.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_reliability_sensitivity/design_of_experiments/plot_create_random_doe.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py:


Create a random design of experiments
=====================================

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Abstract
--------

Random designs of experiments can be generated from probability distributions.

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.. code-block:: Python

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








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We create the underlying distribution: a standard 2-dimensional normal distribution.

.. GENERATED FROM PYTHON SOURCE LINES 19-22

.. code-block:: Python

    distribution = ot.Normal(2)
    size = 50








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The Monte Carlo design of experiments
-------------------------------------

We build the experiment  with the :class:`~openturns.MonteCarloExperiment` class :

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.. code-block:: Python

    experiment = ot.MonteCarloExperiment(distribution, size)
    sample = experiment.generate()








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We draw the design of experiments as a :class:`~openturns.Cloud`

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.. code-block:: Python

    graph = ot.Graph("Monte Carlo design", r"$x_1$", r"$x_2$", True, "")
    cloud = ot.Cloud(sample, "blue", "fsquare", "")
    graph.add(cloud)
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_random_doe_001.svg
   :alt: Monte Carlo design
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_random_doe_001.svg
   :class: sphx-glr-single-img





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Latin Hypercube Sampling
------------------------

We build the LHS design of experiments with the :class:`~openturns.LHSExperiment` class :

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.. code-block:: Python

    experiment = ot.LHSExperiment(distribution, size)
    sample = experiment.generate()








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We draw the LHS design of experiments as a cloud :

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.. code-block:: Python

    graph = ot.Graph("LHS design", r"$x_1$", r"$x_2$", True, "")
    cloud = ot.Cloud(sample, "blue", "fsquare", "")
    graph.add(cloud)
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_random_doe_002.svg
   :alt: LHS design
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_random_doe_002.svg
   :class: sphx-glr-single-img





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Display figures

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.. code-block:: Python

    plt.show()








.. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_create_random_doe.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_create_random_doe.ipynb <plot_create_random_doe.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_create_random_doe.py <plot_create_random_doe.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_create_random_doe.zip <plot_create_random_doe.zip>`