.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/doe/algorithms/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_algorithms_plot_doe_from_array.py>`
        to download the full example code.

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

.. _sphx_glr_examples_doe_algorithms_plot_doe_from_array.py:


DOE from an array
=================

.. GENERATED FROM PYTHON SOURCE LINES 24-33

.. 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"})








.. GENERATED FROM PYTHON SOURCE LINES 38-39

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)








.. GENERATED FROM PYTHON SOURCE LINES 44-46

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])








.. GENERATED FROM PYTHON SOURCE LINES 51-53

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

.. GENERATED FROM PYTHON SOURCE LINES 53-62

.. code-block:: Python

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





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

 .. code-block:: none

        INFO - 16:25:03: *** Start DOEScenario execution ***
        INFO - 16:25:03: DOEScenario
        INFO - 16:25:03:    Disciplines: AnalyticDiscipline
        INFO - 16:25:03:    MDO formulation: DisciplinaryOpt
        INFO - 16:25:03: Optimization problem:
        INFO - 16:25:03:    minimize y(a, b)
        INFO - 16:25:03:    with respect to a, b
        INFO - 16:25:03:    over the design space:
        INFO - 16:25:03:       +------+-------------+-------+-------------+---------+
        INFO - 16:25:03:       | Name | Lower bound | Value | Upper bound | Type    |
        INFO - 16:25:03:       +------+-------------+-------+-------------+---------+
        INFO - 16:25:03:       | a    |      1      |  None |      10     | integer |
        INFO - 16:25:03:       | b    |      1      |  None |      10     | integer |
        INFO - 16:25:03:       +------+-------------+-------+-------------+---------+
        INFO - 16:25:03: Solving optimization problem with algorithm CustomDOE:
        INFO - 16:25:03:     50%|█████     | 1/2 [00:00<00:00, 528.12 it/sec, feas=True, obj=2]
        INFO - 16:25:03:    100%|██████████| 2/2 [00:00<00:00, 877.38 it/sec, feas=True, obj=6]
        INFO - 16:25:03: Optimization result:
        INFO - 16:25:03:    Optimizer info:
        INFO - 16:25:03:       Status: None
        INFO - 16:25:03:       Message: None
        INFO - 16:25:03:    Solution:
        INFO - 16:25:03:       Objective: 2.0
        INFO - 16:25:03:       Design space:
        INFO - 16:25:03:          +------+-------------+-------+-------------+---------+
        INFO - 16:25:03:          | Name | Lower bound | Value | Upper bound | Type    |
        INFO - 16:25:03:          +------+-------------+-------+-------------+---------+
        INFO - 16:25:03:          | a    |      1      |   1   |      10     | integer |
        INFO - 16:25:03:          | b    |      1      |   2   |      10     | integer |
        INFO - 16:25:03:          +------+-------------+-------+-------------+---------+
        INFO - 16:25:03: *** End DOEScenario execution (time: 0:00:00.004791) ***




.. 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>
          <th colspan="2" halign="left">inputs</th>
          <th>outputs</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th>a</th>
          <th>b</th>
          <th>y</th>
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          <td>3</td>
          <td>6.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.016 seconds)


.. _sphx_glr_download_examples_doe_algorithms_plot_doe_from_array.py:

.. only:: html

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

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

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

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


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