.. 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








.. GENERATED FROM PYTHON SOURCE LINES 34-35

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

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

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








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

.. GENERATED FROM PYTHON SOURCE LINES 39-43

.. 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 setting "samples":

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.. code-block:: Python

    scenario = create_scenario(
        [discipline],
        "y",
        design_space,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )
    scenario.execute(algo_name="CustomDOE", samples=samples)








.. GENERATED FROM PYTHON SOURCE LINES 63-66

Note that both the formulation settings passed to :func:`.create_scenario` and the
algorithm settings passed to :meth:`~.BaseDriverLibrary.execute` can be provided via a Pydantic model. For
more information, see :ref:`formulation_settings` and :ref:`algorithm_settings`.

.. GENERATED FROM PYTHON SOURCE LINES 68-72

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 72-75

.. code-block:: Python

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





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          <th>GROUP</th>
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          <th>outputs</th>
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          <th>VARIABLE</th>
          <th>a</th>
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.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.023 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>`

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

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


.. only:: html

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

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