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

.. only:: html

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

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

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

.. _sphx_glr_examples_api_plot_scenario.py:


Scenario
========

.. GENERATED FROM PYTHON SOURCE LINES 25-41

.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_design_space
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo import get_available_scenario_types
    from gemseo import get_scenario_differentiation_modes
    from gemseo import get_scenario_inputs_schema
    from gemseo import get_scenario_options_schema
    from gemseo import monitor_scenario

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 42-56

In this example, we will discover the different functions of the API to
related to scenarios, which are the |g|' objects
dedicated to the resolution of a problem, e.g. optimization or trade-off,
associated with a list of disciplines and a design space. All classes
implementing scenarios inherit from :class:`.Scenario` which is an
abstract class. Classical concrete classes are :class:`.MDOScenario` and
:class:`.DOEScenario`, respectively dedicated to optimization and trade-off
problems.

Get available scenario type
---------------------------
The high-level function :func:`~gemseo.get_available_scenario_types` can be used
to get the available scenario types (:class:`.MDOScenario` and
:class:`.DOEScenario`).

.. GENERATED FROM PYTHON SOURCE LINES 56-58

.. code-block:: Python

    get_available_scenario_types()





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

 .. code-block:: none


    ['MDO', 'DOE']



.. GENERATED FROM PYTHON SOURCE LINES 59-63

Get scenario options schema
---------------------------
The :func:`.get_scenario_options_schema` function can be used
to get the options of a given scenario type:

.. GENERATED FROM PYTHON SOURCE LINES 63-65

.. code-block:: Python

    get_scenario_options_schema("MDO")





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

 .. code-block:: none


    {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name to be given to this scenario. If empty, use the name of the class.'}, 'maximize_objective': {'type': 'boolean', 'description': 'Whether to maximize the objective.'}, 'formulation_settings_model': {'type': 'null', 'description': 'The formulation settings as a Pydantic model. If ``None``, use ``**settings``.'}}}



.. GENERATED FROM PYTHON SOURCE LINES 66-92

Create a scenario
-----------------
The API function :func:`.create_scenario` can be used
to create a scenario:

- The four first arguments are mandatory:

  - ``disciplines``: the list of :class:`.Discipline`
    (or possibly, a single :class:`.Discipline`),
  - ``objective_name``: the name of the objective function
    (one of the discipline outputs),
  - ``design_space``: the :class:`.DesignSpace` or
    the file path of the design space,
  - either a ``formulation_name`` followed by its ``formulation_settings```; or
  - a ``formulation_settings_model`` (see :ref:`formulation_settings`).

- The other arguments are optional:

  - ``name``: scenario name,
  - ``scenario_type``: type of scenario,
    either `"MDO"` (default) or `"DOE"` ,
  - ``**formulation_settings``: settings passed to the formulation as keyword
    arguments when the ``formulation_settings_model`` was not provided.

- This function returns an instance of :class:`.MDOScenario` or
  :class:`.DOEScenario`.

.. GENERATED FROM PYTHON SOURCE LINES 92-113

.. code-block:: Python


    discipline = create_discipline("AnalyticDiscipline", expressions={"y": "x1+x2"})
    design_space = create_design_space()
    design_space.add_variable("x1", 1, "float", 0.0, 1.0)
    design_space.add_variable("x2", 1, "float", 0.0, 1.0)

    scenario = create_scenario(
        discipline,
        "y",
        design_space,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )
    scenario.execute(algo_name="PYDOE_FULLFACT", n_samples=25)
    scenario.post_process(
        post_name="ScatterPlotMatrix",
        variable_names=["x1", "x2", "y"],
        save=False,
        show=True,
    )




.. image-sg:: /examples/api/images/sphx_glr_plot_scenario_001.png
   :alt: plot scenario
   :srcset: /examples/api/images/sphx_glr_plot_scenario_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

        INFO - 11:43:10: *** Start DOEScenario execution ***
        INFO - 11:43:10: DOEScenario
        INFO - 11:43:10:    Disciplines: AnalyticDiscipline
        INFO - 11:43:10:    MDO formulation: DisciplinaryOpt
        INFO - 11:43:10: Optimization problem:
        INFO - 11:43:10:    minimize y(x1, x2)
        INFO - 11:43:10:    with respect to x1, x2
        INFO - 11:43:10:    over the design space:
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:       | x1   |      0      |  None |      1      | float |
        INFO - 11:43:10:       | x2   |      0      |  None |      1      | float |
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10: Solving optimization problem with algorithm PYDOE_FULLFACT:
        INFO - 11:43:10:      4%|▍         | 1/25 [00:00<00:00, 446.16 it/sec, obj=0]
        INFO - 11:43:10:      8%|▊         | 2/25 [00:00<00:00, 769.24 it/sec, obj=0.25]
        INFO - 11:43:10:     12%|█▏        | 3/25 [00:00<00:00, 1030.46 it/sec, obj=0.5]
        INFO - 11:43:10:     16%|█▌        | 4/25 [00:00<00:00, 1257.95 it/sec, obj=0.75]
        INFO - 11:43:10:     20%|██        | 5/25 [00:00<00:00, 1446.51 it/sec, obj=1]
        INFO - 11:43:10:     24%|██▍       | 6/25 [00:00<00:00, 1599.25 it/sec, obj=0.25]
        INFO - 11:43:10:     28%|██▊       | 7/25 [00:00<00:00, 1744.20 it/sec, obj=0.5]
        INFO - 11:43:10:     32%|███▏      | 8/25 [00:00<00:00, 1883.07 it/sec, obj=0.75]
        INFO - 11:43:10:     36%|███▌      | 9/25 [00:00<00:00, 2011.44 it/sec, obj=1]
        INFO - 11:43:10:     40%|████      | 10/25 [00:00<00:00, 2118.44 it/sec, obj=1.25]
        INFO - 11:43:10:     44%|████▍     | 11/25 [00:00<00:00, 2218.57 it/sec, obj=0.5]
        INFO - 11:43:10:     48%|████▊     | 12/25 [00:00<00:00, 2247.95 it/sec, obj=0.75]
        INFO - 11:43:10:     52%|█████▏    | 13/25 [00:00<00:00, 2253.70 it/sec, obj=1]
        INFO - 11:43:10:     56%|█████▌    | 14/25 [00:00<00:00, 2313.55 it/sec, obj=1.25]
        INFO - 11:43:10:     60%|██████    | 15/25 [00:00<00:00, 2381.14 it/sec, obj=1.5]
        INFO - 11:43:10:     64%|██████▍   | 16/25 [00:00<00:00, 2447.44 it/sec, obj=0.75]
        INFO - 11:43:10:     68%|██████▊   | 17/25 [00:00<00:00, 2500.99 it/sec, obj=1]
        INFO - 11:43:10:     72%|███████▏  | 18/25 [00:00<00:00, 2559.06 it/sec, obj=1.25]
        INFO - 11:43:10:     76%|███████▌  | 19/25 [00:00<00:00, 2612.50 it/sec, obj=1.5]
        INFO - 11:43:10:     80%|████████  | 20/25 [00:00<00:00, 2664.32 it/sec, obj=1.75]
        INFO - 11:43:10:     84%|████████▍ | 21/25 [00:00<00:00, 2706.50 it/sec, obj=1]
        INFO - 11:43:10:     88%|████████▊ | 22/25 [00:00<00:00, 2750.61 it/sec, obj=1.25]
        INFO - 11:43:10:     92%|█████████▏| 23/25 [00:00<00:00, 2797.01 it/sec, obj=1.5]
        INFO - 11:43:10:     96%|█████████▌| 24/25 [00:00<00:00, 2839.43 it/sec, obj=1.75]
        INFO - 11:43:10:    100%|██████████| 25/25 [00:00<00:00, 2872.26 it/sec, obj=2]
        INFO - 11:43:10: Optimization result:
        INFO - 11:43:10:    Optimizer info:
        INFO - 11:43:10:       Status: None
        INFO - 11:43:10:       Message: None
        INFO - 11:43:10:       Number of calls to the objective function by the optimizer: 25
        INFO - 11:43:10:    Solution:
        INFO - 11:43:10:       Objective: 0.0
        INFO - 11:43:10:       Design space:
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:          | x1   |      0      |   0   |      1      | float |
        INFO - 11:43:10:          | x2   |      0      |   0   |      1      | float |
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
        INFO - 11:43:10: *** End DOEScenario execution (time: 0:00:00.011125) ***

    <gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x704929cab350>



.. GENERATED FROM PYTHON SOURCE LINES 114-116

- The :func:`.get_scenario_inputs_schema` function can be used
  to get the inputs of a scenario:

.. GENERATED FROM PYTHON SOURCE LINES 116-118

.. code-block:: Python

    get_scenario_inputs_schema(scenario)





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

 .. code-block:: none


    {'$defs': {'_algo_enum': {'enum': ['CustomDOE', 'DiagonalDOE', 'MorrisDOE', 'OATDOE', 'OT_SOBOL', 'OT_RANDOM', 'OT_HASELGROVE', 'OT_REVERSE_HALTON', 'OT_HALTON', 'OT_FAURE', 'OT_MONTE_CARLO', 'OT_FACTORIAL', 'OT_COMPOSITE', 'OT_AXIAL', 'OT_OPT_LHS', 'OT_LHS', 'OT_LHSC', 'OT_FULLFACT', 'OT_SOBOL_INDICES', 'PYDOE_BBDESIGN', 'PYDOE_CCDESIGN', 'PYDOE_FF2N', 'PYDOE_FULLFACT', 'PYDOE_LHS', 'PYDOE_PBDESIGN', 'Halton', 'LHS', 'MC', 'PoissonDisk', 'Sobol'], 'title': 'algo_enum', 'type': 'string'}}, 'properties': {'algo_name': {'$ref': '#/$defs/_algo_enum', 'description': 'The name of the algorithm.'}, 'algo_settings': {'description': 'The settings for the algorithm.', 'title': 'Algo Settings', 'type': 'object'}}, 'required': ['algo_name'], 'title': 'Settings', 'type': 'object'}



.. GENERATED FROM PYTHON SOURCE LINES 119-123

Get scenario differentiation modes
----------------------------------
The :func:`.get_scenario_differentiation_modes` can be used to
get the available differentiation modes of a scenario:

.. GENERATED FROM PYTHON SOURCE LINES 123-125

.. code-block:: Python

    get_scenario_differentiation_modes()





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

 .. code-block:: none


    (<DifferentiationMethod.COMPLEX_STEP: 'complex_step'>, <DifferentiationMethod.FINITE_DIFFERENCES: 'finite_differences'>, <DifferentiationMethod.CENTERED_DIFFERENCES: 'centered_differences'>, <DifferentiationMethod.USER_GRAD: 'user'>, <DifferentiationMethod.NO_DERIVATIVE: 'no_derivative'>)



.. GENERATED FROM PYTHON SOURCE LINES 126-138

Monitor a scenario
------------------
To monitor a scenario execution programmatically,
ie get a notification when a discipline status is changed,
use :func:`.monitor_scenario`.
The first argument is the scenario to monitor, and the second is an
observer object, that is notified by its update(atom) method, which takes an
:class:`~gemseo.core.execution_sequence.ExecutionSequence` as argument.
This method will be called every time a discipline status changes.
The atom represents a discipline's position in the process. One discipline
can have multiple atoms, since one discipline can be used in multiple
positions in the MDO formulation.

.. GENERATED FROM PYTHON SOURCE LINES 138-160

.. code-block:: Python



    class Observer:
        """Observer."""

        def update(self, atom) -> None:
            """Update method.

            :param ExecutionSequence atom: atomic execution sequence.
            """
            print(atom)


    scenario = create_scenario(
        discipline,
        "y",
        design_space,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )
    monitor_scenario(scenario, Observer())
    scenario.execute(algo_name="PYDOE_FULLFACT", n_samples=25)




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

 .. code-block:: none

        INFO - 11:43:10: *** Start DOEScenario execution ***
        INFO - 11:43:10: DOEScenario
        INFO - 11:43:10:    Disciplines: AnalyticDiscipline
        INFO - 11:43:10:    MDO formulation: DisciplinaryOpt
    DOEScenario(RUNNING)
        INFO - 11:43:10: Optimization problem:
        INFO - 11:43:10:    minimize y(x1, x2)
        INFO - 11:43:10:    with respect to x1, x2
        INFO - 11:43:10:    over the design space:
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:       | x1   |      0      |   0   |      1      | float |
        INFO - 11:43:10:       | x2   |      0      |   0   |      1      | float |
        INFO - 11:43:10:       +------+-------------+-------+-------------+-------+
        INFO - 11:43:10: Solving optimization problem with algorithm PYDOE_FULLFACT:
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:      4%|▍         | 1/25 [00:00<00:00, 2069.22 it/sec, obj=0]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:      8%|▊         | 2/25 [00:00<00:00, 2316.02 it/sec, obj=0.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     12%|█▏        | 3/25 [00:00<00:00, 2437.13 it/sec, obj=0.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     16%|█▌        | 4/25 [00:00<00:00, 2591.88 it/sec, obj=0.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     20%|██        | 5/25 [00:00<00:00, 2709.50 it/sec, obj=1]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     24%|██▍       | 6/25 [00:00<00:00, 2673.52 it/sec, obj=0.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     28%|██▊       | 7/25 [00:00<00:00, 2728.13 it/sec, obj=0.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     32%|███▏      | 8/25 [00:00<00:00, 2759.18 it/sec, obj=0.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     36%|███▌      | 9/25 [00:00<00:00, 2800.35 it/sec, obj=1]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     40%|████      | 10/25 [00:00<00:00, 2849.20 it/sec, obj=1.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     44%|████▍     | 11/25 [00:00<00:00, 2893.71 it/sec, obj=0.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     48%|████▊     | 12/25 [00:00<00:00, 2908.67 it/sec, obj=0.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     52%|█████▏    | 13/25 [00:00<00:00, 2920.36 it/sec, obj=1]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     56%|█████▌    | 14/25 [00:00<00:00, 2947.21 it/sec, obj=1.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     60%|██████    | 15/25 [00:00<00:00, 2959.01 it/sec, obj=1.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     64%|██████▍   | 16/25 [00:00<00:00, 2956.34 it/sec, obj=0.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     68%|██████▊   | 17/25 [00:00<00:00, 2975.55 it/sec, obj=1]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     72%|███████▏  | 18/25 [00:00<00:00, 2978.32 it/sec, obj=1.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     76%|███████▌  | 19/25 [00:00<00:00, 2995.82 it/sec, obj=1.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     80%|████████  | 20/25 [00:00<00:00, 3011.20 it/sec, obj=1.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     84%|████████▍ | 21/25 [00:00<00:00, 3016.35 it/sec, obj=1]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     88%|████████▊ | 22/25 [00:00<00:00, 3030.27 it/sec, obj=1.25]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     92%|█████████▏| 23/25 [00:00<00:00, 3046.93 it/sec, obj=1.5]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:     96%|█████████▌| 24/25 [00:00<00:00, 3063.87 it/sec, obj=1.75]
    AnalyticDiscipline(RUNNING)
    AnalyticDiscipline(DONE)
        INFO - 11:43:10:    100%|██████████| 25/25 [00:00<00:00, 3069.24 it/sec, obj=2]
        INFO - 11:43:10: Optimization result:
        INFO - 11:43:10:    Optimizer info:
        INFO - 11:43:10:       Status: None
        INFO - 11:43:10:       Message: None
        INFO - 11:43:10:       Number of calls to the objective function by the optimizer: 25
        INFO - 11:43:10:    Solution:
        INFO - 11:43:10:       Objective: 0.0
        INFO - 11:43:10:       Design space:
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:          | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
        INFO - 11:43:10:          | x1   |      0      |   0   |      1      | float |
        INFO - 11:43:10:          | x2   |      0      |   0   |      1      | float |
        INFO - 11:43:10:          +------+-------------+-------+-------------+-------+
    DOEScenario(DONE)
        INFO - 11:43:10: *** End DOEScenario execution (time: 0:00:00.010658) ***





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

   **Total running time of the script:** (0 minutes 0.532 seconds)


.. _sphx_glr_download_examples_api_plot_scenario.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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