.. 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.

:author: Michel Bierlaire, EPFL
:date: Wed Apr 12 21:02:19 2023

.. GENERATED FROM PYTHON SOURCE LINES 12-30

.. code-block:: Python


    import sys
    import numpy as np

    try:
        import matplotlib.pyplot as plt

        can_plot = True
    except ModuleNotFoundError:
        can_plot = False
    from tqdm import tqdm
    from biogeme import models
    from biogeme.exceptions import BiogemeError
    import biogeme.biogeme as bio
    import biogeme.results as res
    from biogeme.data.optima import read_data, normalized_weight
    from scenarios import scenario








.. GENERATED FROM PYTHON SOURCE LINES 31-32

Read the estimation results from the file.

.. GENERATED FROM PYTHON SOURCE LINES 32-41

.. code-block:: Python

    try:
        results = res.bioResults(pickle_file='saved_results/b02estimation.pickle')
    except BiogemeError:
        sys.exit(
            'Run first the script b02simulation.py '
            'in order to generate the '
            'file b02estimation.pickle.'
        )








.. 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 48-90

.. 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.

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

        # Obtain the expression for the choice probability of each alternative
        prob_pt = models.nested(V, 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 = bio.BIOGEME(database, simulate)
        simulated_values = the_biogeme.simulate(results.get_beta_values())

        # We also calculate confidence intervals for the calculated quantities

        betas = the_biogeme.free_beta_names
        beta_bootstrap = results.get_betas_for_sensitivity_analysis(betas)
        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()
        return revenues_pt, revenues_pt_left, revenues_pt_right









.. GENERATED FROM PYTHON SOURCE LINES 91-92

Current revenues for public transportation

.. GENERATED FROM PYTHON SOURCE LINES 92-99

.. 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

 .. code-block:: none

    Total revenues for public transportation (for the sample): 2878.3 CHF [2360.6 CHF, 3424.8 CHF]




.. GENERATED FROM PYTHON SOURCE LINES 100-101

We now investigate how the revenues vary with the multiplicative factor

.. GENERATED FROM PYTHON SOURCE LINES 101-112

.. code-block:: Python


    factors = np.arange(0.0, 5.0, 0.1)
    plot_revenues = [revenues(s) for s in tqdm(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

 .. code-block:: none


      0%|          | 0/50 [00:00<?, ?it/s]
      2%|▏         | 1/50 [00:11<09:25, 11.54s/it]
      4%|▍         | 2/50 [00:22<08:50, 11.06s/it]
      6%|▌         | 3/50 [00:33<08:37, 11.00s/it]
      8%|▊         | 4/50 [00:41<07:28,  9.75s/it]
     10%|█         | 5/50 [00:47<06:23,  8.52s/it]
     12%|█▏        | 6/50 [00:53<05:44,  7.82s/it]
     14%|█▍        | 7/50 [01:00<05:16,  7.35s/it]
     16%|█▌        | 8/50 [01:06<04:56,  7.06s/it]
     18%|█▊        | 9/50 [01:13<04:41,  6.86s/it]
     20%|██        | 10/50 [01:19<04:28,  6.71s/it]
     22%|██▏       | 11/50 [01:25<04:19,  6.65s/it]
     24%|██▍       | 12/50 [01:32<04:10,  6.59s/it]
     26%|██▌       | 13/50 [01:38<04:02,  6.55s/it]
     28%|██▊       | 14/50 [01:45<03:55,  6.53s/it]
     30%|███       | 15/50 [01:52<03:55,  6.74s/it]
     32%|███▏      | 16/50 [02:05<04:55,  8.69s/it]
     34%|███▍      | 17/50 [02:18<05:23,  9.79s/it]
     36%|███▌      | 18/50 [02:30<05:41, 10.68s/it]
     38%|███▊      | 19/50 [02:42<05:40, 10.99s/it]
     40%|████      | 20/50 [02:53<05:31, 11.04s/it]
     42%|████▏     | 21/50 [03:05<05:22, 11.13s/it]
     44%|████▍     | 22/50 [03:16<05:09, 11.07s/it]
     46%|████▌     | 23/50 [03:26<04:50, 10.75s/it]
     48%|████▊     | 24/50 [03:35<04:26, 10.23s/it]
     50%|█████     | 25/50 [03:45<04:17, 10.30s/it]
     52%|█████▏    | 26/50 [03:57<04:17, 10.71s/it]
     54%|█████▍    | 27/50 [04:08<04:10, 10.91s/it]
     56%|█████▌    | 28/50 [04:17<03:49, 10.43s/it]
     58%|█████▊    | 29/50 [04:24<03:13,  9.20s/it]
     60%|██████    | 30/50 [04:30<02:47,  8.35s/it]
     62%|██████▏   | 31/50 [04:36<02:27,  7.74s/it]
     64%|██████▍   | 32/50 [04:43<02:12,  7.34s/it]
     66%|██████▌   | 33/50 [04:49<02:00,  7.08s/it]
     68%|██████▊   | 34/50 [04:56<01:50,  6.89s/it]
     70%|███████   | 35/50 [05:02<01:41,  6.77s/it]
     72%|███████▏  | 36/50 [05:09<01:33,  6.67s/it]
     74%|███████▍  | 37/50 [05:15<01:26,  6.63s/it]
     76%|███████▌  | 38/50 [05:22<01:19,  6.59s/it]
     78%|███████▊  | 39/50 [05:28<01:12,  6.56s/it]
     80%|████████  | 40/50 [05:37<01:11,  7.11s/it]
     82%|████████▏ | 41/50 [05:49<01:18,  8.74s/it]
     84%|████████▍ | 42/50 [06:01<01:18,  9.79s/it]
     86%|████████▌ | 43/50 [06:15<01:16, 10.96s/it]
     88%|████████▊ | 44/50 [06:26<01:06, 11.11s/it]
     90%|█████████ | 45/50 [06:37<00:53, 10.79s/it]
     92%|█████████▏| 46/50 [06:47<00:42, 10.72s/it]
     94%|█████████▍| 47/50 [06:57<00:31, 10.59s/it]
     96%|█████████▌| 48/50 [07:08<00:21, 10.53s/it]
     98%|█████████▊| 49/50 [07:17<00:10, 10.11s/it]
    100%|██████████| 50/50 [07:28<00:00, 10.28s/it]
    100%|██████████| 50/50 [07:28<00:00,  8.96s/it]




.. GENERATED FROM PYTHON SOURCE LINES 113-118

.. 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: 2879.6 obtained with factor 0.9




.. GENERATED FROM PYTHON SOURCE LINES 119-128

.. 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:** (7 minutes 37.609 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>`_