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

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

.. _sphx_glr__auto_examples_traffic_assignment_plot_sparse_matrix_assignment.py:


.. _example_assign_sparse:

Assigning sparse matrices
=========================

Modern Activity-Based models (and even some trip-based and tour-based ones) result on incredibly sparse
demand matrices, which opens up a significant opportunity to save time during assignment by using early-exiting
during the path-computation phase of assignment.

To take advantage of this, while still computing assignment skims, AequilibraE has a built-in method to
skim the last iteration after the assignment is done.

.. GENERATED FROM PYTHON SOURCE LINES 16-19

.. admonition:: Technical references

  * :doc:`../../traffic_assignment/assignment_procedures`

.. GENERATED FROM PYTHON SOURCE LINES 21-27

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

    * :func:`aequilibrae.paths.Graph`
    * :func:`aequilibrae.paths.TrafficClass`
    * :func:`aequilibrae.paths.TrafficAssignment`

.. GENERATED FROM PYTHON SOURCE LINES 29-39

.. code-block:: Python


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

    from aequilibrae.utils.create_example import create_example
    from aequilibrae.paths import TrafficAssignment, TrafficClass









.. GENERATED FROM PYTHON SOURCE LINES 41-48

.. code-block:: Python


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

    project = create_example(fldr)
    logger = project.logger








.. GENERATED FROM PYTHON SOURCE LINES 49-50

Traffic assignment

.. GENERATED FROM PYTHON SOURCE LINES 52-53

We build all graphs

.. GENERATED FROM PYTHON SOURCE LINES 53-67

.. code-block:: Python

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

    # We grab the graph for cars
    graph = project.network.graphs["c"]

    # Let's say we want to minimize the free_flow_time
    graph.set_graph("free_flow_time")

    # And we will allow paths to be computed going through other centroids/centroid connectors
    # required for the Sioux Falls network, as all nodes are centroids
    graph.set_blocked_centroid_flows(False)








.. GENERATED FROM PYTHON SOURCE LINES 68-69

Let's get the demand matrix directly from the project record, and inspect what matrices we have in the project.

.. GENERATED FROM PYTHON SOURCE LINES 69-72

.. code-block:: Python

    proj_matrices = project.matrices
    proj_matrices.list()






.. 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>name</th>
          <th>file_name</th>
          <th>cores</th>
          <th>procedure</th>
          <th>procedure_id</th>
          <th>timestamp</th>
          <th>description</th>
          <th>status</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>demand_omx</td>
          <td>demand.omx</td>
          <td>1</td>
          <td>None</td>
          <td>None</td>
          <td>2020-11-24 08:47:18</td>
          <td>Original data imported to OMX format</td>
          <td></td>
        </tr>
        <tr>
          <th>1</th>
          <td>demand_mc</td>
          <td>demand_mc.omx</td>
          <td>3</td>
          <td>None</td>
          <td>None</td>
          <td>2021-02-24 00:51:35</td>
          <td>None</td>
          <td></td>
        </tr>
        <tr>
          <th>2</th>
          <td>skims</td>
          <td>skims.omx</td>
          <td>2</td>
          <td>None</td>
          <td>None</td>
          <td>None</td>
          <td>Example skim</td>
          <td></td>
        </tr>
        <tr>
          <th>3</th>
          <td>demand_aem</td>
          <td>demand.aem</td>
          <td>1</td>
          <td>None</td>
          <td>None</td>
          <td>2020-11-24 08:46:42</td>
          <td>Original data imported to AEM format</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 73-74

We get the demand matrix, and prepare it for computation

.. GENERATED FROM PYTHON SOURCE LINES 74-77

.. code-block:: Python

    demand = proj_matrices.get_matrix("demand_omx")
    demand.computational_view(["matrix"])








.. GENERATED FROM PYTHON SOURCE LINES 78-79

Let's perform the traffic assignment

.. GENERATED FROM PYTHON SOURCE LINES 79-108

.. code-block:: Python


    # Create the assignment class
    assigclass = TrafficClass(name="car", graph=graph, matrix=demand)

    assig = TrafficAssignment()

    # We start by adding the list of traffic classes to be assigned
    assig.add_class(assigclass)

    # Then we set these parameters, which an only be configured after adding one class to the assignment
    assig.set_vdf("BPR")  # This is not case-sensitive 

    # Then we set the volume delay function and its parameters
    assig.set_vdf_parameters({"alpha": "b", "beta": "power"})

    # The capacity and free flow travel times as they exist in the graph
    assig.set_capacity_field("capacity")
    assig.set_time_field("free_flow_time")

    # And the algorithm we want to use to assign
    assig.set_algorithm("bfw")

    # Let's set parameters that make this example run very fast
    assig.max_iter = 10
    assig.rgap_target = 0.01

    # we then execute the assignment
    assig.execute()





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

 .. code-block:: none


    car                                               :   0%|          | 0/24 [00:00<?, ?it/s]
    Equilibrium Assignment                            :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car               :  10%|█         | 1/10 [00:00<00:00, 39945.75it/s]
    All-or-Nothing - Traffic Class: car               :  10%|█         | 1/10 [00:00<00:00, 7073.03it/s] 
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]          
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 1/10 - RGap: inf:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  20%|██        | 2/10 [00:00<00:00, 435.05it/s]
    All-or-Nothing - Traffic Class: car               :  20%|██        | 2/10 [00:00<00:00, 422.51it/s]
    All-or-Nothing - Traffic Class: car               :  20%|██        | 2/10 [00:00<00:00, 411.47it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]         
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 2/10 - RGap: 0.855075:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  30%|███       | 3/10 [00:00<00:00, 656.66it/s]
    All-or-Nothing - Traffic Class: car               :  30%|███       | 3/10 [00:00<00:00, 638.05it/s]
    All-or-Nothing - Traffic Class: car               :  30%|███       | 3/10 [00:00<00:00, 621.32it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]         
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 3/10 - RGap: 0.476346:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  40%|████      | 4/10 [00:00<00:00, 861.65it/s]
    All-or-Nothing - Traffic Class: car               :  40%|████      | 4/10 [00:00<00:00, 837.44it/s]
    All-or-Nothing - Traffic Class: car               :  40%|████      | 4/10 [00:00<00:00, 815.85it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]         
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 4/10 - RGap: 0.235513:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  50%|█████     | 5/10 [00:00<00:00, 1046.59it/s]
    All-or-Nothing - Traffic Class: car               :  50%|█████     | 5/10 [00:00<00:00, 1017.69it/s]
    All-or-Nothing - Traffic Class: car               :  50%|█████     | 5/10 [00:00<00:00, 986.62it/s] 
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]         
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 5/10 - RGap: 0.109241:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  60%|██████    | 6/10 [00:00<00:00, 1253.90it/s]
    All-or-Nothing - Traffic Class: car               :  60%|██████    | 6/10 [00:00<00:00, 1218.62it/s]
    All-or-Nothing - Traffic Class: car               :  60%|██████    | 6/10 [00:00<00:00, 1187.74it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]          
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 6/10 - RGap: 0.198095:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  70%|███████   | 7/10 [00:00<00:00, 1422.69it/s]
    All-or-Nothing - Traffic Class: car               :  70%|███████   | 7/10 [00:00<00:00, 1384.19it/s]
    All-or-Nothing - Traffic Class: car               :  70%|███████   | 7/10 [00:00<00:00, 1350.08it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]          
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 7/10 - RGap: 0.0668172:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  80%|████████  | 8/10 [00:00<00:00, 1714.85it/s]
    All-or-Nothing - Traffic Class: car               :  80%|████████  | 8/10 [00:00<00:00, 1666.31it/s]
    All-or-Nothing - Traffic Class: car               :  80%|████████  | 8/10 [00:00<00:00, 1623.26it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]          
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 8/10 - RGap: 0.0679212:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  90%|█████████ | 9/10 [00:00<00:00, 1936.03it/s]
    All-or-Nothing - Traffic Class: car               :  90%|█████████ | 9/10 [00:00<00:00, 1881.98it/s]
    All-or-Nothing - Traffic Class: car               :  90%|█████████ | 9/10 [00:00<00:00, 1834.33it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]          
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 9/10 - RGap: 0.107056:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            : 100%|██████████| 10/10 [00:00<00:00, 2032.62it/s]
    All-or-Nothing - Traffic Class: car               : 100%|██████████| 10/10 [00:00<00:00, 1978.82it/s]
    All-or-Nothing - Traffic Class: car               : 100%|██████████| 10/10 [00:00<00:00, 1930.99it/s]
    All-or-Nothing - Traffic Class: car               :   0%|          | 0/10 [00:00<?, ?it/s]           
    All-or-Nothing - Traffic Class: car - Zones: 0/24 :   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 10/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 20/24:   0%|          | 0/10 [00:00<?, ?it/s]
    All-or-Nothing - Traffic Class: car - Zones: 24/24:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment - Iteration: 10/10 - RGap: 0.0403881:   0%|          | 0/10 [00:00<?, ?it/s]
                                                                                                   



.. GENERATED FROM PYTHON SOURCE LINES 109-110

After finishing the assignment, we can skim the last iteration

.. GENERATED FROM PYTHON SOURCE LINES 110-116

.. code-block:: Python

    skims = assig.skim_congested(["distance"], return_matrices=True)

    # Skims are returned as a dictionary, with the class names as keys
    # Let's see all skims we have inside it:
    print(skims["car"].names)





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

 .. code-block:: none


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    24/24                                             : 100%|██████████| 24/24 [00:00<00:00, 4113.24it/s]
    Saving Outputs                                    : 100%|██████████| 24/24 [00:00<00:00, 3761.43it/s]
                                                                                                     
    ['distance', '__assignment_cost__', '__congested_time__']




.. GENERATED FROM PYTHON SOURCE LINES 117-118

We can save the skims, but we need to choose to only save the final ones, as the blended were not generated

.. GENERATED FROM PYTHON SOURCE LINES 118-120

.. code-block:: Python

    assig.save_skims("base_year_assignment_skims", which_ones="final", format="omx")








.. GENERATED FROM PYTHON SOURCE LINES 121-122

Close the project

.. GENERATED FROM PYTHON SOURCE LINES 122-123

.. code-block:: Python

    project.close()








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

   **Total running time of the script:** (0 minutes 0.610 seconds)


.. _sphx_glr_download__auto_examples_traffic_assignment_plot_sparse_matrix_assignment.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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