.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/parametric_design_study_manager/02_optimization_on_mop.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_parametric_design_study_manager_02_optimization_on_mop.py>`
        to download the full example code.

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

.. _sphx_glr_examples_parametric_design_study_manager_02_optimization_on_mop.py:


.. _ref_optimization_on_mop:

Optimization on MOP with proxy solver
=====================================

This example demonstrates how to create and execute optimization on MOP using ``ParametricDesignStudyManager``

It creates multiple parametric systems using ``ProxySolver`` and ``Mopsolver`` nodes as solvers and then runs the created design study.

.. GENERATED FROM PYTHON SOURCE LINES 35-38

Perform required imports
------------------------
Perform the required imports.

.. GENERATED FROM PYTHON SOURCE LINES 38-58

.. code-block:: Python



    from math import sin
    from pathlib import Path

    import ansys.optislang.core.node_types as nt
    from ansys.optislang.core.project_parametric import (
        Design,
        DesignVariable,
        ObjectiveCriterion,
        OptimizationParameter,
        Response,
    )
    from ansys.optislang.parametric.design_study import ParametricDesignStudyManager
    from ansys.optislang.parametric.design_study_templates import (
        GeneralAlgorithmTemplate,
        OptimizationOnMOPTemplate,
        ProxySolverNodeSettings,
    )


.. GENERATED FROM PYTHON SOURCE LINES 59-63

Create template inputs
----------------------
Define template inputs, that are used by the parametric design study manager
to create and execute the design study.

.. GENERATED FROM PYTHON SOURCE LINES 63-97

.. code-block:: Python



    project_path = Path().cwd() / "design_manager_example.opf"
    parameters = [
        OptimizationParameter(name=f"X{i}", reference_value=0.0, range=(-3.14, 3.14))
        for i in range(1, 6)
    ]
    responses = [Response(name="Y", reference_value=0.0)]


    def callback(designs: list[Design]) -> list[Design]:
        """Calculate coupled function for provided designs."""
        results_designs = []
        for design in designs:
            X1 = design.parameters[design.parameters_names.index("X1")].value
            X2 = design.parameters[design.parameters_names.index("X2")].value
            X3 = design.parameters[design.parameters_names.index("X3")].value
            X4 = design.parameters[design.parameters_names.index("X4")].value
            X5 = design.parameters[design.parameters_names.index("X5")].value

            Y = 0.5 * X1 + X2 + 0.5 * X1 * X2 + 5 * sin(X3) + 0.2 * X4 + 0.1 * X5
            id = design.id

            # create instance of design with new values
            output_design = Design(
                responses=[DesignVariable("Y", Y)],
                design_id=id,
            )
            results_designs.append(output_design)
        return results_designs


    solver_settings = ProxySolverNodeSettings(callback=callback, multi_design_launch_num=-1)


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Create AMOP template
--------------------
Define template that will be transformed into an AMOP system with proxy solver.

.. GENERATED FROM PYTHON SOURCE LINES 101-112

.. code-block:: Python


    amop_template = GeneralAlgorithmTemplate(
        parameters=parameters,
        criteria=[],
        responses=responses,
        algorithm_type=nt.AMOP,
        solver_type=nt.ProxySolver,
        solver_settings=solver_settings,
    )



.. GENERATED FROM PYTHON SOURCE LINES 113-116

Create AMOP system
------------------
Instantiate parametric design study manager, create and execute the design study.

.. GENERATED FROM PYTHON SOURCE LINES 116-123

.. code-block:: Python


    design_study_manager = ParametricDesignStudyManager(project_path=project_path)
    study = design_study_manager.create_design_study(template=amop_template)
    study.execute()
    design_study_manager.save()



.. GENERATED FROM PYTHON SOURCE LINES 124-128

Create Optimization on MOP
--------------------------
Create optimization with MOP solver and validation system, execute.
Database from the previous design study is used as an input for the MOP Solver.

.. GENERATED FROM PYTHON SOURCE LINES 128-144

.. code-block:: Python


    criteria = [ObjectiveCriterion("minY", expression="Y")]
    amop_system = study.managed_instances[0].instance

    optimization_template = OptimizationOnMOPTemplate(
        parameters=parameters,
        criteria=criteria,
        responses=responses,
        mop_predecessor=amop_system,
        callback=callback,
    )

    optimization_study = design_study_manager.create_design_study(optimization_template)
    optimization_study.execute()
    design_study_manager.save()


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Stop and cancel project
~~~~~~~~~~~~~~~~~~~~~~~
Stop and cancel the project.

.. GENERATED FROM PYTHON SOURCE LINES 148-152

.. code-block:: Python


    design_study_manager.optislang.dispose()



.. GENERATED FROM PYTHON SOURCE LINES 153-160

View generated workflow
-----------------------
This image shows the generated workflow.

.. image:: ../../_static/02_optimization_on_mop.png
 :width: 1200
 :alt: Result of script.


.. _sphx_glr_download_examples_parametric_design_study_manager_02_optimization_on_mop.py:

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