{{ 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__ == 'ComposedCopula':
        distribution = ot.ComposedCopula([ot.ClaytonCopula(2.0), ot.GumbelCopula(3.0)])
    elif ot.{{ objname }}().__class__.__name__ == 'ComposedDistribution':
        R = ot.CorrelationMatrix(3)
        R[0,1]=0.5
        R[0,2]=0.3
        R[1,2]=-0.2
        copula = ot.NormalCopula(R)
        marginals = [ot.Uniform(1.0, 2.0), ot.Normal(2.0, 3.0), ot.Gamma(5.5, 2.0)]
        distribution = ot.ComposedDistribution(marginals, copula)
    elif ot.{{ objname }}().__class__.__name__ == 'BlockIndependentDistribution':
        R = ot.CorrelationMatrix(2)
        R[0,1]=0.5
        atom1 = ot.ComposedDistribution([ot.Exponential(2.0), ot.WeibullMax(2.0, 2.0)], ot.NormalCopula(R))
        atom2 = ot.ComposedDistribution([ot.Normal(2.0, 1.0), ot.Triangular(2.0, 3.0, 4.0)], ot.ClaytonCopula(3.0))
        distribution = ot.BlockIndependentDistribution([atom1, atom2])
    else:
        distribution = ot.{{ objname }}()
    dimension = distribution.getDimension()
    mean = distribution.getMean()
    std = distribution.getStandardDeviation()
    xMin = distribution.getRange().getLowerBound()
    xMax = distribution.getRange().getUpperBound()
    xMin = [max(xMin[i], mean[i] - 3.0 * std[i]) for i in range(dimension)]
    xMax = [min(xMax[i], mean[i] + 3.0 * std[i]) for i in range(dimension)]
    size = 1000
    sample = distribution.getSample(size)
    sMin = sample.getMin()
    sMax = sample.getMax()
    xMin = [min(xMin[i], sMin[i]) for i in range(dimension)]
    xMax = [max(xMax[i], sMax[i]) for i in range(dimension)]
    n_rows = dimension
    n_cols = dimension
    fig = plt.figure(figsize=(10, 10))
    for j in range(dimension):
        for i in range(dimension):
            pdf_axis = fig.add_subplot(n_rows, n_cols, j*dimension+i+1)
            if i == j:
                pdf_graph = distribution.drawMarginal1DPDF(i, xMin[i], xMax[i], 256)
            else:
                pdf_graph = distribution.drawMarginal2DPDF(i, j, [xMin[i], xMin[j]], [xMax[i], xMax[j]], [256]*2)            
                cloud = ot.Cloud(sample.getMarginal([i, j]))
                cloud.setPointStyle("dot")
                pdf_graph.add(cloud)
            pdf_graph.setTitle("")
            pdf_graph.setXTitle("")
            pdf_graph.setYTitle("")
            if i == 0:
                pdf_graph.setYTitle(r"$x_" + str(j) + r"$")
            if j == dimension-1:
                pdf_graph.setXTitle(r"$x_" + str(i) + r"$")
            View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False, square_axes=distribution.isCopula())
    title = str(distribution)[: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
