import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
factory = ot.LogNormalFactory()
ref = factory.build()
dimension = ref.getDimension()
if dimension <= 2:
sample = ref.getSample(50)
distribution = factory.build(sample)
if dimension == 1:
distribution.setDescription(['$t$'])
pdf_graph = distribution.drawPDF(256)
cloud = ot.Cloud(sample, ot.Sample(sample.getSize(), 1))
cloud.setColor('blue')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
pdf_axis = fig.add_subplot(111)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
else:
sample = ref.getSample(500)
distribution.setDescription(['$t_0$', '$t_1$'])
pdf_graph = distribution.drawPDF([256] * 2)
cloud = ot.Cloud(sample)
cloud.setColor('red')
cloud.setPointStyle('fcircle')
pdf_graph.add(cloud)
fig = plt.figure(figsize=(10, 4))
plt.suptitle(str(distribution))
# graph.draw('curve7.png')
view = View(graph)
# view.save('curve7.png')
view.ShowAll(block=True)
# Pie
graph = ot.SobolIndicesAlgorithm.DrawImportanceFactors(
[.4, .3, .2, .1], ['a0', 'a1', 'a2', 'a3'], 'Zou')
# graph.draw('curve8.png')
view = View(graph)
# view.save('curve8.png')
view.show()
# Convergence graph curve
aCollection = []
aCollection.append(ot.LogNormalFactory().build(
ot.LogNormalMuSigma()([300.0, 30.0, 0.0])))
aCollection.append(ot.Normal(75e3, 5e3))
myDistribution = ot.ComposedDistribution(aCollection)
vect = ot.RandomVector(myDistribution)
LimitState = ot.SymbolicFunction(('R', 'F'), ('R-F/(pi_*100.0)', ))
G = ot.CompositeRandomVector(LimitState, vect)
myEvent = ot.ThresholdEvent(G, ot.Less(), 0.0)
experiment = ot.MonteCarloExperiment()
myAlgo = ot.ProbabilitySimulationAlgorithm(myEvent, experiment)
myAlgo.setMaximumCoefficientOfVariation(0.05)
myAlgo.setMaximumOuterSampling(int(1e5))
myAlgo.run()
graph = myAlgo.drawProbabilityConvergence()
# graph.draw('curve10.png')
view = View(graph)
# view.save('curve10.png')
from __future__ import print_function
import openturns as ot
from openturns.viewer import View
# Generate some data
size = 250
sample = ot.LogNormal(0.0, 0.4).getSample(size)
# Estimate the distribution
parametric_estimate = ot.LogNormalFactory().build(sample)
nonparametric_estimate = ot.KernelSmoothing().build(sample)
# Draw a non parametric estimate and the parametric estimate
graph = parametric_estimate.drawPDF(0.0, 4.0)
graph.add(nonparametric_estimate.drawPDF(0.0, 4.0))
graph.add(ot.Cloud(sample, ot.Sample(size, 1)))
graph.setLegendPosition("topright")
graph.setXTitle(r"$x$")
graph.setYTitle(r"$p_X$")
graph.setTitle(r"Parametric vs nonparametric estimation")
graph.setColors(["red", "blue", "green"])
graph.setLegends(["parametric", "nonparametric", "data"])
view = View(graph, (800, 600))
view.save("../plot_distribution_fitting.png")
view.close()