.. 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.traffic_class.TrafficClass`
    * :func:`aequilibrae.paths.traffic_assignment.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 49-51

Traffic assignment
------------------

.. GENERATED FROM PYTHON SOURCE LINES 53-54

We build all graphs

.. GENERATED FROM PYTHON SOURCE LINES 54-68

.. 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)





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

 .. code-block:: none

    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:218: ChainedAssignmentError: A value is being set on a copy of a DataFrame or Series through chained assignment.
    Such chained assignment never works to update the original DataFrame or Series, because the intermediate object on which we are setting values always behaves as a copy (due to Copy-on-Write).

    Try using '.loc[row_indexer, col_indexer] = value' instead, to perform the assignment in a single step.

    See the documentation for a more detailed explanation: https://pandas.pydata.org/pandas-docs/stable/user_guide/copy_on_write.html#chained-assignment
      build_compressed_graph(self, remove_dead_ends)




.. GENERATED FROM PYTHON SOURCE LINES 69-70

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 70-73

.. 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>NaN</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>NaN</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 74-75

We get the demand matrix, and prepare it for computation

.. GENERATED FROM PYTHON SOURCE LINES 75-78

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 79-80

Let's perform the traffic assignment

.. GENERATED FROM PYTHON SOURCE LINES 80-109

.. 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, 30393.51it/s]
    All-or-Nothing - Traffic Class: car               :  10%|█         | 1/10 [00:00<00:00, 7639.90it/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, 61.52it/s]
    All-or-Nothing - Traffic Class: car               :  20%|██        | 2/10 [00:00<00:00, 61.34it/s]
    All-or-Nothing - Traffic Class: car               :  20%|██        | 2/10 [00:00<00:00, 61.15it/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: 0.852989:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  30%|███       | 3/10 [00:00<00:00, 51.68it/s]
    All-or-Nothing - Traffic Class: car               :  30%|███       | 3/10 [00:00<00:00, 51.60it/s]
    All-or-Nothing - Traffic Class: car               :  30%|███       | 3/10 [00:00<00:00, 51.53it/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: 0.478664:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  40%|████      | 4/10 [00:00<00:00, 73.26it/s]
    All-or-Nothing - Traffic Class: car               :  40%|████      | 4/10 [00:00<00:00, 73.15it/s]
    All-or-Nothing - Traffic Class: car               :  40%|████      | 4/10 [00:00<00:00, 73.06it/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: 0.240469:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  50%|█████     | 5/10 [00:00<00:00, 96.40it/s]
    All-or-Nothing - Traffic Class: car               :  50%|█████     | 5/10 [00:00<00:00, 96.23it/s]
    All-or-Nothing - Traffic Class: car               :  50%|█████     | 5/10 [00:00<00:00, 96.10it/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: 0.156466:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  60%|██████    | 6/10 [00:00<00:00, 81.27it/s]
    All-or-Nothing - Traffic Class: car               :  60%|██████    | 6/10 [00:00<00:00, 81.16it/s]
    All-or-Nothing - Traffic Class: car               :  60%|██████    | 6/10 [00:00<00:00, 81.07it/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: 0.119963:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  70%|███████   | 7/10 [00:00<00:00, 99.03it/s]
    All-or-Nothing - Traffic Class: car               :  70%|███████   | 7/10 [00:00<00:00, 98.92it/s]
    All-or-Nothing - Traffic Class: car               :  70%|███████   | 7/10 [00:00<00:00, 98.81it/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: 0.0747566:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  80%|████████  | 8/10 [00:00<00:00, 101.49it/s]
    All-or-Nothing - Traffic Class: car               :  80%|████████  | 8/10 [00:00<00:00, 101.38it/s]
    All-or-Nothing - Traffic Class: car               :  80%|████████  | 8/10 [00:00<00:00, 101.29it/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: 0.105012:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            :  90%|█████████ | 9/10 [00:00<00:00, 135.02it/s]
    All-or-Nothing - Traffic Class: car               :  90%|█████████ | 9/10 [00:00<00:00, 134.86it/s]
    All-or-Nothing - Traffic Class: car               :  90%|█████████ | 9/10 [00:00<00:00, 134.72it/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: 0.0818315:   0%|          | 0/10 [00:00<?, ?it/s]
    Equilibrium Assignment                            : 100%|██████████| 10/10 [00:00<00:00, 142.91it/s]
    All-or-Nothing - Traffic Class: car               : 100%|██████████| 10/10 [00:00<00:00, 142.75it/s]
    All-or-Nothing - Traffic Class: car               : 100%|██████████| 10/10 [00:00<00:00, 142.60it/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: 0.062379:   0%|          | 0/10 [00:00<?, ?it/s]
                                                                                                  



.. GENERATED FROM PYTHON SOURCE LINES 110-111

After finishing the assignment, we can skim the last iteration

.. GENERATED FROM PYTHON SOURCE LINES 111-117

.. 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, 1038.82it/s]
    Saving Outputs                                    : 100%|██████████| 24/24 [00:00<00:00, 1004.78it/s]
                                                                                                     
    ['distance', '__assignment_cost__', '__congested_time__']




.. GENERATED FROM PYTHON SOURCE LINES 118-119

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 119-121

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 122-123

Close the project

.. GENERATED FROM PYTHON SOURCE LINES 123-124

.. code-block:: Python

    project.close()








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

   **Total running time of the script:** (0 minutes 2.161 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>`_