.. 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_deterministic_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_deterministic_doe.py>`
        to download the full example code

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

.. _sphx_glr_auto_reliability_sensitivity_design_of_experiments_plot_create_deterministic_doe.py:


Create a deterministic design of experiments
============================================

.. GENERATED FROM PYTHON SOURCE LINES 6-13

.. code-block:: Python

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

    ot.Log.Show(ot.Log.NONE)









.. GENERATED FROM PYTHON SOURCE LINES 14-24

Four types of deterministic designs of experiments are available:

- :class:`~openturns.Axial`
- :class:`~openturns.Factorial`
- :class:`~openturns.Composite`
- :class:`~openturns.Box`

Each type of deterministic design is discretized differently according to a number of levels.

Functionally speaking, a design is a :class:`~openturns.Sample` that lies within the unit cube :math:`(0,1)^d` and can be scaled and moved to cover the desired box.

.. GENERATED FROM PYTHON SOURCE LINES 27-28

We will use the following function to plot bi-dimensional samples.

.. GENERATED FROM PYTHON SOURCE LINES 31-40

.. code-block:: Python

    def drawBidimensionalSample(sample, title):
        n = sample.getSize()
        graph = ot.Graph("%s, size=%d" % (title, n), r"$X_1$", r"$X_2$", True, "")
        # cloud = ot.Cloud(sample)
        cloud = ot.Cloud(sample, "blue", "fsquare", "")
        graph.add(cloud)
        return graph









.. GENERATED FROM PYTHON SOURCE LINES 41-43

Axial design
------------

.. GENERATED FROM PYTHON SOURCE LINES 45-51

.. code-block:: Python

    levels = [1.0, 1.5, 3.0]
    experiment = ot.Axial(2, levels)
    sample = experiment.generate()
    graph = drawBidimensionalSample(sample, "Axial")
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_001.png
   :alt: Axial, size=13
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 52-54

Use \*= to rescale and += to move a design.
Pay attention to the grid in the next graph.

.. GENERATED FROM PYTHON SOURCE LINES 56-61

.. code-block:: Python

    sample *= 2.0
    sample += [5.0, 8.0]
    graph = drawBidimensionalSample(sample, "Axial")
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_002.png
   :alt: Axial, size=13
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 62-65

Factorial design
----------------


.. GENERATED FROM PYTHON SOURCE LINES 67-74

.. code-block:: Python

    experiment = ot.Factorial(2, levels)
    sample = experiment.generate()
    sample *= 2.0
    sample += [5.0, 8.0]
    graph = drawBidimensionalSample(sample, "Factorial")
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_003.png
   :alt: Factorial, size=13
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 75-81

Composite design
----------------

A composite design is a stratified design of experiments enabling to create a pattern as the union of an Axial pattern and a Factorial one.
The number of points generated is :math:`1 + n_{\mathrm{level}}(2n+2^n)`.


.. GENERATED FROM PYTHON SOURCE LINES 83-90

.. code-block:: Python

    experiment = ot.Composite(2, levels)
    sample = experiment.generate()
    sample *= 2.0
    sample += [5.0, 8.0]
    graph = drawBidimensionalSample(sample, "Composite")
    view = viewer.View(graph)




.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_004.png
   :alt: Composite, size=25
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 91-94

Grid design
-----------


.. GENERATED FROM PYTHON SOURCE LINES 96-104

.. code-block:: Python

    levels = [3, 4]
    experiment = ot.Box(levels)
    sample = experiment.generate()
    sample *= 2.0
    sample += [5.0, 8.0]
    graph = drawBidimensionalSample(sample, "Box")
    view = viewer.View(graph)
    plt.show()



.. image-sg:: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_005.png
   :alt: Box, size=30
   :srcset: /auto_reliability_sensitivity/design_of_experiments/images/sphx_glr_plot_create_deterministic_doe_005.png
   :class: sphx-glr-single-img






.. _sphx_glr_download_auto_reliability_sensitivity_design_of_experiments_plot_create_deterministic_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_deterministic_doe.ipynb <plot_create_deterministic_doe.ipynb>`

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

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