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class=SymbolicPhysicalModel inputs=[class=Input name=X0 value=1 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07,class=Input name=X1 value=2 description= finiteDifferenceStep=1e-07] outputs=[class=Output name=Y0 description=output_1] copula=class=BlockIndependentCopula name=BlockIndependentCopula dimension=1 copula[0]=class=IndependentCopula name=IndependentCopula dimension=1 isParallel=false formulas=[sin(X0)+8*X1]
function= MemoizeEvaluation([X0,X1]->[sin(X0)+8*X1])
formulas= [sin(X0)+8*X1]
formula_Y0= sin(X0)+8*X1
stochastic var= [X0]
distribution= JointDistribution(Normal(mu = 0, sigma = 1))
copula= BlockIndependentCopula(IndependentCopula(dimension = 1))
output_YO= class=Output name=Y0 description=output_1
input_XO= class=Input name=X0 value=1 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07
inputs names= [X0,X1]
outputs names= [Y0]
hasY0 True
hasX0 True
inputs= [class=Input name=X0 value=1 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07,class=Input name=X1 value=2 description= distribution=class=LogNormal name=LogNormal dimension=1 muLog=0 sigmaLog=1 gamma=0 finiteDifferenceStep=1e-05]
stochastic var= [X0,X1]
distribution= JointDistribution(Normal(mu = 0, sigma = 1), LogNormal(muLog = 0, sigmaLog = 1, gamma = 0), BlockIndependentCopula(NormalCopula(R = [[ 1 0.25 ]
[ 0.25 1 ]])))
copula= BlockIndependentCopula(NormalCopula(R = [[ 1 0.25 ]
[ 0.25 1 ]]))
outputs= [class=Output name=Y0 description=output_1]
inputs= [class=Input name=X0 value=1 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07,class=Input name=X1 value=2 description= distribution=class=LogNormal name=LogNormal dimension=1 muLog=0 sigmaLog=1 gamma=0 finiteDifferenceStep=1e-05,class=Input name=X_2 value=10 description= finiteDifferenceStep=1e-07]
copula= BlockIndependentCopula(NormalCopula(R = [[ 1 0.25 ]
[ 0.25 1 ]]))
stochastic var= [X0,X1,X4,X5]
distribution= JointDistribution(Normal(mu = 0, sigma = 1), LogNormal(muLog = 0, sigmaLog = 1, gamma = 0), Normal(mu = 0, sigma = 1), Normal(mu = 0, sigma = 1), BlockIndependentCopula(NormalCopula(R = [[ 1 0.25 ]
[ 0.25 1 ]]), IndependentCopula(dimension = 2)))
copula= BlockIndependentCopula(NormalCopula(R = [[ 1 0.25 ]
[ 0.25 1 ]]), IndependentCopula(dimension = 2))
formula_Y1= sin(X0)+8*X1+X2
outputs= [class=Output name=Y0 description=output_1,class=Output name=Y_1 description=]
selected outputs= [Y0]
inputs= [class=Input name=X0 value=1 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07,class=Input name=X1 value=2 description= distribution=class=LogNormal name=LogNormal dimension=1 muLog=0 sigmaLog=1 gamma=0 finiteDifferenceStep=1e-05,class=Input name=X4 value=0 description= distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07,class=Input name=X5 value=0 description=aDescription distribution=class=Normal name=Normal dimension=1 mean=class=Point name=Unnamed dimension=1 values=[0] sigma=class=Point name=Unnamed dimension=1 values=[1] correlationMatrix=class=CorrelationMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1] finiteDifferenceStep=1e-07]
outputs= [class=Output name=Y0 description=output_1]
script= dist_X0 = ot.Normal(0, 1)
X0 = persalys.Input('X0', 1, dist_X0, 'aDescription')
dist_X1 = ot.LogNormal(0, 1, 0)
X1 = persalys.Input('X1', 2, dist_X1, '')
X1.setFiniteDifferenceStep(1e-05)
dist_X4 = ot.Normal(0, 1)
X4 = persalys.Input('X4', 0, dist_X4, '')
dist_X5 = ot.Normal(0, 1)
X5 = persalys.Input('X5', 0, dist_X5, 'aDescription')
Y0 = persalys.Output('Y0', 'output_1')
inputs = [X0, X1, X4, X5]
outputs = [Y0]
formulas = ['sin(X0)+8*X1+0.5']
aModelPhys = persalys.SymbolicPhysicalModel('aModelPhys', inputs, outputs, formulas)
R = ot.CorrelationMatrix(2)
R[0, 1] = 0.239358526049
copula = ot.NormalCopula(ot.NormalCopula.GetCorrelationFromSpearmanCorrelation(R))
aModelPhys.setCopula(['X0', 'X1'], copula)
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