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#! /usr/bin/env python
import openturns as ot
import persalys
myStudy = persalys.Study("myStudy")
X0 = persalys.Input("X0", ot.Normal(1, 1))
X1 = persalys.Input("X1", ot.Normal(1, 1))
Y00 = persalys.Output("fake_Y0")
Y00.setIsSelected(False)
Y0 = persalys.Output("Y0")
formula_Y00 = "X0"
formula_Y0 = "sin(X0) + 8*X1"
model = persalys.SymbolicPhysicalModel(
"aModelPhys", [X0, X1], [Y00, Y0], [formula_Y00, formula_Y0]
)
myStudy.add(model)
# Taylor Expansion ##
analysis = persalys.TaylorExpansionMomentsAnalysis("aTaylor", model)
myStudy.add(analysis)
print(analysis)
analysis.run()
print("result=", analysis.getResult())
print("\n")
# Taylor Expansion ##
# test with a deterministic variable
X2 = persalys.Input("X2", 10)
model.addInput(X2)
model.addOutput(persalys.Output("Y1"))
model.setFormula("Y1", "sin(X0) + 8*X1 + X2")
analysis2 = persalys.TaylorExpansionMomentsAnalysis("aTaylor2", model)
analysis2.setInterestVariables(["Y1"])
myStudy.add(analysis2)
print(analysis2)
analysis2.run()
print("result=", analysis2.getResult())
print("\n")
# Taylor Expansion ##
# test definition gradient with PythonPhysicalModel
code = "from math import sin\n\ndef _exec(X0, X1):\n Y0 = sin(X0) + 8*X1\n fake_Y0 = X0\n return Y0, fake_Y0\n"
model2 = persalys.PythonPhysicalModel("myPhysicalModel", [X0, X1], [Y0], code)
model2.setProcessNumber(1)
myStudy.add(model2)
# default finite difference steps
analysis3 = persalys.TaylorExpansionMomentsAnalysis("aTaylor3", model2)
analysis3.setInterestVariables(["Y0"])
myStudy.add(analysis3)
print(analysis3)
analysis3.run()
print("result=", analysis3.getResult())
print("\n")
# greater finite difference steps
model2.setFiniteDifferenceStep("X0", 0.005)
model2.setFiniteDifferenceStep("X1", 0.005)
analysis4 = persalys.TaylorExpansionMomentsAnalysis("aTaylor4", model2)
analysis4.setInterestVariables(["Y0"])
myStudy.add(analysis4)
print(analysis4)
analysis4.run()
print("result=", analysis4.getResult())
# script
script = myStudy.getPythonScript()
print(script)
exec(script)
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