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
53
54
55
56
57
58
59
60
61
62
63
64
|
#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Default dimension parameter to evaluate the model
defaultDimension = 1
# Amplitude values
amplitude = NumericalPoint(defaultDimension, 1.0)
# Scale values
scale = NumericalPoint(defaultDimension, 1.0)
# Second order model with parameters
myModel = ExponentialCauchy(amplitude, scale)
# checking the copy-cast*/
mySecondOrderModel = SecondOrderModel(myModel)
points = 8
tMin = 0.0
tStep = 1.0 / (points - 1)
# RegularGrid --> Build list of frequencies using the RegularGrid
myTimeGrid = RegularGrid(tMin, tStep, points)
mySpectralProcess0 = SpectralNormalProcess(myModel, myTimeGrid)
print("mySpectralProcess0 = ", mySpectralProcess0)
print("Realization = ", mySpectralProcess0.getRealization())
# Constructor using maximalFrequency value and size of discretization
maximalFrequency = 10.0
mySpectralProcess1 = SpectralNormalProcess(
myModel, maximalFrequency, points)
tg = RegularGrid(mySpectralProcess1.getTimeGrid())
print("mySpectralProcess1 = ", mySpectralProcess1)
print("Realization = ", mySpectralProcess1.getRealization())
# 3D model
highDimension = 3
amplitude = NumericalPoint(highDimension, 1.0)
# Second order model with parameters
mySpecModel = CauchyModel(amplitude, scale)
print("mySpecModel = ", mySpecModel)
mySpectralProcess2 = SpectralNormalProcess(mySpecModel, myTimeGrid)
print("mySpectralProcess2 = ", mySpectralProcess2)
print("Realization = ", mySpectralProcess2.getRealization())
mySpectralProcess3 = SpectralNormalProcess(
mySpecModel, maximalFrequency, points)
print("mySpectralProcess3 = ", mySpectralProcess3)
print("Realization = ", mySpectralProcess3.getRealization())
except:
import sys
print("t_SpectralNormalProcess_std.py",
sys.exc_info()[0], sys.exc_info()[1])
|
