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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
size = 64
dimension = 1
timeGrid = RegularGrid(0.0, 0.1, size)
amplitude = NumericalPoint(dimension, 2.0)
scale = NumericalPoint(dimension, 1.0)
model = ExponentialCauchy(amplitude, scale)
myProcess = SpectralNormalProcess(model, timeGrid)
# Create a Process sample
N = 100
sample = ProcessSample(myProcess.getSample(N))
# Filtering Windows
myFactory = WelchFactory()
# Build a UserDefinedSpectralModel using the Wellch method
mySpectralModel = myFactory.build(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.computeSpectralDensity(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.build(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.computeSpectralDensity(frequency)[i, j]).real
print("Frequency = %.6f" % frequency, ", evaluation = %.8f" %
estimatedValue, " model = %.8f" % modelValue)
except:
import sys
print("t_WelchFactory_std.py", sys.exc_info()[0], sys.exc_info()[1])
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