.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/discipline/plot_analyticdiscipline.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_discipline_plot_analyticdiscipline.py>`
        to download the full example code

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

.. _sphx_glr_examples_discipline_plot_analyticdiscipline.py:


Create a discipline from analytical expressions
===============================================

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.. code-block:: default

    from gemseo.api import configure_logger
    from gemseo.api import create_discipline
    from numpy import array








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Import
------

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.. code-block:: default


    configure_logger()





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

 Out:

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 36-42

Introduction
------------
A simple :class:`.MDODiscipline` can be created
using analytic formulas, e.g. :math:`y_1=2x^2` and :math:`y_2=5+3x^2z^3`,
thanks to the :class:`.AnalyticDiscipline` class
which is a quick alternative to model a simple analytic MDO problem.

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Create the dictionary of analytic outputs
-----------------------------------------
First of all, we have to define the output expressions in a dictionary
where keys are output names and values are formula with :code:`string`
format:

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: default

    expressions = {"y_1": "2*x**2", "y_2": "5+3*x**2+z**3"}








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Create the discipline
---------------------
Then, we create and instantiate the corresponding
:class:`.AnalyticDiscipline`,
which is a particular :class:`.MDODiscipline`.
For that, we use the API function :meth:`~gemseo.api.create_discipline` with:

- :code:`discipline_name="AnalyticDiscipline"`,
- :code:`name="analytic"`,
- :code:`expressions=expr_dict`.

In practice, we write:

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.. code-block:: default

    disc = create_discipline("AnalyticDiscipline", expressions=expressions)








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.. note::

   |g| takes care of the grammars and
   :meth:`!MDODiscipline._run` method generation
   from the :code:`expressions` argument.
   In the background, |g| considers that :code:`x` is a monodimensional
   float input parameter and :code:`y_1` and :code:`y_2` are
   monodimensional float output parameters.

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Execute the discipline
----------------------
Lastly, we can execute this discipline any other:

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.. code-block:: default

    input_data = {"x": array([2.0]), "z": array([3.0])}
    out = disc.execute(input_data)
    print(("y_1 =", out["y_1"]))
    print(("y_2 =", out["y_2"]))





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

 Out:

 .. code-block:: none

    ('y_1 =', array([8.]))
    ('y_2 =', array([44.]))




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About the analytic jacobian
---------------------------
The discipline will provide analytic derivatives (Jacobian) automatically
using the `sympy library <https://www.sympy.org/fr/>`_.

This can be checked easily using
:meth:`.MDODiscipline.check_jacobian`:

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: default

    disc.check_jacobian(
        input_data, derr_approx=disc.FINITE_DIFFERENCES, step=1e-5, threshold=1e-3
    )




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

 Out:

 .. code-block:: none

        INFO - 07:19:10: Jacobian: dp y_1/dp x succeeded.
        INFO - 07:19:10: Jacobian: dp y_1/dp z succeeded.
        INFO - 07:19:10: Jacobian: dp y_2/dp x succeeded.
        INFO - 07:19:10: Jacobian: dp y_2/dp z succeeded.
        INFO - 07:19:10: Linearization of MDODiscipline: AnalyticDiscipline is correct.

    True




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

   **Total running time of the script:** ( 0 minutes  0.029 seconds)


.. _sphx_glr_download_examples_discipline_plot_analyticdiscipline.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_analyticdiscipline.py <plot_analyticdiscipline.py>`



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

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


.. only:: html

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

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