.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.py: Create a Monte Carlo design of experiments ========================================== In this example we are going to create a MonteCarlo probabilistic design experiment. .. code-block:: default from __future__ import print_function import openturns as ot import openturns.viewer as viewer from matplotlib import pylab as plt ot.Log.Show(ot.Log.NONE) Define underlying distribution, size .. code-block:: default distribution = ot.Normal(2) size = 50 Create the design .. code-block:: default experiment = ot.MonteCarloExperiment(distribution, size) sample = experiment.generate() Plot the design .. code-block:: default graph = ot.Graph("MC design", "x1", "x2", True, "") cloud = ot.Cloud(sample, "blue", "fsquare", "") graph.add(cloud) view = viewer.View(graph) plt.show() .. image:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_monte_carlo_experiment_001.png :alt: MC design :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.064 seconds) .. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_monte_carlo_experiment.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_monte_carlo_experiment.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_monte_carlo_experiment.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_