# 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
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)
#!/usr/bin/env python
# coding: utf-8
from __future__ import print_function
import openturns as ot
import persalys
# mesh model
meshModel0 = persalys.GridMeshModel()
print(meshModel0)
# mesh model
meshModel1 = persalys.GridMeshModel([persalys.Variable('x', 'aDescription')],
ot.Interval(0., 12.), ot.Indices(1, 20))
print(meshModel1)
filename = 'mesh.csv'
mesh = ot.IntervalMesher(ot.Indices(1, 20)).build(ot.Interval(0., 12.))
mesh.getVertices().exportToCSVFile(filename)
# mesh model
meshModel2 = persalys.ImportedMeshModel()
print(meshModel2)
# mesh model
meshModel3 = persalys.ImportedMeshModel(
[persalys.Variable('z', 'aDescription')], 'mesh.csv', ot.Indices(1, 0))