def getXEvent(b, t, mu_S, covariance, R, delta_t):
full = buildCrossing(b, t, mu_S, covariance, R, delta_t)
X = ot.RandomVector(full)
f1 = ot.SymbolicFunction(["R", "X1", "X2"], ["X1 - R"])
e1 = ot.ThresholdEvent(ot.CompositeRandomVector(f1, X), ot.Less(), 0.0)
f2 = ot.SymbolicFunction(["R", "X1", "X2"], ["X2 - R"])
e2 = ot.ThresholdEvent(ot.CompositeRandomVector(f2, X),
ot.GreaterOrEqual(), 0.0)
event = ot.IntersectionEvent([e1, e2])
return X, event
def test_DrawFilledEvent(self):
# Distribution
R = ot.Normal(4.0, 1.0)
R.setDescription("R")
S = ot.Normal(2.0, 1.0)
S.setDescription("S")
g = ot.SymbolicFunction(["R", "S"], ["R - S"])
# Event
distribution = ot.ComposedDistribution([R, S])
inputRV = ot.RandomVector(distribution)
outputRV = ot.CompositeRandomVector(g, inputRV)
eventF = ot.ThresholdEvent(outputRV, ot.GreaterOrEqual(), 0)
alpha = 1.0 - 1.0e-2
(
bounds,
marginalProb,
) = distribution.computeMinimumVolumeIntervalWithMarginalProbability(
alpha)
#
drawEvent = otbenchmark.DrawEvent(eventF)
# Avoid failing on CircleCi
# _tkinter.TclError: no display name and no $DISPLAY environment variable
try:
_ = drawEvent.fillEvent(bounds)
except Exception as e:
print(e)
def test_ReliabilityBenchmarkProblem(self):
limitStateFunction = ot.SymbolicFunction(["R", "S"], ["R - S"])
muR = 4.0
sigmaR = 1.0
muS = 2.0
sigmaS = 1.0
threshold = 0.0
R = ot.Normal(muR, sigmaR)
S = ot.Normal(muS, sigmaS)
myDistribution = ot.ComposedDistribution([R, S])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "R-S"
probability = 0.123456789
problem = otb.ReliabilityBenchmarkProblem(name, thresholdEvent,
probability)
#
print(problem)
print(problem.toFullString())
p = problem.getProbability()
assert p == probability
s = problem.getName()
assert s == name
def create_monte_carlo(model, inputRandomVector, coefficient_variation):
"""Create a Monte Carlo algorithm.
Parameters
----------
model : OpenTURNS Function.
inputRandomVector : OpenTURNS RandomVector, vector of random inputs.
coefficient_variation : Float, target for the coefficient of variation of
the estimator.
"""
outputVariableOfInterest = ot.CompositeRandomVector(model, inputRandomVector)
# Create an Event from this RandomVector
threshold = 30
myEvent = ot.ThresholdEvent(outputVariableOfInterest, ot.Greater(), threshold)
myEvent.setName("Deviation > %g cm" % threshold)
# Create a Monte Carlo algorithm
experiment = ot.MonteCarloExperiment()
myAlgoMonteCarlo = ot.ProbabilitySimulationAlgorithm(myEvent, experiment)
myAlgoMonteCarlo.setBlockSize(100)
myAlgoMonteCarlo.setMaximumCoefficientOfVariation(coefficient_variation)
return myAlgoMonteCarlo
def __init__(self, threshold=0.0, expo=1):
"""
Creates a reliability problem RP54.
The event is {g(X) < threshold} where
X = (x1, x2, ...., x20)
g(X) = (x1 + x2 + ... + x20) - 8.951
Parameters
----------
threshold : float
The threshold.
expo : float
Rate parameter of the Xi Exponential distribution
for i in {1, 2, ..., 20}.
"""
formula = "x1+x2+x3+x4+x5+x6+x7+x8+x9+x10"
formula = formula + " + x11+x12+x13+x14+x15+x16+x17+x18+x19+x20-8.951"
print(formula)
limitStateFunction = ot.SymbolicFunction(
[
"x1",
"x2",
"x3",
"x4",
"x5",
"x6",
"x7",
"x8",
"x9",
"x10",
"x11",
"x12",
"x13",
"x14",
"x15",
"x16",
"x17",
"x18",
"x19",
"x20",
],
[formula],
)
X = [ot.Exponential(expo) for i in range(20)]
myDistribution = ot.ComposedDistribution(X)
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(
limitStateFunction, inputRandomVector
)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(), threshold)
name = "RP54"
probability = 0.000998
super(ReliabilityProblem54, self).__init__(name, thresholdEvent, probability)
return None
def __init__(self, threshold=0.0, mu=[0.0] * 2, sigma=[1.0] * 2):
"""
Creates a reliability problem RP35.
The event is {g(X) < threshold} where
g(x1, x2) = min(g1, g2) with
g1 = 2 - x2 + exp(-0.1 * x1^2) + (0.2 * x1) ^ 4
g2 = 4.5 - x1 * x2
We have x1 ~ Normal(mu[0], sigma[0]) and x2 ~ Normal(mu[1], sigma[1]).
Parameters
----------
threshold : float
The threshold.
mu : sequence of floats
The list of two items representing the means of the gaussian distributions.
sigma : float
The list of two items representing the standard deviations of
the gaussian distributions.
"""
equations = ["var g1 := 2 - x2 + exp(-0.1 * x1^2) + (0.2 * x1) ^ 4"]
equations.append("var g2 := 4.5 - x1 * x2")
equations.append("gsys := min(g1, g2)")
formula = ";".join(equations)
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], ["gsys"],
formula)
inputDimension = len(mu)
if inputDimension != 2:
raise Exception(
"The dimension of mu is %d, but the expected dimension is 2." %
(inputDimension))
inputDimension = len(sigma)
if inputDimension != 2:
raise Exception(
"The dimension of sigma is %d, but the expected dimension is 2."
% (inputDimension))
X1 = ot.Normal(mu[0], sigma[0])
X1.setDescription(["X1"])
X2 = ot.Normal(mu[1], sigma[1])
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP35"
probability = 0.00354
super(ReliabilityProblem35, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self, threshold=0.0, muR=4.0, sigmaR=1.0, muS=2.0, sigmaS=1.0):
"""
Create a R-S reliability problem.
The event is {g(X) < threshold} where
g(R, S) = R - S
We have R ~ Normal(muR, sigmaR) and S ~ Normal(muS, sigmaS).
Waarts (2000) uses muR = 7.0 and muS = 2.0 leading to
beta = 3.54.
Parameters
----------
threshold : float
The threshold.
muR : float
The mean of the R gaussian distribution.
sigmaR : float
The standard deviation of the R gaussian distribution.
muS : float
The mean of the S gaussian distribution.
sigmaS : float
The standard deviation of the S gaussian distribution.
Example
-------
problem = RminusSReliabilityBenchmarkProblem()
"""
limitStateFunction = ot.SymbolicFunction(["R", "S"], ["R - S"])
R = ot.Normal(muR, sigmaR)
R.setDescription("R")
S = ot.Normal(muS, sigmaS)
S.setDescription("S")
myDistribution = ot.ComposedDistribution([R, S])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(
limitStateFunction, inputRandomVector
)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(), threshold)
name = "R-S"
diff = R - S
probability = diff.computeCDF(threshold)
super(RminusSReliabilityBenchmarkProblem, self).__init__(
name, thresholdEvent, probability
)
return None
def __init__(self,
threshold=0.0,
mu1=1.5,
sigma1=1.0,
mu2=2.5,
sigma2=1.0):
"""
Creates a reliability problem RP53.
The event is {g(X) < threshold} where
g(X1, X2) = sin(5 * X1 / 2) + 2 - (X1^2 + 4) * (X2 - 1) / 20
We have X1 ~ Normal(mu1, sigma1) and X2 ~ Normal(mu2, sigma2).
Parameters
----------
threshold : float
The threshold.
mu1 : float
The mean of the X1 gaussian distribution.
sigma1 : float
The standard deviation of the X1 gaussian distribution.
mu2 : float
The mean of the X2 gaussian distribution.
sigma2 : float
The standard deviation of the X2 gaussian distribution.
"""
formula = "sin(5 * x1 / 2) + 2 - ( x1 * x1 + 4 ) * ( x2 - 1 ) / 20"
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], [formula])
print("sin(5 * x1 / 2) + 2 - ( x1 * x1 + 4 ) * ( x2 - 1 ) / 20")
X1 = ot.Normal(mu1, sigma1)
X1.setDescription(["X1"])
X2 = ot.Normal(mu2, sigma2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP53"
probability = 0.0313
super(ReliabilityProblem53, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self,
threshold=0.0,
mu1=0.0,
sigma1=1.0,
mu2=0.0,
sigma2=1.0):
"""
Creates a reliability problem RP25.
The event is {g(X) < threshold} where
---
g(x1, x2) = max(x1^2 -8 * x2 + 16, -16 * x1 + x2 + 32)
---
We have x1 ~ Normal(mu1, sigma1) and x2 ~ Normal(mu2, sigma2).
---
Parameters
----------
threshold : float
The threshold.
***
mu1 : float
The mean of the X1 gaussian distribution.
***
sigma1 : float
The standard deviation of the X1 gaussian distribution.
***
mu2 : float
The mean of the X2 gaussian distribution.
***
sigma2 : float
The standard deviation of the X2 gaussian distribution.
"""
equations = ["var g1 := x1^2 -8 * x2 + 16"]
equations.append("var g2 := -16 * x1 + x2 + 32")
equations.append("gsys := max(g1, g2)")
formula = ";".join(equations)
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], ["gsys"],
formula)
print(formula)
X1 = ot.Normal(mu1, sigma1)
X1.setDescription(["X1"])
X2 = ot.Normal(mu2, sigma2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP25"
probability = 0.00000614
super(ReliabilityProblem25, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self,
threshold=0.0,
mu1=0.0,
sigma1=1.0,
mu2=0.0,
sigma2=1.0):
"""
Creates a reliability problem RP57.
The event is {g(X) < threshold} where
g(x1, x2) = min(max(g1, g2), g3) with
g1 = -x1^2 + x2^3 + 3
g2 = 2 - x1 - 8 * x2
g3 = (x1 + 3)^2 + (x2 + 3)^2 - 4
We have x1 ~ Normal(mu1, sigma1) and x2 ~ Normal(mu2, sigma2).
***
Parameters
----------
threshold : float
The threshold.
mu1 : float
The mean of the X1 gaussian distribution.
sigma1 : float
The standard deviation of the X1 gaussian distribution.
mu2 : float
The mean of the X2 gaussian distribution.
sigma2 : float
The standard deviation of the X2 gaussian distribution.
"""
equations = ["var g1 := -x1^2 + x2^3 + 3"]
equations.append("var g2 := 2 - x1 - 8 * x2")
equations.append("var g3 := (x1 + 3)^2 + (x2 + 3)^2 - 4")
equations.append("gsys := min(max(g1, g2), g3) ")
formula = ";".join(equations)
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], ["gsys"],
formula)
print(formula)
X1 = ot.Normal(mu1, sigma1)
X1.setDescription(["X1"])
X2 = ot.Normal(mu2, sigma2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP57"
probability = 0.0284
super(ReliabilityProblem57, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self, threshold=0.0, mu=[10.0] * 2, sigma=[3.0] * 2):
"""
Creates a reliability problem RP24.
The event is {g(X) < threshold} where
g(x1, x2) = 2.5 - 0.2357 * (x1 - x2) + 0.00463 * (x1 + x2 - 20)^4
We have x1 ~ Normal(mu[0], sigma[0]) and x2 ~ Normal(mu[1], sigma[1]).
Parameters
----------
threshold : float
The threshold.
mu : sequence of floats
The list of two items representing the means of the gaussian distributions.
sigma : float
The list of two items representing the standard deviations of
the gaussian distributions.
"""
formula = "2.5 - 0.2357 * (x1 - x2) + 0.00463 * (x1 + x2 - 20)^4"
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], [formula])
inputDimension = len(mu)
if inputDimension != 2:
raise Exception(
"The dimension of mu is %d, but the expected dimension is 2." %
(inputDimension))
inputDimension = len(sigma)
if inputDimension != 2:
raise Exception(
"The dimension of sigma is %d, but the expected dimension is 2."
% (inputDimension))
X1 = ot.Normal(mu[0], sigma[0])
X1.setDescription(["X1"])
X2 = ot.Normal(mu[1], sigma[1])
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP24"
probability = 0.00286
super(ReliabilityProblem24, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self,
threshold=146.14,
mu1=78064.0,
sigma1=11710.0,
mu2=0.0104,
sigma2=0.00156):
"""
Creates a reliability problem RP28.
The event is {g(X) < threshold} where
g(x1, x2) = x1 * x2 - threshold
with threshold = 146.14.
We have x1 ~ Normal(mu1, sigma1) and x2 ~ Normal(mu2, sigma2).
Parameters
----------
threshold : float
The threshold.
mu1 : float
The mean of the X1 gaussian distribution.
sigma1 : float
The standard deviation of the X1 gaussian distribution.
mu2 : float
The mean of the X2 gaussian distribution.
sigma2 : float
The standard deviation of the X2 gaussian distribution.
"""
formula = "x1 * x2"
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], [formula])
X1 = ot.Normal(mu1, sigma1)
X1.setDescription(["X1"])
X2 = ot.Normal(mu2, sigma2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP28"
Y = X1 * X2
probability = Y.computeCDF(threshold)
super(ReliabilityProblem28, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self):
"""
Creates the four-branch serial system from Waarts.
References
----------
Waarts, P.-H. (2000). Structural reliability using finite element
methods: an appraisal of DARS: Directional Adaptive Response Surface
Sampling. Ph. D. thesis, Technical University of Delft, The
Netherlands. Pages 58, 69, 160.
Thèse Vincent Dubourg 2011, Méta-modèles adaptatifs pour l’analyse
de fiabilité et l’optimisation sous contrainte fiabiliste,
section "A two-dimensional four-branch serial system", page 182
Parameters
----------
None.
Example
-------
problem = FourBranchSerialSystemReliabilityBenchmarkProblem()
"""
formulaList = [
"var y0 := 3 + 0.1 * (x0 - x1)^2 - (x0 + x1) / sqrt(2)",
"var y1 := 3 + 0.1 * (x0 - x1)^2 + (x0 + x1) / sqrt(2)",
"var y2 := x0 - x1 + 7 / sqrt(2)",
"var y3 := x1 - x0 + 7 / sqrt(2)",
"y := min(y0,y1,y2,y3)",
]
formula = ";".join(formulaList)
limitStateFunction = ot.SymbolicFunction(["x0", "x1"], ["y"], formula)
x0 = ot.Normal(0.0, 1.0)
x1 = ot.Normal(0.0, 1.0)
inputDistribution = ot.ComposedDistribution((x0, x1))
inputRandomVector = ot.RandomVector(inputDistribution)
outputRandomVector = ot.CompositeRandomVector(
limitStateFunction, inputRandomVector
)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(), 0.0)
name = "Four-branch serial system (Waarts, 2000)"
beta = 2.85
probability = ot.Normal().computeComplementaryCDF(beta)
super(FourBranchSerialSystemReliabilityBenchmarkProblem, self).__init__(
name, thresholdEvent, probability
)
return None
def __init__(self, threshold=0.0):
"""
Create a axial stressed beam reliability problem.
The inputs are R, the Yield strength, and F, the traction load.
The event is {g(X) < threshold} where
g(R, F) = R - F/(pi_ * 100.0)
We have R ~ LogNormalMuSigma() and F ~ Normal().
Parameters
----------
threshold : float
The threshold.
Example
-------
problem = AxialStressedBeamReliability()
"""
limitStateFunction = ot.SymbolicFunction(["R", "F"], ["R - F/(pi_ * 100.0)"])
R_dist = ot.LogNormalMuSigma(300.0, 30.0, 0.0).getDistribution()
R_dist.setName("Yield strength")
R_dist.setDescription("R")
F_dist = ot.Normal(75000.0, 5000.0)
F_dist.setName("Traction load")
F_dist.setDescription("F")
myDistribution = ot.ComposedDistribution([R_dist, F_dist])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(
limitStateFunction, inputRandomVector
)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(), 0.0)
name = "Axial stressed beam"
diff = R_dist - F_dist / (np.pi * 100.0)
probability = diff.computeCDF(threshold)
super(AxialStressedBeamReliability, self).__init__(
name, thresholdEvent, probability
)
return None
def __init__(self,
threshold=0.0,
mu1=10.0,
sigma1=3.0,
mu2=10.0,
sigma2=3.0):
"""
Creates a reliability problem RP24.
The event is {g(X) < threshold} where
g(x1, x2) = 2.5 - 0.2357 * (x1 - x2) + 0.00463 * (x1 + x2 - 20)^4
We have x1 ~ Normal(mu1, sigma1) and x2 ~ Normal(mu2, sigma2).
***
Parameters
----------
threshold : float
The threshold.
mu1 : float
The mean of the X1 gaussian distribution.
sigma1 : float
The standard deviation of the X1 gaussian distribution.
mu2 : float
The mean of the X2 gaussian distribution.
sigma2 : float
The standard deviation of the X2 gaussian distribution.
"""
formula = "2.5 - 0.2357 * (x1 - x2) + 0.00463 * (x1 + x2 - 20)^4"
limitStateFunction = ot.SymbolicFunction(["x1", "x2"], [formula])
print(formula)
X1 = ot.Normal(mu1, sigma1)
X1.setDescription(["X1"])
X2 = ot.Normal(mu2, sigma2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP24"
probability = 0.00286
super(ReliabilityProblem24, self).__init__(name, thresholdEvent,
probability)
return None
def _runMonteCarlo(self, defect):
# set a parametric function where the first parameter = given defect
g = ot.ParametricFunction(self._metamodel, [0], [defect])
g = ot.MemoizeFunction(g)
g.enableHistory()
g.clearHistory()
g.clearCache()
output = ot.CompositeRandomVector(g,
ot.RandomVector(self._distribution))
event = ot.ThresholdEvent(output, ot.Greater(), self._detectionBoxCox)
##### Monte Carlo ########
algo_MC = ot.ProbabilitySimulationAlgorithm(event)
algo_MC.setMaximumOuterSampling(self._samplingSize)
# set negative coef of variation to be sure the stopping criterion is the sampling size
algo_MC.setMaximumCoefficientOfVariation(-1)
algo_MC.run()
return algo_MC.getResult()
def __init__(self, threshold=0.0, a1=-1.0, b1=1.0, a2=-1.0, b2=1.0):
"""
Creates a reliability problem RP55.
The event is {g(X) < threshold} where
We have x1 ~ Uniform(a1, b1) and x2 ~ Uniform(a2, b2).
***
Parameters
----------
threshold : float
The threshold.
a1 , b1 : float
Parameters of the X1 uniform distribution.
a2 , b2 : float
Parameters of the X2 uniform distribution.
"""
equations = ["var g1 := 0.2 + 0.6 * (x0 - x1)^4 - (x0 - x1) / sqrt(2)"]
equations.append(
"var g2 := 0.2 + 0.6 * (x0 - x1)^4 + (x0 - x1) / sqrt(2)")
equations.append("var g3 := (x0 - x1) + 5 / sqrt(2) - 2.2")
equations.append("var g4 := (x1 - x0) + 5 / sqrt(2) - 2.2")
equations.append("gsys := min(g1, g2, g3, g4)")
formula = ";".join(equations)
limitStateFunction = ot.SymbolicFunction(["x0", "x1"], ["gsys"],
formula)
print(formula)
X1 = ot.Uniform(a1, b1)
X1.setDescription(["X1"])
X2 = ot.Uniform(a2, b2)
X2.setDescription(["X2"])
myDistribution = ot.ComposedDistribution([X1, X2])
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP55"
probability = 0.36
super(ReliabilityProblem55, self).__init__(name, thresholdEvent,
probability)
return None
def __init__(self, threshold=0.0, mu=0, sigma=1):
"""
Creates a reliability problem RP107.
The event is {g(X) < threshold} where
X = (x1, x2, ...., x10)
g(X) = 5*sqrt(10) -(x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10).
We have xi ~ Normal(0, 1) for i in {1, 2, ...,10}
Parameters
----------
threshold : float
The threshold.
mu : float
The mean of the Xi Normal distribution for i in {1, 2, ..., 10}.
sigma : float
The standard deviation of the Xi Normal distribution
for i in {1, 2, ..., 10}.
"""
formula = "5*sqrt(10)-(x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10)"
limitStateFunction = ot.SymbolicFunction(
["x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8", "x9", "x10"],
[formula])
X = [ot.Normal(mu, sigma) for i in range(10)]
myDistribution = ot.ComposedDistribution(X)
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(limitStateFunction,
inputRandomVector)
thresholdEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(),
threshold)
name = "RP107"
probability = 0.000000292
super(ReliabilityProblem107, self).__init__(name, thresholdEvent,
probability)
return None
def test_DrawSample(self):
# Distribution
R = ot.Normal(4.0, 1.0)
R.setDescription("R")
S = ot.Normal(2.0, 1.0)
S.setDescription("S")
g = ot.SymbolicFunction(["R", "S"], ["R - S"])
# Event
distribution = ot.ComposedDistribution([R, S])
inputRV = ot.RandomVector(distribution)
outputRV = ot.CompositeRandomVector(g, inputRV)
eventF = ot.ThresholdEvent(outputRV, ot.GreaterOrEqual(), 0)
#
sampleSize = 500
drawEvent = otbenchmark.DrawEvent(eventF)
_ = drawEvent.drawSampleCrossCut(sampleSize, 0, 1)
# Avoid failing on CircleCi
# _tkinter.TclError: no display name and no $DISPLAY environment variable
try:
_ = drawEvent.drawSample(sampleSize)
except Exception as e:
print(e)
# %%
# We consider three functions `f1`, `f2` and `f3` :
f1 = ot.SymbolicFunction(['x0', 'x1'], ['x0'])
f2 = ot.SymbolicFunction(['x0', 'x1'], ['x1'])
f3 = ot.SymbolicFunction(['x0', 'x1'], ['x0+x1'])
# %%
# We build :class:`~openturns.CompositeRandomVector` from these functions and the initial distribution.
Y1 = ot.CompositeRandomVector(f1, X)
Y2 = ot.CompositeRandomVector(f2, X)
Y3 = ot.CompositeRandomVector(f3, X)
# %%
# We define three basic events :math:`E_1=\{(x_0,x_1)~:~x_0 < 0 \}`, :math:`E_2=\{(x_0,x_1)~:~x_1 > 0 \}` and :math:`E_3=\{(x_0,x_1)~:~x_0+x_1>0 \}`.
e1 = ot.ThresholdEvent(Y1, ot.Less(), 0.0)
e2 = ot.ThresholdEvent(Y2, ot.Greater(), 0.0)
e3 = ot.ThresholdEvent(Y3, ot.Greater(), 0.0)
# %%
# The restriction of the domain :math:`E_1` to :math:`[-4,4] \times [-4, 4]` is the grey area.
myGraph = ot.Graph(r'Representation of the event $E_1$', r'$x_1$', r'$x_2$',
True, '')
data = [[-4, -4], [0, -4], [0, 4], [-4, 4]]
myPolygon = ot.Polygon(data)
myPolygon.setColor('grey')
myPolygon.setEdgeColor('black')
myGraph.add(myPolygon)
view = otv.View(myGraph)
axes = view.getAxes()
_ = axes[0].set_xlim(-4.0, 4.0)
# %%
g = ot.MemoizeFunction(g)
# %%
# Create the output random vector :math:`Y = g(X)`:
# %%
Y = ot.CompositeRandomVector(g, X)
# %%
# Create the event :math:`\{ Y = g(X) \leq 0 \}`
# ----------------------------------------------
# %%
myEvent = ot.ThresholdEvent(Y, ot.LessOrEqual(), 0.0)
# %%
# Evaluate the probability with the subset sampling technique
# -----------------------------------------------------------
# %%
algo = ot.SubsetSampling(myEvent)
# %%
# In order to get all the inputs and outputs that realize the event, you have to mention it now:
# %%
algo.setKeepEventSample(True)
# %%
ot.Interval([low] * 2, [up] * 2, [False, True], [True, False]),
ot.Interval([low] * 2, [up] * 2, [False, True], [False, True]),
ot.Interval([low] * 2, [up] * 2, [False, True], [False, False]),
ot.Interval([low] * 2, [up] * 2, [False, False], [True, True]),
ot.Interval([low] * 2, [up] * 2, [False, False], [True, False]),
ot.Interval([low] * 2, [up] * 2, [False, False], [False, True]),
ot.Interval([low] * 2, [up] * 2, [False, False], [False, False])
]
for domain in intervals:
print('#' * 50)
print('domain=\n', domain)
outDim = domain.getDimension()
f = ot.SymbolicFunction(inVars, inVars[0:outDim])
Y = ot.CompositeRandomVector(f, X)
event = ot.ThresholdEvent(Y, domain)
ot.RandomGenerator.SetSeed(0)
# algo = getattr(openturns, algoName)(event)
algo = ot.ProbabilitySimulationAlgorithm(event, ot.MonteCarloExperiment())
algo.run()
res = algo.getResult().getProbabilityEstimate()
print('MC p=%.6g' % res)
ot.RandomGenerator.SetSeed(0)
# algo = getattr(openturns, algoName)(event)
algo = ot.FORM(ot.Cobyla(), event, X.getMean())
algo.run()
res = algo.getResult().getEventProbability()
print('FORM p=%.2f' % res)
# %%
# We use the input parameters distribution from the data class :
distribution = cb.distribution
distribution.setDescription(['E', 'F', 'L', 'I'])
# %%
# We define the model
model = cb.model
# %%
# Create the event whose probability we want to estimate.
# %%
vect = ot.RandomVector(distribution)
G = ot.CompositeRandomVector(model, vect)
event = ot.ThresholdEvent(G, ot.Greater(), 0.3)
event.setName("deviation")
# %%
# Define a solver
optimAlgo = ot.Cobyla()
optimAlgo.setMaximumEvaluationNumber(1000)
optimAlgo.setMaximumAbsoluteError(1.0e-10)
optimAlgo.setMaximumRelativeError(1.0e-10)
optimAlgo.setMaximumResidualError(1.0e-10)
optimAlgo.setMaximumConstraintError(1.0e-10)
# %%
# Run FORM
algo = ot.FORM(optimAlgo, event, distribution.getMean())
algo.run()
mycopula = ot.NormalCopula(R)
# %%
# And we endly create the composed input probability distribution.
inputDistribution = ot.ComposedDistribution([E, F, L, I], mycopula)
inputDistribution.setDescription(("E", "F", "L", "I"))
# %%
# Create the event whose probability we want to estimate:
inputRandomVector = ot.RandomVector(inputDistribution)
outputVariableOfInterest = ot.CompositeRandomVector(model_fmu,
inputRandomVector)
threshold = 30
event = ot.ThresholdEvent(outputVariableOfInterest, ot.Greater(), threshold)
event.setName("Deviation > %g cm" % threshold)
# %%
# Parameterize and run the Monte Carlo algorithm:
ot.RandomGenerator.SetSeed(23091926) # set seed for reproducibility
experiment = ot.MonteCarloExperiment()
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
algo.setMaximumOuterSampling(200)
algo.setMaximumCoefficientOfVariation(0.2)
algo.run()
# %%
# Draw the distribution of threshold excedance probability:
matrix = ot.Matrix(2, 3)
matrix[0, 0] = 0
matrix[0, 1] = 1
matrix[0, 2] = 2
matrix[1, 0] = 3
matrix[1, 1] = 4
matrix[1, 2] = 5
myStudy.add('m', matrix)
# Create a Point that we will try to reinstaciate after reloading
point = ot.Point(2, 1000.)
point.setName('point')
myStudy.add('point', point)
# Create a Simulation::Result
simulationResult = ot.ProbabilitySimulationResult(ot.ThresholdEvent(), 0.5,
0.01, 150, 4)
myStudy.add('simulationResult', simulationResult)
cNameList = [
'LHS', 'DirectionalSampling', 'SimulationSensitivityAnalysis',
'ProbabilitySimulationAlgorithm'
]
for cName in cNameList:
otClass = getattr(ot, cName)
instance = otClass()
print('--', cName, instance)
myStudy.add(cName, instance)
# Create a Beta distribution
beta = ot.Beta(3.0, 2.0, -1.0, 4.0)
# %%
# We want to estimate the probability :math:`P_f` of the output variable to be greater than a prescribed threshold :math:`s=10` : this is the failure event. This probability is simply expressed as an integral :
#
# .. math::
#
# P_f = \int_{\mathcal{D}} \mathbf{1}_{\mathcal{D}}(x) df_{X_1,X_2}(x)
#
# where :math:`\mathcal{D} = \{ (x_1, x_2) \in [0,+\infty[ \times \mathbb{R} / x_1 x_2 \geq s \}` is the failure domain.
# In the general case the probability density function :math:`f_{X_1,X_2}` and the domain of integration :math:`\mathcal{D}` are difficult to handle.
# %%
# We first define RandomVector objects and the failure event associated to the ouput random variable.
vectorX = ot.RandomVector(distX)
vectorY = ot.CompositeRandomVector(f, vectorX)
s = 10.0
event = ot.ThresholdEvent(vectorY, ot.Greater(), s)
# %%
# This event can easily be represented with a 1D curve as it is a branch of an hyperbole.
# If :math:`y = x_1 x_2 = 10.0`, then the boundary of the domain of failure is the curve :
#
# .. math::
#
# h : x_1 \mapsto \frac{10.0}{x_1}
#
# %%
# We shall represent this curve using a :class:`~openturns.Contour` object.
nx, ny = 15, 15
xx = ot.Box([nx], ot.Interval([0.0], [10.0])).generate()
yy = ot.Box([ny], ot.Interval([-10.0], [10.0])).generate()
graph = F_dist.drawPDF()
view = viewer.View(graph)
# %%
# These independent marginals define the joint distribution of the input parameters :
myDistribution = sm.distribution
# %%
# We create a `RandomVector` from the `Distribution`, then a composite random vector. Finally, we create a `ThresholdEvent` from this `RandomVector`.
# %%
inputRandomVector = ot.RandomVector(myDistribution)
outputRandomVector = ot.CompositeRandomVector(
limitStateFunction, inputRandomVector)
myEvent = ot.ThresholdEvent(outputRandomVector, ot.Less(), 0.0)
# %%
# Using Monte Carlo simulations
# -----------------------------
# %%
cv = 0.05
NbSim = 100000
experiment = ot.MonteCarloExperiment()
algoMC = ot.ProbabilitySimulationAlgorithm(myEvent, experiment)
algoMC.setMaximumOuterSampling(NbSim)
algoMC.setBlockSize(1)
algoMC.setMaximumCoefficientOfVariation(cv)
model = ot.SymbolicFunction(["x1", "x2"], ["x1+2*x2"])
# Create the input distribution and random vector X
inputDist = ot.Normal(2)
inputDist.setDescription(['X1', 'X2'])
inputVector = ot.RandomVector(inputDist)
# Create the output random vector Y=model(X)
output = ot.CompositeRandomVector(model, inputVector)
output.setName("MyOutputY")
# %%
# Create the physical event Y > 4
threshold = 4
myEvent = ot.ThresholdEvent(output, ot.Greater(), threshold)
# Create the associated standard event in the standard space
myStandardEvent = ot.StandardEvent(myEvent)
# %%
# First : FORM analyses to get the design point
myCobyla = ot.Cobyla()
myStartingPoint = inputDist.getMean()
myAlgoFORM = ot.FORM(myCobyla, myEvent, myStartingPoint)
myAlgoFORM.run()
FORMResult = myAlgoFORM.getResult()
standardSpaceDesignPoint = FORMResult.getStandardSpaceDesignPoint()
# %%
# Fix the importance level epsilon of the test
from matplotlib import pylab as plt import math as m import time ot.Log.Show(ot.Log.NONE) # %% # Define an event to compute a probability myFunction = ot.SymbolicFunction(['E', 'F', 'L', 'I'], ['-F*L^3/(3.0*E*I)']) dim = myFunction.getInputDimension() mean = [50.0, 1.0, 10.0, 5.0] sigma = [1.0] * dim R = ot.IdentityMatrix(dim) myDistribution = ot.Normal(mean, sigma, R) vect = ot.RandomVector(myDistribution) output = ot.CompositeRandomVector(myFunction, vect) myEvent = ot.ThresholdEvent(output, ot.Less(), -3.0) # %% # **Stop a FORM algorithm using a calls number limit** # # A FORM algorithm termination can be controlled by the maximum number of iterations # # of its underlying optimization solver, but not directly by a maximum number of evaluations. # %% # Create the optimization algorithm myCobyla = ot.Cobyla() myCobyla.setMaximumEvaluationNumber(400) myCobyla.setMaximumAbsoluteError(1.0e-10) myCobyla.setMaximumRelativeError(1.0e-10) myCobyla.setMaximumResidualError(1.0e-10)
model = ot.MemoizeFunction(model) # %% # Remove all the values stored in the history mechanism. # Care : it is done regardless the status of the History mechanism. # %% model.clearHistory() # %% # Create the event whose probability we want to estimate. # %% vect = ot.RandomVector(distribution) G = ot.CompositeRandomVector(model, vect) event = ot.ThresholdEvent(G, ot.Less(), 0.0) # %% # Create a Monte Carlo algorithm. # %% experiment = ot.MonteCarloExperiment() algo = ot.ProbabilitySimulationAlgorithm(event, experiment) algo.setMaximumCoefficientOfVariation(0.1) algo.setMaximumStandardDeviation(0.001) algo.setMaximumOuterSampling(int(1e4)) # %% # Define the HistoryStrategy to store the values of :math:`P_n` and :math:`\sigma_n` used ot draw the convergence graph. # Compact strategy : N points