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

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

.. _sphx_glr_examples_scenario_plot_store_observables.py:


Store observables
=================

.. GENERATED FROM PYTHON SOURCE LINES 25-43

Introduction
------------
In this example,
we will learn how to store the history of state variables using the
:meth:`~gemseo.core.scenario.Scenario.add_observable` method.
This is useful in situations where we wish to access, post-process,
or save the values of discipline outputs that are not design variables,
constraints or objective functions.

The Sellar problem
------------------
We will consider in this example the Sellar problem:

.. include:: /problems/sellar_problem_definition.inc

Imports
-------
All the imports needed for the tutorials are performed here.

.. GENERATED FROM PYTHON SOURCE LINES 43-55

.. code-block:: default

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.algos.design_space import DesignSpace
    from numpy import array
    from numpy import ones

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 56-64

Create the problem disciplines
------------------------------
In this section,
we use the available classes :class:`.Sellar1`, :class:`.Sellar2`
and :class:`.SellarSystem` to define the disciplines of the problem.
The :func:`.create_discipline` API function allows us to
carry out this task easily, as well as store the instances in a list
to be used later on.

.. GENERATED FROM PYTHON SOURCE LINES 64-66

.. code-block:: default

    disciplines = create_discipline(["Sellar1", "Sellar2", "SellarSystem"])








.. GENERATED FROM PYTHON SOURCE LINES 67-74

Create and execute the scenario
-------------------------------

Create the design space
^^^^^^^^^^^^^^^^^^^^^^^
In this section,
we define the design space which will be used for the creation of the MDOScenario.

.. GENERATED FROM PYTHON SOURCE LINES 74-80

.. code-block:: default

    design_space = DesignSpace()
    design_space.add_variable("x_local", l_b=0.0, u_b=10.0, value=ones(1))
    design_space.add_variable(
        "x_shared", 2, l_b=(-10, 0.0), u_b=(10.0, 10.0), value=array([4.0, 3.0])
    )








.. GENERATED FROM PYTHON SOURCE LINES 81-86

Create the scenario
^^^^^^^^^^^^^^^^^^^
In this section,
we build the MDO scenario which links the disciplines with the formulation,
the design space and the objective function.

.. GENERATED FROM PYTHON SOURCE LINES 86-90

.. code-block:: default

    scenario = create_scenario(
        disciplines, formulation="MDF", objective_name="obj", design_space=design_space
    )








.. GENERATED FROM PYTHON SOURCE LINES 91-95

Add the constraints
^^^^^^^^^^^^^^^^^^^
Then,
we have to set the design constraints

.. GENERATED FROM PYTHON SOURCE LINES 95-98

.. code-block:: default

    scenario.add_constraint("c_1", "ineq")
    scenario.add_constraint("c_2", "ineq")








.. GENERATED FROM PYTHON SOURCE LINES 99-108

Add the observables
^^^^^^^^^^^^^^^^^^^
Only the design variables, objective function and constraints are stored by
default. In order to be able to recover the data from the state variables,
y1 and y2, we have to add them as observables. All we have to do is enter
the variable name as a string to the
:meth:`~gemseo.core.scenario.Scenario.add_observable` method.
If more than one output name is provided (as a list of strings),
the observable function returns a concatenated array of the output values.

.. GENERATED FROM PYTHON SOURCE LINES 108-109

.. code-block:: default

    scenario.add_observable("y_1")







.. GENERATED FROM PYTHON SOURCE LINES 110-112

It is also possible to add the observable with a custom name,
using the option `observable_name`. Let us store the variable `y_2` as `y2`.

.. GENERATED FROM PYTHON SOURCE LINES 112-114

.. code-block:: default

    scenario.add_observable("y_2", observable_name="y2")








.. GENERATED FROM PYTHON SOURCE LINES 115-121

Execute the scenario
^^^^^^^^^^^^^^^^^^^^
Then,
we execute the MDO scenario with the inputs of the MDO scenario as a dictionary.
In this example,
the gradient-based `SLSQP` optimizer is selected, with 10 iterations at maximum:

.. GENERATED FROM PYTHON SOURCE LINES 121-123

.. code-block:: default

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





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

 .. code-block:: none

        INFO - 16:25:55:  
        INFO - 16:25:55: *** Start MDOScenario execution ***
        INFO - 16:25:55: MDOScenario
        INFO - 16:25:55:    Disciplines: Sellar1 Sellar2 SellarSystem
        INFO - 16:25:55:    MDO formulation: MDF
        INFO - 16:25:55: Optimization problem:
        INFO - 16:25:55:    minimize obj(x_local, x_shared)
        INFO - 16:25:55:    with respect to x_local, x_shared
        INFO - 16:25:55:    subject to constraints:
        INFO - 16:25:55:       c_1(x_local, x_shared) <= 0.0
        INFO - 16:25:55:       c_2(x_local, x_shared) <= 0.0
        INFO - 16:25:55:    over the design space:
        INFO - 16:25:55:    +-------------+-------------+-------+-------------+-------+
        INFO - 16:25:55:    | name        | lower_bound | value | upper_bound | type  |
        INFO - 16:25:55:    +-------------+-------------+-------+-------------+-------+
        INFO - 16:25:55:    | x_local     |      0      |   1   |      10     | float |
        INFO - 16:25:55:    | x_shared[0] |     -10     |   4   |      10     | float |
        INFO - 16:25:55:    | x_shared[1] |      0      |   3   |      10     | float |
        INFO - 16:25:55:    +-------------+-------------+-------+-------------+-------+
        INFO - 16:25:55: Solving optimization problem with algorithm SLSQP:
        INFO - 16:25:55: ...   0%|          | 0/10 [00:00<?, ?it]
        INFO - 16:25:55: ...  10%|█         | 1/10 [00:00<00:00, 30.74 it/sec, obj=21.8+j]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.0/lib/python3.9/site-packages/scipy/optimize/_slsqp_py.py:422: ComplexWarning: Casting complex values to real discards the imaginary part
      slsqp(m, meq, x, xl, xu, fx, c, g, a, acc, majiter, mode, w, jw,
        INFO - 16:25:55: ...  20%|██        | 2/10 [00:00<00:00, 24.23 it/sec, obj=5.39+j]
        INFO - 16:25:56: ...  30%|███       | 3/10 [00:00<00:00, 23.32 it/sec, obj=3.41+j]
        INFO - 16:25:56: ...  40%|████      | 4/10 [00:00<00:00, 22.78 it/sec, obj=3.19+j]
        INFO - 16:25:56: ...  50%|█████     | 5/10 [00:00<00:00, 22.44 it/sec, obj=3.18+j]
        INFO - 16:25:56: ...  60%|██████    | 6/10 [00:00<00:00, 22.26 it/sec, obj=3.18+j]
        INFO - 16:25:56: ...  70%|███████   | 7/10 [00:00<00:00, 22.11 it/sec, obj=3.18+j]
        INFO - 16:25:56: ...  80%|████████  | 8/10 [00:00<00:00, 24.41 it/sec, obj=3.18+j]
        INFO - 16:25:56: Optimization result:
        INFO - 16:25:56:    Optimizer info:
        INFO - 16:25:56:       Status: None
        INFO - 16:25:56:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
        INFO - 16:25:56:       Number of calls to the objective function by the optimizer: 9
        INFO - 16:25:56:    Solution:
        INFO - 16:25:56:       The solution is feasible.
        INFO - 16:25:56:       Objective: (3.1833939452393087+0j)
        INFO - 16:25:56:       Standardized constraints:
        INFO - 16:25:56:          c_1 = (6.486713388653698e-09+0j)
        INFO - 16:25:56:          c_2 = (-20.244722236724627+0j)
        INFO - 16:25:56:       Design space: 
        INFO - 16:25:56:       +-------------+-------------+-------------------+-------------+-------+
        INFO - 16:25:56:       | name        | lower_bound |       value       | upper_bound | type  |
        INFO - 16:25:56:       +-------------+-------------+-------------------+-------------+-------+
        INFO - 16:25:56:       | x_local     |      0      |         0         |      10     | float |
        INFO - 16:25:56:       | x_shared[0] |     -10     | 1.977638881636786 |      10     | float |
        INFO - 16:25:56:       | x_shared[1] |      0      |         0         |      10     | float |
        INFO - 16:25:56:       +-------------+-------------+-------------------+-------------+-------+
        INFO - 16:25:56: *** End MDOScenario execution (time: 0:00:00.342063) ***

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



.. GENERATED FROM PYTHON SOURCE LINES 124-130

Access the observable variables
-------------------------------
Retrieve observables from a dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In order to create a dataset, we use the
corresponding :class:`.OptimizationProblem`:

.. GENERATED FROM PYTHON SOURCE LINES 130-131

.. code-block:: default

    opt_problem = scenario.formulation.opt_problem







.. GENERATED FROM PYTHON SOURCE LINES 132-135

We can easily build an :class:`.OptimizationDataset` from this :class:`.OptimizationProblem`:
either by separating the design parameters from the functions
(default option):

.. GENERATED FROM PYTHON SOURCE LINES 135-137

.. code-block:: default

    dataset = opt_problem.to_dataset("sellar_problem")
    print(dataset)




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

 .. code-block:: none

    GROUP       designs                          ...  functions                     
    VARIABLE    x_local  x_shared                ...        obj         y2       y_1
    COMPONENT         0         0             1  ...          0          0         0
    1          1.000000  4.000000  3.000000e+00  ...  21.757227  11.213931  4.213931
    2          0.000000  2.520049  0.000000e+00  ...   5.390534   4.840098  2.320049
    3          0.003071  2.040410  1.594470e-08  ...   3.410649   3.881611  1.841201
    4          0.000000  1.978711  5.354587e-12  ...   3.187156   3.757421  1.778711
    5          0.000000  1.977639  5.040991e-13  ...   3.183395   3.755278  1.777639
    6          0.000000  1.977639  3.942508e-13  ...   3.183394   3.755278  1.777639
    7          0.000000  1.977639  0.000000e+00  ...   3.183394   3.755278  1.777639
    8          0.000000  1.977639  1.971886e-13  ...   3.183394   3.755278  1.777639

    [8 rows x 8 columns]




.. GENERATED FROM PYTHON SOURCE LINES 138-139

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 139-141

.. code-block:: default

    dataset = opt_problem.to_dataset("sellar_problem", categorize=False)
    print(dataset)




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

 .. code-block:: none

    GROUP     parameters                          ...                                
    VARIABLE     x_local  x_shared                ...        obj         y2       y_1
    COMPONENT          0         0             1  ...          0          0         0
    0           1.000000  4.000000  3.000000e+00  ...  21.757227  11.213931  4.213931
    1           0.000000  2.520049  0.000000e+00  ...   5.390534   4.840098  2.320049
    2           0.003071  2.040410  1.594470e-08  ...   3.410649   3.881611  1.841201
    3           0.000000  1.978711  5.354587e-12  ...   3.187156   3.757421  1.778711
    4           0.000000  1.977639  5.040991e-13  ...   3.183395   3.755278  1.777639
    5           0.000000  1.977639  3.942508e-13  ...   3.183394   3.755278  1.777639
    6           0.000000  1.977639  0.000000e+00  ...   3.183394   3.755278  1.777639
    7           0.000000  1.977639  1.971886e-13  ...   3.183394   3.755278  1.777639

    [8 rows x 8 columns]




.. GENERATED FROM PYTHON SOURCE LINES 142-143

or by using an input-output naming rather than an optimization naming:

.. GENERATED FROM PYTHON SOURCE LINES 143-145

.. code-block:: default

    dataset = opt_problem.to_dataset("sellar_problem", opt_naming=False)
    print(dataset)




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

 .. code-block:: none

    GROUP        inputs                          ...    outputs                     
    VARIABLE    x_local  x_shared                ...        obj         y2       y_1
    COMPONENT         0         0             1  ...          0          0         0
    0          1.000000  4.000000  3.000000e+00  ...  21.757227  11.213931  4.213931
    1          0.000000  2.520049  0.000000e+00  ...   5.390534   4.840098  2.320049
    2          0.003071  2.040410  1.594470e-08  ...   3.410649   3.881611  1.841201
    3          0.000000  1.978711  5.354587e-12  ...   3.187156   3.757421  1.778711
    4          0.000000  1.977639  5.040991e-13  ...   3.183395   3.755278  1.777639
    5          0.000000  1.977639  3.942508e-13  ...   3.183394   3.755278  1.777639
    6          0.000000  1.977639  0.000000e+00  ...   3.183394   3.755278  1.777639
    7          0.000000  1.977639  1.971886e-13  ...   3.183394   3.755278  1.777639

    [8 rows x 8 columns]




.. GENERATED FROM PYTHON SOURCE LINES 146-149

Access observables by name
~~~~~~~~~~~~~~~~~~~~~~~~~~
We can get the observable data by variable names:

.. GENERATED FROM PYTHON SOURCE LINES 149-151

.. code-block:: default

    print(dataset.get_view(variable_names=["y_1", "y2"]))





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

 .. code-block:: none

    GROUP       outputs           
    VARIABLE        y_1         y2
    COMPONENT         0          0
    0          4.213931  11.213931
    1          2.320049   4.840098
    2          1.841201   3.881611
    3          1.778711   3.757421
    4          1.777639   3.755278
    5          1.777639   3.755278
    6          1.777639   3.755278
    7          1.777639   3.755278




.. GENERATED FROM PYTHON SOURCE LINES 152-157

Use the observables in a post-processing method
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Finally,
we can generate plots with the observable variables. Have a look at the
Basic History plot and the Scatter Plot Matrix:

.. GENERATED FROM PYTHON SOURCE LINES 157-169

.. code-block:: default

    scenario.post_process(
        "BasicHistory",
        variable_names=["obj", "y_1", "y2"],
        save=False,
        show=True,
    )
    scenario.post_process(
        "ScatterPlotMatrix",
        variable_names=["obj", "c_1", "c_2", "y2", "y_1"],
        save=False,
        show=True,
    )



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/scenario/images/sphx_glr_plot_store_observables_001.png
         :alt: History plot
         :srcset: /examples/scenario/images/sphx_glr_plot_store_observables_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scenario/images/sphx_glr_plot_store_observables_002.png
         :alt: plot store observables
         :srcset: /examples/scenario/images/sphx_glr_plot_store_observables_002.png
         :class: sphx-glr-multi-img


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

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.0/lib/python3.9/site-packages/genson/schema/strategies/base.py:42: UserWarning: Schema incompatible. Keyword 'description' has conflicting values ('The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.' vs. 'The width and height of the figure in inches, e.g. `(w, h)`.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'). Using 'The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'
      warn(('Schema incompatible. Keyword {0!r} has conflicting '
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/5.0.0/lib/python3.9/site-packages/genson/schema/strategies/base.py:42: UserWarning: Schema incompatible. Keyword 'description' has conflicting values ('The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.' vs. 'The width and height of the figure in inches, e.g. `(w, h)`.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'). Using 'The width and height of the figure in inches, e.g. ``(w, h)``.\nIf ``None``, use the :attr:`.OptPostProcessor.DEFAULT_FIG_SIZE`\nof the post-processor.'
      warn(('Schema incompatible. Keyword {0!r} has conflicting '

    <gemseo.post.scatter_mat.ScatterPlotMatrix object at 0x7fea47b36e80>




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

   **Total running time of the script:** ( 0 minutes  2.003 seconds)


.. _sphx_glr_download_examples_scenario_plot_store_observables.py:

.. only:: html

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




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

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

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


.. only:: html

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