.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples\08-python-operators\01-package_python_operators.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_08-python-operators_01-package_python_operators.py>`
        to download the full example code.

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

.. _sphx_glr_examples_08-python-operators_01-package_python_operators.py:


.. _ref_python_plugin_package:

Create a plug-in package with multiple operators
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This example shows how to create a plug-in package with multiple operators.
The benefits of writing a package rather than simple scripts are:

- **Componentization:** You can split the code into several Python modules or files.
- **Distribution:** You can use standard Python tools to upload and download packages.
- **Documentation:** You can add README files, documentation, tests, and examples to the package.

For this example, the plug-in package contains two different operators:

- One that returns all scoping IDs having data higher than the average
- One that returns all scoping IDs having data lower than the average

.. note::
    This example requires DPF 4.0 (Ansys 2022R2) or above.
    For more information, see :ref:`ref_compatibility`.

.. GENERATED FROM PYTHON SOURCE LINES 48-57

Create the plug-in package
--------------------------
Each operator implementation derives from the
:class:`ansys.dpf.core.custom_operator.CustomOperatorBase` class
and a call to the :py:func:`ansys.dpf.core.custom_operator.record_operator`
method, which records the operators of the plug-in package.

Download the ``average_filter_plugin`` plug-in package that has already been
created for you.

.. GENERATED FROM PYTHON SOURCE LINES 57-61

.. code-block:: Python

    from ansys.dpf.core import examples

    plugin_folder = examples.download_average_filter_plugin()








.. GENERATED FROM PYTHON SOURCE LINES 62-74

Load the plug-in package
------------------------
You use the function :py:func:`ansys.dpf.core.core.load_library` to load the
plug-in package.

- The first argument is the path to the directory where the plug-in package
  is located.
- The second argument is ``py_<package>``, where ``<package>`` is the name
  identifying the plug-in package.
- The third argument is the name of the function exposed in the ``__init__`` file
  for the plug-in package that is used to record operators.


.. GENERATED FROM PYTHON SOURCE LINES 74-89

.. code-block:: Python


    from ansys.dpf import core as dpf
    from ansys.dpf.core import examples

    # Python plugins are not supported in process.
    dpf.start_local_server(config=dpf.AvailableServerConfigs.GrpcServer)

    tmp = dpf.make_tmp_dir_server()
    dpf.upload_files_in_folder(dpf.path_utilities.join(tmp, "average_filter_plugin"), plugin_folder)
    dpf.load_library(
        dpf.path_utilities.join(tmp, "average_filter_plugin"),
        "py_average_filter",
        "load_operators",
    )





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

 .. code-block:: none


    'py_average_filter successfully loaded'



.. GENERATED FROM PYTHON SOURCE LINES 90-91

Instantiate the operator.

.. GENERATED FROM PYTHON SOURCE LINES 91-94

.. code-block:: Python


    new_operator = dpf.Operator("ids_with_data_lower_than_average")








.. GENERATED FROM PYTHON SOURCE LINES 95-102

Connect a workflow
------------------
Connect a workflow that computes the norm of the displacement
to the ``ids_with_data_lower_than_average`` operator.
Methods of the ``ids_with_data_lower_than_average`` class are dynamically
added because specifications for the operator are defined in the plug-in
package.

.. GENERATED FROM PYTHON SOURCE LINES 104-116

.. graphviz::

   digraph foo {
      graph [pad="0.5", nodesep="0.3", ranksep="0.3"]
      node [shape=box, style=filled, fillcolor="#ffcc00", margin="0"];
      rankdir=LR;
      splines=line;
      ds [label="ds", shape=box, style=filled, fillcolor=cadetblue2];
      ds -> displacement [style=dashed];
      displacement -> norm;
      norm -> ids_with_data_lower_than_average;
   }

.. GENERATED FROM PYTHON SOURCE LINES 118-120

Use the operator
----------------

.. GENERATED FROM PYTHON SOURCE LINES 120-131

.. code-block:: Python


    ds = dpf.DataSources(dpf.upload_file_in_tmp_folder(examples.find_static_rst()))
    displacement = dpf.operators.result.displacement(data_sources=ds)
    norm = dpf.operators.math.norm(displacement)
    new_operator.inputs.connect(norm)


    new_scoping = new_operator.outputs.scoping()
    print("scoping in was:", norm.outputs.field().scoping)
    print("----------------------------------------------")
    print("scoping out is:", new_scoping)




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

 .. code-block:: none

    scoping in was: DPF  Scoping: 
      with Nodal location and 81 entities

    ----------------------------------------------
    scoping out is: DPF  Scoping: 
      with Nodal location and 35 entities






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

   **Total running time of the script:** (0 minutes 5.680 seconds)


.. _sphx_glr_download_examples_08-python-operators_01-package_python_operators.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 01-package_python_operators.ipynb <01-package_python_operators.ipynb>`

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

      :download:`Download Python source code: 01-package_python_operators.py <01-package_python_operators.py>`

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

      :download:`Download zipped: 01-package_python_operators.zip <01-package_python_operators.zip>`


.. only:: html

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

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