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

.. only:: html

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

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

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

.. _sphx_glr__auto_examples_plot_find_disconnected.py:


Finding disconnected links
==========================

On this example, we show how to find disconnected links in an AequilibraE network

We use the Nauru example to find disconnected links

.. GENERATED FROM PYTHON SOURCE LINES 11-19

.. code-block:: python

    import numpy as np
    import pandas as pd
    from PIL import Image
    import matplotlib.pyplot as plt

    img = Image.open("disconnected_network.png")
    plt.imshow(img)




.. image-sg:: /_auto_examples/images/sphx_glr_plot_find_disconnected_001.png
   :alt: plot find disconnected
   :srcset: /_auto_examples/images/sphx_glr_plot_find_disconnected_001.png
   :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none


    <matplotlib.image.AxesImage object at 0x7fd571067970>



.. GENERATED FROM PYTHON SOURCE LINES 20-21

# Imports

.. GENERATED FROM PYTHON SOURCE LINES 21-28

.. code-block:: python

    from uuid import uuid4
    from tempfile import gettempdir
    from os.path import join
    from datetime import datetime
    from aequilibrae.utils.create_example import create_example
    from aequilibrae.paths.results import PathResults








.. GENERATED FROM PYTHON SOURCE LINES 29-30

We create an empty project on an arbitrary folder

.. GENERATED FROM PYTHON SOURCE LINES 30-38

.. code-block:: python

    fldr = join(gettempdir(), uuid4().hex)

    # Let's use the Nauru example project for display
    project = create_example(fldr, "nauru")

    # Let's analyze the mode car, or 'c' in our model
    mode = 'c'








.. GENERATED FROM PYTHON SOURCE LINES 39-40

We need to create the graph, but before that we need to have at least one centroid in our network

.. GENERATED FROM PYTHON SOURCE LINES 40-62

.. code-block:: python


    # We get an arbitrary node to set as centroid and allow for the construction of graphs
    centroid_count = project.conn.execute('select count(*) from nodes where is_centroid=1').fetchone()[0]

    if centroid_count == 0:
        arbitrary_node = project.conn.execute('select node_id from nodes limit 1').fetchone()[0]
        nodes = project.network.nodes
        nd = nodes.get(arbitrary_node)
        nd.is_centroid = 1
        nd.save()

    network = project.network
    network.build_graphs(modes=[mode])
    graph = network.graphs[mode]
    graph.set_blocked_centroid_flows(False)

    if centroid_count == 0:
        # Let's revert of setting up that node as centroid in case we had to do it

        nd.is_centroid = 0
        nd.save()








.. GENERATED FROM PYTHON SOURCE LINES 63-64

We set the graph for computation

.. GENERATED FROM PYTHON SOURCE LINES 64-77

.. code-block:: python


    graph.set_graph('distance')
    graph.set_skimming('distance')

    # Get the nodes that are part of the car network
    missing_nodes = [x[0] for x in
                     project.conn.execute(f"Select node_id from nodes where instr(modes, '{mode}')").fetchall()]
    missing_nodes = np.array(missing_nodes)

    # And prepare the path computation structure
    res = PathResults()
    res.prepare(graph)








.. GENERATED FROM PYTHON SOURCE LINES 78-79

Now we can compute all the path islands we have

.. GENERATED FROM PYTHON SOURCE LINES 79-97

.. code-block:: python


    islands = []
    idx_islands = 0
    #
    while missing_nodes.shape[0] >= 2:
        print(datetime.now().strftime("%H:%M:%S"), f' - Computing island: {idx_islands}')
        res.reset()
        res.compute_path(missing_nodes[0], missing_nodes[1])
        res.predecessors[graph.nodes_to_indices[missing_nodes[0]]] = 0
        connected = graph.all_nodes[np.where(res.predecessors >= 0)]
        connected = np.intersect1d(missing_nodes, connected)
        missing_nodes = np.setdiff1d(missing_nodes, connected)
        print(f'    Nodes to find: {missing_nodes.shape[0]:,}')
        df = pd.DataFrame({'node_id': connected, 'island': idx_islands})
        islands.append(df)
        idx_islands += 1

    print(f'\nWe found {idx_islands} islands')




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

 Out:

 .. code-block:: none

    06:33:02  - Computing island: 0
        Nodes to find: 2
    06:33:02  - Computing island: 1
        Nodes to find: 0

    We found 2 islands




.. GENERATED FROM PYTHON SOURCE LINES 98-99

consolidate everything into a single DataFrame

.. GENERATED FROM PYTHON SOURCE LINES 99-107

.. code-block:: python

    islands = pd.concat(islands)

    # And save to disk alongside our model
    islands.to_csv(join(fldr, 'island_outputs_complete.csv'), index=False)

    # If you join the node_id field in the csv file generated above with the a_node or b_node fields
    # in the links table, you will have the corresponding links in each disjoint island found








.. GENERATED FROM PYTHON SOURCE LINES 108-109

.. code-block:: python

    project.close()








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

   **Total running time of the script:** ( 0 minutes  0.940 seconds)


.. _sphx_glr_download__auto_examples_plot_find_disconnected.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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