InverseNormalFactory ============================================================ .. plot:: :include-source: False import openturns as ot from matplotlib import pyplot as plt from openturns.viewer import View ot.RandomGenerator.SetSeed(0) factory = ot.InverseNormalFactory() ref = factory.build() dimension = ref.getDimension() if dimension <= 2: if dimension == 1: sample = ref.getSample(50) distribution = factory.build(sample) distribution.setDescription(['$t$']) pdf_graph = distribution.drawPDF(256) cloud = ot.Cloud(sample, ot.Sample(sample.getSize(), 1)) cloud.setColor('blue') cloud.setPointStyle('fcircle') pdf_graph.add(cloud) pdf_graph.setTitle(str(distribution)) fig = plt.figure(figsize=(10, 4)) pdf_axis = fig.add_subplot(111) View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False) else: sample = ref.getSample(500) distribution = factory.build(sample) distribution.setDescription(['$t_0$', '$t_1$']) pdf_graph = distribution.drawPDF([256]*2) cloud = ot.Cloud(sample) cloud.setColor('red') cloud.setPointStyle('fcircle') pdf_graph.add(cloud) pdf_graph.setTitle(str(distribution)) fig = plt.figure(figsize=(10, 4)) pdf_axis = fig.add_subplot(111) View(pdf_graph, figure=fig, axes=[pdf_axis], add_legend=False, square_axes=True) .. currentmodule:: openturns .. autoclass:: InverseNormalFactory .. automethod:: __init__