def test():
Study_0 = persalys.Study('Study_0')
persalys.Study.Add(Study_0)
X0 = persalys.Input('X0', 0, '')
Y0 = persalys.Output('Y0', '')
Y0.setValue(0)
inputCollection = []
inputCollection.append(X0)
outputCollection = []
outputCollection.append(Y0)
code = 'def _exec(X0):\n Y0 = X0\n return Y0'
PhysicalModel_0 = persalys.PythonPhysicalModel('PhysicalModel_0',
inputCollection,
outputCollection, code)
Study_0.add(PhysicalModel_0)
print(Study_0.getPythonScript())
#!/usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
myStudy = persalys.Study('myStudy')
# Model
R = persalys.Input('R', 0., ot.LogNormalMuSigma(
300., 30.).getDistribution(), 'Yield strength')
F = persalys.Input('F', 0., ot.Normal(75000., 5000.), 'Traction load')
G = persalys.Output('G', 'deviation')
code = 'from math import pi\n\ndef _exec(R, F):\n G = R-F/(pi*100.0)\n return G\n'
model = persalys.PythonPhysicalModel('myPhysicalModel', [R, F], [G], code)
myStudy.add(model)
f = model.getFunction()
print(f([300., 75000.]))
# several outputs
model.setCode(
'from math import pi\n\ndef _exec(R, F):\n G = 6*F\n H = 42.0\n return G, H\n')
f = model.getFunction()
print(f([300., 75000.]))
# test operator() (sample)
model.setCode(
'from math import pi\n\ndef _exec(R, F):\n G = 2*R-F/(pi*100.0)\n return G\n')
f = model.getFunction()
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)
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)
y0 = persalys.Output('y0')
fake_y0 = persalys.Output('fake_y0')
y1 = persalys.Output('y1')
# symboilc model ##
formula_fake_var = 'x1'
formula_y0 = 'cos(0.5*x1) + sin(x2)'
formula_y1 = 'cos(0.5*x1) + sin(x2) + x3'
symbolicModel = persalys.SymbolicPhysicalModel('symbolicModel', [x1, x2, x3], [fake_var, y0, fake_y0, y1], [
formula_fake_var, formula_y0, formula_y0, formula_y1])
myStudy.add(symbolicModel)
# python model ##
code = 'from math import cos, sin, sqrt\n\ndef _exec(x1, x2, x3):\n y0 = cos(0.5*x1) + sin(x2) + sqrt(x3)\n return y0\n'
pythonModel = persalys.PythonPhysicalModel('pythonModel', [x1, x2, x3], [y0], code)
myStudy.add(pythonModel)
filename = 'data.csv'
cDist = ot.ComposedDistribution([ot.Normal(), ot.Gumbel(), ot.Normal(), ot.Uniform()],
ot.ComposedCopula([ot.IndependentCopula(2), ot.GumbelCopula()]))
sample = cDist.getSample(200)
sample.exportToCSVFile(filename, ' ')
# Designs of Experiment ##
# fixed design ##
ot.RandomGenerator.SetSeed(0)
fixedDesign = persalys.FixedDesignOfExperiment('fixedDesign', symbolicModel)
inputSample = ot.LHSExperiment(ot.ComposedDistribution([ot.Uniform(0., 10.), ot.Uniform(0., 10.)]), 10).generate()
inputSample.stack(ot.Sample(10, [0.5]))
#! /usr/bin/env python
import openturns as ot
import persalys
import os
# Model with error
X0 = persalys.Input('X0', 1, ot.Normal())
X1 = persalys.Input('X1', 2)
Y0 = persalys.Output('Y0')
Y1 = persalys.Output('Y1')
model2 = persalys.PythonPhysicalModel('model2', [X0, X1], [Y0], 'from math import pi\n\ndef _exec(X0, X1):\n Y0 = 2*X1 - 1/X0\n return Y0\n')
model2.setParallel(True)
design5 = persalys.FixedDesignOfExperiment('design5', model2)
design5.setOriginalInputSample([[0.9, 1.1], [1.8, 2.2], [0, 2.2]])
design5.setBlockSize(3)
design5.run()
assert design5.getResult().getDesignOfExperiment().getOutputSample().getSize() == 2, "whole block must no fail"
model3 = persalys.PythonPhysicalModel('model3', [X0, X1], [Y0, Y1], 'from math import pi\n\ndef _exec(X0, X1):\n Y0 = 2*X1 - 1/X0\n Y1=5\n return Y0, Y1\n')
model3.setParallel(True)
# same with multiple outputs
design6 = persalys.FixedDesignOfExperiment('design6', model3)
design6.setInterestVariables(['Y0'])
design6.setOriginalInputSample([[0.9, 1.1], [1.8, 2.2], [0, 2.2]])
design6.setBlockSize(3)
design6.run()
assert design6.getResult().getDesignOfExperiment().getOutputSample().getSize() == 2, "whole block must no fail"
y_fake = 0.
return y
"""
myStudy = persalys.Study('myStudy')
inputs = []
for i in range(20):
inputs.append(persalys.Input('x' + str(i + 1), ot.Uniform(0, 1)))
y = persalys.Output('y')
y_fake = persalys.Output('y_fake')
print(inputs)
model = persalys.PythonPhysicalModel('MorrisModel', inputs, [y, y_fake], code)
myStudy.add(model)
# Morris ##
analysis = persalys.MorrisAnalysis('aMorris', model)
analysis.setInterestVariables(['y'])
analysis.setLevel(4)
analysis.setTrajectoriesNumber(4)
analysis.setSeed(2)
myStudy.add(analysis)
print(analysis)
analysis.run()
print("error message=", analysis.getErrorMessage())