def test_DrawSampleMultidimensional(self):
problem = otbenchmark.ReliabilityProblem33()
event = problem.getEvent()
sampleSize = 500
drawEvent = otbenchmark.DrawEvent(event)
# Avoid failing on CircleCi
# _tkinter.TclError: no display name and no $DISPLAY environment variable
try:
_ = drawEvent.drawSample(sampleSize)
except Exception as e:
print(e)
def test_ReliabilityBenchmarkProblem33(self):
problem = otb.ReliabilityProblem33()
print(problem)
# Check probability
pf = problem.getProbability()
pf_exacte = 0.00257
np.testing.assert_allclose(pf, pf_exacte, rtol=1.0e-15)
# Check function
event = problem.getEvent()
function = event.getFunction()
X = [0.0, 0.0, 0.0]
Y = function(X)
assert type(Y) is ot.Point
np.testing.assert_allclose(Y[0], 3)
def test_DrawLimitStateMultidimensional(self):
problem = otbenchmark.ReliabilityProblem33()
event = problem.getEvent()
inputVector = event.getAntecedent()
distribution = inputVector.getDistribution()
alpha = 1.0 - 1.0e-2
(
bounds,
marginalProb,
) = distribution.computeMinimumVolumeIntervalWithMarginalProbability(alpha)
# bounds
drawEvent = otbenchmark.DrawEvent(event)
# Avoid failing on CircleCi
# _tkinter.TclError: no display name and no $DISPLAY environment variable
try:
_ = drawEvent.drawLimitState(bounds)
except Exception as e:
print(e)
def test_CrossCutFunction(self):
problem = otbenchmark.ReliabilityProblem33()
event = problem.getEvent()
g = event.getFunction()
inputVector = event.getAntecedent()
distribution = inputVector.getDistribution()
alpha = 1 - 0.00001
(
bounds,
marginalProb,
) = distribution.computeMinimumVolumeIntervalWithMarginalProbability(
alpha)
referencePoint = distribution.getMean()
crossCut = otbenchmark.CrossCutFunction(g, referencePoint)
try:
_ = crossCut.draw(bounds)
except Exception as e:
print(e)
def test_UseCase(self):
problem = otb.ReliabilityProblem33()
event = problem.getEvent()
# Create a Monte Carlo algorithm
experiment = ot.MonteCarloExperiment()
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
algo.setMaximumCoefficientOfVariation(0.05)
algo.setMaximumOuterSampling(int(1e3))
algo.run()
# Retrieve results
result = algo.getResult()
computed_pf = result.getProbabilityEstimate()
exact_pf = problem.getProbability()
print("exact_pf=", exact_pf)
print("computed_pf=", computed_pf)
samplesize = result.getOuterSampling() * result.getBlockSize()
print("Sample size : ", samplesize)
atol = 1.0 / np.sqrt(samplesize)
np.testing.assert_allclose(computed_pf, exact_pf, atol=atol)
def ReliabilityBenchmarkProblemList():
"""
Returns the list of reliability benchmark problems.
Returns
-------
problems : list
A list of ReliabilityProblem.
"""
p8 = otb.ReliabilityProblem8()
p14 = otb.ReliabilityProblem14()
p22 = otb.ReliabilityProblem22()
p24 = otb.ReliabilityProblem24()
p25 = otb.ReliabilityProblem25()
p28 = otb.ReliabilityProblem28()
p31 = otb.ReliabilityProblem31()
p33 = otb.ReliabilityProblem33()
p35 = otb.ReliabilityProblem35()
p38 = otb.ReliabilityProblem38()
p53 = otb.ReliabilityProblem53()
p55 = otb.ReliabilityProblem55()
p54 = otb.ReliabilityProblem54()
p57 = otb.ReliabilityProblem57()
p75 = otb.ReliabilityProblem75()
p89 = otb.ReliabilityProblem89()
p107 = otb.ReliabilityProblem107()
p110 = otb.ReliabilityProblem110()
p111 = otb.ReliabilityProblem111()
p63 = otb.ReliabilityProblem63()
p91 = otb.ReliabilityProblem91()
p60 = otb.ReliabilityProblem60()
p77 = otb.ReliabilityProblem77()
pFBS = otb.FourBranchSerialSystemReliability()
pRS = otb.RminusSReliability()
pBeam = otb.AxialStressedBeamReliability()
problemslist = [
p8,
p14,
p22,
p24,
p25,
p28,
p31,
p33,
p35,
p38,
p53,
p55,
p54,
p57,
p75,
p89,
p107,
p110,
p111,
p63,
p91,
p60,
p77,
pFBS,
pRS,
pBeam,
]
return problemslist
