import openturns as ot
import persalys
myStudy = persalys.Study('myStudy')
# Model
X0 = persalys.Input('X0', ot.Normal(1, 1))
X1 = persalys.Input('X1', ot.Normal(1, 1))
Y0 = persalys.Output('Y0')
model = persalys.SymbolicPhysicalModel('aModelPhys', [X0, X1], [Y0],
['sin(X0) + 8*X1'])
myStudy.add(model)
# limit state ##
limitState = persalys.LimitState('aLimitState', model, 'Y0', ot.Greater(), 20.)
print(limitState)
myStudy.add(limitState)
limitState.setThreshold(10.)
limitState.setOperator(ot.Less())
print(limitState)
# limit state ##
limitState2 = persalys.LimitState('aLimitState2', model)
print(limitState2)
myStudy.add(limitState2)
limitState2.setThreshold(15.)
print(limitState2)
ot.Normal(30.0, 7.5), 0, ot.TruncatedDistribution.LOWER)
dist_Zv = ot.Uniform(49.0, 51.0)
dist_Zm = ot.Uniform(54.0, 56.0)
Q = persalys.Input('Q', 1000., dist_Q, 'Débit maximal annuel (m3/s)')
Ks = persalys.Input('Ks', 30., dist_Ks, 'Strickler (m^(1/3)/s)')
Zv = persalys.Input('Zv', 50., dist_Zv, 'Côte de la rivière en aval (m)')
Zm = persalys.Input('Zm', 55., dist_Zm, 'Côte de la rivière en amont (m)')
S = persalys.Output('S', 'Surverse (m)')
model = persalys.SymbolicPhysicalModel('myPhysicalModel', [Q, Ks, Zv, Zm], [
S], ['(Q/(Ks*300.*sqrt((Zm-Zv)/5000)))^(3.0/5.0)+Zv-55.5-3.'])
myStudy.add(model)
# limit state ##
limitState = persalys.LimitState('limitState1', model, 'S', ot.Greater(), 0.)
myStudy.add(limitState)
# Monte Carlo ##
montecarlo = persalys.MonteCarloReliabilityAnalysis(
'myMonteCarlo', limitState)
montecarlo.setMaximumCalls(10000)
myStudy.add(montecarlo)
montecarlo.run()
montecarloResult = montecarlo.getResult()
# Comparaison
openturns.testing.assert_almost_equal(montecarloResult.getSimulationResult().getProbabilityEstimate(), 0.0, 1e-6)
# FORM-IS ##
myStudy.add(monteCarlo)
# 2-b Taylor Expansion ##
taylor = persalys.TaylorExpansionMomentsAnalysis('Taylor', model1)
taylor.setInterestVariables(['y0', 'y1'])
myStudy.add(taylor)
# 2-c Taylor Expansion which generate an error
taylor2 = persalys.TaylorExpansionMomentsAnalysis('Taylor2', model1)
taylor2.setInterestVariables(['fake_var'])
myStudy.add(taylor2)
# 3- reliability ##
# limit state ##
limitState = persalys.LimitState('aLimitState', model1, 'y1', ot.Greater(),
0.5)
myStudy.add(limitState)
optimAlgo = ot.AbdoRackwitz()
optimAlgo.setMaximumIterationNumber(150)
optimAlgo.setMaximumAbsoluteError(1e-3)
# 3-a Monte Carlo ##
monteCarloReliability = persalys.MonteCarloReliabilityAnalysis(
'MonteCarloReliability', limitState)
monteCarloReliability.setMaximumCoefficientOfVariation(-1.)
monteCarloReliability.setMaximumElapsedTime(1000)
monteCarloReliability.setMaximumCalls(20)
monteCarloReliability.setSeed(2)
myStudy.add(monteCarloReliability)
