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

.. only:: html

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

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

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

.. _sphx_glr_sources_visualisation_sankey.py:


Sankey Plots
============

.. GENERATED FROM PYTHON SOURCE LINES 7-43

Sankey plots (often called `Sankey diagrams <https://en.wikipedia.org/wiki/Sankey_diagram>`_) can be generated using the :py:func:`~isaricanalytics.visualisation.fig_sankey` function, which returns a :py:class:`Plotly Go Figure <plotly.graph_objs._figure.Figure>` object.

The plot below was generated using a synthetic dataset for a hypothetical community of 1200 people who are hospitalised. **Click** the image to view the full interactive and fully annotated Plotly Go figure.

The synthetic dataset is given as a table (which can easily be converted to a CSV).

.. list-table:: Synthetic dataset for a disease outbreak response in a small community
   :widths: 33 33 33
   :header-rows: 1

   * - Source
     - Target
     - Value
   * - Community
     - Hospitalised
     - 1200
   * - Hospitalised
     - ICU
     - 300
   * - Hospitalised
     - Ward
     - 900
   * - ICU
     - Death
     - 80
   * - ICU
     - Recovered
     - 220
   * - Ward
     - Recovered
     - 850
   * - Ward
     - Death
     - 50

The data source can be in any appropriate form, such as, typically, a CSV. Here are the Python steps you need to generate the plot using the :py:func:`~isaricanalytics.visualisation.fig_sankey` function:

.. GENERATED FROM PYTHON SOURCE LINES 43-86

.. code-block:: Python

    import io, pandas as pd
    from isaricanalytics.visualisation import fig_sankey
    # Load the CSV data from a string buffer
    data = pd.read_csv(io.StringIO(
       """source,target,value\n
          Community,Hospitalised,1200\n
          Hospitalised,ICU,300\n
          Hospitalised,Ward,900\n
          ICU,Death,80\n
          ICU,Recovered,220\n
          Ward,Recovered,850\n
          Ward,Death,50"""
    ), skipinitialspace=True)

    # Create the labels, nodes, flows/arrows and annotations
    labels = pd.Series(pd.unique(data[["source", "target"]].values.ravel()))
    nodes = pd.DataFrame({
       "label": labels,
       "customdata": labels.apply(lambda x: f"{x} (synthetic)")
    })
    label_to_idx = {label: i for i, label in enumerate(labels)}
    arrows = pd.DataFrame({
       "source": data["source"].map(label_to_idx),
       "target": data["target"].map(label_to_idx),
       "value": data["value"],
       "customdata": data.apply(lambda r: f"{r['source']} → {r['target']}: {r['value']} cases", axis=1)
    })
    annotations = pd.DataFrame([{
       "text": "Sankey plot of Synthetic Outbreak Patient Case Flow",
       "x": 0.5,
       "y": 1.08,
       "xref": "paper",
       "yref": "paper",
       "showarrow": False,
       "font": {"size": 14}
    }])

    fig = fig_sankey(
        [nodes, arrows, annotations],
    )
    fig.update_layout(autosize=True)
    fig




.. raw:: html

    <style>html[data-theme="dark"] div.output_subarea:has(.plotly-graph-div){background:#fff;border-radius:0.25rem;padding:0.5rem}@media (prefers-color-scheme: dark){html:not([data-theme="light"]) div.output_subarea:has(.plotly-graph-div){background:#fff;border-radius:0.25rem;padding:0.5rem}}</style><script>if (!window.plotlySphinxGalleryResize) {window.plotlySphinxGalleryResize = true;window.addEventListener("load", function () {document.querySelectorAll(".plotly-graph-div").forEach(function (gd) { Plotly.Plots.resize(gd); });});}</script>



.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style="height:500px; width:100%;">                        <script>window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
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                       {"responsive": true}                    )                };            </script>        </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 87-100

You should see the plot appearing as given above.

.. note::

   Any dataframe or CSV column names, or dictionary field labels, in the
   example above that are not specific to the dataset must be as given,
   otherwise the function may throw an exception or return an incorrect figure.

   The figure height and width parameters can be set using the ``height``
   and ``width`` parameters, but it may be more convenient to let Plotly handle
   this using the figure layout `autosize <https://plotly.com/python/reference/layout/#layout-autosize>`_
   parameter. Refer to the :py:func:`~isaricanalytics.visualisation.fig_sankey`
   function docstring for more information.


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

   **Total running time of the script:** (0 minutes 1.803 seconds)


.. _sphx_glr_download_sources_visualisation_sankey.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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