{{ objname }} distribution
{{ underline }}{{ underline }}

.. plot::
    :include-source: False

    import openturns as ot
    from matplotlib import pyplot as plt
    from openturns.viewer import View
    if (ot.{{ objname }}().__class__.__name__=='ComposedDistribution'):
        correlation = ot.CorrelationMatrix(2)
        correlation[1, 0] = 0.25
        aCopula = ot.NormalCopula(correlation)
        marginals = [ot.Normal(1.0, 2.0), ot.Normal(2.0, 3.0)]
        distribution = ot.ComposedDistribution(marginals, aCopula)
    elif (ot.{{ objname }}().__class__.__name__=='CumulativeDistributionNetwork'):
        distribution = ot.CumulativeDistributionNetwork([ot.Normal(2),ot.Dirichlet([0.5, 1.0, 1.5])], ot.BipartiteGraph([[0,1], [0,1]]))
    elif (ot.{{ objname }}().__class__.__name__=='Histogram'):
        distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15])
    else:
        distribution = ot.{{ objname }}()
    dimension = distribution.getDimension()
    if dimension <= 2:
        if distribution.getDimension() == 1:
            distribution.setDescription(['$x$'])
            pdf_graph = distribution.drawPDF()
            cdf_graph = distribution.drawCDF()
            fig = plt.figure(figsize=(10, 4))
            plt.suptitle(str(distribution))
            pdf_axis = fig.add_subplot(121)
            cdf_axis = fig.add_subplot(122)
            View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)
            View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False)
        else:
            distribution.setDescription(['$x_1$', '$x_2$'])
            pdf_graph = distribution.drawPDF()
            fig = plt.figure(figsize=(10, 5))
            plt.suptitle(str(distribution))
            pdf_axis = fig.add_subplot(111)
            View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False)

.. currentmodule:: {{ module }}

.. autoclass:: {{ objname }}

   {% block methods %}
   .. automethod:: __init__
   {% endblock %}
