.. 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; %(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

    2022-03-30 06:35:13,913;aequilibrae; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2022-03-30 06:35:13,968;aequilibrae; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2022-03-30 06:35:14,021;aequilibrae; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2022-03-30 06:35:14,075;aequilibrae; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2022-03-30 06:35:14,129;aequilibrae; Field(s) name, lanes has(ve) at least one NaN value. Check your computations
    2022-03-30 06:35:14,183;aequilibrae; 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:453: 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_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 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(name='car', graph=graph, matrix=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:453: UserWarning: Cost field with wrong type. Converting to float64
      warn("Cost field with wrong type. Converting to float64")
    2022-03-30 06:35:14,753;aequilibrae; bfw Assignment STATS
    2022-03-30 06:35:14,753;aequilibrae; Iteration, RelativeGap, stepsize
    2022-03-30 06:35:14,762;aequilibrae; 1,inf,1.0
    2022-03-30 06:35:14,771;aequilibrae; 2,0.8485503636986156,0.3649733931991619
    2022-03-30 06:35:14,780;aequilibrae; 3,0.38139263975960314,0.22983569243524993
    2022-03-30 06:35:14,789;aequilibrae; 4,0.19621280093105328,0.18591303407405754
    2022-03-30 06:35:14,798;aequilibrae; 5,0.09069069564886302,0.709081525570193
    2022-03-30 06:35:14,806;aequilibrae; 6,0.20600049841796414,0.12290139708465252
    2022-03-30 06:35:14,815;aequilibrae; 7,0.0671057020569446,0.3863865464644097
    2022-03-30 06:35:14,824;aequilibrae; 8,0.10307514522959232,0.10930550628245674
    2022-03-30 06:35:14,832;aequilibrae; 9,0.042221488560558955,0.24878058909094228
    2022-03-30 06:35:14,841;aequilibrae; 10,0.05926436280283587,0.15904812211073494
    2022-03-30 06:35:14,850;aequilibrae; 11,0.034539501887818985,0.5180973982981508
    2022-03-30 06:35:14,860;aequilibrae; 12,0.059426522740212366,0.101971242325629
    2022-03-30 06:35:14,871;aequilibrae; 13,0.023239892828420625,0.1780595247621586
    2022-03-30 06:35:14,880;aequilibrae; 14,0.01787378174233466,0.9787872892076548
    2022-03-30 06:35:14,889;aequilibrae; 15,0.04966137112489825,0.08320656230754342
    2022-03-30 06:35:14,899;aequilibrae; 16,0.021382882927382725,0.11517403372477297
    2022-03-30 06:35:14,909;aequilibrae; 17,0.01314154305058098,0.1064036961470442
    2022-03-30 06:35:14,927;aequilibrae; 18,0.009902228306191804,0.10710395852809111
    2022-03-30 06:35:14,941;aequilibrae; 19,0.008834657124558089,0.252467895277828
    2022-03-30 06:35:14,950;aequilibrae; 20,0.010371041656588888,0.6727839455896467
    2022-03-30 06:35:14,964;aequilibrae; 21,0.011090096024277666,0.07468062458005388
    2022-03-30 06:35:14,984;aequilibrae; 22,0.006512685864200749,0.12485018976276105
    2022-03-30 06:35:14,995;aequilibrae; 23,0.00523888532832478,0.06316016036308214
    2022-03-30 06:35:15,004;aequilibrae; 24,0.003949527733957799,0.09717892036982514
    2022-03-30 06:35:15,013;aequilibrae; 25,0.003282221717998643,0.15775902884958926
    2022-03-30 06:35:15,025;aequilibrae; 26,0.0057928324189390375,0.4524765555189632
    2022-03-30 06:35:15,035;aequilibrae; 27,0.006682358714677215,0.7851865434128832
    2022-03-30 06:35:15,044;aequilibrae; 28,0.00599261954468699,0.039098102583274626
    2022-03-30 06:35:15,053;aequilibrae; 29,0.004031364721003652,0.04253017715977122
    2022-03-30 06:35:15,065;aequilibrae; 30,0.00276965205224939,0.02251286694569934
    2022-03-30 06:35:15,074;aequilibrae; 31,0.002484645375580033,0.045801689794576335
    2022-03-30 06:35:15,087;aequilibrae; 32,0.0016385526291291498,0.034295834113305265
    2022-03-30 06:35:15,097;aequilibrae; 33,0.0014956909894299761,0.035391999643557624
    2022-03-30 06:35:15,105;aequilibrae; 34,0.0011355396506463338,0.053589857140533936
    2022-03-30 06:35:15,114;aequilibrae; 35,0.0012151969842906466,0.04757129085244828
    2022-03-30 06:35:15,126;aequilibrae; 36,0.0012393652042090905,0.06536474008117803
    2022-03-30 06:35:15,135;aequilibrae; 37,0.0010684964499824193,0.1070395355360692
    2022-03-30 06:35:15,153;aequilibrae; 38,0.0010899690746481111,0.09864062890245766
    2022-03-30 06:35:15,164;aequilibrae; 39,0.0009886949061956609,0.06027624172548778
    2022-03-30 06:35:15,165;aequilibrae; bfw Assignment finished. 39 iterations and 0.0009886949061956609 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>0.025655</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

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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            text-align: right;
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    </style>
    <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>4535.923098</td>
          <td>NaN</td>
          <td>4535.923098</td>
          <td>6.000847</td>
          <td>NaN</td>
          <td>6.000847</td>
          <td>1.000141</td>
          <td>NaN</td>
          <td>1.000141</td>
          <td>0.175131</td>
          <td>NaN</td>
          <td>0.175131</td>
          <td>4535.923098</td>
          <td>NaN</td>
          <td>4535.923098</td>
        </tr>
        <tr>
          <th>2</th>
          <td>8186.151917</td>
          <td>NaN</td>
          <td>8186.151917</td>
          <td>4.008982</td>
          <td>NaN</td>
          <td>4.008982</td>
          <td>1.002245</td>
          <td>NaN</td>
          <td>1.002245</td>
          <td>0.349784</td>
          <td>NaN</td>
          <td>0.349784</td>
          <td>8186.151917</td>
          <td>NaN</td>
          <td>8186.151917</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4586.028377</td>
          <td>NaN</td>
          <td>4586.028377</td>
          <td>6.000885</td>
          <td>NaN</td>
          <td>6.000885</td>
          <td>1.000147</td>
          <td>NaN</td>
          <td>1.000147</td>
          <td>0.177065</td>
          <td>NaN</td>
          <td>0.177065</td>
          <td>4586.028377</td>
          <td>NaN</td>
          <td>4586.028377</td>
        </tr>
        <tr>
          <th>4</th>
          <td>6024.212847</td>
          <td>NaN</td>
          <td>6024.212847</td>
          <td>6.634455</td>
          <td>NaN</td>
          <td>6.634455</td>
          <td>1.326891</td>
          <td>NaN</td>
          <td>1.326891</td>
          <td>1.215005</td>
          <td>NaN</td>
          <td>1.215005</td>
          <td>6024.212847</td>
          <td>NaN</td>
          <td>6024.212847</td>
        </tr>
        <tr>
          <th>5</th>
          <td>8136.046638</td>
          <td>NaN</td>
          <td>8136.046638</td>
          <td>4.008764</td>
          <td>NaN</td>
          <td>4.008764</td>
          <td>1.002191</td>
          <td>NaN</td>
          <td>1.002191</td>
          <td>0.347643</td>
          <td>NaN</td>
          <td>0.347643</td>
          <td>8136.046638</td>
          <td>NaN</td>
          <td>8136.046638</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

    2022-03-30 06:35:15,435;aequilibrae; 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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            vertical-align: middle;
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        .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>
        <tr>
          <th>4</th>
          <td>base_year_assignment_skims_car</td>
          <td>base_year_assignment_skims_car.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>2a9f1dccac1c4640997dd4ac2ad6562a</td>
          <td>2022-03-30 06:35:14.384605</td>
          <td>Skimming for assignment procedure. Class car</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_car")








.. 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 157-174

.. 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 175-180

.. 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 181-186

.. 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 187-191

.. code-block:: python


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








.. GENERATED FROM PYTHON SOURCE LINES 192-206

.. 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:315: RuntimeWarning: divide by zero encountered in power
      self.output.matrix_view[i, :] = (np.power(self.impedance.matrix_view[i, :], -self.model.alpha) * p
    /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 207-212

.. 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 213-218

.. code-block:: python


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








.. GENERATED FROM PYTHON SOURCE LINES 219-242

.. 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 243-251

.. code-block:: python


    # Impedance
    imped = proj_matrices.get_matrix("base_year_assignment_skims_car")
    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 252-275

.. 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

    2022-03-30 06:35:16,732;aequilibrae; Matrix Record has been saved to the database
    2022-03-30 06:35:17,098;aequilibrae; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 276-281

.. 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_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>
        <tr>
          <th>4</th>
          <td>base_year_assignment_skims_car</td>
          <td>base_year_assignment_skims_car.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>2a9f1dccac1c4640997dd4ac2ad6562a</td>
          <td>2022-03-30 06:35:14.384605</td>
          <td>Skimming for assignment procedure. Class car</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_power_modeled</td>
          <td>demand_power_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>29d0cd1b9e5545928422b917f536defd</td>
          <td>2022-03-30 06:35:16.549166</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>6</th>
          <td>demand_expo_modeled</td>
          <td>demand_expo_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>4139bc61fb6a465c989d7ea757cb2ab4</td>
          <td>2022-03-30 06:35:16.915684</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 282-285

.. code-block:: python


    ### We now run IPF for the future vectors








.. GENERATED FROM PYTHON SOURCE LINES 286-301

.. 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

    2022-03-30 06:35:17,341;aequilibrae; Matrix Record has been saved to the database
    2022-03-30 06:35:17,533;aequilibrae; Matrix Record has been saved to the database

    <aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7f0bef448ca0>



.. GENERATED FROM PYTHON SOURCE LINES 302-305

.. 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_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>
        <tr>
          <th>4</th>
          <td>base_year_assignment_skims_car</td>
          <td>base_year_assignment_skims_car.omx</td>
          <td>4</td>
          <td>Traffic Assignment</td>
          <td>2a9f1dccac1c4640997dd4ac2ad6562a</td>
          <td>2022-03-30 06:35:14.384605</td>
          <td>Skimming for assignment procedure. Class car</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_power_modeled</td>
          <td>demand_power_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>29d0cd1b9e5545928422b917f536defd</td>
          <td>2022-03-30 06:35:16.549166</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>6</th>
          <td>demand_expo_modeled</td>
          <td>demand_expo_modeled.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>4139bc61fb6a465c989d7ea757cb2ab4</td>
          <td>2022-03-30 06:35:16.915684</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
        <tr>
          <th>7</th>
          <td>demand_ipfd</td>
          <td>demand_ipfd.aem</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>5f59b16b87934553b3340f8ad97b4ecc</td>
          <td>2022-03-30 06:35:17.198626</td>
          <td>None</td>
          <td></td>
        </tr>
        <tr>
          <th>8</th>
          <td>demand_ipfd_omx</td>
          <td>demand_ipfd.omx</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>5f59b16b87934553b3340f8ad97b4ecc</td>
          <td>2022-03-30 06:35:17.198626</td>
          <td>None</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 306-309

.. code-block:: python


    ## Future traffic assignment








.. GENERATED FROM PYTHON SOURCE LINES 310-314

.. code-block:: python


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








.. GENERATED FROM PYTHON SOURCE LINES 315-318

.. code-block:: python


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





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

 Out:

 .. code-block:: none

    2022-03-30 06:35:17,605;aequilibrae; 


     TRAFFIC ASSIGNMENT FOR FUTURE YEAR




.. GENERATED FROM PYTHON SOURCE LINES 319-325

.. code-block:: python


    demand = proj_matrices.get_matrix("demand_ipfd")

    # 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


    ['matrix']



.. GENERATED FROM PYTHON SOURCE LINES 326-354

.. code-block:: python


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

    assig = TrafficAssignment()

    # Creates the assignment class
    assigclass = TrafficClass(name='car', graph=graph, matrix=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 = 500
    assig.rgap_target = 0.00001

    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:453: UserWarning: Cost field with wrong type. Converting to float64
      warn("Cost field with wrong type. Converting to float64")
    2022-03-30 06:35:18,058;aequilibrae; bfw Assignment STATS
    2022-03-30 06:35:18,058;aequilibrae; Iteration, RelativeGap, stepsize
    2022-03-30 06:35:18,071;aequilibrae; 1,inf,1.0
    2022-03-30 06:35:18,084;aequilibrae; 2,0.8640822421161786,0.37292935195231486
    2022-03-30 06:35:18,098;aequilibrae; 3,0.40024694275335776,0.17844919022612446
    2022-03-30 06:35:18,111;aequilibrae; 4,0.20182146926554542,0.25092398016447004
    2022-03-30 06:35:18,124;aequilibrae; 5,0.14777343987636024,0.25463241986636725
    2022-03-30 06:35:18,138;aequilibrae; 6,0.1353590905785671,0.1798897068483709
    2022-03-30 06:35:18,153;aequilibrae; 7,0.08251683407277165,0.3385597919367376
    2022-03-30 06:35:18,166;aequilibrae; 8,0.13010592291417183,0.13070564747408114
    2022-03-30 06:35:18,178;aequilibrae; 9,0.053777329464136325,0.24384177537366378
    2022-03-30 06:35:18,194;aequilibrae; 10,0.07698484783814527,0.15043289622601314
    2022-03-30 06:35:18,207;aequilibrae; 11,0.04871899017281356,0.36723565474618985
    2022-03-30 06:35:18,221;aequilibrae; 12,0.06444096966611908,0.14621750937022399
    2022-03-30 06:35:18,238;aequilibrae; 13,0.03115886806536968,0.37069792708493243
    2022-03-30 06:35:18,250;aequilibrae; 14,0.05785384890361702,0.0841544833611363
    2022-03-30 06:35:18,268;aequilibrae; 15,0.024642006497786768,0.20589779402995073
    2022-03-30 06:35:18,277;aequilibrae; 16,0.029776037175472572,0.759006770427189
    2022-03-30 06:35:18,288;aequilibrae; 17,0.05232068798997916,0.08681566374265748
    2022-03-30 06:35:18,302;aequilibrae; 18,0.02285513076700145,0.14564578669240189
    2022-03-30 06:35:18,314;aequilibrae; 19,0.017565437789536478,0.1923058715302588
    2022-03-30 06:35:18,330;aequilibrae; 20,0.01132432219256183,0.18265444475584058
    2022-03-30 06:35:18,342;aequilibrae; 21,0.013857026386117584,0.43319644765256143
    2022-03-30 06:35:18,353;aequilibrae; 22,0.01663069216809704,0.8561745035309122
    2022-03-30 06:35:18,372;aequilibrae; 23,0.017532874169244997,0.07861447298706668
    2022-03-30 06:35:18,383;aequilibrae; 24,0.008641317980830298,0.11139754834694682
    2022-03-30 06:35:18,394;aequilibrae; 25,0.007998325045162201,0.16205217333031008
    2022-03-30 06:35:18,405;aequilibrae; 26,0.00803440141886617,0.32938046600562837
    2022-03-30 06:35:18,415;aequilibrae; 27,0.009808514085828609,0.6797088116630732
    2022-03-30 06:35:18,429;aequilibrae; 28,0.007541771630230079,0.061368183446967026
    2022-03-30 06:35:18,441;aequilibrae; 29,0.005451140795173988,0.03636730878643076
    2022-03-30 06:35:18,452;aequilibrae; 30,0.0036438837554848757,0.037906463779132905
    2022-03-30 06:35:18,465;aequilibrae; 31,0.002344228466927835,0.033421474112148936
    2022-03-30 06:35:18,477;aequilibrae; 32,0.0025251931872718493,0.05477821789629168
    2022-03-30 06:35:18,488;aequilibrae; 33,0.0031269714283486557,0.12195784870427219
    2022-03-30 06:35:18,498;aequilibrae; 34,0.002970909340975315,0.15955188001141868
    2022-03-30 06:35:18,509;aequilibrae; 35,0.00342876964409134,0.2580128112407674
    2022-03-30 06:35:18,522;aequilibrae; 36,0.004291044710031676,0.9459640185361629
    2022-03-30 06:35:18,537;aequilibrae; 37,0.00578461168312251,0.022121979134642825
    2022-03-30 06:35:18,553;aequilibrae; 38,0.003336360937181334,0.02551246960902463
    2022-03-30 06:35:18,567;aequilibrae; 39,0.0023334944046044317,0.04807923203397432
    2022-03-30 06:35:18,580;aequilibrae; 40,0.0027959267362353173,0.0656958923562387
    2022-03-30 06:35:18,592;aequilibrae; 41,0.002287611715877224,0.0478937547516726
    2022-03-30 06:35:18,603;aequilibrae; 42,0.0019895396740052013,0.044775203333665525
    2022-03-30 06:35:18,613;aequilibrae; 43,0.0018430924388742366,0.05811037776869034
    2022-03-30 06:35:18,627;aequilibrae; 44,0.0015555132228279678,0.04726075730261095
    2022-03-30 06:35:18,639;aequilibrae; 45,0.0013143066189709644,0.03728183781182998
    2022-03-30 06:35:18,652;aequilibrae; 46,0.0009021191252855489,0.02127066221641698
    2022-03-30 06:35:18,666;aequilibrae; 47,0.0008483883741159835,0.03234679941672534
    2022-03-30 06:35:18,676;aequilibrae; 48,0.0009266177002097058,0.03395779763559276
    2022-03-30 06:35:18,687;aequilibrae; 49,0.0008953822588090007,0.06270771965115957
    2022-03-30 06:35:18,698;aequilibrae; 50,0.0011549366136067109,0.15072758680627008
    2022-03-30 06:35:18,708;aequilibrae; 51,0.0011097140152600442,0.15538163333241756
    2022-03-30 06:35:18,718;aequilibrae; 52,0.0011474096105997535,0.17999717373373902
    2022-03-30 06:35:18,731;aequilibrae; 53,0.0008333645100889973,0.22275692478089423
    2022-03-30 06:35:18,746;aequilibrae; 54,0.0007607763289351458,0.16549754335749928
    2022-03-30 06:35:18,757;aequilibrae; 55,0.0007368905402904042,0.2498364877092027
    2022-03-30 06:35:18,774;aequilibrae; 56,0.000884053221262679,0.538714504205792
    2022-03-30 06:35:18,787;aequilibrae; 57,0.001005443348680526,0.8084648236622918
    2022-03-30 06:35:18,798;aequilibrae; 58,0.000747461579483486,0.006069358671767987
    2022-03-30 06:35:18,808;aequilibrae; 59,0.0006400285373093239,0.01612750859229421
    2022-03-30 06:35:18,820;aequilibrae; 60,0.0005820180437978713,0.008724694815418986
    2022-03-30 06:35:18,835;aequilibrae; 61,0.00042948855599335396,0.011009044788899124
    2022-03-30 06:35:18,848;aequilibrae; 62,0.0004244010028385218,0.00991688299964967
    2022-03-30 06:35:18,858;aequilibrae; 63,0.00032061306947600966,0.012824515991954158
    2022-03-30 06:35:18,874;aequilibrae; 64,0.0003930569682652042,0.010866054914672968
    2022-03-30 06:35:18,887;aequilibrae; 65,0.00022323468235573693,0.008835066036782862
    2022-03-30 06:35:18,899;aequilibrae; 66,0.0002089742307188207,0.004424554100754541
    2022-03-30 06:35:18,916;aequilibrae; 67,0.00018382077988359419,0.007836275302565246
    2022-03-30 06:35:18,928;aequilibrae; 68,0.00015286991089337644,0.006433699255677444
    2022-03-30 06:35:18,939;aequilibrae; 69,0.0002484143327024828,0.009507983184299846
    2022-03-30 06:35:19,047;aequilibrae; 70,0.00022490043530284733,0.009695251108478095
    2022-03-30 06:35:19,073;aequilibrae; 71,0.00016947534616318952,0.004619535021213213
    2022-03-30 06:35:19,085;aequilibrae; 72,0.00012516551312058446,0.0029559754987165676
    2022-03-30 06:35:19,095;aequilibrae; 73,0.00010435546054245256,0.0021707566409139493
    2022-03-30 06:35:19,110;aequilibrae; 74,0.00010692496771692026,0.002497887214058174
    2022-03-30 06:35:19,122;aequilibrae; 75,0.00011579800928728354,0.005260475727459672
    2022-03-30 06:35:19,137;aequilibrae; 76,0.0001384075537735438,0.005661230105417702
    2022-03-30 06:35:19,148;aequilibrae; 77,0.0001498854930016576,0.006934149847735108
    2022-03-30 06:35:19,159;aequilibrae; 78,0.00014089075020361287,0.011185259173635104
    2022-03-30 06:35:19,173;aequilibrae; 79,0.00013592775811622536,0.01388139861729522
    2022-03-30 06:35:19,187;aequilibrae; 80,0.00016597587325851114,0.02474520377361046
    2022-03-30 06:35:19,199;aequilibrae; 81,0.0002248529184988508,0.02828217720467378
    2022-03-30 06:35:19,214;aequilibrae; 82,0.00022526755220986046,0.043287987927642047
    2022-03-30 06:35:19,226;aequilibrae; 83,0.0002995988222583355,0.05443440558600097
    2022-03-30 06:35:19,236;aequilibrae; 84,0.0002943676866778313,0.06493852537024743
    2022-03-30 06:35:19,247;aequilibrae; 85,0.0003293671083765304,0.07614441239762093
    2022-03-30 06:35:19,259;aequilibrae; 86,0.00026831353424313406,0.06259001869507097
    2022-03-30 06:35:19,275;aequilibrae; 87,0.0002829898151723774,0.04086780273092652
    2022-03-30 06:35:19,289;aequilibrae; 88,0.00020451627040555314,0.0392116684544161
    2022-03-30 06:35:19,300;aequilibrae; 89,0.00019053353576253002,0.016698061680574902
    2022-03-30 06:35:19,313;aequilibrae; 90,0.0001548538639462388,0.03542109125365637
    2022-03-30 06:35:19,324;aequilibrae; 91,0.0001881421544741506,0.030771112054799783
    2022-03-30 06:35:19,337;aequilibrae; 92,0.0001500447948956757,0.052299402355829895
    2022-03-30 06:35:19,347;aequilibrae; 93,0.00019178097718779778,0.045952336399559
    2022-03-30 06:35:19,359;aequilibrae; 94,0.00022050598243492542,0.03523947316950556
    2022-03-30 06:35:19,369;aequilibrae; 95,0.0002273732542280226,0.04990425947044629
    2022-03-30 06:35:19,386;aequilibrae; 96,0.00020422731252323865,0.05276344674682311
    2022-03-30 06:35:19,397;aequilibrae; 97,0.00019155910855896214,0.02236081775186587
    2022-03-30 06:35:19,408;aequilibrae; 98,0.00016736631923181872,0.03655813792701671
    2022-03-30 06:35:19,423;aequilibrae; 99,0.00017752644791664305,0.03242786057976886
    2022-03-30 06:35:19,433;aequilibrae; 100,0.00012338576045857637,0.014732568011475997
    2022-03-30 06:35:19,444;aequilibrae; 101,0.00013887392950292078,0.014843470059113073
    2022-03-30 06:35:19,455;aequilibrae; 102,0.00013281959881746266,0.015176275085905473
    2022-03-30 06:35:19,468;aequilibrae; 103,0.00012693218834403374,0.00980908264598729
    2022-03-30 06:35:19,483;aequilibrae; 104,9.30776404496239e-05,0.00991723076546758
    2022-03-30 06:35:19,496;aequilibrae; 105,9.942177515794031e-05,0.01354962523655338
    2022-03-30 06:35:19,508;aequilibrae; 106,0.00010369951115410869,0.013622236941332405
    2022-03-30 06:35:19,521;aequilibrae; 107,0.00013286107056316625,0.015150508728010246
    2022-03-30 06:35:19,531;aequilibrae; 108,0.00014766203366773533,0.02684371945415645
    2022-03-30 06:35:19,543;aequilibrae; 109,0.00015524988809836621,0.01623704253115988
    2022-03-30 06:35:19,555;aequilibrae; 110,0.00010659754380363564,0.01069634919222407
    2022-03-30 06:35:19,565;aequilibrae; 111,8.44143183091351e-05,0.0036904282324187227
    2022-03-30 06:35:19,579;aequilibrae; 112,8.565971418548702e-05,0.007436795377352814
    2022-03-30 06:35:19,592;aequilibrae; 113,7.251323824042111e-05,0.0045488184766465795
    2022-03-30 06:35:19,602;aequilibrae; 114,6.561203356177781e-05,0.006055010381754182
    2022-03-30 06:35:19,613;aequilibrae; 115,8.106612876853072e-05,0.00490019946570833
    2022-03-30 06:35:19,623;aequilibrae; 116,8.018603126364028e-05,0.0065943175023865305
    2022-03-30 06:35:19,636;aequilibrae; 117,8.188038123706757e-05,0.006965201568159744
    2022-03-30 06:35:19,645;aequilibrae; 118,8.639998049955129e-05,0.008233897071969856
    2022-03-30 06:35:19,656;aequilibrae; 119,8.343384481489934e-05,0.010577656796395577
    2022-03-30 06:35:19,666;aequilibrae; 120,9.28315126697004e-05,0.01191344820708073
    2022-03-30 06:35:19,677;aequilibrae; 121,8.712170184392309e-05,0.007257867424435106
    2022-03-30 06:35:19,688;aequilibrae; 122,7.799120019269096e-05,0.016453580793161685
    2022-03-30 06:35:19,699;aequilibrae; 123,8.172443007465513e-05,0.011212941579839828
    2022-03-30 06:35:19,709;aequilibrae; 124,7.387464906311946e-05,0.023554813245845414
    2022-03-30 06:35:19,720;aequilibrae; 125,0.0001082685588756055,0.037900504037610194
    2022-03-30 06:35:19,730;aequilibrae; 126,0.00013681986756328095,0.04267093165844508
    2022-03-30 06:35:19,740;aequilibrae; 127,0.00013693243921027195,0.05188763988423357
    2022-03-30 06:35:19,753;aequilibrae; 128,0.00013733032648599423,0.052241850277523386
    2022-03-30 06:35:19,764;aequilibrae; 129,0.00011043923669432712,0.017096792169477612
    2022-03-30 06:35:19,775;aequilibrae; 130,0.0001077416876509317,0.0068693379112062195
    2022-03-30 06:35:19,786;aequilibrae; 131,9.63209727562745e-05,0.01823187806587189
    2022-03-30 06:35:19,805;aequilibrae; 132,8.311254607139367e-05,0.0123411940400802
    2022-03-30 06:35:19,818;aequilibrae; 133,7.58696827884544e-05,0.007409206646628601
    2022-03-30 06:35:19,828;aequilibrae; 134,7.67965476245599e-05,0.008940084348594265
    2022-03-30 06:35:19,840;aequilibrae; 135,6.195799599729911e-05,0.005342224625468388
    2022-03-30 06:35:19,851;aequilibrae; 136,5.682679180035631e-05,0.005233117892059727
    2022-03-30 06:35:19,862;aequilibrae; 137,7.109173410886536e-05,0.006060773838685758
    2022-03-30 06:35:19,874;aequilibrae; 138,6.320410663567255e-05,0.0062678586117379444
    2022-03-30 06:35:19,885;aequilibrae; 139,7.231857684913043e-05,0.012993495256721851
    2022-03-30 06:35:19,895;aequilibrae; 140,6.900047525418267e-05,0.008485542994760448
    2022-03-30 06:35:19,906;aequilibrae; 141,5.975643044834034e-05,0.008369294913624483
    2022-03-30 06:35:19,918;aequilibrae; 142,3.985952388008899e-05,0.004165633285972503
    2022-03-30 06:35:19,928;aequilibrae; 143,4.310189268186914e-05,0.0026957774622298173
    2022-03-30 06:35:19,939;aequilibrae; 144,3.914818920886589e-05,0.00501202673555048
    2022-03-30 06:35:19,952;aequilibrae; 145,3.413300063964538e-05,0.0019788056804002853
    2022-03-30 06:35:19,963;aequilibrae; 146,3.249624295537848e-05,0.0029229049025055387
    2022-03-30 06:35:19,974;aequilibrae; 147,2.8412502047261662e-05,0.0034810875647030524
    2022-03-30 06:35:19,988;aequilibrae; 148,3.348460214897343e-05,0.0018374835902372243
    2022-03-30 06:35:20,001;aequilibrae; 149,3.090056732847303e-05,0.0027431294879373173
    2022-03-30 06:35:20,014;aequilibrae; 150,3.8526696026758314e-05,0.00449782159627854
    2022-03-30 06:35:20,025;aequilibrae; 151,3.865728442826923e-05,0.0052528900886873574
    2022-03-30 06:35:20,036;aequilibrae; 152,4.4323893029449545e-05,0.0058211097892819684
    2022-03-30 06:35:20,048;aequilibrae; 153,3.61832113365804e-05,0.003370457684091816
    2022-03-30 06:35:20,061;aequilibrae; 154,3.142461590978521e-05,0.002280725603266664
    2022-03-30 06:35:20,074;aequilibrae; 155,3.0846328340976406e-05,0.0045290905631862124
    2022-03-30 06:35:20,087;aequilibrae; 156,3.661303806058412e-05,0.005044331153582943
    2022-03-30 06:35:20,098;aequilibrae; 157,3.4069921108311114e-05,0.004939935736792037
    2022-03-30 06:35:20,111;aequilibrae; 158,3.2526603461971433e-05,0.004418315695652205
    2022-03-30 06:35:20,121;aequilibrae; 159,3.518570852360373e-05,0.005720752195921868
    2022-03-30 06:35:20,132;aequilibrae; 160,3.901145970134266e-05,0.010504108581306574
    2022-03-30 06:35:20,150;aequilibrae; 161,5.7244571174156904e-05,0.021042883915905322
    2022-03-30 06:35:20,161;aequilibrae; 162,6.558227087535522e-05,0.025763436497034144
    2022-03-30 06:35:20,173;aequilibrae; 163,8.34031960284889e-05,0.02266275296763202
    2022-03-30 06:35:20,185;aequilibrae; 164,8.064852105498755e-05,0.020959016506469638
    2022-03-30 06:35:20,196;aequilibrae; 165,9.817930839221043e-05,0.032566115250585566
    2022-03-30 06:35:20,209;aequilibrae; 166,8.408358062700785e-05,0.02443460873055362
    2022-03-30 06:35:20,225;aequilibrae; 167,8.800811145699258e-05,0.02908070024000702
    2022-03-30 06:35:20,238;aequilibrae; 168,7.144187246761685e-05,0.01848394983085679
    2022-03-30 06:35:20,250;aequilibrae; 169,8.261228684765797e-05,0.022972376682869448
    2022-03-30 06:35:20,261;aequilibrae; 170,7.730341417126756e-05,0.02140761691793548
    2022-03-30 06:35:20,272;aequilibrae; 171,8.351487328979362e-05,0.02911909029684487
    2022-03-30 06:35:20,283;aequilibrae; 172,6.558009295502121e-05,0.032720058571128516
    2022-03-30 06:35:20,296;aequilibrae; 173,8.1460486510221e-05,0.050436688377210895
    2022-03-30 06:35:20,312;aequilibrae; 174,7.352610783949715e-05,0.031073484347687037
    2022-03-30 06:35:20,322;aequilibrae; 175,7.20834985314472e-05,0.02228819454912721
    2022-03-30 06:35:20,336;aequilibrae; 176,7.069618596455959e-05,0.02142248932164551
    2022-03-30 06:35:20,348;aequilibrae; 177,8.409940984664768e-05,0.03354366174283368
    2022-03-30 06:35:20,359;aequilibrae; 178,7.439496669302533e-05,0.020677561897787883
    2022-03-30 06:35:20,369;aequilibrae; 179,6.007358195988385e-05,0.026639714824788285
    2022-03-30 06:35:20,380;aequilibrae; 180,5.461665433947615e-05,0.022744121105807955
    2022-03-30 06:35:20,391;aequilibrae; 181,4.7275808753786506e-05,0.018558845038839547
    2022-03-30 06:35:20,402;aequilibrae; 182,5.200670488705403e-05,0.01948906141108359
    2022-03-30 06:35:20,414;aequilibrae; 183,3.6487187323093606e-05,0.0076462618741046295
    2022-03-30 06:35:20,426;aequilibrae; 184,3.6865510701699965e-05,0.0053078670417365855
    2022-03-30 06:35:20,439;aequilibrae; 185,3.916130470156447e-05,0.007036779616811502
    2022-03-30 06:35:20,449;aequilibrae; 186,3.962993890789461e-05,0.005536671618470833
    2022-03-30 06:35:20,467;aequilibrae; 187,4.6237043321175434e-05,0.010892693966829932
    2022-03-30 06:35:20,481;aequilibrae; 188,5.020836239315733e-05,0.01868651262672999
    2022-03-30 06:35:20,492;aequilibrae; 189,7.300567369333636e-05,0.025336841997635955
    2022-03-30 06:35:20,503;aequilibrae; 190,6.404195580641755e-05,0.02402694732258743
    2022-03-30 06:35:20,514;aequilibrae; 191,6.184310923774902e-05,0.021052267044510485
    2022-03-30 06:35:20,527;aequilibrae; 192,6.038886713021122e-05,0.012860126951572737
    2022-03-30 06:35:20,537;aequilibrae; 193,4.6936452899112676e-05,0.010978431477204327
    2022-03-30 06:35:20,548;aequilibrae; 194,3.636021592934711e-05,0.005393245209964354
    2022-03-30 06:35:20,559;aequilibrae; 195,3.3656017220639456e-05,0.0058738381743069015
    2022-03-30 06:35:20,569;aequilibrae; 196,4.038743185285662e-05,0.011962108097798647
    2022-03-30 06:35:20,580;aequilibrae; 197,4.745471349840089e-05,0.011789712548040447
    2022-03-30 06:35:20,593;aequilibrae; 198,3.505867666803582e-05,0.012307152742698292
    2022-03-30 06:35:20,605;aequilibrae; 199,4.642255848980489e-05,0.013268156376934532
    2022-03-30 06:35:20,617;aequilibrae; 200,3.9711676389985166e-05,0.014225792233677769
    2022-03-30 06:35:20,632;aequilibrae; 201,3.4871415171198675e-05,0.003307293448942469
    2022-03-30 06:35:20,645;aequilibrae; 202,2.3756526688255493e-05,0.002593882491174012
    2022-03-30 06:35:20,656;aequilibrae; 203,2.2030166604475446e-05,0.0020959585556396703
    2022-03-30 06:35:20,667;aequilibrae; 204,2.408163874597062e-05,0.0015406082073595234
    2022-03-30 06:35:20,679;aequilibrae; 205,2.595361419820379e-05,0.0028492973337620395
    2022-03-30 06:35:20,690;aequilibrae; 206,2.7394125180559924e-05,0.0023871998507281602
    2022-03-30 06:35:20,702;aequilibrae; 207,2.1290829339898705e-05,0.004344874324705038
    2022-03-30 06:35:20,714;aequilibrae; 208,2.428870494848446e-05,0.004166711724851157
    2022-03-30 06:35:20,727;aequilibrae; 209,2.1624402147632748e-05,0.003751676047851544
    2022-03-30 06:35:20,738;aequilibrae; 210,1.8483984963892405e-05,0.0054268381625540724
    2022-03-30 06:35:20,750;aequilibrae; 211,2.799175457374037e-05,0.006654793474008459
    2022-03-30 06:35:20,760;aequilibrae; 212,2.8299950420165198e-05,0.009859096815843259
    2022-03-30 06:35:20,774;aequilibrae; 213,3.061338281472309e-05,0.0037204564303195947
    2022-03-30 06:35:20,785;aequilibrae; 214,2.873602639579616e-05,0.011984276374615074
    2022-03-30 06:35:20,797;aequilibrae; 215,4.3175208420947824e-05,0.01263481412177762
    2022-03-30 06:35:20,808;aequilibrae; 216,3.91588111786499e-05,0.010474392269716032
    2022-03-30 06:35:20,820;aequilibrae; 217,3.6378504649382026e-05,0.016346758842171624
    2022-03-30 06:35:20,834;aequilibrae; 218,4.088583444821159e-05,0.021340715536920143
    2022-03-30 06:35:20,846;aequilibrae; 219,4.3006174292222325e-05,0.028934239207787007
    2022-03-30 06:35:20,859;aequilibrae; 220,6.159044536023042e-05,0.07188767651189076
    2022-03-30 06:35:20,871;aequilibrae; 221,7.996165426203744e-05,0.061880594055853416
    2022-03-30 06:35:20,882;aequilibrae; 222,5.780677561009128e-05,0.08708903400330371
    2022-03-30 06:35:20,894;aequilibrae; 223,6.968793662653245e-05,0.05370810485869615
    2022-03-30 06:35:20,906;aequilibrae; 224,6.909417731200611e-05,0.14071722987411867
    2022-03-30 06:35:20,918;aequilibrae; 225,0.00010124514994858672,0.1849002078509574
    2022-03-30 06:35:20,930;aequilibrae; 226,8.983580771243411e-05,0.21332966046502
    2022-03-30 06:35:20,942;aequilibrae; 227,9.0593106097032e-05,0.29785375765312333
    2022-03-30 06:35:20,954;aequilibrae; 228,8.630782157899777e-05,0.6035944558591091
    2022-03-30 06:35:20,965;aequilibrae; 229,8.554887358184628e-05,0.8744768783481298
    2022-03-30 06:35:20,976;aequilibrae; 230,6.87253053082474e-05,0.0005395317699496178
    2022-03-30 06:35:20,988;aequilibrae; 231,4.506883016392143e-05,0.0009982656991577997
    2022-03-30 06:35:20,999;aequilibrae; 232,7.314638735107332e-05,0.0015363342041115114
    2022-03-30 06:35:21,010;aequilibrae; 233,5.685277489459044e-05,0.0018008264707584946
    2022-03-30 06:35:21,020;aequilibrae; 234,6.220335522920968e-05,0.0023737313776120393
    2022-03-30 06:35:21,031;aequilibrae; 235,5.352422681343023e-05,0.002433346586019852
    2022-03-30 06:35:21,042;aequilibrae; 236,5.0129546897928204e-05,0.001956538489443802
    2022-03-30 06:35:21,053;aequilibrae; 237,4.68151456966112e-05,0.002428204654193711
    2022-03-30 06:35:21,065;aequilibrae; 238,6.115288815385408e-05,0.0034325288356155882
    2022-03-30 06:35:21,075;aequilibrae; 239,6.398390651012139e-05,0.003614734878575821
    2022-03-30 06:35:21,089;aequilibrae; 240,4.318650410810957e-05,0.001803408074624398
    2022-03-30 06:35:21,101;aequilibrae; 241,4.645501143872556e-05,0.0021872018183055303
    2022-03-30 06:35:21,112;aequilibrae; 242,3.1731173499134464e-05,0.0013139122225321544
    2022-03-30 06:35:21,124;aequilibrae; 243,3.5171901346269106e-05,0.0008453297275525358
    2022-03-30 06:35:21,135;aequilibrae; 244,2.683533293023639e-05,0.0018257183433974778
    2022-03-30 06:35:21,145;aequilibrae; 245,3.1203263239777164e-05,0.001359301581766235
    2022-03-30 06:35:21,156;aequilibrae; 246,1.8921441921264987e-05,0.0010518564699650245
    2022-03-30 06:35:21,166;aequilibrae; 247,2.3390582354712506e-05,0.001076341913587382
    2022-03-30 06:35:21,177;aequilibrae; 248,2.5097951791505416e-05,0.0010325834490978903
    2022-03-30 06:35:21,187;aequilibrae; 249,1.9215146908946588e-05,0.0007148976579949088
    2022-03-30 06:35:21,199;aequilibrae; 250,1.7810800392343976e-05,0.000374138211066436
    2022-03-30 06:35:21,213;aequilibrae; 251,1.289871330248871e-05,0.00044252548634874744
    2022-03-30 06:35:21,224;aequilibrae; 252,1.2551006764435497e-05,0.0004011613364555553
    2022-03-30 06:35:21,235;aequilibrae; 253,9.369438355684967e-06,0.0003972318191172314
    2022-03-30 06:35:21,235;aequilibrae; bfw Assignment finished. 253 iterations and 9.369438355684967e-06 final gap




.. GENERATED FROM PYTHON SOURCE LINES 355-362

.. 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

    2022-03-30 06:35:21,457;aequilibrae; Matrix Record has been saved to the database




.. GENERATED FROM PYTHON SOURCE LINES 363-364

We can also plot convergence

.. GENERATED FROM PYTHON SOURCE LINES 364-380

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





.. GENERATED FROM PYTHON SOURCE LINES 381-382

Close the project

.. GENERATED FROM PYTHON SOURCE LINES 382-383

.. code-block:: python

    project.close()




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

 Out:

 .. code-block:: none

    2022-03-30 06:35:22,127;aequilibrae; Closed project on /tmp/7913d028c032499ea017a89703807e34





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

   **Total running time of the script:** ( 0 minutes  8.603 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>`_