ams_parameters = ot.ArcsineMuSigma(8.4, 2.25)
myStudy.add('ams_parameters', ams_parameters)
# BetaMuSigma parameter save
bms_parameters = ot.BetaMuSigma(0.2, 0.6, -1, 2)
myStudy.add('bms_parameters', bms_parameters)
# GammaMuSigma parameter save
gmms_parameters = ot.GammaMuSigma(1.5, 2.5, -0.5)
myStudy.add('gmms_parameters', gmms_parameters)
# GumbelMuSigma parameter save
gms_parameters = ot.GumbelMuSigma(1.5, 1.3)
myStudy.add('gms_parameters', gms_parameters)
# LogNormalMuSigma parameter save
lnms_parameters = ot.LogNormalMuSigma(30000.0, 9000.0, 15000)
myStudy.add('lnms_parameters', lnms_parameters)
# LogNormalMuSigmaOverMu parameter save
lnmsm_parameters = ot.LogNormalMuSigmaOverMu(0.63, 5.24, -0.5)
myStudy.add('lnmsm_parameters', lnmsm_parameters)
# WeibullMinMuSigma parameter save
wms_parameters = ot.WeibullMinMuSigma(1.3, 1.23, -0.5)
myStudy.add('wms_parameters', wms_parameters)
# MemoizeFunction
f = ot.SymbolicFunction(['x1', 'x2'], ['x1*x2'])
memoize = ot.MemoizeFunction(f)
memoize([5, 6])
myStudy.add('memoize', memoize)
# print ('Study = ' , myStudy)
myStudy.save()
# Create a new Study Object
#! /usr/bin/env python
import openturns as ot
distParams = []
distParams.append(ot.ArcsineMuSigma(8.4, 2.25))
distParams.append(ot.BetaMuSigma(0.2, 0.6, -1, 2))
distParams.append(ot.GammaMuSigma(1.5, 2.5, -0.5))
distParams.append(ot.GumbelLambdaGamma(0.6, 6.0))
distParams.append(ot.GumbelMuSigma(1.5, 1.3))
distParams.append(ot.LogNormalMuErrorFactor(0.63, 1.5, -0.5))
distParams.append(ot.LogNormalMuSigma(0.63, 3.3, -0.5))
distParams.append(ot.LogNormalMuSigmaOverMu(0.63, 5.24, -0.5))
distParams.append(ot.WeibullMaxMuSigma(1.3, 1.23, 3.1))
distParams.append(ot.WeibullMinMuSigma(1.3, 1.23, -0.5))
for distParam in distParams:
print('Distribution Parameters ', repr(distParam))
print('Distribution Parameters ', distParam)
non_native = distParam.getValues()
desc = distParam.getDescription()
print('non-native=', non_native, desc)
native = distParam.evaluate()
print('native=', native)
non_native = distParam.inverse(native)
print('non-native=', non_native)
print('built dist=', distParam.getDistribution())
# derivative of the native parameters with regards the parameters of the
#!/usr/bin/env python
from __future__ import print_function
import openturns as ot
import otmorris
poutre = ot.SymbolicFunction(['L', 'b', 'h', 'E', 'F'],
['F * L^3 / (48 * E * b * h^3 / 12)'])
# define the model
L = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(5., 0.02))
b = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(2., 0.05))
h = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(0.4, 0.05))
E = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(3e4, 0.12))
F = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(0.1, 0.20))
list_marginals = [L, b, h, E, F]
distribution = ot.ComposedDistribution(list_marginals)
distribution.setDescription(('L', 'b', 'h', 'E', 'F'))
dim = distribution.getDimension()
level_number = 4
trajectories = 10
jump_step = int(level_number / 2)
levels = [level_number] * dim
# set the bounds of the grid experiment
bound = ot.Interval(
[marginal.computeQuantile(0.01)[0] for marginal in list_marginals],
[marginal.computeQuantile(0.99)[0] for marginal in list_marginals])
experiment = otmorris.MorrisExperimentGrid(levels, bound, trajectories)
experiment.setJumpStep(ot.Indices([jump_step] * dim))
ref_sampleSize, ref_nrepetitions)
# Martinez
runConvergence(model_ishigami, distribution_ishigami,
MartinezSensitivityAlgorithm, list_sampleSize,
ref_sampleSize, ref_nrepetitions)
################################################################################
################# POUTRE #####################
################################################################################
model_poutre = ot.SymbolicFunction(['L', 'b', 'h', 'E', 'F'],
['F * L^3 / (48 * E * b * h^3 / 12)'])
model_poutre.setName("poutre")
L = ot.LogNormal()
L.setParameter(ot.LogNormalMuSigmaOverMu()([5., .02, 0.]))
b = ot.LogNormal()
b.setParameter(ot.LogNormalMuSigmaOverMu()([.2, .05, 0.]))
h = ot.LogNormal()
h.setParameter(ot.LogNormalMuSigmaOverMu()([.4, .05, 0.]))
E = ot.LogNormal()
E.setParameter(ot.LogNormalMuSigmaOverMu()([3e4, .12, 0.]))
F = ot.LogNormal()
F.setParameter(ot.LogNormalMuSigmaOverMu()([.1, .20, 0.]))
distribution_poutre = ot.ComposedDistribution([L, b, h, E, F])
# Saltelli
runConvergence(model_poutre, distribution_poutre,
SaltelliSensitivityAlgorithm, list_sampleSize,
ref_sampleSize, ref_nrepetitions)
# Martinez
runConvergence(
model_ishigami,
distribution_ishigami,
MartinezSensitivityAlgorithm,
list_sampleSize,
)
# POUTRE
model_poutre = ot.SymbolicFunction(
["L", "b", "h", "E", "F"], ["F * L^3 / (48 * E * b * h^3 / 12)"]
)
model_poutre.setName("poutre")
L = ot.LogNormal()
L.setParameter(ot.LogNormalMuSigmaOverMu()([5.0, 0.02, 0.0]))
b = ot.LogNormal()
b.setParameter(ot.LogNormalMuSigmaOverMu()([0.2, 0.05, 0.0]))
h = ot.LogNormal()
h.setParameter(ot.LogNormalMuSigmaOverMu()([0.4, 0.05, 0.0]))
E = ot.LogNormal()
E.setParameter(ot.LogNormalMuSigmaOverMu()([3e4, 0.12, 0.0]))
F = ot.LogNormal()
F.setParameter(ot.LogNormalMuSigmaOverMu()([0.1, 0.20, 0.0]))
distribution_poutre = ot.ComposedDistribution([L, b, h, E, F])
# Saltelli
runConvergence(
model_poutre,
distribution_poutre,
SaltelliSensitivityAlgorithm,
