.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_auto_examples/path_computation/plot_graph_from_arbitrary_data.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_graph_from_arbitrary_data.py>`
        to download the full example code.

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

.. _sphx_glr__auto_examples_path_computation_plot_graph_from_arbitrary_data.py:


.. _plot_graph_from_arbitrary_data:

Graph from arbitrary data
=========================

In this example, we demonstrate how to create an AequilibraE Graph from an arbitrary network.

We are using 
`Sioux Falls data <https://github.com/bstabler/TransportationNetworks/tree/master/SiouxFalls>`_, 
from TNTP.

.. GENERATED FROM PYTHON SOURCE LINES 15-19

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

    * :func:`aequilibrae.paths.Graph`

.. GENERATED FROM PYTHON SOURCE LINES 21-28

.. code-block:: Python


    # Imports
    import numpy as np
    import pandas as pd

    from aequilibrae.paths import Graph








.. GENERATED FROM PYTHON SOURCE LINES 30-31

We start by adding the path to load our arbitrary network.

.. GENERATED FROM PYTHON SOURCE LINES 31-33

.. code-block:: Python

    net_file = "https://raw.githubusercontent.com/bstabler/TransportationNetworks/master/SiouxFalls/SiouxFalls_net.tntp"








.. GENERATED FROM PYTHON SOURCE LINES 34-37

Let's read our data! We'll be using Sioux Falls transportation network data, but without
geometric information. The data will be stored in a Pandas DataFrame containing information
about initial and final nodes, link distances, travel times, etc.

.. GENERATED FROM PYTHON SOURCE LINES 37-39

.. code-block:: Python

    net = pd.read_csv(net_file, skiprows=8, sep="\t", lineterminator="\n", usecols=np.arange(1, 11))








.. GENERATED FROM PYTHON SOURCE LINES 40-44

The Graph object requires several default fields: link_id, a_node, b_node, and direction.

We need to manipulate the data to add the missing fields (link_id and direction) and
rename the node columns accordingly.

.. GENERATED FROM PYTHON SOURCE LINES 44-48

.. code-block:: Python

    net.insert(0, "link_id", np.arange(1, net.shape[0] + 1))
    net = net.assign(direction=1)
    net.rename(columns={"init_node": "a_node", "term_node": "b_node"}, inplace=True)








.. GENERATED FROM PYTHON SOURCE LINES 49-50

Now we can take a look in our network file

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: Python

    net.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>link_id</th>
          <th>a_node</th>
          <th>b_node</th>
          <th>capacity</th>
          <th>length</th>
          <th>free_flow_time</th>
          <th>b</th>
          <th>power</th>
          <th>speed</th>
          <th>toll</th>
          <th>link_type</th>
          <th>direction</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>1</td>
          <td>2</td>
          <td>25900.200640</td>
          <td>6</td>
          <td>6</td>
          <td>0.15</td>
          <td>4</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>1</td>
          <td>3</td>
          <td>23403.473190</td>
          <td>4</td>
          <td>4</td>
          <td>0.15</td>
          <td>4</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>2</td>
          <td>1</td>
          <td>25900.200640</td>
          <td>6</td>
          <td>6</td>
          <td>0.15</td>
          <td>4</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>2</td>
          <td>6</td>
          <td>4958.180928</td>
          <td>5</td>
          <td>5</td>
          <td>0.15</td>
          <td>4</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>3</td>
          <td>1</td>
          <td>23403.473190</td>
          <td>4</td>
          <td>4</td>
          <td>0.15</td>
          <td>4</td>
          <td>0</td>
          <td>0</td>
          <td>1</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 53-55

Building an AequilibraE graph from our network is pretty straightforward. We assign
our network to be the graph's network ...

.. GENERATED FROM PYTHON SOURCE LINES 55-58

.. code-block:: Python

    graph = Graph()
    graph.network = net








.. GENERATED FROM PYTHON SOURCE LINES 59-60

... and then set the graph's configurations.

.. GENERATED FROM PYTHON SOURCE LINES 60-70

.. code-block:: Python


    graph.prepare_graph(np.arange(1, 25))  # sets the centroids for which we will perform computation

    graph.set_graph("length")  # sets the cost field for path computation

    graph.set_skimming(["length", "free_flow_time"]) # sets the skims to be computed

    graph.set_blocked_centroid_flows(False)  # we don't block flows through centroids because all nodes
                                             # in the Sioux Falls network are centroids





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

 .. code-block:: none

    Cost field with wrong type. Converting to float64




.. GENERATED FROM PYTHON SOURCE LINES 71-74

Two of AequilibraE's new features consist in directly computing path or skims.

Let's compute the path between nodes 1 and 17...

.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

    res = graph.compute_path(1, 17)








.. GENERATED FROM PYTHON SOURCE LINES 77-78

... and print the corresponding nodes...

.. GENERATED FROM PYTHON SOURCE LINES 78-80

.. code-block:: Python

    res.path_nodes





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

 .. code-block:: none


    array([ 1,  2,  6,  8, 16, 17])



.. GENERATED FROM PYTHON SOURCE LINES 81-82

... and the path links.

.. GENERATED FROM PYTHON SOURCE LINES 82-83

.. code-block:: Python

    res.path




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

 .. code-block:: none


    array([ 1,  4, 16, 22, 49])



.. GENERATED FROM PYTHON SOURCE LINES 84-89

For path computation, when we call the method ``graph.compute_path(1, 17)``, we are calling the class
``PathComputation`` and storing its results into a variable.

Notice that other methods related to path computation, such as ``milepost`` can also be used with
``res``.

.. GENERATED FROM PYTHON SOURCE LINES 91-93

For skim computation, the process is quite similar. When calligng the method ``graph.compute_skims()``
we are actually calling the class ``NetworkSkimming``, and storing its results into ``skm``.

.. GENERATED FROM PYTHON SOURCE LINES 93-96

.. code-block:: Python


    skm = graph.compute_skims()





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

 .. code-block:: none


                                                      :   0%|          | 0/24 [00:00<?, ?it/s]
    1/24                                              :   4%|▍         | 1/24 [00:00<00:00, 515.65it/s]
    2/24                                              :   8%|▊         | 2/24 [00:00<00:00, 922.43it/s]
    3/24                                              :  12%|█▎        | 3/24 [00:00<00:00, 1274.74it/s]
    4/24                                              :  17%|█▋        | 4/24 [00:00<00:00, 1580.82it/s]
    5/24                                              :  21%|██        | 5/24 [00:00<00:00, 1848.85it/s]
    6/24                                              :  25%|██▌       | 6/24 [00:00<00:00, 2086.55it/s]
    7/24                                              :  29%|██▉       | 7/24 [00:00<00:00, 2281.11it/s]
    8/24                                              :  33%|███▎      | 8/24 [00:00<00:00, 2468.51it/s]
    9/24                                              :  38%|███▊      | 9/24 [00:00<00:00, 2638.48it/s]
    10/24                                             :  42%|████▏     | 10/24 [00:00<00:00, 2793.22it/s]
    11/24                                             :  46%|████▌     | 11/24 [00:00<00:00, 2932.89it/s]
    12/24                                             :  50%|█████     | 12/24 [00:00<00:00, 3050.96it/s]
    13/24                                             :  54%|█████▍    | 13/24 [00:00<00:00, 3166.62it/s]
    14/24                                             :  58%|█████▊    | 14/24 [00:00<00:00, 3278.81it/s]
    15/24                                             :  62%|██████▎   | 15/24 [00:00<00:00, 3381.05it/s]
    16/24                                             :  67%|██████▋   | 16/24 [00:00<00:00, 3475.52it/s]
    17/24                                             :  71%|███████   | 17/24 [00:00<00:00, 3565.34it/s]
    18/24                                             :  75%|███████▌  | 18/24 [00:00<00:00, 3635.63it/s]
    19/24                                             :  79%|███████▉  | 19/24 [00:00<00:00, 3711.08it/s]
    20/24                                             :  83%|████████▎ | 20/24 [00:00<00:00, 3784.79it/s]
    21/24                                             :  88%|████████▊ | 21/24 [00:00<00:00, 3854.89it/s]
    22/24                                             :  92%|█████████▏| 22/24 [00:00<00:00, 3918.91it/s]
    23/24                                             :  96%|█████████▌| 23/24 [00:00<00:00, 3980.73it/s]
    24/24                                             : 100%|██████████| 24/24 [00:00<00:00, 4027.02it/s]
    Saving Outputs                                    : 100%|██████████| 24/24 [00:00<00:00, 3672.77it/s]
                                                                                                     



.. GENERATED FROM PYTHON SOURCE LINES 97-98

Let's get the values for 'free_flow_time' matrix.

.. GENERATED FROM PYTHON SOURCE LINES 98-101

.. code-block:: Python

    skims = skm.results.skims
    skims.get_matrix("free_flow_time")





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

 .. code-block:: none


    array([[ 0.,  6.,  4.,  8., 10., 11., 16., 13., 15., 18., 14.,  8., 11.,
            18., 23., 18., 20., 18., 22., 22., 18., 20., 17., 15.],
           [ 6.,  0., 10., 11.,  9.,  5., 10.,  7., 14., 16., 17., 14., 17.,
            21., 19., 12., 14., 12., 16., 16., 22., 21., 23., 21.],
           [ 4., 10.,  0.,  4.,  6., 10., 15., 12., 11., 14., 10.,  4.,  7.,
            14., 19., 17., 19., 17., 21., 20., 14., 16., 13., 11.],
           [ 8., 11.,  4.,  0.,  2.,  6., 11.,  8.,  7., 10.,  6.,  8., 11.,
            10., 15., 13., 15., 13., 17., 17., 18., 18., 14., 15.],
           [10.,  9.,  6.,  2.,  0.,  4.,  9.,  6.,  5.,  8.,  8., 10., 13.,
            12., 14., 11., 13., 11., 15., 15., 19., 17., 16., 17.],
           [11.,  5., 10.,  6.,  4.,  0.,  5.,  2.,  9., 11., 12., 14., 17.,
            16., 14.,  7.,  9.,  7., 11., 11., 17., 16., 20., 20.],
           [16., 10., 15., 11.,  9.,  5.,  0.,  3., 12.,  9., 14., 19., 19.,
            17., 12.,  5.,  7.,  2.,  9.,  6., 12., 11., 15., 15.],
           [13.,  7., 12.,  8.,  6.,  2.,  3.,  0., 10.,  9., 14., 16., 19.,
            17., 12.,  5.,  7.,  5.,  9.,  9., 15., 14., 18., 18.],
           [15., 14., 11.,  7.,  5.,  9., 12., 10.,  0.,  3.,  8., 14., 17.,
            12.,  9.,  7.,  9., 10., 11., 14., 14., 12., 16., 17.],
           [18., 16., 14., 10.,  8., 11.,  9.,  9.,  3.,  0.,  5., 11., 14.,
             9.,  6.,  4.,  6.,  7.,  8., 11., 11.,  9., 13., 14.],
           [14., 17., 10.,  6.,  8., 12., 14., 14.,  8.,  5.,  0.,  6.,  9.,
             4.,  9.,  9., 11., 12., 12., 16., 13., 12.,  8., 10.],
           [ 8., 14.,  4.,  8., 10., 14., 19., 16., 14., 11.,  6.,  0.,  3.,
            10., 15., 15., 17., 18., 18., 16., 10., 12.,  9.,  7.],
           [11., 17.,  7., 11., 13., 17., 19., 19., 17., 14.,  9.,  3.,  0.,
            10., 12., 18., 17., 17., 15., 13.,  7.,  9.,  6.,  4.],
           [18., 21., 14., 10., 12., 16., 17., 17., 12.,  9.,  4., 10., 10.,
             0.,  5., 12., 10., 15.,  8., 12.,  9.,  8.,  4.,  6.],
           [23., 19., 19., 15., 14., 14., 12., 12.,  9.,  6.,  9., 15., 12.,
             5.,  0.,  7.,  5., 10.,  3.,  7.,  5.,  3.,  7.,  8.],
           [18., 12., 17., 13., 11.,  7.,  5.,  5.,  7.,  4.,  9., 15., 18.,
            12.,  7.,  0.,  2.,  3.,  4.,  7., 12., 10., 14., 15.],
           [20., 14., 19., 15., 13.,  9.,  7.,  7.,  9.,  6., 11., 17., 17.,
            10.,  5.,  2.,  0.,  5.,  2.,  6., 10.,  8., 12., 13.],
           [18., 12., 17., 13., 11.,  7.,  2.,  5., 10.,  7., 12., 18., 17.,
            15., 10.,  3.,  5.,  0.,  7.,  4., 10.,  9., 13., 13.],
           [22., 16., 21., 17., 15., 11.,  9.,  9., 11.,  8., 12., 18., 15.,
             8.,  3.,  4.,  2.,  7.,  0.,  4.,  8.,  6., 10., 11.],
           [22., 16., 20., 17., 15., 11.,  6.,  9., 14., 11., 16., 16., 13.,
            12.,  7.,  7.,  6.,  4.,  4.,  0.,  6.,  5.,  9.,  9.],
           [18., 22., 14., 18., 19., 17., 12., 15., 14., 11., 13., 10.,  7.,
             9.,  5., 12., 10., 10.,  8.,  6.,  0.,  2.,  5.,  3.],
           [20., 21., 16., 18., 17., 16., 11., 14., 12.,  9., 12., 12.,  9.,
             8.,  3., 10.,  8.,  9.,  6.,  5.,  2.,  0.,  4.,  5.],
           [17., 23., 13., 14., 16., 20., 15., 18., 16., 13.,  8.,  9.,  6.,
             4.,  7., 14., 12., 13., 10.,  9.,  5.,  4.,  0.,  2.],
           [15., 21., 11., 15., 17., 20., 15., 18., 17., 14., 10.,  7.,  4.,
             6.,  8., 15., 13., 13., 11.,  9.,  3.,  5.,  2.,  0.]])



.. GENERATED FROM PYTHON SOURCE LINES 102-103

Now we're all set!

.. GENERATED FROM PYTHON SOURCE LINES 105-106

Graph image credits to
`Behance-network icons created by Sumitsaengtong - Flaticon <https://www.flaticon.com/free-icons/behance-network>`_


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

   **Total running time of the script:** (0 minutes 0.166 seconds)


.. _sphx_glr_download__auto_examples_path_computation_plot_graph_from_arbitrary_data.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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