.. 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.network_skimming`

.. 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.


.. 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))))


.. 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()


.. 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, :]


.. 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]


.. 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")


.. 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()


.. _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>`_