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#!/usr/bin/env python
import openturns as ot
import openturns.testing
import persalys
myStudy = persalys.Study("myStudy")
# Model
dist_Q = ot.Normal(10200, 100)
dist_E = ot.Normal(3000, 15)
dist_C = ot.Normal(4000, 60)
Q = persalys.Input("Q", 10200, dist_Q, "Primary energy (W.h)")
E = persalys.Input("E", 3000, dist_E, "Produced electric energy (W.h)")
C = persalys.Input("C", 4000, dist_C, "Valued thermal energy (W.h)")
Ep = persalys.Output("Ep", "Primary energy savings (W.h)")
model = persalys.SymbolicPhysicalModel(
"myPhysicalModel", [Q, E, C], [Ep], ["1-(Q/((E/((1-0.05)*0.54))+(C/0.8)))"]
)
myStudy.add(model)
outputSample = [
[0.0600385],
[0.0812696],
[0.0684305],
[0.0892884],
[0.0292232],
[0.0511503],
[0.0378903],
[0.059432],
]
# Design of Experiment - Parametric analysis ##
bounds = ot.Interval([10035.5, 2975.33, 3901.31], [10364.5, 3024.67, 4098.69])
values = [[bounds.getLowerBound()[i], bounds.getUpperBound()[i]] for i in range(3)]
aDesign = persalys.GridDesignOfExperiment("aDesign_0", model, values)
myStudy.add(aDesign)
aDesign.run()
# Comparaison
openturns.testing.assert_almost_equal(
outputSample, aDesign.getResult().getDesignOfExperiment().getOutputSample(), 1e-5
)
# Taylor Expansions ##
taylorExpansionsMoments = persalys.TaylorExpansionMomentsAnalysis(
"myTaylorExpansionMoments", model
)
myStudy.add(taylorExpansionsMoments)
taylorExpansionsMoments.run()
taylorExpansionsMomentsResult = taylorExpansionsMoments.getResult()
# Comparaison
openturns.testing.assert_almost_equal(
0.059730458221, taylorExpansionsMomentsResult.getMeanFirstOrder()[0], 1e-13
)
# Monte Carlo ##
montecarlo = persalys.MonteCarloAnalysis("myMonteCarlo", model)
montecarlo.setMaximumCalls(1000)
montecarlo.setMaximumCoefficientOfVariation(-1)
myStudy.add(montecarlo)
montecarlo.run()
montecarloResult = montecarlo.getResult()
# Comparaison
openturns.testing.assert_almost_equal(
0.0597109963361, montecarloResult.getMean()[3][0], 1e-13
)
openturns.testing.assert_almost_equal(
0.0114128746587, montecarloResult.getStandardDeviation()[3][0], 1e-13
)
meanCI = montecarloResult.getMeanConfidenceInterval()
openturns.testing.assert_almost_equal(0.0590036320343, meanCI.getLowerBound()[3], 1e-13)
openturns.testing.assert_almost_equal(0.0604183606379, meanCI.getUpperBound()[3], 1e-13)
stdCi = montecarloResult.getStdConfidenceInterval()
openturns.testing.assert_almost_equal(0.0109336748621, stdCi.getLowerBound()[3], 1e-13)
openturns.testing.assert_almost_equal(0.0119363302339, stdCi.getUpperBound()[3], 1e-13)
# Sobol ##
sobol = persalys.SobolAnalysis("mySobol", model)
sobol.setReplicationSize(200)
sobol.setMaximumCalls(1000)
myStudy.add(sobol)
sobol.run()
sobolResult = sobol.getResult()
# Comparaison
firstOrderIndicesValues = [[0.643987, 0.0183602, 0.255834]]
totalIndicesValues = [[0.610267, 0.0494237, 0.280706]]
openturns.testing.assert_almost_equal(
firstOrderIndicesValues, sobolResult.getFirstOrderIndices(), 1e-6
)
openturns.testing.assert_almost_equal(
totalIndicesValues, sobolResult.getTotalIndices(), 1e-6
)
# SRC ##
src = persalys.SRCAnalysis("mySRC", model)
myStudy.add(src)
src.run()
srcResult = src.getResult()
# Comparaison
openturns.testing.assert_almost_equal(0.628946, srcResult.getIndices()[0][0], 3e-2)
openturns.testing.assert_almost_equal(0.0476118, srcResult.getIndices()[0][1], 3e-2)
openturns.testing.assert_almost_equal(0.318226, srcResult.getIndices()[0][2], 3e-2)
# Chaos ##
values = [
[
10035.5,
10072.1,
10108.6,
10145.2,
10181.7,
10218.3,
10254.8,
10291.4,
10327.9,
10364.5,
],
[
2975.33,
2980.81,
2986.29,
2991.78,
2997.26,
3002.74,
3008.22,
3013.71,
3019.19,
3024.67,
],
[
3901.31,
3923.24,
3945.17,
3967.1,
3989.03,
4010.97,
4032.9,
4054.83,
4076.76,
4098.69,
],
]
design_1 = persalys.GridDesignOfExperiment("aDesign_1", model, values)
design_1.run()
myStudy.add(design_1)
chaos = persalys.FunctionalChaosAnalysis("chaos_0", design_1)
chaos.setChaosDegree(2)
chaos.setSparseChaos(False)
myStudy.add(chaos)
chaos.run()
chaosResult = chaos.getResult()
# Comparaison
openturns.testing.assert_almost_equal(chaosResult.getSobolResult().getFirstOrderIndices()[0], [0.63572, 0.0480642, 0.316216])
openturns.testing.assert_almost_equal(chaosResult.getSobolResult().getTotalIndices()[0], [0.63572, 0.0480642, 0.316216])
# script
script = myStudy.getPythonScript()
print(script)
exec(script)
|
