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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 = 10
timeGrid = ot.RegularGrid(0.0, 0.1, size)
# Absolute model
model = ot.AbsoluteExponential(scale, amplitude)
myProcess = ot.GaussianProcess(model, timeGrid)
# Create a Process sample of size N
N = 10000
sample = myProcess.getSample(N)
# NonStationaryCovarianceModelFactory using default parameter - Factory
# initiate
myFactory = ot.NonStationaryCovarianceModelFactory()
# Build a UserDefinedCovarianceModel using the Welch method
myCovarianceModel = myFactory.buildAsUserDefinedCovarianceModel(sample)
for i in range(size):
t = timeGrid.getValue(i)
for j in range(size):
s = timeGrid.getValue(j)
estimatedValue = myCovarianceModel(t, s)[0, 0]
modelValue = model(t, s)[0, 0]
print(
"Covariance C( %.6g" % t,
", %.6g" % s,
") : ",
" evaluation = %.6g" % estimatedValue,
" model = %.6g" % modelValue,
)
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