# Graphical copula validation
# ---------------------------
#
# In this paragraph we visualize an estimated copula versus the data in the rank space.
#
# %%
# First we create data
marginals = [ot.Normal()] * 2
dist = ot.ComposedDistribution(marginals, ot.ClaytonCopula(3))
N = 500
sample = dist.getSample(N)
# %%
# We build a estimate copula from the previous sample using the :class:`~openturns.ClaytonCopulaFactory` :
estimated = ot.ClaytonCopulaFactory().build(sample)
# %%
# We represent data as a cloud in the rank space :
ranksTransf = ot.MarginalTransformationEvaluation(
marginals, ot.MarginalTransformationEvaluation.FROM)
rankSample = ranksTransf(sample)
rankCloud = ot.Cloud(rankSample, 'blue', 'plus', 'sample')
# %%
# We can plot the graph with rank sample and estimated copula :
myGraph = ot.Graph('Parametric estimation of the copula', 'X', 'Y', True,
'topleft')
myGraph.setLegendPosition('bottomright')
myGraph.add(rankCloud)
import openturns as ot
from matplotlib import pyplot as plt
from openturns.viewer import View
ot.RandomGenerator.SetSeed(0)
factory = ot.ClaytonCopulaFactory()
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))