.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/_501_simulating_networks.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples__501_simulating_networks.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples__501_simulating_networks.py:


Simulating a Network in Parallel
================================

While the ultimate objective is to process multiple fractionated samples together (with sample as a dim),
this pattern may be useful in the mean-time. It demonstrates how to process multiple samples in parallel,
with a progressbar to provide feedback.

The function my_simulator and the class TqdmParallel is defined in simulating_networks_tools.py, and are
imported here to demonstrate.

.. GENERATED FROM PYTHON SOURCE LINES 13-23

.. code-block:: default

    import pandas as pd
    import plotly
    from joblib import delayed

    from elphick.mass_composition import MassComposition
    from elphick.mass_composition.datasets.sample_data import sample_data
    from elphick.mass_composition.flowsheet import Flowsheet
    from elphick.mass_composition.utils.parallel import TqdmParallel
    from examples._simulating_network_functions import my_simulator








.. GENERATED FROM PYTHON SOURCE LINES 24-26

Execute multiple simulations
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 26-37

.. code-block:: default


    df_data: pd.DataFrame = sample_data()
    obj_mc: MassComposition = MassComposition(df_data, name='sample')
    d_inputs: dict[int, MassComposition] = {1: obj_mc, 2: obj_mc.add(obj_mc), 3: obj_mc.add(obj_mc).add(obj_mc)}

    results: list[tuple[int, Flowsheet]] = TqdmParallel(n_jobs=3, prefer="processes", total=len(d_inputs))(
        delayed(my_simulator)(item) for item in d_inputs.items()
    )

    d_results = {sid: fs for sid, fs in results}





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

 .. code-block:: none


      0%|          | 0/3 [00:00<?, ?it/s]
     33%|###3      | 1/3 [00:06<00:13,  6.66s/it]
     67%|######6   | 2/3 [00:07<00:03,  3.23s/it]
    100%|##########| 3/3 [00:07<00:00,  2.53s/it]




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Print the results

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: default

    print(d_results)





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

 .. code-block:: none

    {1: <elphick.mass_composition.flowsheet.Flowsheet object at 0x7f3959bc0460>, 2: <elphick.mass_composition.flowsheet.Flowsheet object at 0x7f3959bc3160>, 3: <elphick.mass_composition.flowsheet.Flowsheet object at 0x7f39580e71f0>}




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View the network for a sample
-----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 44-47

.. code-block:: default


    fig = d_results[1].table_plot()
    plotly.io.show(fig)



.. raw:: html
    :file: images/sphx_glr__501_simulating_networks_001.html






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

   **Total running time of the script:** ( 0 minutes  8.025 seconds)


.. _sphx_glr_download_auto_examples__501_simulating_networks.py:

.. only:: html

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


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

      :download:`Download Python source code: _501_simulating_networks.py <_501_simulating_networks.py>`

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

      :download:`Download Jupyter notebook: _501_simulating_networks.ipynb <_501_simulating_networks.ipynb>`


.. only:: html

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

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