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

.. only:: html

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

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

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

.. _sphx_glr_examples_metro_graph_signal.py:


Metro Graph Signal.
==================

Example of a graph signal for the Santiago Metro.

This example shows a graph signal defined over a graph induced by the Santiago
Metro (https://en.wikipedia.org/wiki/Santiago_Metro). Each station is a
node. Two nodes are connected if the corresponding stations are connected. The
data for each node is the daily average of people leaving each station.

To run this example, you need to download three files and place them in the
same directory as this script.

1. Download the file `Tablas de subidas y bajadas nov23.zip` from this link:

https://www.dtpm.cl/descargas/modelos_y_matrices/Tablas%20de%20subidas%20y%20bajadas%20nov23.zip

Then, uncompress the zip file and copy `2023.11 Matriz_baj_SS_MH.xlsb` to the
same location as this script.

2. Download the file `santiago_metro_stations_coords.geojson` from this link:

https://zenodo.org/records/11637462/files/santiago_metro_stations_coords.geojson

3. Download the file `santiago_metro_stations_connections.txt` from this link:

https://zenodo.org/records/11637462/files/santiago_metro_stations_connections.txt

.. GENERATED FROM PYTHON SOURCE LINES 31-43

.. code-block:: Python

    import os
    import sys

    import matplotlib.pyplot as plt
    import pandas as pd
    from unidecode import unidecode

    from pygsp2.utils_examples import (fetch_data, make_metro_graph, metro_database_preprocessing, plot_signal_in_graph)

    current_dir = os.getcwd()
    os.chdir(current_dir)








.. GENERATED FROM PYTHON SOURCE LINES 44-56

.. code-block:: Python

    assets_dir = os.path.join(current_dir, 'data')
    fetch_data(assets_dir, 'metro')

    try:
        commutes = pd.read_excel(os.path.join(assets_dir, '2023.11 Matriz_baj_SS_MH.xlsb'), header=1,
                                 sheet_name='bajadas_prom_laboral')
    except FileNotFoundError:
        print(f'Data file was not found in:\n {os.getcwd()}')
        print('Download it from:\n' +
              r'https://www.dtpm.cl/descargas/modelos_y_matrices/Tablas%20de%20subidas%20y%20bajadas%20nov23.zip')
        sys.exit(1)





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

 .. code-block:: none

    Downloading data file to:
     /home/docs/checkouts/readthedocs.org/user_builds/pygsp2/checkouts/main/examples/data/2023.11 Matriz_baj_SS_MH.xlsb
    File downloaded successfully.
    Downloading data file to:
     /home/docs/checkouts/readthedocs.org/user_builds/pygsp2/checkouts/main/examples/data/santiago_metro_stations_coords.geojson
    File downloaded successfully.
    Downloading data file to:
     /home/docs/checkouts/readthedocs.org/user_builds/pygsp2/checkouts/main/examples/data/santiago_metro_stations_connections.txt
    File downloaded successfully.




.. GENERATED FROM PYTHON SOURCE LINES 57-66

.. code-block:: Python


    G, pos = make_metro_graph(edgesfile=os.path.join(assets_dir, 'santiago_metro_stations_connections.txt'),
                              coordsfile=os.path.join(assets_dir, 'santiago_metro_stations_coords.geojson'))

    stations = [name.upper() for name in list(G.nodes)]
    stations = [unidecode(station) for station in stations]

    metro_commutes, signal = metro_database_preprocessing(commutes, stations)








.. GENERATED FROM PYTHON SOURCE LINES 67-78

.. code-block:: Python


    available_stations = list(metro_commutes['paradero'])

    stations_missing = []
    for i, station in enumerate(stations):
        if station not in available_stations:
            stations_missing.append(station)

    print(f'{len(stations_missing)} Stations Not Meassured:')
    _ = [print(f'\t{i+1}. {station}') for i, station in enumerate(stations_missing)]





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

 .. code-block:: none

    4 Stations Not Meassured:
            1. EL BOSQUE
            2. OBSERVATORIO
            3. COPA LO MARTINEZ
            4. HOSPITAL EL PINO




.. GENERATED FROM PYTHON SOURCE LINES 79-82

.. code-block:: Python

    fig, ax = plot_signal_in_graph(G, signal, 'Promedio Diario\nBajadas de Metro')
    #fig.savefig('metro_graph_signal.png')
    plt.show()



.. image-sg:: /examples/images/sphx_glr_metro_graph_signal_001.png
   :alt: Promedio Diario Bajadas de Metro
   :srcset: /examples/images/sphx_glr_metro_graph_signal_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 21.628 seconds)

**Estimated memory usage:**  346 MB


.. _sphx_glr_download_examples_metro_graph_signal.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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