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

.. only:: html

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

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

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

.. _sphx_glr_examples_scenario_plot_gantt_chart.py:


Gantt Chart
===========

In this example, we illustrate the use of the Gantt chart plot
on the Sobieski's SSBJ problem.

.. GENERATED FROM PYTHON SOURCE LINES 30-34

Import
------
The first step is to import some high-level functions
and a method to get the design space.

.. GENERATED FROM PYTHON SOURCE LINES 34-46

.. code-block:: Python

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.core.discipline import MDODiscipline
    from gemseo.post.core.gantt_chart import create_gantt_chart
    from gemseo.problems.sobieski.core.design_space import SobieskiDesignSpace

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 47-51

Create disciplines
------------------
Then, we instantiate the disciplines of the Sobieski's SSBJ problem:
Propulsion, Aerodynamics, Structure and Mission

.. GENERATED FROM PYTHON SOURCE LINES 51-58

.. code-block:: Python

    disciplines = create_discipline([
        "SobieskiPropulsion",
        "SobieskiAerodynamics",
        "SobieskiStructure",
        "SobieskiMission",
    ])








.. GENERATED FROM PYTHON SOURCE LINES 59-62

Create design space
-------------------
We also create the :class:`.SobieskiDesignSpace`.

.. GENERATED FROM PYTHON SOURCE LINES 62-64

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. GENERATED FROM PYTHON SOURCE LINES 65-72

Create and execute scenario
---------------------------
The next step is to build an MDO scenario in order to maximize the range,
encoded 'y_4', with respect to the design parameters, while satisfying the
inequality constraints 'g_1', 'g_2' and 'g_3'. We can use the MDF formulation,
the SLSQP optimization algorithm
and a maximum number of iterations equal to 100.

.. GENERATED FROM PYTHON SOURCE LINES 72-83

.. code-block:: Python

    scenario = create_scenario(
        disciplines,
        "MDF",
        "y_4",
        design_space,
        maximize_objective=True,
    )

    for constraint in ["g_1", "g_2", "g_3"]:
        scenario.add_constraint(constraint, constraint_type="ineq")








.. GENERATED FROM PYTHON SOURCE LINES 84-88

Activate time stamps
--------------------
In order to record all time stamps recording, we have to call this method
before the execution of the scenarios

.. GENERATED FROM PYTHON SOURCE LINES 88-92

.. code-block:: Python

    MDODiscipline.activate_time_stamps()

    scenario.execute({"algo": "SLSQP", "max_iter": 10})





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

 .. code-block:: none

        INFO - 13:53:21:  
        INFO - 13:53:21: *** Start MDOScenario execution ***
        INFO - 13:53:21: MDOScenario
        INFO - 13:53:21:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 13:53:21:    MDO formulation: MDF
        INFO - 13:53:21: Optimization problem:
        INFO - 13:53:21:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 13:53:21:    with respect to x_1, x_2, x_3, x_shared
        INFO - 13:53:21:    subject to constraints:
        INFO - 13:53:21:       g_1(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:53:21:       g_2(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:53:21:       g_3(x_shared, x_1, x_2, x_3) <= 0.0
        INFO - 13:53:21:    over the design space:
        INFO - 13:53:21:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:53:21:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 13:53:21:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:53:21:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 13:53:21:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 13:53:21:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 13:53:21:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 13:53:21:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 13:53:21:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 13:53:21:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 13:53:21:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 13:53:21:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 13:53:21:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 13:53:21:       +-------------+-------------+-------+-------------+-------+
        INFO - 13:53:21: Solving optimization problem with algorithm SLSQP:
        INFO - 13:53:21:     10%|█         | 1/10 [00:00<00:01,  4.95 it/sec, obj=-536]
        INFO - 13:53:21:     20%|██        | 2/10 [00:00<00:02,  3.68 it/sec, obj=-2.12e+3]
     WARNING - 13:53:22: MDAJacobi has reached its maximum number of iterations but the normed residual 1.7130677857005655e-05 is still above the tolerance 1e-06.
        INFO - 13:53:22:     30%|███       | 3/10 [00:01<00:02,  2.97 it/sec, obj=-3.75e+3]
        INFO - 13:53:22:     40%|████      | 4/10 [00:01<00:02,  2.85 it/sec, obj=-3.96e+3]
        INFO - 13:53:23:     50%|█████     | 5/10 [00:01<00:01,  2.78 it/sec, obj=-3.96e+3]
        INFO - 13:53:23: Optimization result:
        INFO - 13:53:23:    Optimizer info:
        INFO - 13:53:23:       Status: 8
        INFO - 13:53:23:       Message: Positive directional derivative for linesearch
        INFO - 13:53:23:       Number of calls to the objective function by the optimizer: 6
        INFO - 13:53:23:    Solution:
        INFO - 13:53:23:       The solution is feasible.
        INFO - 13:53:23:       Objective: -3963.408265187933
        INFO - 13:53:23:       Standardized constraints:
        INFO - 13:53:23:          g_1 = [-0.01806104 -0.03334642 -0.04424946 -0.0518346  -0.05732607 -0.13720865
        INFO - 13:53:23:  -0.10279135]
        INFO - 13:53:23:          g_2 = 3.333278582928756e-06
        INFO - 13:53:23:          g_3 = [-7.67181773e-01 -2.32818227e-01  8.30379541e-07 -1.83255000e-01]
        INFO - 13:53:23:       Design space:
        INFO - 13:53:23:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:53:23:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 13:53:23:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:53:23:          | x_shared[0] |     0.01    | 0.06000083331964572 |     0.09    | float |
        INFO - 13:53:23:          | x_shared[1] |    30000    |        60000        |    60000    | float |
        INFO - 13:53:23:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 13:53:23:          | x_shared[3] |     2.5     |         2.5         |     8.5     | float |
        INFO - 13:53:23:          | x_shared[4] |      40     |          70         |      70     | float |
        INFO - 13:53:23:          | x_shared[5] |     500     |         1500        |     1500    | float |
        INFO - 13:53:23:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 13:53:23:          | x_1[1]      |     0.75    |         0.75        |     1.25    | float |
        INFO - 13:53:23:          | x_2         |     0.75    |         0.75        |     1.25    | float |
        INFO - 13:53:23:          | x_3         |     0.1     |  0.1562448753887276 |      1      | float |
        INFO - 13:53:23:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 13:53:23: *** End MDOScenario execution (time: 0:00:01.950136) ***

    {'max_iter': 10, 'algo': 'SLSQP'}



.. GENERATED FROM PYTHON SOURCE LINES 93-96

Post-process scenario
---------------------
Lastly, we plot the Gantt chart.

.. GENERATED FROM PYTHON SOURCE LINES 96-100

.. code-block:: Python

    create_gantt_chart(save=False, show=True)

    # Finally, we deactivate the time stamps for other executions
    MDODiscipline.deactivate_time_stamps()



.. image-sg:: /examples/scenario/images/sphx_glr_plot_gantt_chart_001.png
   :alt: plot gantt chart
   :srcset: /examples/scenario/images/sphx_glr_plot_gantt_chart_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.238 seconds)


.. _sphx_glr_download_examples_scenario_plot_gantt_chart.py:

.. only:: html

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

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

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

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

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


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