory()
# Build a UserDefinedSpectralModel using the Wellch method
mySpectralModel = myFactory.buildAsUserDefinedSpectralModel(sample)
# Get the frequency grid of the model
myFrequencyGrid = mySpectralModel.getFrequencyGrid()
for i in range(dimension):
for j in range(dimension):
print("Spectre ", i, "-", j)
for k in range(myFrequencyGrid.getN()):
frequency = myFrequencyGrid.getStart() + k * myFrequencyGrid.getStep()
estimatedValue = (mySpectralModel(frequency)[i, j]).real
modelValue = (model(frequency)[i, j]).real
print(
"Frequency = %.6f" % frequency,
", evaluation = %.8f" % estimatedValue,
" model = %.8f" % modelValue,
)
# Create a Time Series
timeSeries = myProcess.getRealization()
# Build a UserDefinedSpectralModel using the Wellch method
mySpectralModel2 = myFactory.buildAsUserDefinedSpectralModel(timeSeries)
# Get the frequency grid of the model
myFrequencyGrid = mySpectralModel2.getFrequencyGrid()
for i in range(dimension):
for j in range(dimension):
print("Spectre ", i, "-", j)
for k in range(myFrequencyGrid.getN()):
frequency = myFrequencyGrid.getStart() + k * myFrequencyGrid.getStep()
estimatedValue = (mySpectralModel2(frequency)[i, j]).real
modelValue = (model(frequency)[i, j]).real
print(
"Frequency = %.6f" % frequency,
", evaluation = %.8f" % estimatedValue,
" model = %.8f" % modelValue,
)
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