.. 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 28-36

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.

.. GENERATED FROM PYTHON SOURCE LINES 38-43

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

.. include:: /tutorials/_description/sellar_problem_definition.inc

.. GENERATED FROM PYTHON SOURCE LINES 45-49

Imports
-------
All the imports needed for the tutorials are performed here.
Note that some of the imports are related to the Python 2/3 compatibility.

.. GENERATED FROM PYTHON SOURCE LINES 49-61

.. code-block:: default


    from __future__ import division, unicode_literals

    from matplotlib import pyplot as plt
    from numpy import array, ones

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

    configure_logger()






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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 62-70

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.

.. GENERATED FROM PYTHON SOURCE LINES 70-72

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 73-80

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 80-86

.. 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 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 92-96

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 97-101

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

.. GENERATED FROM PYTHON SOURCE LINES 101-104

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 105-114

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 114-115

.. code-block:: default

    scenario.add_observable("y_1")







.. GENERATED FROM PYTHON SOURCE LINES 116-118

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 118-120

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 121-127

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 127-129

.. code-block:: default

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





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

 Out:

 .. code-block:: none

        INFO - 21:50:03:  
        INFO - 21:50:03: *** Start MDO Scenario execution ***
        INFO - 21:50:03: MDOScenario
        INFO - 21:50:03:    Disciplines: Sellar1 Sellar2 SellarSystem
        INFO - 21:50:03:    MDOFormulation: MDF
        INFO - 21:50:03:    Algorithm: SLSQP
        INFO - 21:50:03: Optimization problem:
        INFO - 21:50:03:    Minimize: obj(x_local, x_shared)
        INFO - 21:50:03:    With respect to: x_local, x_shared
        INFO - 21:50:03:    Subject to constraints:
        INFO - 21:50:03:       c_1(x_local, x_shared) <= 0.0
        INFO - 21:50:03:       c_2(x_local, x_shared) <= 0.0
        INFO - 21:50:03: Design space:
        INFO - 21:50:03: +----------+-------------+-------+-------------+-------+
        INFO - 21:50:03: | name     | lower_bound | value | upper_bound | type  |
        INFO - 21:50:03: +----------+-------------+-------+-------------+-------+
        INFO - 21:50:03: | x_local  |      0      |   1   |      10     | float |
        INFO - 21:50:03: | x_shared |     -10     |   4   |      10     | float |
        INFO - 21:50:03: | x_shared |      0      |   3   |      10     | float |
        INFO - 21:50:03: +----------+-------------+-------+-------------+-------+
        INFO - 21:50:03: Optimization:   0%|          | 0/10 [00:00<?, ?it]
    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.2.0/lib/python3.8/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:407: SparseEfficiencyWarning: splu requires CSC matrix format
      warn('splu requires CSC matrix format', SparseEfficiencyWarning)
        INFO - 21:50:03: Optimization:  50%|█████     | 5/10 [00:00<00:00, 80.99 it/sec, obj=3.18]
        INFO - 21:50:03: Optimization:  80%|████████  | 8/10 [00:00<00:00, 54.19 it/sec, obj=3.18]
        INFO - 21:50:03: Optimization result:
        INFO - 21:50:03: Objective value = 3.1833939505271727
        INFO - 21:50:03: The result is feasible.
        INFO - 21:50:03: Status: None
        INFO - 21:50:03: Optimizer message: Successive iterates of the objective function are closer than ftol_rel or ftol_abs. GEMSEO Stopped the driver
        INFO - 21:50:03: Number of calls to the objective function by the optimizer: 9
        INFO - 21:50:03: Constraints values:
        INFO - 21:50:03:    c_1 = 1.1283325385136322e-09
        INFO - 21:50:03:    c_2 = -20.244722233710302
        INFO - 21:50:03: Design space:
        INFO - 21:50:03: +----------+-------------+-------------------+-------------+-------+
        INFO - 21:50:03: | name     | lower_bound |       value       | upper_bound | type  |
        INFO - 21:50:03: +----------+-------------+-------------------+-------------+-------+
        INFO - 21:50:03: | x_local  |      0      |         0         |      10     | float |
        INFO - 21:50:03: | x_shared |     -10     | 1.977638883143948 |      10     | float |
        INFO - 21:50:03: | x_shared |      0      |         0         |      10     | float |
        INFO - 21:50:03: +----------+-------------+-------------------+-------------+-------+
        INFO - 21:50:03: *** MDO Scenario run terminated in 0:00:00.191938 ***

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



.. GENERATED FROM PYTHON SOURCE LINES 130-136

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 136-137

.. code-block:: default

    opt_problem = scenario.formulation.opt_problem







.. GENERATED FROM PYTHON SOURCE LINES 138-141

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 141-143

.. 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: y2 (1), y_1 (1), obj (1), c_1 (1), c_2 (1)
       Number of dimensions (total = 8) by group:
          design_parameters: 3
          functions: 5




.. GENERATED FROM PYTHON SOURCE LINES 144-145

or by considering all features as default parameters:

.. GENERATED FROM PYTHON SOURCE LINES 145-147

.. 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), y2 (1), y_1 (1), obj (1), c_1 (1), c_2 (1)
       Number of dimensions (total = 8) by group:
          parameters: 8




.. GENERATED FROM PYTHON SOURCE LINES 148-149

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

.. GENERATED FROM PYTHON SOURCE LINES 149-151

.. 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: y2 (1), y_1 (1), obj (1), c_1 (1), c_2 (1)
       Number of dimensions (total = 8) by group:
          inputs: 3
          outputs: 5




.. GENERATED FROM PYTHON SOURCE LINES 152-156

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 156-157

.. 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],
           [2.32004905],
           [1.8412013 ],
           [1.77871073],
           [1.77763921],
           [1.77763888],
           [1.77763888],
           [1.77763888]]), 'y2': array([[11.21393092],
           [ 4.84009809],
           [ 3.8816114 ],
           [ 3.75742147],
           [ 3.75527841],
           [ 3.75527777],
           [ 3.75527777],
           [ 3.75527777]])}




.. GENERATED FROM PYTHON SOURCE LINES 158-159

or as an array:

.. GENERATED FROM PYTHON SOURCE LINES 159-161

.. 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 11.21393092]
     [ 2.32004905  4.84009809]
     [ 1.8412013   3.8816114 ]
     [ 1.77871073  3.75742147]
     [ 1.77763921  3.75527841]
     [ 1.77763888  3.75527777]
     [ 1.77763888  3.75527777]
     [ 1.77763888  3.75527777]]




.. GENERATED FROM PYTHON SOURCE LINES 162-167

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 167-178

.. code-block:: default

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



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


    *

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

    *

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


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

 Out:

 .. code-block:: none

    /home/docs/checkouts/readthedocs.org/user_builds/gemseo/conda/3.2.0/lib/python3.8/site-packages/gemseo/algos/database.py:1373: 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  1.500 seconds)


.. _sphx_glr_download_examples_scenario_plot_store_observables.py:


.. only :: html

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



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

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


.. only:: html

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

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