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
mcAnalysis1 = persalys.FieldMonteCarloAnalysis("mcAnalysis", PhysicalModel_1)
mcAnalysis1.setMaximumCalls(10)
mcAnalysis1.setMaximumElapsedTime(1000)
mcAnalysis1.setBlockSize(5)
mcAnalysis1.setSeed(2)
mcAnalysis1.setInterestVariables(['z'])
mcAnalysis1.setKarhunenLoeveThreshold(2e-5)
Study_0.add(mcAnalysis1)
# evaluation
evaluation1 = persalys.FieldModelEvaluation("evaluation", PhysicalModel_1)
evaluation1.setInterestVariables(['z'])
Study_0.add(evaluation1)
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)
# evaluation
analysis1 = persalys.FieldModelEvaluation("analysis1", PhysicalModel_1)
Study_0.add(analysis1)
print(analysis1)
analysis1.run()
result1 = analysis1.getProcessSample()
print("result=", result1)
analysis2 = persalys.FieldModelEvaluation("analysis2", PhysicalModel_1,
[100, 55, 81, 17])
analysis2.setInterestVariables(['z2'])
Study_0.add(analysis2)
print(analysis2)
analysis2.run()
result2 = analysis2.getProcessSample()