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
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
|
#! /usr/bin/env python
from __future__ import print_function
from openturns import *
TESTPREAMBLE()
RandomGenerator.SetSeed(0)
try:
# Uncertain parameters
distribution = Normal(
NumericalPoint(3, 1.0), NumericalPoint(3, 2.0), CorrelationMatrix(3))
distribution.setName("Unnamed")
# Model
inputVar = Description(["x", "y", "z"])
formulas = Description(["x-1.5*y+2*z"])
outputVar = Description(["out"])
f = NumericalMathFunction(inputVar, formulas)
# Must activate the history mechanism if one want to perform sensitivity
# analysis
f.enableHistory()
# Sampling
size = 100
inputSample = distribution.getSample(size)
outputSample = f(inputSample)
comparisonOperators = [Less(), LessOrEqual(), Greater(), GreaterOrEqual()]
threshold = 3.0
ResourceMap.SetAsUnsignedInteger(
"SimulationSensitivityAnalysis-DefaultSampleMargin", 10)
for i in range(4):
algo = SimulationSensitivityAnalysis(
inputSample, outputSample, distribution.getIsoProbabilisticTransformation(), comparisonOperators[i], threshold)
print("algo=", algo)
# Perform the analysis
print("Mean point in event domain=",
algo.computeMeanPointInEventDomain())
print("Importance factors at ", threshold,
" =", algo.computeImportanceFactors())
print("Importance factors at ", threshold / 2, " =",
algo.computeImportanceFactors(threshold / 2))
importanceFactorsGraph = algo.drawImportanceFactors()
print("importanceFactorsGraph=", importanceFactorsGraph)
# Importance factors evolution on probability scale
importanceFactorsRangeGraphProbability = algo.drawImportanceFactorsRange(
)
print("importanceFactorsRangeGraphProbability=",
importanceFactorsRangeGraphProbability)
# Importance factors evolution on threshold scale
importanceFactorsRangeGraphThreshold = algo.drawImportanceFactorsRange(
False)
print("importanceFactorsRangeGraphThreshold=",
importanceFactorsRangeGraphThreshold)
# Reset the history of the model
f.clearHistory()
# Analysis based on an event
X = RandomVector(distribution)
Y = RandomVector(f, X)
event = Event(Y, comparisonOperators[i], threshold)
# Get a sample of the event to simulate a Monte Carlo analysis. We
# don't care of the result as the interesting values are stored in the
# model history
event.getSample(size)
algo = SimulationSensitivityAnalysis(event)
print("algo=", algo)
# Perform the analysis
print("Mean point in event domain=",
algo.computeMeanPointInEventDomain())
print("Importance factors at threshold ", threshold,
" =", algo.computeImportanceFactors())
print("Importance factors at threshold/2 ", threshold / 2,
" =", algo.computeImportanceFactors(threshold / 2))
importanceFactorsGraph = algo.drawImportanceFactors()
print("importanceFactorsGraph=", importanceFactorsGraph)
# Importance factors evolution on probability scale
importanceFactorsRangeGraphProbability = algo.drawImportanceFactorsRange(
)
print("importanceFactorsRangeGraphProbability=",
importanceFactorsRangeGraphProbability)
# Importance factors evolution on threshold scale
importanceFactorsRangeGraphThreshold = algo.drawImportanceFactorsRange(
False)
print("importanceFactorsRangeGraphThreshold=",
importanceFactorsRangeGraphThreshold)
except:
import sys
print("t_SimulationSensitivityAnalysis_std.py",
sys.exc_info()[0], sys.exc_info()[1])
|
