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

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

.. _sphx_glr__auto_examples_public_transport_plot_import_gtfs.py:


.. _example_gtfs:

Import GTFS
===========

In this example, we import a GTFS feed to our model and perform map matching. 

We use data from Coquimbo, a city in La Serena Metropolitan Area in Chile.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. seealso::
    Several functions, methods, classes and modules are used in this example:

    * :func:`aequilibrae.transit.Transit`
    * :func:`aequilibrae.transit.lib_gtfs.GTFSRouteSystemBuilder`

.. GENERATED FROM PYTHON SOURCE LINES 19-35

.. code-block:: Python


    # Imports
    from uuid import uuid4
    from os import remove
    from os.path import join
    from tempfile import gettempdir

    import folium
    import geopandas as gpd
    import pandas as pd

    from aequilibrae.project.database_connection import database_connection
    from aequilibrae.transit import Transit
    from aequilibrae.utils.create_example import create_example









.. GENERATED FROM PYTHON SOURCE LINES 37-43

.. code-block:: Python


    # Let's create an empty project on an arbitrary folder.
    fldr = join(gettempdir(), uuid4().hex)

    project = create_example(fldr, "coquimbo")








.. GENERATED FROM PYTHON SOURCE LINES 44-46

As the Coquimbo example already has a complete GTFS model, we shall remove its public transport
database for the sake of this example.

.. GENERATED FROM PYTHON SOURCE LINES 46-48

.. code-block:: Python

    remove(join(fldr, "public_transport.sqlite"))








.. GENERATED FROM PYTHON SOURCE LINES 49-50

Let's import the GTFS feed.

.. GENERATED FROM PYTHON SOURCE LINES 50-52

.. code-block:: Python

    dest_path = join(fldr, "gtfs_coquimbo.zip")








.. GENERATED FROM PYTHON SOURCE LINES 53-55

Now we create our Transit object and import the GTFS feed into our model.
This will automatically create a new public transport database.

.. GENERATED FROM PYTHON SOURCE LINES 55-60

.. code-block:: Python


    data = Transit(project)

    transit = data.new_gtfs_builder(agency="Lisanco", file_path=dest_path)








.. GENERATED FROM PYTHON SOURCE LINES 61-64

To load the data, we must choose one date. We're going to continue with 2016-04-13 but feel free
to experiment with any other available dates. Transit class has a function allowing you to check
dates for the GTFS feed. It should take approximately 2 minutes to load the data.

.. GENERATED FROM PYTHON SOURCE LINES 64-76

.. code-block:: Python


    transit.load_date("2016-04-13")

    # Now we execute the map matching to find the real paths.
    # Depending on the GTFS size, this process can be really time-consuming.

    # transit.set_allow_map_match(True)
    # transit.map_match()

    # Finally, we save our GTFS into our model.
    transit.save_to_disk()





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

 .. code-block:: none


    Loading routes (Step: 1/12)                       : 0it [00:00, ?it/s]
                                                                      

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    De-conflicting stop times (Step: 6/12)            :   0%|          | 0/1 [00:00<?, ?it/s]
    Loading fare data (Step: 7/12)                    : 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]
                                                                                                 

    Loading data for 2016-04-13 (Step: 9/12) -        :   0%|          | 0/1 [00:00<?, ?it/s]
                                                                                         

    Saving patterns (Step: 10/12)                     :   0%|          | 0/2 [00:00<?, ?it/s]
                                                                                         

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    Saving stops (Step: 12/12)                        :  99%|█████████▊| 77/78 [00:01<00:00, 42.35it/s]



.. GENERATED FROM PYTHON SOURCE LINES 77-78

Now we will plot one of the route's patterns we just imported

.. GENERATED FROM PYTHON SOURCE LINES 78-83

.. code-block:: Python

    conn = database_connection("transit")

    patterns = pd.read_sql("SELECT pattern_id, ST_AsText(geometry) geom FROM routes;", con=conn)
    stops = pd.read_sql("""SELECT stop_id, ST_X(geometry) X, ST_Y(geometry) Y FROM stops""", con=conn)








.. GENERATED FROM PYTHON SOURCE LINES 84-85

We turn the patterns and stops DataFrames into GeoDataFrames so we can plot them more easily.

.. GENERATED FROM PYTHON SOURCE LINES 85-88

.. code-block:: Python

    patterns = gpd.GeoDataFrame(patterns, geometry=gpd.GeoSeries.from_wkt(patterns["geom"]), crs=4326)
    stops = gpd.GeoDataFrame(stops, geometry=gpd.GeoSeries.from_xy(stops["X"], stops["Y"]), crs=4326)








.. GENERATED FROM PYTHON SOURCE LINES 89-90

And plot out data!

.. GENERATED FROM PYTHON SOURCE LINES 90-97

.. code-block:: Python


    map = patterns.explore(color=["#146DB3", "#EB9719"], style_kwds={"weight": 4}, name="links")
    map = stops.explore(m=map, color="black", style_kwds={"radius": 2, "fillOpacity": 1.0}, name="stops")

    folium.LayerControl().add_to(map)
    map






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        geo_json_15b4337071dc4d626c1c2a4e9eeb9093.bindTooltip(
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                    style: geo_json_2e7ecb9e6a4285dff5ae80a6e3466dd5_styler,
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.. GENERATED FROM PYTHON SOURCE LINES 98-99

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.. _sphx_glr_download__auto_examples_public_transport_plot_import_gtfs.py:

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