import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
factory = ot.GumbelCopulaFactory()
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))
sobol.setReplicationSize(3)
sobol.setBlockSize(1)
sobol.setSeed(2)
sobol.setInterestVariables(['y0', 'y1'])
myStudy.add(sobol)
# 4-b SRC ##
src = persalys.SRCAnalysis('SRC', model1)
src.setSimulationsNumber(20)
src.setSeed(2)
src.setInterestVariables(['y0', 'y1'])
myStudy.add(src)
# 7- data analysis ##
dataAnalysis = persalys.DataAnalysis('DataAnalysis', model3)
myStudy.add(dataAnalysis)
# 8- Marginals inference ##
inference = persalys.InferenceAnalysis('inference', model3)
inference.setInterestVariables(['x_0', 'x_3'])
factories = [ot.NormalFactory(), ot.GumbelFactory()]
inference.setDistributionsFactories('x_3', factories)
inference.setLevel(0.1)
myStudy.add(inference)
# 9- Copula inference ##
copulaInference = persalys.CopulaInferenceAnalysis('copulaInference', model3)
factories = [ot.NormalCopulaFactory(), ot.GumbelCopulaFactory()]
copulaInference.setDistributionsFactories(['x_0', 'x_3'], factories)
myStudy.add(copulaInference)