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{{ objname }}
{{ underline }}{{ underline }}
.. plot::
:include-source: False
import openturns as ot
import openturns.experimental as otexp
from matplotlib import pyplot as plt
from openturns.viewer import View
if "{{ objname }}" == "EmpiricalBernsteinCopula":
sample = ot.Dirichlet([1.0, 2.0, 3.0]).getSample(100)
copula = ot.EmpiricalBernsteinCopula(sample, 4)
elif "{{ objname }}" == "ExtremeValueCopula":
copula = ot.ExtremeValueCopula(ot.SymbolicFunction("t", "t^3/2-t/2+1"))
elif "{{ objname }}" == "MaximumEntropyOrderStatisticsCopula":
marginals = [ot.Beta(1.5, 3.2, 0.0, 1.0), ot.Beta(2.0, 4.3, 0.5, 1.2)]
copula = ot.MaximumEntropyOrderStatisticsCopula(marginals)
elif "{{ objname }}" == "NormalCopula":
R = ot.CorrelationMatrix(2)
R[1, 0] = 0.8
copula = ot.NormalCopula(R)
elif "{{ objname }}" == "SklarCopula":
student = ot.Student(3.0, [1.0] * 2, [3.0] * 2, ot.CorrelationMatrix(2))
copula = ot.SklarCopula(student)
elif "{{ objname }}" == "StudentCopula":
R = ot.CorrelationMatrix(2)
R[1, 0] = 0.3
copula = ot.StudentCopula(3.0, R)
else:
copula = ot.{{ objname }}()
if copula.getDimension() == 1:
copula = ot.{{ objname }}(2)
copula.setDescription(["$u_1$", "$u_2$"])
pdf_graph = copula.drawPDF()
cdf_graph = copula.drawCDF()
fig = plt.figure(figsize=(10, 4))
pdf_axis = fig.add_subplot(121)
cdf_axis = fig.add_subplot(122)
View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False, square_axes=True)
View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False, square_axes=True)
title = str(copula)[:100].split("\n")[0]
fig.suptitle(title)
.. currentmodule:: {{ module }}
.. autoclass:: {{ objname }}
{% block methods %}
.. automethod:: __init__
{% endblock %}
.. minigallery:: {{module}}.{{objname}}
:add-heading: Examples using the class
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