.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_example/p2f/epid/plot_dyn_init.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_example_p2f_epid_plot_dyn_init.py>`
        to download the full example code.

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

.. _sphx_glr_auto_example_p2f_epid_plot_dyn_init.py:


Initialize FMUPointToFieldFunction
==================================

.. GENERATED FROM PYTHON SOURCE LINES 7-10

The interest of using FMUs in Python lies in the ease to change its input
/ parameter values. This notably enables to study the behavior of the FMU
with uncertain inputs / parameters.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

Initialization scripts can gather a large number of initial values.
The use of initialization scripts (*.mos* files) is common in Dymola :

- to save the value of all the variables of a model after simulation,
- to restart simulation from a given point.

.. GENERATED FROM PYTHON SOURCE LINES 19-20

First, retrieve the path to the FMU *epid.fmu*.

.. GENERATED FROM PYTHON SOURCE LINES 20-28

.. code-block:: Python

    import otfmi.example.utility
    import otfmi
    import openturns as ot
    from os.path import abspath
    import openturns.viewer as viewer

    path_fmu = otfmi.example.utility.get_path_fmu("epid")








.. GENERATED FROM PYTHON SOURCE LINES 29-31

The initialization script must be provided to `FMUPointToFieldFunction`
constructor. We thus create it now (using Python for clarity).

.. GENERATED FROM PYTHON SOURCE LINES 33-35

.. note::
   The initialization script can be automatically created in Dymola.

.. GENERATED FROM PYTHON SOURCE LINES 35-41

.. code-block:: Python


    temporary_file = "initialization.mos"
    with open(temporary_file, "w") as f:
        f.write("total_pop = 500;\n")
        f.write("healing_rate = 0.5;\n")








.. GENERATED FROM PYTHON SOURCE LINES 42-44

If no initial value is provided for an input / parameter, it is set to its
default initial value (as set in the FMU).

.. GENERATED FROM PYTHON SOURCE LINES 46-49

We are interested in the model output during the 2 first seconds of simulation
(which corresponds to a specific moment in the epidemic spreading).
We must create the time grid on which the model output will be interpolated.

.. GENERATED FROM PYTHON SOURCE LINES 49-54

.. code-block:: Python


    mesh = ot.RegularGrid(0.0, 0.1, 20)
    meshSample = mesh.getVertices()
    print(meshSample)





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

 .. code-block:: none

         [ t   ]
     0 : [ 0   ]
     1 : [ 0.1 ]
     2 : [ 0.2 ]
     3 : [ 0.3 ]
     4 : [ 0.4 ]
     5 : [ 0.5 ]
     6 : [ 0.6 ]
     7 : [ 0.7 ]
     8 : [ 0.8 ]
     9 : [ 0.9 ]
    10 : [ 1   ]
    11 : [ 1.1 ]
    12 : [ 1.2 ]
    13 : [ 1.3 ]
    14 : [ 1.4 ]
    15 : [ 1.5 ]
    16 : [ 1.6 ]
    17 : [ 1.7 ]
    18 : [ 1.8 ]
    19 : [ 1.9 ]




.. GENERATED FROM PYTHON SOURCE LINES 55-59

We can now build the `FMUPointToFieldFunction`. In the example below, we use
the initialization script to fix the (non-default) values of ``total_pop`` and
``healing_rate`` in the FMU. We can thus observe the evolution of ``infected``
depending on the ``infection_rate``.

.. GENERATED FROM PYTHON SOURCE LINES 59-70

.. code-block:: Python


    function = otfmi.FMUPointToFieldFunction(
        path_fmu,
        mesh,
        inputs_fmu=["infection_rate"],
        outputs_fmu=["infected"],
        initialization_script=abspath("initialization.mos"),
        start_time=0.0,
        final_time=5.0,
    )








.. GENERATED FROM PYTHON SOURCE LINES 71-75

``total_pop`` and ``healing_rate`` values are defined in the initialization
script, and remain constant over time. We can now set probability laws on the
function input variable ``infection_rate`` to propagate its uncertainty
through the model:

.. GENERATED FROM PYTHON SOURCE LINES 75-80

.. code-block:: Python


    law_infection_rate = ot.Normal(2.0, 0.25)
    inputSample = law_infection_rate.getSample(10)
    outputProcessSample = function(inputSample)








.. GENERATED FROM PYTHON SOURCE LINES 81-83

Visualize the time evolution of the ``infected`` over time, depending on the
`ìnfection_rate`` value:

.. GENERATED FROM PYTHON SOURCE LINES 83-91

.. code-block:: Python

    gridLayout = outputProcessSample.draw()
    graph = gridLayout.getGraph(0, 0)
    graph.setTitle("")
    graph.setXTitle("FMU simulation time (s)")
    graph.setYTitle("Number of infected")
    graph.setLegends([f"{line[0]:.4f}" for line in inputSample])
    view = viewer.View(graph, legend_kw={"title": "infection rate", "loc": "upper left"})
    view.ShowAll()



.. image-sg:: /auto_example/p2f/epid/images/sphx_glr_plot_dyn_init_001.png
   :alt: plot dyn init
   :srcset: /auto_example/p2f/epid/images/sphx_glr_plot_dyn_init_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.283 seconds)


.. _sphx_glr_download_auto_example_p2f_epid_plot_dyn_init.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

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