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

.. only:: html

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

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

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

.. _sphx_glr__auto_examples_path_computation_plot_skimming.py:


.. _example_usage_skimming:

Network skimming
================

In this example, we show how to perform network skimming for Coquimbo, a city in La Serena Metropolitan Area in Chile.

.. GENERATED FROM PYTHON SOURCE LINES 27-32

.. seealso::
    Several functions, methods, classes and modules are used in this example:

    * :func:`aequilibrae.paths.Graph`
    * :func:`aequilibrae.paths.NetworkSkimming`

.. GENERATED FROM PYTHON SOURCE LINES 34-41

.. code-block:: Python


    # Imports
    from uuid import uuid4
    from tempfile import gettempdir
    from os.path import join
    from aequilibrae.utils.create_example import create_example








.. GENERATED FROM PYTHON SOURCE LINES 43-49

.. code-block:: Python


    # We create the example project inside our temp folder
    fldr = join(gettempdir(), uuid4().hex)

    project = create_example(fldr, "coquimbo")








.. GENERATED FROM PYTHON SOURCE LINES 50-53

.. code-block:: Python

    import logging
    import sys








.. GENERATED FROM PYTHON SOURCE LINES 54-55

When the project opens, we can tell the logger to direct all messages to the terminal as well

.. GENERATED FROM PYTHON SOURCE LINES 55-61

.. code-block:: Python

    logger = project.logger
    stdout_handler = logging.StreamHandler(sys.stdout)
    formatter = logging.Formatter("%(asctime)s;%(levelname)s ; %(message)s")
    stdout_handler.setFormatter(formatter)
    logger.addHandler(stdout_handler)








.. GENERATED FROM PYTHON SOURCE LINES 62-64

Network Skimming
----------------

.. GENERATED FROM PYTHON SOURCE LINES 66-68

.. code-block:: Python

    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 69-70

Let's build all graphs

.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: Python

    project.network.build_graphs()
    # We get warnings that several fields in the project are filled with ``NaN``s.
    # This is true, but we won't use those fields.





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

 .. code-block:: none

    2025-06-17 01:06:18,804;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
    [  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
      19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
      37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
      55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
      73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
      91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
     109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
     127 128 129 130 131 132 133]
      warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))
    2025-06-17 01:06:18,881;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
    2025-06-17 01:06:18,973;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
    2025-06-17 01:06:19,060;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:248: UserWarning: Found centroids not present in the graph!
    [  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
      19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
      37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
      55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
      73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
      91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
     109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
     127 128 129 130 131 132 133]
      warnings.warn("Found centroids not present in the graph!\n" + str(centroids[~present_centroids]))




.. GENERATED FROM PYTHON SOURCE LINES 75-76

We grab the graph for cars

.. GENERATED FROM PYTHON SOURCE LINES 76-92

.. code-block:: Python

    graph = project.network.graphs["c"]

    # we also see what graphs are available
    project.network.graphs.keys()

    # let's say we want to minimize the distance
    graph.set_graph("distance")

    # And will skim distance while we are at it, other fields like ``free_flow_time`` or ``travel_time`` 
    # can be added here as well
    graph.set_skimming(["distance"])

    # But let's say we only want a skim matrix for nodes 28-40, and 49-60 (inclusive), 
    # these happen to be a selection of western centroids.
    graph.prepare_graph(np.array(list(range(28, 41)) + list(range(49, 91))))





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

 .. code-block:: none

    2025-06-17 01:06:19,354;WARNING ; Field(s) speed, travel_time, capacity, osm_id, lanes has(ve) at least one NaN value. Check your computations




.. GENERATED FROM PYTHON SOURCE LINES 93-94

And run the skimming

.. GENERATED FROM PYTHON SOURCE LINES 94-96

.. code-block:: Python

    skm = graph.compute_skims()





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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 97-99

Building network skims directly from the graph is more straightforward, though  
we could alternatively use the class ``NetworkSkimming`` to achieve the same result.

.. GENERATED FROM PYTHON SOURCE LINES 99-105

.. code-block:: Python


    # from aequilibrae.paths import NetworkSkimming

    # skm = NetworkSkimming(graph)
    # skm.execute()








.. GENERATED FROM PYTHON SOURCE LINES 106-107

The result is an AequilibraEMatrix object

.. GENERATED FROM PYTHON SOURCE LINES 107-112

.. code-block:: Python

    skims = skm.results.skims

    # Which we can manipulate directly from its temp file, if we wish
    skims.matrices[:3, :3, :]





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

 .. code-block:: none


    array([[[   0.        ],
            [4166.92919206],
            [5532.32681478]],

           [[3733.4499255 ],
            [   0.        ],
            [3311.30654014]],

           [[5446.26074416],
            [3596.12274848],
            [   0.        ]]])



.. GENERATED FROM PYTHON SOURCE LINES 113-114

Or access each matrix, lets just look at the first 3x3

.. GENERATED FROM PYTHON SOURCE LINES 114-116

.. code-block:: Python

    skims.distance[:3, :3]





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

 .. code-block:: none


    array([[   0.        , 4166.92919206, 5532.32681478],
           [3733.4499255 ,    0.        , 3311.30654014],
           [5446.26074416, 3596.12274848,    0.        ]])



.. GENERATED FROM PYTHON SOURCE LINES 117-118

We can save it to the project if we want

.. GENERATED FROM PYTHON SOURCE LINES 118-120

.. code-block:: Python

    skm.save_to_project("base_skims")





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

 .. code-block:: none

    2025-06-17 01:06:19,457;WARNING ; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 121-122

We can also retrieve this skim record to write something to its description

.. GENERATED FROM PYTHON SOURCE LINES 122-127

.. code-block:: Python

    matrices = project.matrices
    mat_record = matrices.get_record("base_skims")
    mat_record.description = "minimized distance while also skimming distance for just a few nodes"
    mat_record.save()








.. GENERATED FROM PYTHON SOURCE LINES 128-129

.. code-block:: Python

    project.close()




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

 .. code-block:: none

    This project at /tmp/174a883e747443d7b0ce8814f963981b is already closed





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

   **Total running time of the script:** (0 minutes 0.993 seconds)


.. _sphx_glr_download__auto_examples_path_computation_plot_skimming.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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