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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download__auto_examples_trip_distribution_plot_ipf_without_model.py>`
        to download the full example code.

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

.. _sphx_glr__auto_examples_trip_distribution_plot_ipf_without_model.py:


.. _plot_ipf_without_model:

Running IPF without an AequilibraE model
========================================

In this example, we show you how to use AequilibraE's IPF function without a model.
This is a compliment to the application in :ref:`Trip Distribution <example_usage_distribution>`.

Let's consider that you have an OD-matrix, the future production and future attraction values.
*How would your trip distribution matrix using IPF look like?*
The data used in this example comes from Table 5.6 in Ortúzar & Willumsen (2011).

.. GENERATED FROM PYTHON SOURCE LINES 17-26

.. code-block:: Python


    # Imports
    import numpy as np

    from aequilibrae.distribution import Ipf
    from os.path import join
    from tempfile import gettempdir
    from aequilibrae.matrix import AequilibraeMatrix, AequilibraeData








.. GENERATED FROM PYTHON SOURCE LINES 28-30

.. code-block:: Python

    folder = gettempdir()








.. GENERATED FROM PYTHON SOURCE LINES 31-38

.. code-block:: Python

    matrix = np.array([[5, 50, 100, 200], [50, 5, 100, 300], 
                       [50, 100, 5, 100], [100, 200, 250, 20]], dtype="float64")
    future_prod = np.array([400, 460, 400, 702], dtype="float64")
    future_attr = np.array([260, 400, 500, 802], dtype="float64")

    num_zones = matrix.shape[0]








.. GENERATED FROM PYTHON SOURCE LINES 39-45

.. code-block:: Python

    mtx = AequilibraeMatrix()
    mtx.create_empty(file_name=join(folder, "matrix.aem"), zones=num_zones)
    mtx.index[:] = np.arange(1, num_zones + 1)[:]
    mtx.matrices[:, :, 0] = matrix[:]
    mtx.computational_view()








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.. code-block:: Python

    args = {
        "entries": mtx.index.shape[0],
        "field_names": ["productions", "attractions"],
        "data_types": [np.float64, np.float64],
        "file_path": join(folder, "vectors.aem"),
    }

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

    vectors.productions[:] = future_prod[:]
    vectors.attractions[:] = future_attr[:]

    vectors.index[:] = mtx.index[:]








.. GENERATED FROM PYTHON SOURCE LINES 62-73

.. code-block:: Python

    args = {
        "matrix": mtx,
        "rows": vectors,
        "row_field": "productions",
        "columns": vectors,
        "column_field": "attractions",
        "nan_as_zero": True,
    }
    fratar = Ipf(**args)
    fratar.fit()








.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: Python

    fratar.output.matrix_view





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

 .. code-block:: none


    array([[  5.19523253,  43.60117896,  97.1907419 , 254.02026689],
           [ 44.70942645,   3.75225496,  83.64095928, 327.90930057],
           [ 76.67583276, 128.70094592,   7.17213428, 187.45277887],
           [133.41950826, 223.94562016, 311.99616454,  32.61765367]])



.. GENERATED FROM PYTHON SOURCE LINES 77-80

.. code-block:: Python

    for line in fratar.report:
        print(line)





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

 .. code-block:: none

      #####    IPF computation    #####  

    Target convergence criteria: 0.0001
    Maximum iterations: 5000

    Rows:4
    Columns: 4
    Total of seed matrix:                   1,635.0000
    Total of target vectors:                1,962.0000

    Iteration,   Convergence
    7   ,   0.0000902085

    Running time: 0.002s




.. GENERATED FROM PYTHON SOURCE LINES 81-84

Reference
~~~~~~~~~

ORTÚZAR, J.D., WILLUMSEN, L.G. (2011) *Modelling Transport* (4th ed.). Wiley-Blackwell.


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

   **Total running time of the script:** (0 minutes 0.039 seconds)


.. _sphx_glr_download__auto_examples_trip_distribution_plot_ipf_without_model.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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