def gsobolDistribution(d):
distribution = ComposedDistribution([Uniform(0, 1)] * d)
return distribution
B = 300.0 # Largeur de la rivière
myParametricWrapper = CrueFunction(H_d, Z_b, L, B)
myWrapper = Function(myParametricWrapper)
# 2. Random vector definition
Q = Gumbel(1.0 / 558.0, 1013.0)
Q = TruncatedDistribution(Q, 0.0, TruncatedDistribution.LOWER)
K_s = Normal(30.0, 7.5)
K_s = TruncatedDistribution(K_s, 0.0, TruncatedDistribution.LOWER)
Z_v = Uniform(49.0, 51.0)
Z_m = Uniform(54.0, 56.0)
# 3. Create the joint distribution function,
# the output and the event.
inputDistribution = ComposedDistribution([Q, K_s, Z_v, Z_m])
inputRandomVector = RandomVector(inputDistribution)
outputRandomVector = RandomVector(myWrapper, inputRandomVector)
# 4. Get a sample of the output
sampleS = outputRandomVector.getSample(500)
# 5. Plot the histogram
barsNumber = int(sqrt(sampleS.getSize()))
histoGraph = VisualTest.DrawHistogram(sampleS, barsNumber)
histoGraph.setTitle("Histogramme de la surverse")
histoGraph.setXTitle("S (m)")
histoGraph.setYTitle("Frequence")
histoGraph.setLegends([""])
View(histoGraph).show()
def ishigamiDistribution():
distribution = ComposedDistribution([Uniform(-pi, pi)] * 3)
return distribution
def create_uniform_distribution():
distribution = ComposedDistribution([Uniform(0., 1.)] * 2)
return distribution
Z_m = Uniform(54.0, 56.0) # 3. View the PDF Q.setDescription(["Q (m3/s)"]) K_s.setDescription(["Ks (m^(1/3)/s)"]) Z_v.setDescription(["Zv (m)"]) Z_m.setDescription(["Zm (m)"]) View(Q.drawPDF()).show() View(K_s.drawPDF()).show() View(Z_v.drawPDF()).show() View(Z_m.drawPDF()).show() # 4. Create the joint distribution function, # the output and the event. inputRandomVector = ComposedDistribution([Q, K_s, Z_v, Z_m]) outputRandomVector = RandomVector(f, RandomVector(inputRandomVector)) eventF = Event(outputRandomVector, GreaterOrEqual(), 0) # 4.bis Draw pairs sample = inputRandomVector.getSample(500) myPairs = Pairs(sample, "N=500", sample.getDescription(), "red", "bullet") View(myPairs).show() # 5. Create the Monte-Carlo algorithm algoProb = MonteCarlo(eventF) algoProb.setMaximumOuterSampling(100000) algoProb.run() # 6. Get the results resultAlgo = algoProb.getResult()
Zm = Uniform(54.0, 56.0) # 3. View the PDF Q.setDescription(["Q (m3/s)"]) Ks.setDescription(["Ks (m^(1/3)/s)"]) Zv.setDescription(["Zv (m)"]) Zm.setDescription(["Zm (m)"]) View(Q.drawPDF()).show() View(Ks.drawPDF()).show() View(Zv.drawPDF()).show() View(Zm.drawPDF()).show() # 4. Create the joint distribution function, # the output and the event. inputvector = ComposedDistribution([Q, Ks, Zv, Zm]) outputvector = RandomVector(f, RandomVector(inputvector)) eventF = Event(outputvector, GreaterOrEqual(), 0) # 4.bis Draw pairs sample = inputvector.getSample(500) myPairs = Pairs(sample, "N=500", sample.getDescription(), "red", "bullet") View(myPairs).show() # 5. Create the Monte-Carlo algorithm algoProb = MonteCarlo(eventF) algoProb.setMaximumOuterSampling(100000) algoProb.run() # 6. Get the results resultAlgo = algoProb.getResult()