IndividualChanceMeasure distribution
================================================================

.. plot::
    :include-source: False

    import openturns as ot
    import otrobopt
    from matplotlib import pyplot as plt
    from openturns.viewer import View

    thetaDist = ot.Normal(2.0, 0.1)
    if 'IndividualChanceMeasure' == 'WorstCaseMeasure':
        thetaDist = ot.Uniform(-1.0, 4.0)
    elif 'ChanceMeasure' in 'IndividualChanceMeasure':
        thetaDist = ot.Normal(1.0, 1.0)

    f_base = ot.SymbolicFunction(['x', 'theta'], ['x*theta'])
    f = ot.ParametricFunction(f_base, [1], thetaDist.getMean())

    if 'IndividualChanceMeasure' == 'JointChanceMeasure':
        measure = otrobopt.JointChanceMeasure(f, thetaDist, ot.GreaterOrEqual(), 0.95)
    elif 'IndividualChanceMeasure' == 'IndividualChanceMeasure':
        measure = otrobopt.IndividualChanceMeasure(f,thetaDist, ot.GreaterOrEqual(), [0.95])
    elif 'IndividualChanceMeasure' == 'MeanStandardDeviationTradeoffMeasure':
        measure =  otrobopt.MeanStandardDeviationTradeoffMeasure(f, thetaDist, [0.8])
    elif 'IndividualChanceMeasure' == 'QuantileMeasure':
        measure =  otrobopt.QuantileMeasure(f, thetaDist, 0.99)
    else:
        measure = otrobopt.IndividualChanceMeasure(f, thetaDist)

    N = 10
    experiment = ot.LHSExperiment(N)
    factory = otrobopt.MeasureFactory(experiment)
    discretizedMeasure = factory.build(measure)

    continuous_measure = otrobopt.MeasureFunction(measure)
    discretized_measure = otrobopt.MeasureFunction(discretizedMeasure)

    x_min = -2.0
    x_max = 2.0
    n_points = 128

    parametric_graph = f.draw(x_min, x_max, n_points)
    continuous_graph = continuous_measure.draw(x_min, x_max, n_points)
    discretized_graph = discretized_measure.draw(x_min, x_max, n_points)

    parametric_curve = parametric_graph.getDrawable(0)
    discretized_curve = discretized_graph.getDrawable(0)

    left_graph = ot.Graph(continuous_graph)
    left_graph.add(parametric_curve)
    #left_graph.setLegends(['measure', 'parametric function'])
    #left_graph.setLegendPosition('topright')
    left_graph.setColors(['blue', 'red'])

    right_graph = ot.Graph(continuous_graph)
    right_graph.add(discretized_curve)
    #right_graph.setLegends(['measure', 'discretized measure'])
    #right_graph.setLegendPosition('topright')
    right_graph.setColors(['blue', 'red'])

    fig = plt.figure(figsize=(10, 4))
    plt.suptitle(str(measure))
    left_axis = fig.add_subplot(121)
    right_axis = fig.add_subplot(122)

    View(left_graph, figure=fig, axes=[left_axis], add_legend=False)
    View(right_graph, figure=fig, axes=[right_axis], add_legend=False)


.. currentmodule:: otrobopt

.. autoclass:: IndividualChanceMeasure

   
   .. automethod:: __init__