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 persalys
import os
anOTStudy = persalys.Study('anOTStudy')
# Model
X0 = persalys.Input('X0', 1, ot.Normal())
X1 = persalys.Input('X1', 2, ot.Normal())
Y0 = persalys.Output('Y0')
model = persalys.SymbolicPhysicalModel('aModelPhys', [X0, X1], [Y0],
['sin(X0)+8*X1'])
anOTStudy.add(model)
# Design of Experiment ##
aDesign = persalys.GridDesignOfExperiment('aDesign_1', model,
[[0.9, 1.1], [1.8, 2.2]])
anOTStudy.add(aDesign)
aDesign.run()
print('outs=', aDesign.getResult().getDesignOfExperiment().getOutputSample())
# Design of Experiment ##
filename = 'normal.csv'
ot.Normal(3).getSample(10).exportToCSVFile(filename)
aDesign2 = persalys.ImportedDesignOfExperiment('aDesign_2', model, filename,
[0, 2])
anOTStudy.add(aDesign2)
#!/usr/bin/env python
import openturns as ot
import persalys
Study_0 = persalys.Study('Study_0')
persalys.Study.Add(Study_0)
# variables
dist_z0 = ot.Uniform(100, 150)
z0 = persalys.Input('z0', 100, dist_z0, '')
dist_v0 = ot.Normal(55, 10)
v0 = persalys.Input('v0', 55, dist_v0, '')
dist_m = ot.Normal(80, 8)
m = persalys.Input('m', 80, dist_m, '')
dist_c = ot.Uniform(0, 30)
c = persalys.Input('c', 16, dist_c, '')
z1 = persalys.Output('z1', '')
z2 = persalys.Output('z2', 'fake output')
inputs = [z0, v0, m, c]
outputs = [z1, z2]
# mesh model
meshModel = persalys.GridMeshModel(ot.Interval(0., 12.), [20])
# Python model
code = 'from math import exp\n\ndef _exec(z0,v0,m,c):\n g = 9.81\n zmin = 0.\n tau = m / c\n vinf = -m * g / c\n\n # mesh nodes\n t = getMesh().getVertices()\n\n z = [max(z0 + vinf * t_i[0] + tau * (v0 - vinf) * (1 - exp(-t_i[0] / tau)), zmin) for t_i in t]\n z2 = [2*max(z0 + vinf * t_i[0] + tau * (v0 - vinf) * (1 - exp(-t_i[0] / tau)), zmin) for t_i in t]\n\n return z, z2'
PhysicalModel_1 = persalys.PythonFieldModel('PhysicalModel_1', meshModel, inputs, outputs, code)
Study_0.add(PhysicalModel_1)
# central tendency
#! /usr/bin/env python
import openturns as ot
import persalys
myStudy = persalys.Study('myStudy')
# Model
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)
# Monte Carlo ##
analysis = persalys.MonteCarloAnalysis('myMonteCarlo', model)
analysis.setLevelConfidenceInterval(0.93)
analysis.setMaximumCalls(1000)
analysis.setMaximumCoefficientOfVariation(-1)
analysis.setMaximumElapsedTime(30)
analysis.setSeed(2)
myStudy.add(analysis)
print(analysis)
analysis.run()
#! /usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
import os
ot.RandomGenerator.SetSeed(0)
anOTStudy = persalys.Study('anOTStudy')
# Model
X0 = persalys.Input('X0', 1, ot.Normal())
X1 = persalys.Input('X1', 2)
X2 = persalys.Input('X2', 2)
X3 = persalys.Input('X3', 2)
Y0 = persalys.Output('Y0')
Y1 = persalys.Output('Y1')
Y2 = persalys.Output('Y2')
model = persalys.SymbolicPhysicalModel('aModelPhys', [X0, X1, X2, X3],
[Y0, Y1, Y2], ['sin(X0)+8*X1'] * 3)
anOTStudy.add(model)
# Observations ##
filename = 'normal2.csv'
sample = ot.Normal(8).getSample(10)
sample.exportToCSVFile(filename)
obs = persalys.Observations("obs", model, filename, [7, 3], [6, 2],
["X3", "X1"], ["Y2", "Y1"])
