.. 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

        Click :ref:`here <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:: /tutorials/_description/sellar_problem_definition.inc
#############################################################################
 Imports
 -------
 All the imports needed for the tutorials are performed here.

.. GENERATED FROM PYTHON SOURCE LINES 43-54

.. code-block:: default

    from gemseo.algos.design_space import DesignSpace
    from gemseo.api import configure_logger
    from gemseo.api import create_discipline
    from gemseo.api import create_scenario
    from matplotlib import pyplot as plt
    from numpy import array
    from numpy import ones

    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



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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 :meth:`~gemseo.api.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.

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.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 66-73

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 73-79

.. code-block:: default

    design_space = DesignSpace()
    design_space.add_variable("x_local", 1, 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 80-85

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 85-89

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 90-94

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

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 98-107

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 107-108

.. code-block:: default

    scenario.add_observable("y_1")







.. GENERATED FROM PYTHON SOURCE LINES 109-111

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 111-113

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 114-120

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 120-122

.. code-block:: default

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





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

 Out:

 .. code-block:: none

        INFO - 07:17:21:  
        INFO - 07:17:21: *** Start MDOScenario execution ***
        INFO - 07:17:21: MDOScenario
        INFO - 07:17:21:    Disciplines: Sellar1 Sellar2 SellarSystem
        INFO - 07:17:21:    MDO formulation: MDF
        INFO - 07:17:21: Optimization problem:
        INFO - 07:17:21:    minimize obj(x_local, x_shared)
        INFO - 07:17:21:    with respect to x_local, x_shared
        INFO - 07:17:21:    subject to constraints:
        INFO - 07:17:21:       c_1(x_local, x_shared) <= 0.0
        INFO - 07:17:21:       c_2(x_local, x_shared) <= 0.0
        INFO - 07:17:21:    over the design space:
        INFO - 07:17:21:    +----------+-------------+-------+-------------+-------+
        INFO - 07:17:21:    | name     | lower_bound | value | upper_bound | type  |
        INFO - 07:17:21:    +----------+-------------+-------+-------------+-------+
        INFO - 07:17:21:    | x_local  |      0      |   1   |      10     | float |
        INFO - 07:17:21:    | x_shared |     -10     |   4   |      10     | float |
        INFO - 07:17:21:    | x_shared |      0      |   3   |      10     | float |
        INFO - 07:17:21:    +----------+-------------+-------+-------------+-------+
        INFO - 07:17:21: Solving optimization problem with algorithm SLSQP:
        INFO - 07:17:21: ...   0%|          | 0/10 [00:00<?, ?it]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.0/lib/python3.9/site-packages/scipy/optimize/slsqp.py:427: 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 - 07:17:21: ...  30%|███       | 3/10 [00:00<00:00, 97.89 it/sec, obj=3.41+j]
        INFO - 07:17:21: ...  60%|██████    | 6/10 [00:00<00:00, 47.36 it/sec, obj=3.18+j]
        INFO - 07:17:21: ...  80%|████████  | 8/10 [00:00<00:00, 38.40 it/sec, obj=3.18+j]
        INFO - 07:17:21: Optimization result:
        INFO - 07:17:21:    Optimizer info:
        INFO - 07:17:21:       Status: None
        INFO - 07:17:21:       Message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
        INFO - 07:17:21:       Number of calls to the objective function by the optimizer: 9
        INFO - 07:17:21:    Solution:
        INFO - 07:17:21:       The solution is feasible.
        INFO - 07:17:21:       Objective: (3.1833939452393087+0j)
        INFO - 07:17:21:       Standardized constraints:
        INFO - 07:17:21:          c_1 = (6.486713388653698e-09+0j)
        INFO - 07:17:21:          c_2 = (-20.244722236724627+0j)
        INFO - 07:17:21:       Design space:
        INFO - 07:17:21:       +----------+-------------+-------------------+-------------+-------+
        INFO - 07:17:21:       | name     | lower_bound |       value       | upper_bound | type  |
        INFO - 07:17:21:       +----------+-------------+-------------------+-------------+-------+
        INFO - 07:17:21:       | x_local  |      0      |         0         |      10     | float |
        INFO - 07:17:21:       | x_shared |     -10     | 1.977638881636786 |      10     | float |
        INFO - 07:17:21:       | x_shared |      0      |         0         |      10     | float |
        INFO - 07:17:21:       +----------+-------------+-------------------+-------------+-------+
        INFO - 07:17:21: *** End MDOScenario execution (time: 0:00:00.271076) ***

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



.. GENERATED FROM PYTHON SOURCE LINES 123-129

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 129-130

.. code-block:: default

    opt_problem = scenario.formulation.opt_problem







.. GENERATED FROM PYTHON SOURCE LINES 131-134

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

.. GENERATED FROM PYTHON SOURCE LINES 134-136

.. code-block:: default

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




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

 Out:

 .. code-block:: none

    sellar_problem
       Number of samples: 8
       Number of variables: 7
       Variables names and sizes by group:
          design_parameters: x_local (1), x_shared (2)
          functions: c_1 (1), c_2 (1), obj (1), y2 (1), y_1 (1)
       Number of dimensions (total = 8) by group:
          design_parameters: 3
          functions: 5




.. GENERATED FROM PYTHON SOURCE LINES 137-138

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 138-140

.. code-block:: default

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




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

 Out:

 .. code-block:: none

    sellar_problem
       Number of samples: 8
       Number of variables: 7
       Variables names and sizes by group:
          parameters: x_local (1), x_shared (2), c_1 (1), c_2 (1), obj (1), y2 (1), y_1 (1)
       Number of dimensions (total = 8) by group:
          parameters: 8




.. GENERATED FROM PYTHON SOURCE LINES 141-142

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

.. GENERATED FROM PYTHON SOURCE LINES 142-144

.. code-block:: default

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




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

 Out:

 .. code-block:: none

    sellar_problem
       Number of samples: 8
       Number of variables: 7
       Variables names and sizes by group:
          inputs: x_local (1), x_shared (2)
          outputs: c_1 (1), c_2 (1), obj (1), y2 (1), y_1 (1)
       Number of dimensions (total = 8) by group:
          inputs: 3
          outputs: 5




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Access observables by name
~~~~~~~~~~~~~~~~~~~~~~~~~~
We can get the observable data by name,
either as a dictionary indexed by the observable names (default option):

.. GENERATED FROM PYTHON SOURCE LINES 149-150

.. code-block:: default

    print(dataset.get_data_by_names(["y_1", "y2"]))




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

 Out:

 .. code-block:: none

    {'y_1': array([[4.21393092+0.j],
           [2.32004903+0.j],
           [1.84120131+0.j],
           [1.77871073+0.j],
           [1.77763921+0.j],
           [1.77763888+0.j],
           [1.77763888+0.j],
           [1.77763888+0.j]]), 'y2': array([[11.21393092+0.j],
           [ 4.84009806+0.j],
           [ 3.88161141+0.j],
           [ 3.75742147+0.j],
           [ 3.75527841+0.j],
           [ 3.75527777+0.j],
           [ 3.75527776+0.j],
           [ 3.75527777+0.j]])}




.. GENERATED FROM PYTHON SOURCE LINES 151-152

or as an array:

.. GENERATED FROM PYTHON SOURCE LINES 152-154

.. code-block:: default

    print(dataset.get_data_by_names(["y_1", "y2"], False))





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

 Out:

 .. code-block:: none

    [[ 4.21393092+0.j 11.21393092+0.j]
     [ 2.32004903+0.j  4.84009806+0.j]
     [ 1.84120131+0.j  3.88161141+0.j]
     [ 1.77871073+0.j  3.75742147+0.j]
     [ 1.77763921+0.j  3.75527841+0.j]
     [ 1.77763888+0.j  3.75527777+0.j]
     [ 1.77763888+0.j  3.75527776+0.j]
     [ 1.77763888+0.j  3.75527777+0.j]]




.. GENERATED FROM PYTHON SOURCE LINES 155-160

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 160-171

.. code-block:: default

    scenario.post_process(
        "BasicHistory", variable_names=["obj", "y_1", "y2"], save=False, show=False
    )
    scenario.post_process(
        "ScatterPlotMatrix",
        save=False,
        show=False,
        variable_names=["obj", "c_1", "c_2", "y2", "y_1"],
    )
    # Workaround for HTML rendering, instead of ``show=True``
    plt.show()



.. 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

 Out:

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.0/lib/python3.9/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part
      return np.asarray(x, float)
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/envs/4.0.0/lib/python3.9/site-packages/gemseo/algos/database.py:1331: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
      f_history = array(flat_vals).real





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

   **Total running time of the script:** ( 0 minutes  1.485 seconds)


.. _sphx_glr_download_examples_scenario_plot_store_observables.py:


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 .. container:: sphx-glr-footer
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  .. 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>`


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