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#!/usr/bin/env python
import openturns as ot
import math as m
ot.TESTPREAMBLE()
dim = 4
def flood_model(X):
L = 5000.0 # m
B = 300.0 # m
Q = X[0] # m^3.s^-1
Ks = X[1] # m^1/3.s^-1
Zv = X[2] # m
Zm = X[3] # m
Hd = 0.0 # m
Zb = 55.5 # m
S = Zv + (Q / (Ks * B * m.sqrt((Zm - Zv) / L))) ** (3.0 / 5) - (Hd + Zb)
return [S]
function = ot.PythonFunction(dim, 1, flood_model)
Q_law = ot.TruncatedDistribution(
ot.Gumbel(558.0, 1013.0), 0.0, ot.TruncatedDistribution.LOWER
)
# alpha=1/b, beta=a | you can use Gumbel(a, b, Gumbel.AB) starting from OT 1.2
Ks_law = ot.TruncatedDistribution(
ot.Normal(30.0, 7.5), 0.0, ot.TruncatedDistribution.LOWER
)
Zv_law = ot.Triangular(49.0, 50.0, 51.0)
Zm_law = ot.Triangular(54.0, 55.0, 56.0)
coll = ot.DistributionCollection([Q_law, Ks_law, Zv_law, Zm_law])
distribution = ot.JointDistribution(coll)
x = [dist.computeQuantile(0.5)[0] for dist in coll]
fx = function(x)
for k in [0.0, 2.0, 5.0, 8.0][0:1]:
randomVector = ot.RandomVector(distribution)
composite = ot.CompositeRandomVector(function, randomVector)
print("--------------------")
print("model flood S <", k, "gamma=", end=" ")
print("f(", ot.Point(x), ")=", fx)
event = ot.ThresholdEvent(composite, ot.Greater(), k)
for n in [100, 1000, 5000][1:2]:
for gamma1 in [0.25, 0.5, 0.75][1:2]:
experiment = ot.MonteCarloExperiment()
algo = ot.ProbabilitySimulationAlgorithm(event, experiment)
algo.setMaximumOuterSampling(100 * n)
# algo.setMaximumCoefficientOfVariation(-1.)
algo.run()
result = algo.getResult()
print(result)
algo = ot.AdaptiveDirectionalStratification(event)
algo.setMaximumOuterSampling(n)
algo.setGamma([gamma1, 1.0 - gamma1])
calls0 = function.getEvaluationCallsNumber()
algo.run()
calls = function.getEvaluationCallsNumber() - calls0
result = algo.getResult()
pf = result.getProbabilityEstimate()
var = result.getVarianceEstimate()
cov = result.getCoefficientOfVariation()
print(result, end=" ")
print("callsNumber=%d" % calls + " gamma=" + str(algo.getGamma()))
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