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

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

.. _sphx_glr_examples_post_process_algorithms_plot_constraints_history.py:


Constraints history
===================

In this example, we illustrate the use of the :class:`.ConstraintsHistory` 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 functions from the API
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 45-54

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

The :class:`.ConstraintsHistory` post-processing
plots the constraints functions history in line charts
with violation indication by color on the background.

This plot is more precise than the constraint plot provided by the
:ref:`opt_history_view` but scales less with the number of constraints.

.. GENERATED FROM PYTHON SOURCE LINES 56-60

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

.. GENERATED FROM PYTHON SOURCE LINES 60-67

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 68-71

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

.. GENERATED FROM PYTHON SOURCE LINES 71-73

.. code-block:: Python

    design_space = SobieskiDesignSpace()








.. GENERATED FROM PYTHON SOURCE LINES 74-81

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 81-94

.. code-block:: Python

    scenario = create_scenario(
        disciplines,
        "y_4",
        design_space,
        formulation_name="MDF",
        maximize_objective=True,
    )
    scenario.set_differentiation_method()
    all_constraints = ["g_1", "g_2", "g_3"]
    for constraint in all_constraints:
        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 - 08:38:31: Unsupported feature 'minItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
     WARNING - 08:38:31: Unsupported feature 'maxItems' in JSONGrammar 'SobieskiMission_discipline_output' for property 'y_4' in conversion to SimpleGrammar.
        INFO - 08:38:31:  
        INFO - 08:38:31: *** Start MDOScenario execution ***
        INFO - 08:38:31: MDOScenario
        INFO - 08:38:31:    Disciplines: SobieskiAerodynamics SobieskiMission SobieskiPropulsion SobieskiStructure
        INFO - 08:38:31:    MDO formulation: MDF
        INFO - 08:38:31: Optimization problem:
        INFO - 08:38:31:    minimize -y_4(x_shared, x_1, x_2, x_3)
        INFO - 08:38:31:    with respect to x_1, x_2, x_3, x_shared
        INFO - 08:38:31:    subject to constraints:
        INFO - 08:38:31:       g_1(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:31:       g_2(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:31:       g_3(x_shared, x_1, x_2, x_3) <= 0
        INFO - 08:38:31:    over the design space:
        INFO - 08:38:31:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:31:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:38:31:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:31:       | x_shared[0] |     0.01    |  0.05 |     0.09    | float |
        INFO - 08:38:31:       | x_shared[1] |    30000    | 45000 |    60000    | float |
        INFO - 08:38:31:       | x_shared[2] |     1.4     |  1.6  |     1.8     | float |
        INFO - 08:38:31:       | x_shared[3] |     2.5     |  5.5  |     8.5     | float |
        INFO - 08:38:31:       | x_shared[4] |      40     |   55  |      70     | float |
        INFO - 08:38:31:       | x_shared[5] |     500     |  1000 |     1500    | float |
        INFO - 08:38:31:       | x_1[0]      |     0.1     |  0.25 |     0.4     | float |
        INFO - 08:38:31:       | x_1[1]      |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:31:       | x_2         |     0.75    |   1   |     1.25    | float |
        INFO - 08:38:31:       | x_3         |     0.1     |  0.5  |      1      | float |
        INFO - 08:38:31:       +-------------+-------------+-------+-------------+-------+
        INFO - 08:38:31: Solving optimization problem with algorithm SLSQP:
        INFO - 08:38:31:     10%|█         | 1/10 [00:00<00:00, 18.89 it/sec, obj=-536]
        INFO - 08:38:31:     20%|██        | 2/10 [00:00<00:00, 13.99 it/sec, obj=-2.12e+3]
     WARNING - 08:38:31: MDAJacobi has reached its maximum number of iterations but the normed residual 5.741449586530469e-06 is still above the tolerance 1e-06.
        INFO - 08:38:31:     30%|███       | 3/10 [00:00<00:00, 11.38 it/sec, obj=-3.46e+3]
        INFO - 08:38:31:     40%|████      | 4/10 [00:00<00:00, 10.85 it/sec, obj=-3.96e+3]
        INFO - 08:38:31:     50%|█████     | 5/10 [00:00<00:00, 11.08 it/sec, obj=-4.61e+3]
        INFO - 08:38:32:     60%|██████    | 6/10 [00:00<00:00, 11.81 it/sec, obj=-4.5e+3]
        INFO - 08:38:32:     70%|███████   | 7/10 [00:00<00:00, 12.22 it/sec, obj=-4.26e+3]
        INFO - 08:38:32:     80%|████████  | 8/10 [00:00<00:00, 12.53 it/sec, obj=-4.11e+3]
        INFO - 08:38:32:     90%|█████████ | 9/10 [00:00<00:00, 12.78 it/sec, obj=-4.02e+3]
        INFO - 08:38:32:    100%|██████████| 10/10 [00:00<00:00, 12.98 it/sec, obj=-3.99e+3]
        INFO - 08:38:32: Optimization result:
        INFO - 08:38:32:    Optimizer info:
        INFO - 08:38:32:       Status: None
        INFO - 08:38:32:       Message: Maximum number of iterations reached. GEMSEO stopped the driver.
        INFO - 08:38:32:       Number of calls to the objective function by the optimizer: 12
        INFO - 08:38:32:    Solution:
        INFO - 08:38:32:       The solution is feasible.
        INFO - 08:38:32:       Objective: -3463.120411437138
        INFO - 08:38:32:       Standardized constraints:
        INFO - 08:38:32:          g_1 = [-0.01112145 -0.02847064 -0.04049911 -0.04878943 -0.05476349 -0.14014207
        INFO - 08:38:32:  -0.09985793]
        INFO - 08:38:32:          g_2 = -0.0020925663903177405
        INFO - 08:38:32:          g_3 = [-0.71359843 -0.28640157 -0.05926796 -0.183255  ]
        INFO - 08:38:32:       Design space:
        INFO - 08:38:32:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:32:          | Name        | Lower bound |        Value        | Upper bound | Type  |
        INFO - 08:38:32:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:32:          | x_shared[0] |     0.01    | 0.05947685840242058 |     0.09    | float |
        INFO - 08:38:32:          | x_shared[1] |    30000    |   59246.692998739   |    60000    | float |
        INFO - 08:38:32:          | x_shared[2] |     1.4     |         1.4         |     1.8     | float |
        INFO - 08:38:32:          | x_shared[3] |     2.5     |   2.64097355362077  |     8.5     | float |
        INFO - 08:38:32:          | x_shared[4] |      40     |  69.32144380869019  |      70     | float |
        INFO - 08:38:32:          | x_shared[5] |     500     |  1478.031626737187  |     1500    | float |
        INFO - 08:38:32:          | x_1[0]      |     0.1     |         0.4         |     0.4     | float |
        INFO - 08:38:32:          | x_1[1]      |     0.75    |  0.7608797907508461 |     1.25    | float |
        INFO - 08:38:32:          | x_2         |     0.75    |  0.7607584987262048 |     1.25    | float |
        INFO - 08:38:32:          | x_3         |     0.1     |  0.1514057659459843 |      1      | float |
        INFO - 08:38:32:          +-------------+-------------+---------------------+-------------+-------+
        INFO - 08:38:32: *** End MDOScenario execution (time: 0:00:00.779888) ***




.. GENERATED FROM PYTHON SOURCE LINES 95-103

Post-process scenario
---------------------
Lastly, we post-process the scenario by means of the
:class:`.ConstraintsHistory` plot which plots the history of constraints
passed as argument by the user. Each constraint history is represented by
a subplot where the value of the constraints is drawn by a line. Moreover,
the background color represents a qualitative view of these values: active
areas are white, violated ones are red and satisfied ones are green.

.. GENERATED FROM PYTHON SOURCE LINES 105-113

.. tip::

   Each post-processing method requires different inputs and offers a variety
   of customization options. Use the API 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 113-120

.. code-block:: Python


    scenario.post_process(
        post_name="ConstraintsHistory",
        constraint_names=all_constraints,
        save=False,
        show=True,
    )



.. image-sg:: /examples/post_process/algorithms/images/sphx_glr_plot_constraints_history_001.png
   :alt: Evolution of the constraints w.r.t. iterations, g_1[0] (inequality), g_1[1] (inequality), g_1[2] (inequality), g_1[3] (inequality), g_1[4] (inequality), g_1[5] (inequality), g_1[6] (inequality), g_2 (inequality), g_3[0] (inequality), g_3[1] (inequality), g_3[2] (inequality), g_3[3] (inequality)
   :srcset: /examples/post_process/algorithms/images/sphx_glr_plot_constraints_history_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <gemseo.post.constraints_history.ConstraintsHistory object at 0x7f6dc1280d00>




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

   **Total running time of the script:** (0 minutes 2.595 seconds)


.. _sphx_glr_download_examples_post_process_algorithms_plot_constraints_history.py:

.. only:: html

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

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

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

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

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

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

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


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