1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
|
#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Default dimension parameter to evaluate the model
dimension = 1
spatialDimension = 1
# Amplitude values
amplitude = NumericalPoint(dimension, 1.0)
# Scale values
scale = NumericalPoint(dimension, 1.0)
# Covariance model
myCovarianceModel = ExponentialModel(spatialDimension, amplitude, scale)
print("myCovarianceModel = ", myCovarianceModel)
# Spectral model
mySpectralModel = CauchyModel(amplitude, scale)
print("mySpectralModel = ", mySpectralModel)
# We build the second order model using covariance and spectral models
myModel = SecondOrderModel(myCovarianceModel, mySpectralModel)
print("myModel = ", myModel)
# Some computations ==> call the sub models methods
instant = 1.0
frequency = 0.5
#
print("covariance matrix at t = ", instant,
" : ", myModel.computeCovariance(instant))
print("covariance matrix at t = ", -1.0 * instant,
" : ", myModel.computeCovariance(-1.0 * instant))
print("spectral density at f = ", frequency, " : ",
myModel.computeSpectralDensity(frequency))
# Discretize the process on a small time grid
timeGrid = RegularGrid(0.0, 1.0, 11)
print("discretized covariance over the time grid = ", timeGrid, " is ")
print(myModel.discretize(timeGrid))
except:
import sys
print("t_SecondOrderModel_std.py", sys.exc_info()[0], sys.exc_info()[1])
|
