.. 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 26-44

Introduction
------------
In this example,
we will learn how to store the history of state variables using the
:meth:`~gemseo.scenarios.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 44-57

.. code-block:: Python

    from __future__ import annotations

    from numpy import array
    from numpy import ones

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo.algos.design_space import DesignSpace

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 58-66

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 66-68

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 69-76

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 76-86

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 87-92

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 92-94

.. code-block:: Python

    scenario = create_scenario(disciplines, "obj", design_space, formulation_name="MDF")





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

 .. code-block:: none

        INFO - 20:38:32: Variable x_local was removed from the Design Space, it is not an input of any discipline.




.. GENERATED FROM PYTHON SOURCE LINES 95-97

Note that the formulation settings passed to :func:`.create_scenario` can be provided
via a Pydantic model. For more information, see :ref:`formulation_settings`.

.. GENERATED FROM PYTHON SOURCE LINES 99-103

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

.. GENERATED FROM PYTHON SOURCE LINES 103-106

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 107-116

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.scenarios.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 116-117

.. code-block:: Python

    scenario.add_observable("y_1")







.. GENERATED FROM PYTHON SOURCE LINES 118-120

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 120-122

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 123-129

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 129-131

.. code-block:: Python

    scenario.execute(algo_name="SLSQP", max_iter=10)





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

 .. code-block:: none

        INFO - 20:38:32: *** Start MDOScenario execution ***
        INFO - 20:38:32: MDOScenario
        INFO - 20:38:32:    Disciplines: Sellar1 Sellar2 SellarSystem
        INFO - 20:38:32:    MDO formulation: MDF
        INFO - 20:38:32: Optimization problem:
        INFO - 20:38:32:    minimize obj(x_shared)
        INFO - 20:38:32:    with respect to x_shared
        INFO - 20:38:32:    under the inequality constraints
        INFO - 20:38:32:       c_1(x_shared) <= 0
        INFO - 20:38:32:       c_2(x_shared) <= 0
        INFO - 20:38:32:    over the design space:
        INFO - 20:38:32:       +-------------+-------------+-------+-------------+-------+
        INFO - 20:38:32:       | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 20:38:32:       +-------------+-------------+-------+-------------+-------+
        INFO - 20:38:32:       | x_shared[0] |     -10     |   4   |      10     | float |
        INFO - 20:38:32:       | x_shared[1] |      0      |   3   |      10     | float |
        INFO - 20:38:32:       +-------------+-------------+-------+-------------+-------+
        INFO - 20:38:32: Solving optimization problem with algorithm SLSQP:
        INFO - 20:38:32:     10%|█         | 1/10 [00:00<00:00, 85.36 it/sec, obj=19.8]
        INFO - 20:38:32:     20%|██        | 2/10 [00:00<00:00, 83.33 it/sec, obj=5.38]
        INFO - 20:38:32:     30%|███       | 3/10 [00:00<00:00, 88.53 it/sec, obj=3.41]
        INFO - 20:38:32:     40%|████      | 4/10 [00:00<00:00, 91.49 it/sec, obj=3.19]
        INFO - 20:38:32:     50%|█████     | 5/10 [00:00<00:00, 93.82 it/sec, obj=3.18]
        INFO - 20:38:32:     60%|██████    | 6/10 [00:00<00:00, 94.25 it/sec, obj=3.18]
        INFO - 20:38:32:     70%|███████   | 7/10 [00:00<00:00, 94.28 it/sec, obj=3.18]
        INFO - 20:38:32:     80%|████████  | 8/10 [00:00<00:00, 101.79 it/sec, obj=3.18]
        INFO - 20:38:32: Optimization result:
        INFO - 20:38:32:    Optimizer info:
        INFO - 20:38:32:       Status: None
        INFO - 20:38:32:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO stopped the driver.
        INFO - 20:38:32:       Number of calls to the objective function by the optimizer: 0
        INFO - 20:38:32:    Solution:
        INFO - 20:38:32:       The solution is feasible.
        INFO - 20:38:32:       Objective: 3.183393960638473
        INFO - 20:38:32:       Standardized constraints:
        INFO - 20:38:32:          c_1 = 1.5724075375089797e-09
        INFO - 20:38:32:          c_2 = -20.244722684911245
        INFO - 20:38:32:       Design space:
        INFO - 20:38:32:          +-------------+-------------+-------------------+-------------+-------+
        INFO - 20:38:32:          | Name        | Lower bound |       Value       | Upper bound | Type  |
        INFO - 20:38:32:          +-------------+-------------+-------------------+-------------+-------+
        INFO - 20:38:32:          | x_shared[0] |     -10     | 1.977638860218251 |      10     | float |
        INFO - 20:38:32:          | x_shared[1] |      0      |         0         |      10     | float |
        INFO - 20:38:32:          +-------------+-------------+-------------------+-------------+-------+
        INFO - 20:38:32: *** End MDOScenario execution (time: 0:00:00.081586) ***




.. GENERATED FROM PYTHON SOURCE LINES 132-134

Note that the algorithm settings passed to :meth:`.DriverLibrary.execute` can be provided
via a Pydantic model. For more information, see :ref:`algorithm_settings`.

.. GENERATED FROM PYTHON SOURCE LINES 136-142

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 142-143

.. code-block:: Python

    opt_problem = scenario.formulation.optimization_problem







.. GENERATED FROM PYTHON SOURCE LINES 144-147

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 147-149

.. code-block:: Python

    dataset = opt_problem.to_dataset("sellar_problem")
    dataset





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="2" halign="left">designs</th>
          <th colspan="5" halign="left">functions</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th colspan="2" halign="left">x_shared</th>
          <th>c_1</th>
          <th>c_2</th>
          <th>obj</th>
          <th>y2</th>
          <th>y_1</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>4.000000</td>
          <td>3.000000e+00</td>
          <td>-1.362071e+01</td>
          <td>-12.903573</td>
          <td>19.780730</td>
          <td>11.096427</td>
          <td>4.096427</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.518818</td>
          <td>0.000000e+00</td>
          <td>-2.216919e+00</td>
          <td>-19.162364</td>
          <td>5.384844</td>
          <td>4.837636</td>
          <td>2.318818</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2.040791</td>
          <td>4.248151e-10</td>
          <td>-2.285105e-01</td>
          <td>-20.118419</td>
          <td>3.409129</td>
          <td>3.881581</td>
          <td>1.840791</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1.978722</td>
          <td>1.358050e-12</td>
          <td>-3.852514e-03</td>
          <td>-20.242556</td>
          <td>3.187196</td>
          <td>3.757444</td>
          <td>1.778722</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>-1.172903e-06</td>
          <td>-20.244722</td>
          <td>3.183395</td>
          <td>3.755278</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1.977639</td>
          <td>8.487813e-14</td>
          <td>1.061817e-12</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>7</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>1.572408e-09</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>8</th>
          <td>1.977639</td>
          <td>4.245252e-14</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 150-151

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 151-153

.. code-block:: Python

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





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>GROUP</th>
          <th colspan="7" halign="left">parameters</th>
        </tr>
        <tr>
          <th>VARIABLE</th>
          <th colspan="2" halign="left">x_shared</th>
          <th>c_1</th>
          <th>c_2</th>
          <th>obj</th>
          <th>y2</th>
          <th>y_1</th>
        </tr>
        <tr>
          <th>COMPONENT</th>
          <th>0</th>
          <th>1</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
          <th>0</th>
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      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>4.000000</td>
          <td>3.000000e+00</td>
          <td>-1.362071e+01</td>
          <td>-12.903573</td>
          <td>19.780730</td>
          <td>11.096427</td>
          <td>4.096427</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2.518818</td>
          <td>0.000000e+00</td>
          <td>-2.216919e+00</td>
          <td>-19.162364</td>
          <td>5.384844</td>
          <td>4.837636</td>
          <td>2.318818</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.040791</td>
          <td>4.248151e-10</td>
          <td>-2.285105e-01</td>
          <td>-20.118419</td>
          <td>3.409129</td>
          <td>3.881581</td>
          <td>1.840791</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1.978722</td>
          <td>1.358050e-12</td>
          <td>-3.852514e-03</td>
          <td>-20.242556</td>
          <td>3.187196</td>
          <td>3.757444</td>
          <td>1.778722</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>-1.172903e-06</td>
          <td>-20.244722</td>
          <td>3.183395</td>
          <td>3.755278</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.977639</td>
          <td>8.487813e-14</td>
          <td>1.061817e-12</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>1.572408e-09</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>7</th>
          <td>1.977639</td>
          <td>4.245252e-14</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 154-155

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

.. GENERATED FROM PYTHON SOURCE LINES 155-157

.. code-block:: Python

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





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
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        <tr>
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          <th colspan="2" halign="left">x_shared</th>
          <th>c_1</th>
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      <tbody>
        <tr>
          <th>0</th>
          <td>4.000000</td>
          <td>3.000000e+00</td>
          <td>-1.362071e+01</td>
          <td>-12.903573</td>
          <td>19.780730</td>
          <td>11.096427</td>
          <td>4.096427</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2.518818</td>
          <td>0.000000e+00</td>
          <td>-2.216919e+00</td>
          <td>-19.162364</td>
          <td>5.384844</td>
          <td>4.837636</td>
          <td>2.318818</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2.040791</td>
          <td>4.248151e-10</td>
          <td>-2.285105e-01</td>
          <td>-20.118419</td>
          <td>3.409129</td>
          <td>3.881581</td>
          <td>1.840791</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1.978722</td>
          <td>1.358050e-12</td>
          <td>-3.852514e-03</td>
          <td>-20.242556</td>
          <td>3.187196</td>
          <td>3.757444</td>
          <td>1.778722</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>-1.172903e-06</td>
          <td>-20.244722</td>
          <td>3.183395</td>
          <td>3.755278</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.977639</td>
          <td>8.487813e-14</td>
          <td>1.061817e-12</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1.977639</td>
          <td>0.000000e+00</td>
          <td>1.572408e-09</td>
          <td>-20.244723</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
        <tr>
          <th>7</th>
          <td>1.977639</td>
          <td>4.245252e-14</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>3.183394</td>
          <td>3.755277</td>
          <td>1.777639</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 158-161

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

.. GENERATED FROM PYTHON SOURCE LINES 161-163

.. code-block:: Python

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






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    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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          <td>4.096427</td>
          <td>11.096427</td>
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          <td>2.318818</td>
          <td>4.837636</td>
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        <tr>
          <th>2</th>
          <td>1.840791</td>
          <td>3.881581</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1.778722</td>
          <td>3.757444</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1.777639</td>
          <td>3.755278</td>
        </tr>
        <tr>
          <th>5</th>
          <td>1.777639</td>
          <td>3.755277</td>
        </tr>
        <tr>
          <th>6</th>
          <td>1.777639</td>
          <td>3.755277</td>
        </tr>
        <tr>
          <th>7</th>
          <td>1.777639</td>
          <td>3.755277</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 164-169

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 169-176

.. code-block:: Python

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




.. 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-single-img


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

 .. code-block:: none


    <gemseo.post.basic_history.BasicHistory object at 0x78592f69de50>



.. GENERATED FROM PYTHON SOURCE LINES 177-180

Note that the post-processor settings passed to :func:`.BaseScenario.post_process` can be
provided via a Pydantic model. For more information, see
:ref:`post_processor_settings`. An example is shown below.

.. GENERATED FROM PYTHON SOURCE LINES 180-189

.. code-block:: Python

    from gemseo.settings.post import ScatterPlotMatrix_Settings  # noqa: E402

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



.. 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-single-img


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

 .. code-block:: none


    <gemseo.post.scatter_plot_matrix.ScatterPlotMatrix object at 0x78592f7cd7c0>




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

   **Total running time of the script:** (0 minutes 0.798 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-jupyter

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

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

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

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

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


.. only:: html

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

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