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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_disciplines_grammars_plot_data_converters.py>`
        to download the full example code.

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

.. _sphx_glr_examples_disciplines_grammars_plot_data_converters.py:


A Sellar problem with custom data converters
============================================

.. GENERATED FROM PYTHON SOURCE LINES 24-46

.. code-block:: Python


    from __future__ import annotations

    import operator
    from math import exp
    from typing import TYPE_CHECKING

    from numpy import array
    from numpy import ones

    from gemseo import configure_logger
    from gemseo import create_scenario
    from gemseo import set_data_converters
    from gemseo.algos.design_space import DesignSpace
    from gemseo.core.discipline import Discipline

    if TYPE_CHECKING:
        from gemseo.typing import StrKeyMapping

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 47-54

As compared to the original Sellar example,
here the ``y_1`` variable is a dictionary.
and the ``y_2`` variable is a 2D array
The grammars are changed to ``SimpleGrammar`` for simplicity,
otherwise,
with the default ``JSONGrammar``,
more complex definitions would be required.

.. GENERATED FROM PYTHON SOURCE LINES 54-124

.. code-block:: Python


    DEFAULT_INPUT_DATA = {
        "x": ones(1),
        "z": array([1.0, 0.0]),
        "y_1": {"dummy": ones(1)},
        "y_2": ones((1, 1)),
    }


    class SellarSystem(Discipline):
        default_grammar_type = Discipline.default_grammar_type.SIMPLE

        def __init__(self) -> None:
            super().__init__()
            default_input_data = DEFAULT_INPUT_DATA.copy()
            self.input_grammar.update_from_data(default_input_data)
            self.output_grammar.update_from_names(["obj", "c_1", "c_2"])
            self.default_input_data = default_input_data

        def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
            x = input_data["x"]
            z = input_data["z"]
            y_1 = input_data["y_1"]["dummy"]
            y_2 = input_data["y_2"][0]
            return {
                "obj": array([x[0] ** 2 + z[1] + y_1[0] ** 2 + exp(-y_2[0])]),
                "c_1": array([3.16 - y_1[0] ** 2]),
                "c_2": array([y_2[0] - 24.0]),
            }


    class Sellar1(Discipline):
        default_grammar_type = Discipline.default_grammar_type.SIMPLE

        def __init__(self) -> None:
            super().__init__()
            default_input_data = DEFAULT_INPUT_DATA.copy()
            # Keep the y_1 data for easily defining the output grammar.
            output_grammar_data = {"y_1": default_input_data.pop("y_1")}
            self.input_grammar.update_from_data(default_input_data)
            self.output_grammar.update_from_data(output_grammar_data)
            self.default_input_data = default_input_data

        def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
            x = input_data["x"]
            z = input_data["z"]
            y_2 = input_data["y_2"][0]
            return {
                "y_1": {"dummy": array([(z[0] ** 2 + z[1] + x[0] - 0.2 * y_2[0]) ** 0.5])}
            }


    class Sellar2(Discipline):
        default_grammar_type = Discipline.default_grammar_type.SIMPLE

        def __init__(self) -> None:
            super().__init__()
            default_input_data = DEFAULT_INPUT_DATA.copy()
            del default_input_data["y_2"]
            del default_input_data["x"]
            self.input_grammar.update_from_data(default_input_data)
            self.output_grammar.update_from_names(["y_2"])
            self.default_input_data = default_input_data

        def _run(self, input_data: StrKeyMapping) -> StrKeyMapping | None:
            z = input_data["z"]
            y_1 = input_data["y_1"]["dummy"]
            return {"y_2": array([[abs(y_1[0]) + z[0] + z[1]]])}









.. GENERATED FROM PYTHON SOURCE LINES 125-128

Define the data converters for the custom types of the variables ``y_1`` and ``y_2``.

This one shall return the value of a variable as a 1D array.

.. GENERATED FROM PYTHON SOURCE LINES 128-189

.. code-block:: Python

    to_array = {
        "y_1": operator.itemgetter("dummy"),
        "y_2": operator.itemgetter(0),
    }


    # This one shall return the value of a variable in the type expected by the discipline from a 1D array.
    def get_dict(array_):
        return {"dummy": array_}


    def get_2d_array(array_):
        return array([array_])


    from_array = {
        "y_1": get_dict,
        "y_2": get_2d_array,
    }


    # This one shall return the size of a value of a variable.
    # The builtin converters already handle NumPy arrays via the ``size`` attribute,
    # so we only need to handle ``y_1``.
    def get_size(data):
        return data["dummy"].size


    to_size = {
        "y_1": get_size,
    }

    set_data_converters(to_array, from_array, to_size)

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

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

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

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

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

    # Reset the data converters to remove the custom converters.
    # This should only be needed if the type of the variables y_1 and y_2 are no longer
    # custom type afterward.
    set_data_converters({}, {}, {})




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

 .. code-block:: none

        INFO - 20:35:54: *** Start MDOScenario execution ***
        INFO - 20:35:54: MDOScenario
        INFO - 20:35:54:    Disciplines: Sellar1 Sellar2 SellarSystem
        INFO - 20:35:54:    MDO formulation: MDF
        INFO - 20:35:54: Optimization problem:
        INFO - 20:35:54:    minimize obj(x, z)
        INFO - 20:35:54:    with respect to x, z
        INFO - 20:35:54:    under the inequality constraints
        INFO - 20:35:54:       c_1(x, z) <= 0
        INFO - 20:35:54:       c_2(x, z) <= 0
        INFO - 20:35:54:    over the design space:
        INFO - 20:35:54:       +------+-------------+-------+-------------+-------+
        INFO - 20:35:54:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 20:35:54:       +------+-------------+-------+-------------+-------+
        INFO - 20:35:54:       | x    |      0      |   1   |      10     | float |
        INFO - 20:35:54:       | z[0] |     -10     |   4   |      10     | float |
        INFO - 20:35:54:       | z[1] |      0      |   3   |      10     | float |
        INFO - 20:35:54:       +------+-------------+-------+-------------+-------+
        INFO - 20:35:54: Solving optimization problem with algorithm NLOPT_COBYLA:
        INFO - 20:35:54:     10%|█         | 1/10 [00:00<00:00, 201.16 it/sec, obj=21.8]
        INFO - 20:35:54:     20%|██        | 2/10 [00:00<00:00, 195.69 it/sec, obj=35.5]
        INFO - 20:35:54:     30%|███       | 3/10 [00:00<00:00, 200.74 it/sec, obj=84.8]
        INFO - 20:35:54:     40%|████      | 4/10 [00:00<00:00, 200.96 it/sec, obj=26.2]
        INFO - 20:35:54:     50%|█████     | 5/10 [00:00<00:00, 200.25 it/sec, obj=15.5]
        INFO - 20:35:54:     60%|██████    | 6/10 [00:00<00:00, 199.68 it/sec, obj=27.9]
        INFO - 20:35:54:     70%|███████   | 7/10 [00:00<00:00, 198.58 it/sec, obj=7.44]
        INFO - 20:35:54:     80%|████████  | 8/10 [00:00<00:00, 194.86 it/sec, obj=3.51]
        INFO - 20:35:54:     90%|█████████ | 9/10 [00:00<00:00, 189.58 it/sec, obj=2.81]
        INFO - 20:35:54:    100%|██████████| 10/10 [00:00<00:00, 186.60 it/sec, obj=3.68]
        INFO - 20:35:54: Optimization result:
        INFO - 20:35:54:    Optimizer info:
        INFO - 20:35:54:       Status: None
        INFO - 20:35:54:       Message: Maximum number of iterations reached. GEMSEO stopped the driver.
        INFO - 20:35:54:       Number of calls to the objective function by the optimizer: 0
        INFO - 20:35:54:    Solution:
        INFO - 20:35:54:       The solution is feasible.
        INFO - 20:35:54:       Objective: 3.506317890821701
        INFO - 20:35:54:       Standardized constraints:
        INFO - 20:35:54:          c_1 = -0.3267639063985981
        INFO - 20:35:54:          c_2 = -20.065423793491785
        INFO - 20:35:54:       Design space:
        INFO - 20:35:54:          +------+-------------+-----------------------+-------------+-------+
        INFO - 20:35:54:          | Name | Lower bound |         Value         | Upper bound | Type  |
        INFO - 20:35:54:          +------+-------------+-----------------------+-------------+-------+
        INFO - 20:35:54:          | x    |      0      |           0           |      10     | float |
        INFO - 20:35:54:          | z[0] |     -10     |   2.067287914435337   |      10     | float |
        INFO - 20:35:54:          | z[1] |      0      | 2.081668171172169e-16 |      10     | float |
        INFO - 20:35:54:          +------+-------------+-----------------------+-------------+-------+
        INFO - 20:35:54: *** End MDOScenario execution ***





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

   **Total running time of the script:** (0 minutes 0.065 seconds)


.. _sphx_glr_download_examples_disciplines_grammars_plot_data_converters.py:

.. only:: html

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

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

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

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

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

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


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