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

.. only:: html

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

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

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

.. _sphx_glr_examples_doe_plot_doe_from_array.py:


Use a design of experiments from an array
=========================================

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.. code-block:: Python


    from __future__ import annotations

    import numpy as np

    from gemseo import create_design_space
    from gemseo import create_discipline
    from gemseo import create_scenario








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Let us consider a discipline implementing the function :math:`y=a*b`

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.. code-block:: Python

    discipline = create_discipline("AnalyticDiscipline", expressions={"y": "a*b"})








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where :math:`a,b\in\{1,2,\ldots,10\}`:

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.. code-block:: Python

    design_space = create_design_space()
    design_space.add_variable("a", 1, design_space.DesignVariableType.INTEGER, 1, 10)
    design_space.add_variable("b", 1, design_space.DesignVariableType.INTEGER, 1, 10)








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We want to evaluate this discipline over this design space
by using the following input samples:

.. GENERATED FROM PYTHON SOURCE LINES 46-50

.. code-block:: Python

    sample_1 = [1.0, 2.0]
    sample_2 = [2.0, 3.0]
    samples = np.array([sample_1, sample_2])








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For that, we can create a scenario and execute it with a :class:`.CustomDOE`
with the option "samples":

.. GENERATED FROM PYTHON SOURCE LINES 53-58

.. code-block:: Python

    scenario = create_scenario(
        [discipline], "DisciplinaryOpt", "y", design_space, scenario_type="DOE"
    )
    scenario.execute({"algo": "CustomDOE", "algo_options": {"samples": samples}})





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    {'eval_jac': False, 'algo_options': {'samples': array([[1., 2.],
           [2., 3.]])}, 'algo': 'CustomDOE'}



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Then,
we can display the content of the database as a :class:`.Dataset`
and check the values of the output,
which should be the product of :math:`a` and :math:`b`:

.. GENERATED FROM PYTHON SOURCE LINES 63-66

.. code-block:: Python

    opt_problem = scenario.formulation.opt_problem
    dataset = opt_problem.to_dataset(name="custom_sampling", opt_naming=False)
    dataset





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          <th>GROUP</th>
          <th colspan="2" halign="left">inputs</th>
          <th>outputs</th>
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        <tr>
          <th>VARIABLE</th>
          <th>a</th>
          <th>b</th>
          <th>y</th>
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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.031 seconds)


.. _sphx_glr_download_examples_doe_plot_doe_from_array.py:

.. only:: html

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

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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_