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

.. only:: html

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

        Click :ref:`here <sphx_glr_download__auto_examples_plot_forecasting.py>`
        to download the full example code

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

.. _sphx_glr__auto_examples_plot_forecasting.py:


Forecasting
============

On this example we present a full forecasting workflow for the Sioux Falls
example model.

.. GENERATED FROM PYTHON SOURCE LINES 8-18

.. 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
    from aequilibrae import logger
    import logging
    import sys








.. GENERATED FROM PYTHON SOURCE LINES 19-20

We create the example project inside our temp folder

.. GENERATED FROM PYTHON SOURCE LINES 20-30

.. code-block:: python

    fldr = join(gettempdir(), uuid4().hex)

    project = create_example(fldr)

    # We the project open, we can tell the logger to direct all messages to the terminal as well
    stdout_handler = logging.StreamHandler(sys.stdout)
    formatter = logging.Formatter("%(asctime)s;%(name)s;%(levelname)s ; %(message)s")
    stdout_handler.setFormatter(formatter)
    logger.addHandler(stdout_handler)








.. GENERATED FROM PYTHON SOURCE LINES 31-34

.. code-block:: python


    ## Traffic assignment with skimming








.. GENERATED FROM PYTHON SOURCE LINES 35-38

.. code-block:: python


    from aequilibrae.paths import TrafficAssignment, TrafficClass








.. GENERATED FROM PYTHON SOURCE LINES 39-44

.. code-block:: python


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





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:33,368;aequilibrae;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2021-02-18 10:20:33,426;aequilibrae;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2021-02-18 10:20:33,484;aequilibrae;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2021-02-18 10:20:33,543;aequilibrae;WARNING ; Field(s) name, lanes has(ve) at least one NaN value. Check your computations




.. GENERATED FROM PYTHON SOURCE LINES 45-59

.. code-block:: python


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

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

    # And will skim time and distance while we are at it
    graph.set_skimming(["free_flow_time", "distance"])

    # And we will allow paths to be compute 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

 Out:

 .. code-block:: none

    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: UserWarning: Cost field with wrong type. Converting to float64
      warn("Cost field with wrong type. Converting to float64")




.. GENERATED FROM PYTHON SOURCE LINES 60-66

.. code-block:: python


    # We get the demand matrix directly from the project record
    # so let's inspect what we have in the project
    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_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\n</td>
          <td></td>
        </tr>
        <tr>
          <th>1</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>2</th>
          <td>skims</td>
          <td>skims.omx</td>
          <td>2</td>
          <td>None</td>
          <td>None</td>
          <td></td>
          <td>Example skim</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 67-72

.. code-block:: python


    # Let's get it in this better way
    demand = proj_matrices.get_matrix("demand_omx")
    demand.computational_view(["matrix"])








.. GENERATED FROM PYTHON SOURCE LINES 73-99

.. code-block:: python


    assig = TrafficAssignment()

    # Creates the assignment class
    assigclass = TrafficClass(graph, demand)

    # The first thing to do is to add at list of traffic classes to be assigned
    assig.add_class(assigclass)

    # We set these parameters only after adding one class to the assignment
    assig.set_vdf("BPR")  # This is not case-sensitive # Then we set the volume delay function

    assig.set_vdf_parameters({"alpha": "b", "beta": "power"})  # And its parameters

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

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

    # since I haven't checked the parameters file, let's make sure convergence criteria is good
    assig.max_iter = 1000
    assig.rgap_target = 0.001

    assig.execute()  # we then execute the assignment





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

 Out:

 .. code-block:: none

    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: UserWarning: Cost field with wrong type. Converting to float64
      warn("Cost field with wrong type. Converting to float64")
    2021-02-18 10:20:34,043;aequilibrae;INFO ; bfw Assignment STATS
    2021-02-18 10:20:34,044;aequilibrae;INFO ; Iteration, RelativeGap, stepsize
    2021-02-18 10:20:34,145;aequilibrae;INFO ; 1,inf,1.0
    2021-02-18 10:20:34,248;aequilibrae;INFO ; 2,0.8485503509703024,0.36497345609427145
    2021-02-18 10:20:34,350;aequilibrae;INFO ; 3,0.3813926225800203,0.2298356924660528
    2021-02-18 10:20:34,453;aequilibrae;INFO ; 4,0.19621277462606984,0.18591312145268074
    2021-02-18 10:20:34,555;aequilibrae;INFO ; 5,0.09069073200924213,0.7090816523174254
    2021-02-18 10:20:34,658;aequilibrae;INFO ; 6,0.20600048221061426,0.1229016022154401
    2021-02-18 10:20:34,761;aequilibrae;INFO ; 7,0.06710568925282254,0.38638656717489844
    2021-02-18 10:20:34,865;aequilibrae;INFO ; 8,0.10307514154369488,0.1093055036410267
    2021-02-18 10:20:34,969;aequilibrae;INFO ; 9,0.04222147191362779,0.2487805192125393
    2021-02-18 10:20:35,072;aequilibrae;INFO ; 10,0.05926435464772421,0.15904810628271004
    2021-02-18 10:20:35,177;aequilibrae;INFO ; 11,0.03453951151333739,0.5180973804491722
    2021-02-18 10:20:35,281;aequilibrae;INFO ; 12,0.059426517206032654,0.10197123112986584
    2021-02-18 10:20:35,384;aequilibrae;INFO ; 13,0.023239895792359067,0.1780595483940025
    2021-02-18 10:20:35,488;aequilibrae;INFO ; 14,0.017873783562205998,0.9787872930853664
    2021-02-18 10:20:35,593;aequilibrae;INFO ; 15,0.049661371545771556,0.08320656307451389
    2021-02-18 10:20:35,696;aequilibrae;INFO ; 16,0.021382882276233087,0.11517403065013918
    2021-02-18 10:20:35,802;aequilibrae;INFO ; 17,0.013141542820482906,0.10640369388111594
    2021-02-18 10:20:35,905;aequilibrae;INFO ; 18,0.009902228209039313,0.10710395766777006
    2021-02-18 10:20:36,007;aequilibrae;INFO ; 19,0.008834657139075773,0.2524678977926848
    2021-02-18 10:20:36,113;aequilibrae;INFO ; 20,0.01037104162273405,0.672783938935575
    2021-02-18 10:20:36,216;aequilibrae;INFO ; 21,0.011090096306112919,0.07468060074370476
    2021-02-18 10:20:36,319;aequilibrae;INFO ; 22,0.006512685266481729,0.12485017624661685
    2021-02-18 10:20:36,422;aequilibrae;INFO ; 23,0.005238884615251675,0.06316016574610187
    2021-02-18 10:20:36,525;aequilibrae;INFO ; 24,0.003949529236289854,0.09717896243712648
    2021-02-18 10:20:36,627;aequilibrae;INFO ; 25,0.003282221301580423,0.1577590171880403
    2021-02-18 10:20:36,732;aequilibrae;INFO ; 26,0.0057928324528667054,0.45247656699511957
    2021-02-18 10:20:36,835;aequilibrae;INFO ; 27,0.0066823585472082035,0.7851865570315525
    2021-02-18 10:20:36,940;aequilibrae;INFO ; 28,0.005992619218871392,0.03909810047559774
    2021-02-18 10:20:37,044;aequilibrae;INFO ; 29,0.0040313646880229485,0.04253018777364977
    2021-02-18 10:20:37,147;aequilibrae;INFO ; 30,0.0027696521195955236,0.022512872266868305
    2021-02-18 10:20:37,251;aequilibrae;INFO ; 31,0.0024846461521885483,0.045801692933285426
    2021-02-18 10:20:37,355;aequilibrae;INFO ; 32,0.0016385526468732255,0.03429583308494769
    2021-02-18 10:20:37,458;aequilibrae;INFO ; 33,0.0014956911143795503,0.03539200254746481
    2021-02-18 10:20:37,560;aequilibrae;INFO ; 34,0.0011355392148568219,0.05358983822746
    2021-02-18 10:20:37,663;aequilibrae;INFO ; 35,0.0012151973759507247,0.047571304687631016
    2021-02-18 10:20:37,768;aequilibrae;INFO ; 36,0.001239365147988278,0.06536473861293134
    2021-02-18 10:20:37,870;aequilibrae;INFO ; 37,0.0010684962819863329,0.10703952287434068
    2021-02-18 10:20:37,976;aequilibrae;INFO ; 38,0.0010899690080088834,0.0986406240944672
    2021-02-18 10:20:38,081;aequilibrae;INFO ; 39,0.0009886948497197317,0.06027623911565202
    2021-02-18 10:20:38,184;aequilibrae;INFO ; 40,0.0012632086912186262,0.22288089471588354
    2021-02-18 10:20:38,286;aequilibrae;INFO ; 41,0.001165571653411883,0.1977596494198537
    2021-02-18 10:20:38,389;aequilibrae;INFO ; 42,0.0012299958308316802,0.3170348486824528
    2021-02-18 10:20:38,491;aequilibrae;INFO ; 43,0.0012902531663786525,0.3422879827743953
    2021-02-18 10:20:38,594;aequilibrae;INFO ; 44,0.0009979531087593776,0.7332060974425909
    2021-02-18 10:20:38,594;aequilibrae;INFO ; bfw Assignment finished. 44 iterations and 0.0009979531087593776 final gap




.. GENERATED FROM PYTHON SOURCE LINES 100-107

.. code-block:: python


    # Convergence report is easy to see
    import pandas as pd

    convergence_report = assig.report()
    convergence_report.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>iteration</th>
          <th>rgap</th>
          <th>alpha</th>
          <th>warnings</th>
          <th>beta0</th>
          <th>beta1</th>
          <th>beta2</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>inf</td>
          <td>1.000000</td>
          <td></td>
          <td>1.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>0.848550</td>
          <td>0.364973</td>
          <td></td>
          <td>1.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>0.381393</td>
          <td>0.229836</td>
          <td></td>
          <td>1.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>0.196213</td>
          <td>0.185913</td>
          <td></td>
          <td>0.959771</td>
          <td>0.040229</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>0.090691</td>
          <td>0.709082</td>
          <td></td>
          <td>0.687640</td>
          <td>0.286705</td>
          <td>0.025654</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 108-112

.. code-block:: python


    volumes = assig.results()
    volumes.head()






.. raw:: html

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    <div>
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    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>matrix_ab</th>
          <th>matrix_ba</th>
          <th>matrix_tot</th>
          <th>Congested_Time_AB</th>
          <th>Congested_Time_BA</th>
          <th>Congested_Time_Max</th>
          <th>Delay_factor_AB</th>
          <th>Delay_factor_BA</th>
          <th>Delay_factor_Max</th>
          <th>VOC_AB</th>
          <th>VOC_BA</th>
          <th>VOC_max</th>
          <th>PCE_AB</th>
          <th>PCE_BA</th>
          <th>PCE_tot</th>
        </tr>
        <tr>
          <th>link_id</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>4570.421761</td>
          <td>NaN</td>
          <td>4570.421761</td>
          <td>6.000883</td>
          <td>NaN</td>
          <td>6.000883</td>
          <td>1.000147</td>
          <td>NaN</td>
          <td>1.000147</td>
          <td>0.176463</td>
          <td>NaN</td>
          <td>0.176463</td>
          <td>4570.421761</td>
          <td>NaN</td>
          <td>4570.421761</td>
        </tr>
        <tr>
          <th>2</th>
          <td>8275.382482</td>
          <td>NaN</td>
          <td>8275.382482</td>
          <td>4.009065</td>
          <td>NaN</td>
          <td>4.009065</td>
          <td>1.002266</td>
          <td>NaN</td>
          <td>1.002266</td>
          <td>0.353596</td>
          <td>NaN</td>
          <td>0.353596</td>
          <td>8275.382482</td>
          <td>NaN</td>
          <td>8275.382482</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4675.373252</td>
          <td>NaN</td>
          <td>4675.373252</td>
          <td>6.000899</td>
          <td>NaN</td>
          <td>6.000899</td>
          <td>1.000150</td>
          <td>NaN</td>
          <td>1.000150</td>
          <td>0.180515</td>
          <td>NaN</td>
          <td>0.180515</td>
          <td>4675.373252</td>
          <td>NaN</td>
          <td>4675.373252</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5900.513362</td>
          <td>NaN</td>
          <td>5900.513362</td>
          <td>6.547982</td>
          <td>NaN</td>
          <td>6.547982</td>
          <td>1.309596</td>
          <td>NaN</td>
          <td>1.309596</td>
          <td>1.190056</td>
          <td>NaN</td>
          <td>1.190056</td>
          <td>5900.513362</td>
          <td>NaN</td>
          <td>5900.513362</td>
        </tr>
        <tr>
          <th>5</th>
          <td>8170.430991</td>
          <td>NaN</td>
          <td>8170.430991</td>
          <td>4.008970</td>
          <td>NaN</td>
          <td>4.008970</td>
          <td>1.002243</td>
          <td>NaN</td>
          <td>1.002243</td>
          <td>0.349112</td>
          <td>NaN</td>
          <td>0.349112</td>
          <td>8170.430991</td>
          <td>NaN</td>
          <td>8170.430991</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 113-117

.. code-block:: python


    # We could export it to CSV or AequilibraE data, but let's put it directly into the results database
    assig.save_results("base_year_assignment")








.. GENERATED FROM PYTHON SOURCE LINES 118-122

.. code-block:: python


    # And save the skims
    assig.save_skims("base_year_assignment_skims", which_ones="all", format="omx")





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:38,884;aequilibrae;WARNING ; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 123-126

.. code-block:: python


    ## Trip distribution








.. GENERATED FROM PYTHON SOURCE LINES 127-131

.. code-block:: python


    ### Calibration
    # We will calibrate synthetic gravity models using the skims for TIME that we just generated








.. GENERATED FROM PYTHON SOURCE LINES 132-136

.. code-block:: python


    import numpy as np
    from aequilibrae.distribution import GravityCalibration








.. GENERATED FROM PYTHON SOURCE LINES 137-141

.. code-block:: python


    # Let's take another look at what we have in terms of matrices in the model
    proj_matrices.list()






.. raw:: html

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    <div>
    <style scoped>
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        }

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

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            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_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\n</td>
          <td></td>
        </tr>
        <tr>
          <th>1</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>2</th>
          <td>skims</td>
          <td>skims.omx</td>
          <td>2</td>
          <td>None</td>
          <td>None</td>
          <td></td>
          <td>Example skim</td>
          <td></td>
        </tr>
        <tr>
          <th>3</th>
          <td>base_year_assignment_skims</td>
          <td>base_year_assignment_skims.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>c43e1c8718d649e3a602ef802ffe2441</td>
          <td>2021-02-18 10:20:33.708364</td>
          <td>Skimming for assignment procedure</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 142-149

.. code-block:: python


    # We need the demand
    demand = proj_matrices.get_matrix("demand_aem")

    # And the skims
    imped = proj_matrices.get_matrix("base_year_assignment_skims")








.. GENERATED FROM PYTHON SOURCE LINES 150-156

.. code-block:: python


    # We can check which matrix cores were created for our skims to decide which one to use
    imped.names

    # Where free_flow_time_final is actually the congested time for the last iteration





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

 Out:

 .. code-block:: none


    ['distance_blended', 'distance_final', 'free_flow_time_blended', 'free_flow_time_final']



.. GENERATED FROM PYTHON SOURCE LINES 157-175

.. code-block:: python


    # But before using the data, let's get some impedance for the intrazonals
    # Let's assume it is 75% of the closest zone

    imped_core = "free_flow_time_final"
    imped.computational_view([imped_core])

    # If we run the code below more than once, we will be overwriting the diagonal values with non-sensical data
    # so let's zero it first
    np.fill_diagonal(imped.matrix_view, 0)

    # We compute it with a little bit of NumPy magic
    intrazonals = np.amin(imped.matrix_view, where=imped.matrix_view > 0, initial=imped.matrix_view.max(), axis=1)
    intrazonals *= 0.75

    # Then we fill in the impedance matrix
    np.fill_diagonal(imped.matrix_view, intrazonals)








.. GENERATED FROM PYTHON SOURCE LINES 176-181

.. code-block:: python


    # Since we are working with an OMX file, we cannot overwrite a matrix on disk
    # So we give a new name to save it
    imped.save(names=["final_time_with_intrazonals"])








.. GENERATED FROM PYTHON SOURCE LINES 182-187

.. code-block:: python


    # This also updates these new matrices as those being used for computation
    # As one can verify below
    imped.view_names





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

 Out:

 .. code-block:: none


    ['final_time_with_intrazonals']



.. GENERATED FROM PYTHON SOURCE LINES 188-193

.. code-block:: python


    # We set the matrices for being used in computation
    demand.computational_view(["matrix"])









.. GENERATED FROM PYTHON SOURCE LINES 194-209

.. code-block:: python


    for function in ["power", "expo"]:
        gc = GravityCalibration(matrix=demand, impedance=imped, function=function, nan_as_zero=True)
        gc.calibrate()
        model = gc.model
        # we save the model
        model.save(join(fldr, f"{function}_model.mod"))

        # We can save the result of applying the model as well
        # we can also save the calibration report
        with open(join(fldr, f"{function}_convergence.log"), "w") as otp:
            for r in gc.report:
                otp.write(r + "\n")






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

 Out:

 .. code-block:: none

    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/distribution/gravity_application.py:316: RuntimeWarning: divide by zero encountered in power
      * a)[:]
    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/distribution/gravity_application.py:326: RuntimeWarning: invalid value encountered in multiply
      self.output.matrix_view[:, :] = self.output.matrix_view[:, :] * non_inf




.. GENERATED FROM PYTHON SOURCE LINES 210-215

.. code-block:: python


    ## Forecast
    #  * We create a set of * 'future' * vectors using some random growth factors
    #  * We apply the model for inverse power, as the TFLD seems to be a better fit for the actual one








.. GENERATED FROM PYTHON SOURCE LINES 216-221

.. code-block:: python


    from aequilibrae.distribution import Ipf, GravityApplication, SyntheticGravityModel
    from aequilibrae.matrix import AequilibraeData
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 222-245

.. code-block:: python


    # We compute the vectors from our matrix
    origins = np.sum(demand.matrix_view, axis=1)
    destinations = np.sum(demand.matrix_view, axis=0)

    args = {
        "file_path": join(fldr, "synthetic_future_vector.aed"),
        "entries": demand.zones,
        "field_names": ["origins", "destinations"],
        "data_types": [np.float64, np.float64],
        "memory_mode": False,
    }

    vectors = AequilibraeData()
    vectors.create_empty(**args)

    vectors.index[:] = demand.index[:]

    # Then grow them with some random growth between 0 and 10% - Plus balance them
    vectors.origins[:] = origins * (1 + np.random.rand(vectors.entries) / 10)
    vectors.destinations[:] = destinations * (1 + np.random.rand(vectors.entries) / 10)
    vectors.destinations *= vectors.origins.sum() / vectors.destinations.sum()








.. GENERATED FROM PYTHON SOURCE LINES 246-254

.. code-block:: python


    # Impedance
    imped = proj_matrices.get_matrix("base_year_assignment_skims")
    imped.computational_view(["final_time_with_intrazonals"])

    # If we wanted the main diagonal to not be considered...
    # np.fill_diagonal(imped.matrix_view, np.nan)








.. GENERATED FROM PYTHON SOURCE LINES 255-278

.. code-block:: python


    for function in ["power", "expo"]:
        model = SyntheticGravityModel()
        model.load(join(fldr, f"{function}_model.mod"))

        outmatrix = join(proj_matrices.fldr, f"demand_{function}_model.aem")
        apply = GravityApplication()
        args = {
            "impedance": imped,
            "rows": vectors,
            "row_field": "origins",
            "model": model,
            "columns": vectors,
            "column_field": "destinations",
            "nan_as_zero": True,
        }

        gravity = GravityApplication(**args)
        gravity.apply()

        # We get the output matrix and save it to OMX too,
        gravity.save_to_project(name=f"demand_{function}_modeled", file_name=f"demand_{function}_modeled.omx")





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:40,076;aequilibrae;WARNING ; Matrix Record has been saved to the database
    2021-02-18 10:20:40,415;aequilibrae;WARNING ; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 279-284

.. code-block:: python


    # We update the matrices table/records and verify that the new matrices are indeed there
    proj_matrices.update_database()
    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_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\n</td>
          <td></td>
        </tr>
        <tr>
          <th>1</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>2</th>
          <td>skims</td>
          <td>skims.omx</td>
          <td>2</td>
          <td>None</td>
          <td>None</td>
          <td></td>
          <td>Example skim</td>
          <td></td>
        </tr>
        <tr>
          <th>3</th>
          <td>base_year_assignment_skims</td>
          <td>base_year_assignment_skims.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>c43e1c8718d649e3a602ef802ffe2441</td>
          <td>2021-02-18 10:20:33.708364</td>
          <td>Skimming for assignment procedure</td>
          <td></td>
        </tr>
        <tr>
          <th>4</th>
          <td>demand_power_modeled</td>
          <td>demand_power_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>648062fa682145e8b96a734f13b1cc64</td>
          <td>2021-02-18 10:20:39.903831</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_expo_modeled</td>
          <td>demand_expo_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>c4290359fda2400296b5481ffbbaf495</td>
          <td>2021-02-18 10:20:40.242892</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 285-288

.. code-block:: python


    ### We now run IPF for the future vectors








.. GENERATED FROM PYTHON SOURCE LINES 289-304

.. code-block:: python

    args = {
        "matrix": demand,
        "rows": vectors,
        "columns": vectors,
        "column_field": "destinations",
        "row_field": "origins",
        "nan_as_zero": True,
    }

    ipf = Ipf(**args)
    ipf.fit()

    ipf.save_to_project(name="demand_ipfd", file_name="demand_ipfd.aem")
    ipf.save_to_project(name="demand_ipfd_omx", file_name="demand_ipfd.omx")





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:40,642;aequilibrae;WARNING ; Matrix Record has been saved to the database
    2021-02-18 10:20:40,832;aequilibrae;WARNING ; Matrix Record has been saved to the database

    <aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7f18e6f193d0>



.. GENERATED FROM PYTHON SOURCE LINES 305-308

.. code-block:: python


    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_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\n</td>
          <td></td>
        </tr>
        <tr>
          <th>1</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>2</th>
          <td>skims</td>
          <td>skims.omx</td>
          <td>2</td>
          <td>None</td>
          <td>None</td>
          <td></td>
          <td>Example skim</td>
          <td></td>
        </tr>
        <tr>
          <th>3</th>
          <td>base_year_assignment_skims</td>
          <td>base_year_assignment_skims.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>c43e1c8718d649e3a602ef802ffe2441</td>
          <td>2021-02-18 10:20:33.708364</td>
          <td>Skimming for assignment procedure</td>
          <td></td>
        </tr>
        <tr>
          <th>4</th>
          <td>demand_power_modeled</td>
          <td>demand_power_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>648062fa682145e8b96a734f13b1cc64</td>
          <td>2021-02-18 10:20:39.903831</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_expo_modeled</td>
          <td>demand_expo_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>c4290359fda2400296b5481ffbbaf495</td>
          <td>2021-02-18 10:20:40.242892</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
        <tr>
          <th>6</th>
          <td>demand_ipfd</td>
          <td>demand_ipfd.aem</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>dd59253dc017483b9104641f72371c8d</td>
          <td>2021-02-18 10:20:40.518877</td>
          <td>None</td>
          <td></td>
        </tr>
        <tr>
          <th>7</th>
          <td>demand_ipfd_omx</td>
          <td>demand_ipfd.omx</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>dd59253dc017483b9104641f72371c8d</td>
          <td>2021-02-18 10:20:40.518877</td>
          <td>None</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 309-312

.. code-block:: python


    ## Future traffic assignment








.. GENERATED FROM PYTHON SOURCE LINES 313-317

.. code-block:: python


    from aequilibrae.paths import TrafficAssignment, TrafficClass
    from aequilibrae import logger








.. GENERATED FROM PYTHON SOURCE LINES 318-321

.. code-block:: python


    logger.info("\n\n\n TRAFFIC ASSIGNMENT FOR FUTURE YEAR")





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:40,922;aequilibrae;INFO ; 


     TRAFFIC ASSIGNMENT FOR FUTURE YEAR




.. GENERATED FROM PYTHON SOURCE LINES 322-328

.. code-block:: python


    demand = proj_matrices.get_matrix("demand_power_modeled")

    # let's see what is the core we ended up getting. It should be 'gravity'
    demand.names





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

 Out:

 .. code-block:: none


    ['gravity']



.. GENERATED FROM PYTHON SOURCE LINES 329-357

.. code-block:: python


    # Let's use the IPF matrix
    demand.computational_view("gravity")

    assig = TrafficAssignment()

    # Creates the assignment class
    assigclass = TrafficClass(graph, demand)

    # The first thing to do is to add at list of traffic classes to be assigned
    assig.add_class(assigclass)

    assig.set_vdf("BPR")  # This is not case-sensitive # Then we set the volume delay function

    assig.set_vdf_parameters({"alpha": "b", "beta": "power"})  # And its parameters

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

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

    # since I haven't checked the parameters file, let's make sure convergence criteria is good
    assig.max_iter = 1000
    assig.rgap_target = 0.0001

    assig.execute()  # we then execute the assignment





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

 Out:

 .. code-block:: none

    /home/runner/work/aequilibrae/aequilibrae/aequilibrae/paths/graph.py:445: UserWarning: Cost field with wrong type. Converting to float64
      warn("Cost field with wrong type. Converting to float64")
    2021-02-18 10:20:41,393;aequilibrae;INFO ; bfw Assignment STATS
    2021-02-18 10:20:41,393;aequilibrae;INFO ; Iteration, RelativeGap, stepsize
    2021-02-18 10:20:41,495;aequilibrae;INFO ; 1,inf,1.0
    2021-02-18 10:20:41,597;aequilibrae;INFO ; 2,0.8869185206419594,0.2949026040834314
    2021-02-18 10:20:41,701;aequilibrae;INFO ; 3,0.588015612525359,0.19768688064832907
    2021-02-18 10:20:41,805;aequilibrae;INFO ; 4,0.2889812379086869,0.2060170031866801
    2021-02-18 10:20:41,908;aequilibrae;INFO ; 5,0.16050523639494185,0.4634702151143747
    2021-02-18 10:20:42,013;aequilibrae;INFO ; 6,0.1816762043788088,0.662460952586707
    2021-02-18 10:20:42,116;aequilibrae;INFO ; 7,0.18233373059700111,0.1531308565130455
    2021-02-18 10:20:42,219;aequilibrae;INFO ; 8,0.09324136985368256,0.217098492066391
    2021-02-18 10:20:42,321;aequilibrae;INFO ; 9,0.0917922227940775,0.40560618410083055
    2021-02-18 10:20:42,424;aequilibrae;INFO ; 10,0.09541307470223052,0.1317007335702223
    2021-02-18 10:20:42,527;aequilibrae;INFO ; 11,0.05532891653016809,0.2542502273921534
    2021-02-18 10:20:42,629;aequilibrae;INFO ; 12,0.07122804426870828,0.7269773288457817
    2021-02-18 10:20:42,732;aequilibrae;INFO ; 13,0.10697840737360402,0.08127463419343302
    2021-02-18 10:20:42,836;aequilibrae;INFO ; 14,0.03704907731458983,0.18521577573018747
    2021-02-18 10:20:42,938;aequilibrae;INFO ; 15,0.037219544060874465,0.24730710646023193
    2021-02-18 10:20:43,041;aequilibrae;INFO ; 16,0.033934442418399435,0.7568432640876742
    2021-02-18 10:20:43,144;aequilibrae;INFO ; 17,0.05541977224186314,0.05976603861950719
    2021-02-18 10:20:43,249;aequilibrae;INFO ; 18,0.026930808809218033,0.14173526628126604
    2021-02-18 10:20:43,352;aequilibrae;INFO ; 19,0.02302945715046113,0.26708632984431163
    2021-02-18 10:20:43,457;aequilibrae;INFO ; 20,0.02282882402297576,0.5185952228353777
    2021-02-18 10:20:43,560;aequilibrae;INFO ; 21,0.018483453607451426,0.8681299394472989
    2021-02-18 10:20:43,664;aequilibrae;INFO ; 22,0.022325299923591578,0.047696010314943915
    2021-02-18 10:20:43,769;aequilibrae;INFO ; 23,0.01004122186821739,0.09378603478281151
    2021-02-18 10:20:43,872;aequilibrae;INFO ; 24,0.011712071636473509,0.17260495615199578
    2021-02-18 10:20:43,976;aequilibrae;INFO ; 25,0.012756996591750655,0.19439731839221525
    2021-02-18 10:20:44,079;aequilibrae;INFO ; 26,0.012536799894475495,0.18547498777793955
    2021-02-18 10:20:44,181;aequilibrae;INFO ; 27,0.009980951374883805,0.403170942161783
    2021-02-18 10:20:44,284;aequilibrae;INFO ; 28,0.013775105104312305,0.8238042610358633
    2021-02-18 10:20:44,388;aequilibrae;INFO ; 29,0.013324549163614113,0.03788228727469217
    2021-02-18 10:20:44,491;aequilibrae;INFO ; 30,0.0077442421944975155,0.07165351790730007
    2021-02-18 10:20:44,593;aequilibrae;INFO ; 31,0.007821532834290087,0.06735814516209085
    2021-02-18 10:20:44,696;aequilibrae;INFO ; 32,0.005592099541250802,0.09660403410175603
    2021-02-18 10:20:44,799;aequilibrae;INFO ; 33,0.004678867820026161,0.18871450868751974
    2021-02-18 10:20:44,904;aequilibrae;INFO ; 34,0.0063504285660024255,0.2757172933476472
    2021-02-18 10:20:45,007;aequilibrae;INFO ; 35,0.006199604607905618,0.37207639120901087
    2021-02-18 10:20:45,110;aequilibrae;INFO ; 36,0.006838687947857594,0.5801728421645576
    2021-02-18 10:20:45,213;aequilibrae;INFO ; 37,0.004418599066521518,0.8912628141579837
    2021-02-18 10:20:45,317;aequilibrae;INFO ; 38,0.005501599096386511,0.016135741720802888
    2021-02-18 10:20:45,421;aequilibrae;INFO ; 39,0.00258796628849951,0.012999709578847086
    2021-02-18 10:20:45,528;aequilibrae;INFO ; 40,0.0020711128857675695,0.6091738008367833
    2021-02-18 10:20:45,632;aequilibrae;INFO ; 41,0.002979403195056885,0.9583859969971712
    2021-02-18 10:20:45,735;aequilibrae;INFO ; 42,0.0021910475349452684,0.011041894435877617
    2021-02-18 10:20:45,838;aequilibrae;INFO ; 43,0.0014926920682489997,0.01947783351919215
    2021-02-18 10:20:45,942;aequilibrae;INFO ; 44,0.00142924488611853,0.017620785894387293
    2021-02-18 10:20:46,045;aequilibrae;INFO ; 45,0.001601648095347361,0.031940760171249256
    2021-02-18 10:20:46,148;aequilibrae;INFO ; 46,0.0018563918994951283,0.037306459268010204
    2021-02-18 10:20:46,252;aequilibrae;INFO ; 47,0.0017343529817500872,0.10351516365981342
    2021-02-18 10:20:46,356;aequilibrae;INFO ; 48,0.0031209343916088835,0.2545573447837624
    2021-02-18 10:20:46,459;aequilibrae;INFO ; 49,0.0031938937914114678,0.1254550551110894
    2021-02-18 10:20:46,561;aequilibrae;INFO ; 50,0.0025165837702627015,0.0989251657124465
    2021-02-18 10:20:46,664;aequilibrae;INFO ; 51,0.001694781216143684,0.13922944824915734
    2021-02-18 10:20:46,766;aequilibrae;INFO ; 52,0.0027111355994742597,0.15280683097306944
    2021-02-18 10:20:46,869;aequilibrae;INFO ; 53,0.0020211380287558627,0.1225621731715659
    2021-02-18 10:20:46,972;aequilibrae;INFO ; 54,0.001555012718187175,0.07629320199949229
    2021-02-18 10:20:47,077;aequilibrae;INFO ; 55,0.0013356817942195211,0.1332807411613251
    2021-02-18 10:20:47,179;aequilibrae;INFO ; 56,0.0013848446173182658,0.08618620116632922
    2021-02-18 10:20:47,282;aequilibrae;INFO ; 57,0.001296959674886513,0.06800613642910683
    2021-02-18 10:20:47,386;aequilibrae;INFO ; 58,0.0014540815135828335,0.08295554076387648
    2021-02-18 10:20:47,490;aequilibrae;INFO ; 59,0.0010113474461896559,0.07008885708748151
    2021-02-18 10:20:47,592;aequilibrae;INFO ; 60,0.0014511817919706216,0.06263780879116987
    2021-02-18 10:20:47,695;aequilibrae;INFO ; 61,0.0012892870749688712,0.09698115906000165
    2021-02-18 10:20:47,798;aequilibrae;INFO ; 62,0.0013452810224726286,0.09088717170826374
    2021-02-18 10:20:47,901;aequilibrae;INFO ; 63,0.0013211320056163863,0.23373943231751143
    2021-02-18 10:20:48,004;aequilibrae;INFO ; 64,0.001505376011811344,0.4563621595817925
    2021-02-18 10:20:48,106;aequilibrae;INFO ; 65,0.001952246715315088,0.6396981239168877
    2021-02-18 10:20:48,209;aequilibrae;INFO ; 66,0.0020711412348888795,0.005122028419646269
    2021-02-18 10:20:48,312;aequilibrae;INFO ; 67,0.0012136112642739017,0.010186676981305454
    2021-02-18 10:20:48,417;aequilibrae;INFO ; 68,0.0008291376869490191,0.008297158885505867
    2021-02-18 10:20:48,521;aequilibrae;INFO ; 69,0.0007505584479753556,0.010393518207368028
    2021-02-18 10:20:48,625;aequilibrae;INFO ; 70,0.0008509479729728319,0.019533368383428087
    2021-02-18 10:20:48,727;aequilibrae;INFO ; 71,0.0008145990545891626,0.017068941823450395
    2021-02-18 10:20:48,833;aequilibrae;INFO ; 72,0.0005646548137295848,0.02734364534558361
    2021-02-18 10:20:48,938;aequilibrae;INFO ; 73,0.000595904442284007,0.03121986122906667
    2021-02-18 10:20:49,040;aequilibrae;INFO ; 74,0.000711798849674591,0.0381576461441203
    2021-02-18 10:20:49,143;aequilibrae;INFO ; 75,0.0006915743781048434,0.032076724662211197
    2021-02-18 10:20:49,247;aequilibrae;INFO ; 76,0.00040057700461107037,0.017912441325390504
    2021-02-18 10:20:49,352;aequilibrae;INFO ; 77,0.0004261302524838105,0.023504260808075515
    2021-02-18 10:20:49,457;aequilibrae;INFO ; 78,0.0004248435881948729,0.04199437153181703
    2021-02-18 10:20:49,561;aequilibrae;INFO ; 79,0.0006898350439722776,0.05149448360541459
    2021-02-18 10:20:49,664;aequilibrae;INFO ; 80,0.000597578393182954,0.04025640547609996
    2021-02-18 10:20:49,767;aequilibrae;INFO ; 81,0.0005757801443597037,0.02181138987415942
    2021-02-18 10:20:49,871;aequilibrae;INFO ; 82,0.0005091197485470857,0.048991234074100706
    2021-02-18 10:20:49,976;aequilibrae;INFO ; 83,0.0004815771039532207,0.039701294903883716
    2021-02-18 10:20:50,079;aequilibrae;INFO ; 84,0.00043451440454763654,0.041377126709858825
    2021-02-18 10:20:50,184;aequilibrae;INFO ; 85,0.0004361428395797134,0.04578682515510653
    2021-02-18 10:20:50,289;aequilibrae;INFO ; 86,0.0005725324652484132,0.11091581087706058
    2021-02-18 10:20:50,394;aequilibrae;INFO ; 87,0.0006028063775225079,0.1275279287512066
    2021-02-18 10:20:50,497;aequilibrae;INFO ; 88,0.0005971650294881656,0.0989899353513331
    2021-02-18 10:20:50,601;aequilibrae;INFO ; 89,0.0006662549381065007,0.12202101737024314
    2021-02-18 10:20:50,704;aequilibrae;INFO ; 90,0.0005399211585659303,0.24826965763266498
    2021-02-18 10:20:50,809;aequilibrae;INFO ; 91,0.0006080343599214994,0.25444251201240337
    2021-02-18 10:20:50,914;aequilibrae;INFO ; 92,0.0005630104886945532,0.5134625344490855
    2021-02-18 10:20:51,017;aequilibrae;INFO ; 93,0.0004617637393901471,0.6707674242526727
    2021-02-18 10:20:51,120;aequilibrae;INFO ; 94,0.00038293806803260254,0.003481604882310088
    2021-02-18 10:20:51,223;aequilibrae;INFO ; 95,0.000498684356756774,0.006307221059531465
    2021-02-18 10:20:51,326;aequilibrae;INFO ; 96,0.0003936058016806088,0.005361302677816026
    2021-02-18 10:20:51,429;aequilibrae;INFO ; 97,0.0003537797193419004,0.0061738195353352255
    2021-02-18 10:20:51,532;aequilibrae;INFO ; 98,0.000384269057994639,0.018576637246314795
    2021-02-18 10:20:51,635;aequilibrae;INFO ; 99,0.0006564033521152243,0.05003480290822521
    2021-02-18 10:20:51,739;aequilibrae;INFO ; 100,0.0007652129935651653,0.04971241299265019
    2021-02-18 10:20:51,845;aequilibrae;INFO ; 101,0.00041661093607663253,0.01481751080499359
    2021-02-18 10:20:51,949;aequilibrae;INFO ; 102,0.0004261093328976528,0.0163781613973041
    2021-02-18 10:20:52,053;aequilibrae;INFO ; 103,0.00029373029117321517,0.010396166570911487
    2021-02-18 10:20:52,158;aequilibrae;INFO ; 104,0.000329320121180928,0.008648024662756562
    2021-02-18 10:20:52,261;aequilibrae;INFO ; 105,0.00022478994702593776,0.01011986024479549
    2021-02-18 10:20:52,365;aequilibrae;INFO ; 106,0.00022782123276748214,0.003650615109348965
    2021-02-18 10:20:52,468;aequilibrae;INFO ; 107,0.00013780454694990951,0.006972676031332353
    2021-02-18 10:20:52,573;aequilibrae;INFO ; 108,0.0002038080138896186,0.006762001397051689
    2021-02-18 10:20:52,676;aequilibrae;INFO ; 109,0.00017092979969150288,0.007029547500354097
    2021-02-18 10:20:52,779;aequilibrae;INFO ; 110,0.00019431956688401288,0.013918518212906725
    2021-02-18 10:20:52,882;aequilibrae;INFO ; 111,0.00018392318338677553,0.014286466583695276
    2021-02-18 10:20:52,985;aequilibrae;INFO ; 112,0.00022416544239709486,0.02125457303906268
    2021-02-18 10:20:53,091;aequilibrae;INFO ; 113,0.00023000989471792346,0.028036474109013602
    2021-02-18 10:20:53,194;aequilibrae;INFO ; 114,0.00028937641608934266,0.03425145990925873
    2021-02-18 10:20:53,297;aequilibrae;INFO ; 115,0.0003225857983508608,0.047067713163790686
    2021-02-18 10:20:53,401;aequilibrae;INFO ; 116,0.00021849218486898837,0.008274744293602889
    2021-02-18 10:20:53,506;aequilibrae;INFO ; 117,0.00020365708997543928,0.007928247277044668
    2021-02-18 10:20:53,609;aequilibrae;INFO ; 118,0.00018381738640249604,0.015853499457346137
    2021-02-18 10:20:53,712;aequilibrae;INFO ; 119,0.0001928454130895287,0.01639716463257601
    2021-02-18 10:20:53,818;aequilibrae;INFO ; 120,0.00022846608624769638,0.02354456168752016
    2021-02-18 10:20:53,921;aequilibrae;INFO ; 121,0.00025066395421076225,0.029956337818356653
    2021-02-18 10:20:54,024;aequilibrae;INFO ; 122,0.00021210688925997164,0.021192450133486262
    2021-02-18 10:20:54,126;aequilibrae;INFO ; 123,0.0001495099878148145,0.015804940773610054
    2021-02-18 10:20:54,229;aequilibrae;INFO ; 124,0.00017380071536776882,0.02587549448866636
    2021-02-18 10:20:54,334;aequilibrae;INFO ; 125,0.0001363109958606895,0.01757762085514827
    2021-02-18 10:20:54,438;aequilibrae;INFO ; 126,0.00013690169578739697,0.016687380236161693
    2021-02-18 10:20:54,541;aequilibrae;INFO ; 127,0.00016667638722538542,0.028681072043548538
    2021-02-18 10:20:54,644;aequilibrae;INFO ; 128,0.0001424780967764896,0.026211121113087966
    2021-02-18 10:20:54,748;aequilibrae;INFO ; 129,0.0001305528398859378,0.034551058346546065
    2021-02-18 10:20:54,852;aequilibrae;INFO ; 130,0.000178124672709283,0.03720786263188335
    2021-02-18 10:20:54,956;aequilibrae;INFO ; 131,0.00015376350099660812,0.03224538138008354
    2021-02-18 10:20:55,058;aequilibrae;INFO ; 132,0.00016245512090658335,0.053728005477472696
    2021-02-18 10:20:55,163;aequilibrae;INFO ; 133,0.00023110692351780205,0.04763837625681554
    2021-02-18 10:20:55,266;aequilibrae;INFO ; 134,0.00014324405570671894,0.03799154084435219
    2021-02-18 10:20:55,372;aequilibrae;INFO ; 135,0.00017271980060685894,0.014509914813839356
    2021-02-18 10:20:55,476;aequilibrae;INFO ; 136,0.0001293585482256135,0.03577650998302448
    2021-02-18 10:20:55,579;aequilibrae;INFO ; 137,0.00011504770327205402,0.014528199895281974
    2021-02-18 10:20:55,682;aequilibrae;INFO ; 138,0.00013340647028784467,0.04142136002359936
    2021-02-18 10:20:55,784;aequilibrae;INFO ; 139,0.00014527124985634212,0.04051031561164441
    2021-02-18 10:20:55,887;aequilibrae;INFO ; 140,0.00014750273237711646,0.04706660050579762
    2021-02-18 10:20:55,990;aequilibrae;INFO ; 141,0.00016767853811447875,0.03573400667071823
    2021-02-18 10:20:56,092;aequilibrae;INFO ; 142,0.00021133551536609474,0.12058203701724501
    2021-02-18 10:20:56,198;aequilibrae;INFO ; 143,0.0002643586476991359,0.13255804681772607
    2021-02-18 10:20:56,302;aequilibrae;INFO ; 144,0.00027023046229496055,0.1348384023743591
    2021-02-18 10:20:56,407;aequilibrae;INFO ; 145,0.00022585620848965927,0.06302691890041356
    2021-02-18 10:20:56,510;aequilibrae;INFO ; 146,0.00016688158636147437,0.0961601071082684
    2021-02-18 10:20:56,612;aequilibrae;INFO ; 147,0.0001411488914643167,0.03748049365917197
    2021-02-18 10:20:56,715;aequilibrae;INFO ; 148,0.0001499698087788164,0.048697610059297285
    2021-02-18 10:20:56,819;aequilibrae;INFO ; 149,0.0001504538087922177,0.03555459799689903
    2021-02-18 10:20:56,923;aequilibrae;INFO ; 150,0.00011647690264123426,0.024645214381956698
    2021-02-18 10:20:57,027;aequilibrae;INFO ; 151,0.00010843795562528193,0.023279041790168698
    2021-02-18 10:20:57,131;aequilibrae;INFO ; 152,9.7970116104514e-05,0.039607937451787596
    2021-02-18 10:20:57,234;aequilibrae;INFO ; 153,0.00012551740307337493,0.02339609871875548
    2021-02-18 10:20:57,338;aequilibrae;INFO ; 154,0.00010291497142593013,0.041920707949084396
    2021-02-18 10:20:57,441;aequilibrae;INFO ; 155,0.00010524327594840423,0.021974955389637667
    2021-02-18 10:20:57,545;aequilibrae;INFO ; 156,9.594869019373251e-05,0.04378530857292892
    2021-02-18 10:20:57,546;aequilibrae;INFO ; bfw Assignment finished. 156 iterations and 9.594869019373251e-05 final gap




.. GENERATED FROM PYTHON SOURCE LINES 358-365

.. code-block:: python


    # We could export it to CSV or AequilibraE data, but let's put it directly into the results database
    assig.save_results("future_year_assignment")

    # And save the skims
    assig.save_skims("future_year_assignment_skims", which_ones="all", format="omx")





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

 Out:

 .. code-block:: none

    2021-02-18 10:20:57,758;aequilibrae;WARNING ; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 366-367

We can also plot convergence

.. GENERATED FROM PYTHON SOURCE LINES 367-383

.. code-block:: python

    import matplotlib.pyplot as plt

    df = assig.report()
    x = df.iteration.values
    y = df.rgap.values

    fig = plt.figure()
    ax = fig.add_subplot(111)

    plt.plot(x, y, "k--")
    plt.yscale("log")
    plt.grid(True, which="both")
    plt.xlabel(r"Iterations")
    plt.ylabel(r"Relative Gap")
    plt.show()




.. image:: /_auto_examples/images/sphx_glr_plot_forecasting_001.png
    :alt: plot forecasting
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 384-385

Close the project

.. GENERATED FROM PYTHON SOURCE LINES 385-386

.. code-block:: python

    project.close()




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

 Out:

 .. code-block:: none

    2021-02-18 10:20:58,361;aequilibrae;INFO ; Closed project on /tmp/fd15f0d73aa34d76a46459e8aac7b6af





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

   **Total running time of the script:** ( 0 minutes  25.325 seconds)


.. _sphx_glr_download__auto_examples_plot_forecasting.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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