MaximumEntropyOrderStatisticsCopula ========================================================================================== .. plot:: :include-source: False import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.MaximumEntropyOrderStatisticsCopula().__class__.__name__ == 'EmpiricalBernsteinCopula': sample = ot.Dirichlet([1.0, 2.0, 3.0]).getSample(100) copula = ot.EmpiricalBernsteinCopula(sample, 4) elif ot.MaximumEntropyOrderStatisticsCopula().__class__.__name__ == 'ExtremeValueCopula': copula = ot.ExtremeValueCopula(ot.SymbolicFunction("t", "t^3/2-t/2+1")) elif ot.MaximumEntropyOrderStatisticsCopula().__class__.__name__ == '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 ot.MaximumEntropyOrderStatisticsCopula().__class__.__name__ == 'NormalCopula': R = ot.CorrelationMatrix(2) R[1, 0] = 0.8 copula = ot.NormalCopula(R) elif ot.MaximumEntropyOrderStatisticsCopula().__class__.__name__ == 'SklarCopula': student = ot.Student(3.0, [1.0]*2, [3.0]*2, ot.CorrelationMatrix(2)) copula = ot.SklarCopula(student) else: copula = ot.MaximumEntropyOrderStatisticsCopula() if copula.getDimension() == 1: copula = ot.MaximumEntropyOrderStatisticsCopula(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:: openturns .. autoclass:: MaximumEntropyOrderStatisticsCopula .. automethod:: __init__