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#! /usr/bin/env python
import openturns as ot
ot.TESTPREAMBLE()
# Uncertain parameters
distribution = ot.Normal([1.0] * 3, [2.0] * 3, ot.CorrelationMatrix(3))
distribution.setName("Unnamed")
# Model
f = ot.SymbolicFunction(["x", "y", "z"], ["x-1.5*y+2*z"])
# Sampling
size = 100
inputSample = distribution.getSample(size)
outputSample = f(inputSample)
comparisonOperators = [ot.Less(), ot.LessOrEqual(), ot.Greater(), ot.GreaterOrEqual()]
threshold = 3.0
ot.ResourceMap.SetAsUnsignedInteger(
"SimulationSensitivityAnalysis-DefaultSampleMargin", 10
)
for i in range(4):
# Analysis based on an event
X = ot.RandomVector(distribution)
Y = ot.CompositeRandomVector(f, X)
event = ot.ThresholdEvent(Y, comparisonOperators[i], threshold)
algo = ot.SimulationSensitivityAnalysis(event, inputSample, outputSample)
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)
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