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

.. only:: html

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

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

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

.. _sphx_glr_examples_post_process_algorithms_plot_obj_constr_hist.py:


Objective and constraints history
=================================

In this example, we illustrate the use of the :class:`.ObjConstrHist` plot
on the Sobieski's SSBJ problem.

.. GENERATED FROM PYTHON SOURCE LINES 28-36

.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace








.. GENERATED FROM PYTHON SOURCE LINES 37-41

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

.. GENERATED FROM PYTHON SOURCE LINES 41-44

.. code-block:: Python


    configure_logger()





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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 45-51

Description
-----------

The :class:`.ObjConstrHist` post-processing
plots the objective history in a line chart
with constraint violation indication by color in the background.

.. GENERATED FROM PYTHON SOURCE LINES 53-57

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

.. GENERATED FROM PYTHON SOURCE LINES 57-64

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 65-68

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

.. GENERATED FROM PYTHON SOURCE LINES 68-70

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. GENERATED FROM PYTHON SOURCE LINES 71-78

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 78-90

.. code-block:: Python

    scenario = create_scenario(
        disciplines,
        "y_4",
        design_space,
        formulation_name="MDF",
        maximize_objective=True,
    )
    scenario.set_differentiation_method()
    for constraint in ["g_1", "g_2", "g_3"]:
        scenario.add_constraint(constraint, constraint_type="ineq")
    scenario.execute(algo_name="SLSQP", max_iter=10)





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

 .. code-block:: none

     WARNING - 08:38:40: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
     WARNING - 08:38:40: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
        INFO - 08:38:40:  
        INFO - 08:38:40: *** Start MDOScenario execution ***
        INFO - 08:38:40: MDOScenario
        INFO - 08:38:40:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 08:38:40:    MDO formulation: MDF
        INFO - 08:38:40: Optimization problem:
        INFO - 08:38:40:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 08:38:40:    with respect to x_1, x_2, x_3, x_shared
        INFO - 08:38:40:    subject to constraints:
        INFO - 08:38:40:       g_1(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:40:       g_2(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:40:       g_3(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:40:    over the design space:
        INFO - 08:38:40:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:40:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:38:40:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:40:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 08:38:40:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 08:38:40:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 08:38:40:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 08:38:40:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 08:38:40:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 08:38:40:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 08:38:40:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:40:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:40:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 08:38:40:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:40: Solving optimization problem with algorithm SLSQP:
        INFO - 08:38:40:     10%|█         | 1/10 [00:00<00:00, 18.68 it/sec, obj=-536]
        INFO - 08:38:40:     20%|██        | 2/10 [00:00<00:00, 13.88 it/sec, obj=-2.12e+3]
     WARNING - 08:38:41: MDAJacobi has reached its maximum number of iterations but the normed residual 5.741449586530469e-06 is still above the tolerance 1e-06.
        INFO - 08:38:41:     30%|███       | 3/10 [00:00<00:00, 11.34 it/sec, obj=-3.46e+3]
        INFO - 08:38:41:     40%|████      | 4/10 [00:00<00:00, 10.83 it/sec, obj=-3.96e+3]
        INFO - 08:38:41:     50%|█████     | 5/10 [00:00<00:00, 11.07 it/sec, obj=-4.61e+3]
        INFO - 08:38:41:     60%|██████    | 6/10 [00:00<00:00, 11.80 it/sec, obj=-4.5e+3]
        INFO - 08:38:41:     70%|███████   | 7/10 [00:00<00:00, 12.21 it/sec, obj=-4.26e+3]
        INFO - 08:38:41:     80%|████████  | 8/10 [00:00<00:00, 12.54 it/sec, obj=-4.11e+3]
        INFO - 08:38:41:     90%|█████████ | 9/10 [00:00<00:00, 12.81 it/sec, obj=-4.02e+3]
        INFO - 08:38:41:    100%|██████████| 10/10 [00:00<00:00, 13.03 it/sec, obj=-3.99e+3]
        INFO - 08:38:41: Optimization result:
        INFO - 08:38:41:    Optimizer info:
        INFO - 08:38:41:       Status: None
        INFO - 08:38:41:       Message: Maximum number of iterations reached. GEMSEO stopped the driver.
        INFO - 08:38:41:       Number of calls to the objective function by the optimizer: 12
        INFO - 08:38:41:    Solution:
        INFO - 08:38:41:       The solution is feasible.
        INFO - 08:38:41:       Objective: -3463.120411437138
        INFO - 08:38:41:       Standardized constraints:
        INFO - 08:38:41:          g_1 = [-0.01112145 -0.02847064 -0.04049911 -0.04878943 -0.05476349 -0.14014207
        INFO - 08:38:41:  -0.09985793]
        INFO - 08:38:41:          g_2 = -0.0020925663903177405
        INFO - 08:38:41:          g_3 = [-0.71359843 -0.28640157 -0.05926796 -0.183255  ]
        INFO - 08:38:41:       Design space:
        INFO - 08:38:41:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:41:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 08:38:41:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:41:          | x_shared[0] |     0.01    | 0.05947685840242058 |     0.09    | float |
        INFO - 08:38:41:          | x_shared[1] |    30000    |   59246.692998739   |    60000    | float |
        INFO - 08:38:41:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 08:38:41:          | x_shared[3] |     2.5     |   2.64097355362077  |     8.5     | float |
        INFO - 08:38:41:          | x_shared[4] |      40     |  69.32144380869019  |      70     | float |
        INFO - 08:38:41:          | x_shared[5] |     500     |  1478.031626737187  |     1500    | float |
        INFO - 08:38:41:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 08:38:41:          | x_1[1]      |     0.75    |  0.7608797907508461 |     1.25    | float |
        INFO - 08:38:41:          | x_2         |     0.75    |  0.7607584987262048 |     1.25    | float |
        INFO - 08:38:41:          | x_3         |     0.1     |  0.1514057659459843 |      1      | float |
        INFO - 08:38:41:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:41: *** End MDOScenario execution (time: 0:00:00.777006) ***




.. GENERATED FROM PYTHON SOURCE LINES 91-95

Post-process scenario
---------------------
Lastly, we post-process the scenario by means of the :class:`.ObjConstrHist`
plot which plots the constraint functions history in lines charts.

.. GENERATED FROM PYTHON SOURCE LINES 97-105

.. tip::

   Each post-processing method requires different inputs and offers a variety
   of customization options. Use the high-level function
   :func:`.get_post_processing_options_schema` to print a table with
   the options for any post-processing algorithm.
   Or refer to our dedicated page:
   :ref:`gen_post_algos`.

.. GENERATED FROM PYTHON SOURCE LINES 105-106

.. code-block:: Python

    scenario.post_process(post_name="ObjConstrHist", save=False, show=True)



.. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_obj_constr_hist_001.png
   :alt: Evolution of the objective and maximum constraint
   :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_obj_constr_hist_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gemseo.post.obj_constr_hist.ObjConstrHist object at 0x7f6dc1b2ef40>




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

   **Total running time of the script:** (0 minutes 1.263 seconds)


.. _sphx_glr_download_examples_post_process_algorithms_plot_obj_constr_hist.py:

.. only:: html

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

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

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

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

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

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


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