Pareto distribution ================================ .. plot:: :include-source: False import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View if ot.Pareto().__class__.__name__ == 'Bernoulli': distribution = ot.Bernoulli(0.7) elif ot.Pareto().__class__.__name__ == 'Binomial': distribution = ot.Binomial(5, 0.2) elif ot.Pareto().__class__.__name__ == 'Hypergeometric': distribution = ot.Hypergeometric(10, 4, 7) elif ot.Pareto().__class__.__name__ == 'CumulativeDistributionNetwork': coll = [ot.Normal(2),ot.Dirichlet([0.5, 1.0, 1.5])] distribution = ot.CumulativeDistributionNetwork(coll, ot.BipartiteGraph([[0,1], [0,1]])) elif ot.Pareto().__class__.__name__ == 'Histogram': distribution = ot.Histogram([-1.0, 0.5, 1.0, 2.0], [0.45, 0.4, 0.15]) elif ot.Pareto().__class__.__name__ == 'KernelMixture': kernel = ot.Uniform() sample = ot.Normal().getSample(5) bandwith = [1.0] distribution = ot.KernelMixture(kernel, bandwith, sample) elif ot.Pareto().__class__.__name__ == 'MaximumDistribution': coll = [ot.Uniform(2.5, 3.5), ot.LogUniform(1.0, 1.2), ot.Triangular(2.0, 3.0, 4.0)] distribution = ot.MaximumDistribution(coll) elif ot.Pareto().__class__.__name__ == 'Multinomial': distribution = ot.Multinomial(5, [0.2]) elif ot.Pareto().__class__.__name__ == 'RandomMixture': coll = [ot.Triangular(0.0, 1.0, 5.0), ot.Uniform(-2.0, 2.0)] weights = [0.8, 0.2] cst = 3.0 distribution = ot.RandomMixture(coll, weights, cst) elif ot.Pareto().__class__.__name__ == 'TruncatedDistribution': distribution = ot.TruncatedDistribution(ot.Normal(2.0, 1.5), 1.0, 4.0) elif ot.Pareto().__class__.__name__ == 'UserDefined': distribution = ot.UserDefined([[0.0], [1.0], [2.0]], [0.2, 0.7, 0.1]) elif ot.Pareto().__class__.__name__ == 'ZipfMandelbrot': distribution = ot.ZipfMandelbrot(10, 2.5, 0.3) else: distribution = ot.Pareto() dimension = distribution.getDimension() title = str(distribution)[:100].split('\n')[0] if dimension == 1: distribution.setDescription(['$x$']) pdf_graph = distribution.drawPDF() cdf_graph = distribution.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) View(cdf_graph, figure=fig, axes=[cdf_axis], add_legend=False) fig.suptitle(title) elif dimension == 2: distribution.setDescription(['$x_1$', '$x_2$']) pdf_graph = distribution.drawPDF() pdf_graph.setTitle(title) fig = plt.figure(figsize=(10, 5)) pdf_axis = fig.add_subplot(111) View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False, square_axes=False) .. currentmodule:: openturns .. autoclass:: Pareto .. automethod:: __init__