print("Morris experiment generated from grid = ", sample1)
print("Use Case #2 : generate trajectories from initial lhs design")
size = 20
# Generate an LHS design
dist = ot.ComposedDistribution(2 * [ot.Uniform(0, 1)])
experiment = ot.LHSExperiment(dist, size, True, False)
lhsDesign = experiment.generate()
print("Initial LHS design = ", lhsDesign)
# Generate designs
morris_experiment_lhs = otmorris.MorrisExperimentLHS(lhsDesign, r)
lhs_bound = morris_experiment_lhs.getBounds()
sample2 = morris_experiment.generate()
print("Morris experiment generated from LHS = ", sample2)
# Define model
model = ot.SymbolicFunction(["x", "y"], ["cos(x)*y + sin(y)*x + x*y -0.1"])
# Define Morris method with two designs
morrisEE1 = otmorris.Morris(sample1, model(sample1), grid_bound)
morrisEE2 = otmorris.Morris(sample2, model(sample2), lhs_bound)
print("Using level grid, E(|EE|) = ",
morrisEE1.getMeanAbsoluteElementaryEffects())
print(" V(|EE|)^{1/2} = ",
morrisEE1.getStandardDeviationElementaryEffects())
print("Using initial LHS, E(|EE|) = ",
morrisEE2.getMeanAbsoluteElementaryEffects())
print(" V(|EE|)^{1/2} = ",
morrisEE2.getStandardDeviationElementaryEffects())
import openturns as ot import otmorris from otmorris.plot_sensitivity import PlotEE # Number of trajectories r = 10 # Define experiments in [0,1]^20 # p-levels p = 5 morris_experiment = otmorris.MorrisExperiment([p] * 20, r) X = morris_experiment.generate() f = ot.NumericalMathFunction(otmorris.MorrisFunction()) Y = f(X) # Evaluate Elementary effects (ee) ee = otmorris.Morris(X, Y) # Compute mu/sigma mean = ee.getMeanAbsoluteElementaryEffects() sigma = ee.getStandardDeviationElementaryEffects() fig = PlotEE(ee) fig.show()
print("Morris experiment generated from grid = ", sample1)
print("Use Case #2 : generate trajectories from initial lhs design")
size = 20
# Generate an LHS design
dist = ot.ComposedDistribution(2 * [ot.Uniform(0, 1)])
experiment = ot.LHSExperiment(dist, size, True, False)
lhsDesign = experiment.generate()
print("Initial LHS design = ", lhsDesign)
# Generate designs
morris_experiment_lhs = otmorris.MorrisExperiment(lhsDesign, r)
sample2 = morris_experiment.generate()
print("Morris experiment generated from LHS = ", sample2)
# Define model
model = ot.NumericalMathFunction(["x", "y"],
["cos(x)*y + sin(y)*x + x*y -0.1"])
# Define Morris method with two designs
morrisEE1 = otmorris.Morris(sample1, model(sample1))
morrisEE2 = otmorris.Morris(sample2, model(sample2))
print("Using level grid, E(|EE|) = ",
morrisEE1.getMeanAbsoluteElementaryEffects())
print(" V(|EE|)^{1/2} = ",
morrisEE1.getStandardDeviationElementaryEffects())
print("Using initial LHS, E(|EE|) = ",
morrisEE2.getMeanAbsoluteElementaryEffects())
print(" V(|EE|)^{1/2} = ",
morrisEE2.getStandardDeviationElementaryEffects())
E = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(3e4, 0.12))
F = ot.ParametrizedDistribution(ot.LogNormalMuSigmaOverMu(0.1, 0.20))
list_marginals = [L, b, h, E, F]
distribution = ot.ComposedDistribution(list_marginals)
distribution.setDescription(('L', 'b', 'h', 'E', 'F'))
dim = distribution.getDimension()
level_number = 4
trajectories = 10
jump_step = int(level_number / 2)
levels = [level_number] * dim
# set the bounds of the grid experiment
bound = ot.Interval(
[marginal.computeQuantile(0.01)[0] for marginal in list_marginals],
[marginal.computeQuantile(0.99)[0] for marginal in list_marginals])
experiment = otmorris.MorrisExperimentGrid(levels, bound, trajectories)
experiment.setJumpStep(ot.Indices([jump_step] * dim))
# create and compute the design of experiments
input_sample = experiment.generate()
print("Morris experiment generated from grid = ")
print(input_sample)
output_sample = poutre(input_sample)
# run the Morris analysis
morris = otmorris.Morris(input_sample, output_sample, bound)
print("E(|EE|) = ", morris.getMeanAbsoluteElementaryEffects())
print("E(EE) = ", morris.getMeanElementaryEffects())
print("V(|EE|)^{1/2} = ", morris.getStandardDeviationElementaryEffects())
import openturns as ot import otmorris from otmorris.plot_sensitivity import PlotEE # Number of trajectories r = 10 dim = 20 # Define an LHS experiment of size 50 in [0, 1]^20 size = 50 dist = ot.ComposedDistribution([ot.Uniform(0, 1)] * dim) lhs_experiment = ot.LHSExperiment(dist, size, True, False) lhsDesign = lhs_experiment.generate() morris_experiment = otmorris.MorrisExperimentLHS(lhsDesign, r) bounds = ot.Interval(dim) # [0, 1]^2 X = morris_experiment.generate() f = ot.Function(otmorris.MorrisFunction()) Y = f(X) # Evaluate Elementary effects (ee) ee = otmorris.Morris(X, Y, bounds) # Compute mu/sigma mean = ee.getMeanAbsoluteElementaryEffects() sigma = ee.getStandardDeviationElementaryEffects() fig = PlotEE(ee, title="Elementary Effects using LHS") fig.show()
