# Get a time series or a sample of time series
# myTimeSeries = gp.getRealization()
mySample = gp.getSample(1000)
mySegmentNumber = 10
myOverlapSize = 0.3
# Build a spectral model factory
myFactory = ot.WelchFactory(ot.Hanning(), mySegmentNumber, myOverlapSize)
# Estimation on a TimeSeries or on a ProcessSample
# myEstimatedModel_TS = myFactory.build(myTimeSeries)
myEstimatedModel_PS = myFactory.buildAsUserDefinedSpectralModel(mySample)
# Change the filtering window
myFactory.setFilteringWindows(ot.Hamming())
# Get the FFT algorithm
myFFT = myFactory.getFFTAlgorithm()
# Get the frequencyGrid
frequencyGrid = myEstimatedModel_PS.getFrequencyGrid()
# With the model, we want to compare values
# We compare values computed with theoritical values
plotSample = ot.Sample(frequencyGrid.getN(), 3)
# Loop of comparison ==> data are saved in plotSample
for k in range(frequencyGrid.getN()):
freq = frequencyGrid.getStart() + k * frequencyGrid.getStep()
plotSample[k, 0] = freq
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
filteringWindow = ot.Hamming()
numPoints = 512
data = ot.Sample(numPoints, 2)
for i in range(numPoints):
x = -0.1 + (1.2 * i) / (numPoints - 1.0)
data[i, 0] = x
data[i, 1] = filteringWindow(x)
graph = ot.Graph(str(filteringWindow), '$tau$', 'W', True)
graph.add(ot.Curve(data))
graph.setColors(['red'])
fig = plt.figure(figsize=(10, 4))
filtering_axis = fig.add_subplot(111)
View(graph, figure=fig, axes=[filtering_axis], add_legend=False)
