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

.. only:: html

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

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

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

.. _sphx_glr__auto_examples_plot_trip_distribution.py:


Trip Distribution
=================

On this example we calibrate a Synthetic Gravity Model that same model plus IPF (Fratar/Furness).

.. GENERATED FROM PYTHON SOURCE LINES 7-16

.. 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
    import pandas as pd
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 17-18

We create the example project inside our temp folder

.. GENERATED FROM PYTHON SOURCE LINES 18-22

.. code-block:: python

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

    project = create_example(fldr)








.. GENERATED FROM PYTHON SOURCE LINES 23-29

.. 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 30-39

.. code-block:: python


    # We get the demand matrix
    demand = proj_matrices.get_matrix('demand_omx')
    demand.computational_view(['matrix'])

    # And the impedance
    impedance = proj_matrices.get_matrix('skims')
    impedance.computational_view(['time_final'])








.. GENERATED FROM PYTHON SOURCE LINES 40-41

Let's have a function to plot the Trip Length Frequency Distribution

.. GENERATED FROM PYTHON SOURCE LINES 41-58

.. code-block:: python

    from math import log10, floor
    import matplotlib.pyplot as plt


    def plot_tlfd(demand, skim, name):
        plt.clf()
        b = floor(log10(skim.shape[0]) * 10)
        n, bins, patches = plt.hist(np.nan_to_num(skim.flatten(), 0), bins=b, weights=np.nan_to_num(demand.flatten()),
                                    density=False, facecolor='g', alpha=0.75)

        plt.xlabel('Trip length')
        plt.ylabel('Probability')
        plt.title('Trip-length frequency distribution')
        plt.savefig(name, format="png")
        return plt









.. GENERATED FROM PYTHON SOURCE LINES 59-61

.. code-block:: python

    from aequilibrae.distribution import GravityCalibration








.. GENERATED FROM PYTHON SOURCE LINES 62-79

.. code-block:: python


    for function in ['power', 'expo']:
        gc = GravityCalibration(matrix=demand, impedance=impedance, 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 an image for the resulting model
        _ = plot_tlfd(gc.result_matrix.matrix_view, impedance.matrix_view, join(fldr, f'{function}_tfld.png'))

        # 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')




.. image:: /_auto_examples/images/sphx_glr_plot_trip_distribution_001.png
    :alt: Trip-length frequency distribution
    :class: sphx-glr-single-img


.. 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 80-81

We save a trip length frequency distribution for the demand itself

.. GENERATED FROM PYTHON SOURCE LINES 81-84

.. code-block:: python


    plt = plot_tlfd(demand.matrix_view, impedance.matrix_view, join(fldr, 'demand_tfld.png'))
    plt.show()



.. image:: /_auto_examples/images/sphx_glr_plot_trip_distribution_002.png
    :alt: Trip-length frequency distribution
    :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 85-90

.. code-block:: python


    ## Forecast
    #  * We create a set of * 'future' * vectors by applying some models
    #  * We apply the model for both deterrence functions








.. GENERATED FROM PYTHON SOURCE LINES 91-96

.. code-block:: python


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








.. GENERATED FROM PYTHON SOURCE LINES 97-118

.. code-block:: python


    zonal_data = pd.read_sql('Select zone_id, population, employment from zones order by zone_id', project.conn)
    # We compute the vectors from our matrix


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

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

    vectors.index[:] = zonal_data.zone_id[:]

    # We apply a trivial regression-based model and balance the vectors
    vectors.origins[:] = zonal_data.population[:] * 2.32
    vectors.destinations[:] = zonal_data.employment[:] * 1.87
    vectors.destinations *= vectors.origins.sum() / vectors.destinations.sum()








.. GENERATED FROM PYTHON SOURCE LINES 119-144

.. code-block:: python


    # We simply apply the models to the same impedance matrix now
    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": impedance,
                "rows": vectors,
                "row_field": "origins",
                "model": model,
                "columns": vectors,
                "column_field": "destinations",
                "nan_as_zero": True
                }

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

        gravity.save_to_project(name=f'demand_{function}_model', file_name=f'demand_{function}_model.aem')

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





.. 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 145-150

.. 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>demand_power_model</td>
          <td>demand_power_model.aem</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>dcfa995d62bf4c0383313aa151c2faf9</td>
          <td>2021-01-14 09:20:51.531536</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>4</th>
          <td>demand_power_model_omx</td>
          <td>demand_power_model.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>dcfa995d62bf4c0383313aa151c2faf9</td>
          <td>2021-01-14 09:20:51.531536</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_expo_model</td>
          <td>demand_expo_model.aem</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>b6c0fa99afee44be9dd4bb21d17c2721</td>
          <td>2021-01-14 09:20:52.001499</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
        <tr>
          <th>6</th>
          <td>demand_expo_model_omx</td>
          <td>demand_expo_model.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>b6c0fa99afee44be9dd4bb21d17c2721</td>
          <td>2021-01-14 09:20:52.001499</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 151-154

.. code-block:: python


    ### We now run IPF for the future vectors








.. GENERATED FROM PYTHON SOURCE LINES 155-168

.. 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_ipf', file_name='demand_ipf.aem')
    ipf.save_to_project(name='demand_ipf_omx', file_name='demand_ipf.omx')





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

 Out:

 .. code-block:: none


    <aequilibrae.project.data.matrix_record.MatrixRecord object at 0x7fb7b04f7750>



.. GENERATED FROM PYTHON SOURCE LINES 169-172

.. 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>demand_power_model</td>
          <td>demand_power_model.aem</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>dcfa995d62bf4c0383313aa151c2faf9</td>
          <td>2021-01-14 09:20:51.531536</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>4</th>
          <td>demand_power_model_omx</td>
          <td>demand_power_model.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>dcfa995d62bf4c0383313aa151c2faf9</td>
          <td>2021-01-14 09:20:51.531536</td>
          <td>Synthetic gravity trip distribution. POWER</td>
          <td></td>
        </tr>
        <tr>
          <th>5</th>
          <td>demand_expo_model</td>
          <td>demand_expo_model.aem</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>b6c0fa99afee44be9dd4bb21d17c2721</td>
          <td>2021-01-14 09:20:52.001499</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
        <tr>
          <th>6</th>
          <td>demand_expo_model_omx</td>
          <td>demand_expo_model.omx</td>
          <td>1</td>
          <td>Synthetic gravity trip distribution</td>
          <td>b6c0fa99afee44be9dd4bb21d17c2721</td>
          <td>2021-01-14 09:20:52.001499</td>
          <td>Synthetic gravity trip distribution. EXPO</td>
          <td></td>
        </tr>
        <tr>
          <th>7</th>
          <td>demand_ipf</td>
          <td>demand_ipf.aem</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>c995cce9e1924ce5a6e7689b7c9772d7</td>
          <td>2021-01-14 09:20:52.381313</td>
          <td>None</td>
          <td></td>
        </tr>
        <tr>
          <th>8</th>
          <td>demand_ipf_omx</td>
          <td>demand_ipf.omx</td>
          <td>1</td>
          <td>Iterative Proportional fitting</td>
          <td>c995cce9e1924ce5a6e7689b7c9772d7</td>
          <td>2021-01-14 09:20:52.381313</td>
          <td>None</td>
          <td></td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 173-175

.. code-block:: python


    project.close()








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

   **Total running time of the script:** ( 0 minutes  2.856 seconds)


.. _sphx_glr_download__auto_examples_plot_trip_distribution.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_trip_distribution.py <plot_trip_distribution.py>`



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

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


.. only:: html

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

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