def run(self):
"""
Compute the Sobol indices with the chosen algorithm.
"""
# create the NumericalMathFunction which computes the POD for a given
# realization and for all defect sizes.
if self._podType == "kriging":
self._PODaggr = ot.NumericalMathFunction(
PODaggrKriging(self._POD, self._dim, self._defectSizes,
self._detectionBoxCox))
elif self._podType == "chaos":
self._PODaggr = ot.NumericalMathFunction(
PODaggrChaos(self._POD, self._dim, self._defectSizes,
self._detectionBoxCox, self._simulationSize))
if self._method == "Saltelli":
self._sa = ot.SaltelliSensitivityAlgorithm(self._distribution,
self._N, self._PODaggr,
False)
elif self._method == "Martinez":
self._sa = ot.MartinezSensitivityAlgorithm(self._distribution,
self._N, self._PODaggr,
False)
elif self._method == "Jansen":
self._sa = ot.JansenSensitivityAlgorithm(self._distribution,
self._N, self._PODaggr,
False)
elif self._method == "MauntzKucherenko":
self._sa = ot.MauntzKucherenkoSensitivityAlgorithm(
self._distribution, self._N, self._PODaggr, False)
def myMauntzKucherenkoSobolEstimator(distribution, size, model):
inputDesign = ot.SobolIndicesExperiment(distribution, size,
True).generate()
outputDesign = model(inputDesign)
sensitivityAnalysis = ot.MauntzKucherenkoSensitivityAlgorithm(
inputDesign, outputDesign, size)
return sensitivityAnalysis
def run(self):
"""
Compute the Sobol indices with the chosen algorithm.
"""
# create the Function which computes the POD for a given
# realization and for all defect sizes.
if self._podType == "kriging":
self._PODaggr = ot.Function(
PODaggrKriging(self._POD, self._dim, self._defectSizes,
self._detectionBoxCox))
elif self._podType == "chaos":
self._PODaggr = ot.Function(
PODaggrChaos(self._POD, self._dim, self._defectSizes,
self._detectionBoxCox, self._simulationSize))
input_design = ot.SobolIndicesExperiment(self._distribution, self._N,
False).generate()
output_design = self._PODaggr(input_design)
# remove the output marginal when the variance is null because it causes
# a failure. Must remove also the associated defect sizes.
selected_marginal_index = []
for index_output in range(output_design.getDimension()):
if output_design.getMarginal(
index_output)[:self._N].computeCovariance()[0, 0] != 0:
selected_marginal_index.append(index_output)
if len(selected_marginal_index) != output_design.getDimension():
self.setDefectSizes(self._defectSizes[selected_marginal_index])
selected_output_design = np.array(
output_design)[:, selected_marginal_index]
logging.warning("Warning : some output variances are null. " + \
"Only the following defect sizes are taken into " + \
"account : {}".format(self._defectSizes))
else:
selected_output_design = output_design
if self._method == "Saltelli":
self._sa = ot.SaltelliSensitivityAlgorithm(input_design,
selected_output_design,
self._N)
elif self._method == "Martinez":
self._sa = ot.MartinezSensitivityAlgorithm(input_design,
selected_output_design,
self._N)
elif self._method == "Jansen":
self._sa = ot.JansenSensitivityAlgorithm(input_design,
selected_output_design,
self._N)
elif self._method == "MauntzKucherenko":
self._sa = ot.MauntzKucherenkoSensitivityAlgorithm(
input_design, selected_output_design, self._N)
self._sa.setUseAsymptoticDistribution(True)
Zc = H + Zv
S = Zc - Zd
return [S]
myFunction = ot.PythonFunction(4, 1, flooding)
myParam = ot.GumbelAB(1013.0, 558.0)
Q = ot.ParametrizedDistribution(myParam)
Q = ot.TruncatedDistribution(Q, 0.0, ot.SpecFunc.MaxScalar)
Ks = ot.Normal(30.0, 7.5)
Ks = ot.TruncatedDistribution(Ks, 0.0, ot.SpecFunc.MaxScalar)
Zv = ot.Uniform(49.0, 51.0)
Zm = ot.Uniform(54.0, 56.0)
inputX = ot.ComposedDistribution([Q, Ks, Zv, Zm])
inputX.setDescription(["Q", "Ks", "Zv", "Zm"])
size = 5000
computeSO = True
inputDesign = ot.SobolIndicesExperiment(inputX, size, computeSO).generate()
outputDesign = myFunction(inputDesign)
sensitivityAnalysis = ot.MauntzKucherenkoSensitivityAlgorithm(
inputDesign, outputDesign, size)
graph = sensitivityAnalysis.draw()
fig = plt.figure(figsize=(8, 4))
plt.suptitle(graph.getTitle())
axis = fig.add_subplot(111)
axis.set_xlim(auto=True)
View(graph, figure=fig, axes=[axis], add_legend=True)
