.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/indicators/plot_b05revenues.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_indicators_plot_b05revenues.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_indicators_plot_b05revenues.py:


Calculation of revenues
=======================

We use an estimated model to calculate revenues.

Michel Bierlaire, EPFL
Sat Jun 28 2025, 18:57:49

.. GENERATED FROM PYTHON SOURCE LINES 11-28

.. code-block:: Python


    import sys

    import numpy as np
    from biogeme.biogeme import BIOGEME
    from biogeme.models import nested
    from biogeme.results_processing import EstimationResults

    try:
        import matplotlib.pyplot as plt

        can_plot = True
    except ModuleNotFoundError:
        can_plot = False
    from biogeme.data.optima import read_data, normalized_weight
    from scenarios import scenario








.. GENERATED FROM PYTHON SOURCE LINES 29-30

Read the estimation results from the file.

.. GENERATED FROM PYTHON SOURCE LINES 30-41

.. code-block:: Python

    try:
        results = EstimationResults.from_yaml_file(
            filename='saved_results/b02estimation.yaml'
        )
    except FileNotFoundError:
        sys.exit(
            'Run first the script b02simulation.py '
            'in order to generate the '
            'file b02estimation.yaml.'
        )








.. GENERATED FROM PYTHON SOURCE LINES 42-43

Read the data

.. GENERATED FROM PYTHON SOURCE LINES 43-46

.. code-block:: Python

    database = read_data()









.. GENERATED FROM PYTHON SOURCE LINES 47-48

Function calculating the revenues

.. GENERATED FROM PYTHON SOURCE LINES 48-103

.. code-block:: Python

    def revenues(factor: float) -> tuple[float, float, float]:
        """Calculate the total revenues generated by public transportation,
            when the price is multiplied by a factor.

        :param factor: factor that multiplies the current cost of public
            transportation

        :return: total revenues, followed by the lower and upper bound of
            the confidence interval.

        """
        filename = f'revenue_{factor:.2f}.txt'
        SEPARATOR = '%'
        try:
            with open(filename, 'r') as f:
                line = f.read()
                revenue, left, right = line.split(SEPARATOR)
                return float(revenue), float(left), float(right)
        except FileNotFoundError:
            ...

        # Obtain the specification for the default scenario
        utilities, nests, _, marginal_cost_scenario = scenario(factor=factor)

        # Obtain the expression for the choice probability of each alternative
        prob_pt = nested(utilities, None, nests, 0)

        # We now simulate the choice probabilities,the weight and the
        # price variable

        simulate = {
            'weight': normalized_weight,
            'Revenue public transportation': prob_pt * marginal_cost_scenario,
        }

        the_biogeme = BIOGEME(database, simulate)
        simulated_values = the_biogeme.simulate(results.get_beta_values())

        # We also calculate confidence intervals for the calculated quantities
        beta_bootstrap = results.get_betas_for_sensitivity_analysis()
        left, right = the_biogeme.confidence_intervals(beta_bootstrap, 0.9)

        revenues_pt = (
            simulated_values['Revenue public transportation'] * simulated_values['weight']
        ).sum()
        revenues_pt_left = (left['Revenue public transportation'] * left['weight']).sum()
        revenues_pt_right = (right['Revenue public transportation'] * right['weight']).sum()
        with open(filename, 'w') as f:
            print(
                f'{revenues_pt} {SEPARATOR} {revenues_pt_left} {SEPARATOR} {revenues_pt_right}',
                file=f,
            )
        return revenues_pt, revenues_pt_left, revenues_pt_right









.. GENERATED FROM PYTHON SOURCE LINES 104-105

Current revenues for public transportation

.. GENERATED FROM PYTHON SOURCE LINES 105-112

.. code-block:: Python

    r, r_left, r_right = revenues(factor=1.0)
    print(
        f'Total revenues for public transportation (for the sample): {r:.1f} CHF '
        f'[{r_left:.1f} CHF, '
        f'{r_right:.1f} CHF]'
    )





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

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    Total revenues for public transportation (for the sample): 3043.3 CHF [2423.5 CHF, 3643.7 CHF]




.. GENERATED FROM PYTHON SOURCE LINES 113-114

We now investigate how the revenues vary with the multiplicative factor

.. GENERATED FROM PYTHON SOURCE LINES 114-125

.. code-block:: Python


    factors = np.arange(0.0, 5.0, 0.1)
    plot_revenues = [revenues(s) for s in factors]
    zipped = zip(*plot_revenues)
    rev = next(zipped)
    lower = next(zipped)
    upper = next(zipped)

    largest_revenue = max(rev)
    max_index = rev.index(largest_revenue)





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

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.. GENERATED FROM PYTHON SOURCE LINES 126-131

.. code-block:: Python

    print(
        f'Largest revenue: {largest_revenue:.1f} obtained with '
        f'factor {factors[max_index]:.1f}'
    )





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

 .. code-block:: none

    Largest revenue: 3062.4 obtained with factor 1.2




.. GENERATED FROM PYTHON SOURCE LINES 132-141

.. code-block:: Python

    if can_plot:
        # We plot the results
        ax = plt.gca()
        ax.plot(factors, rev, label="Revenues")
        ax.plot(factors, lower, label="Lower bound of the CI")
        ax.plot(factors, upper, label="Upper bound of the CI")
        ax.legend()

        plt.show()



.. image-sg:: /auto_examples/indicators/images/sphx_glr_plot_b05revenues_001.png
   :alt: plot b05revenues
   :srcset: /auto_examples/indicators/images/sphx_glr_plot_b05revenues_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (22 minutes 32.842 seconds)


.. _sphx_glr_download_auto_examples_indicators_plot_b05revenues.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_b05revenues.ipynb <plot_b05revenues.ipynb>`

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

      :download:`Download Python source code: plot_b05revenues.py <plot_b05revenues.py>`

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

      :download:`Download zipped: plot_b05revenues.zip <plot_b05revenues.zip>`


.. only:: html

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

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