.. 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`

.. GENERATED FROM PYTHON SOURCE LINES 19-34

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









.. GENERATED FROM PYTHON SOURCE LINES 36-42

.. 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 43-45

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 45-47

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 48-49

Let's import the GTFS feed.

.. GENERATED FROM PYTHON SOURCE LINES 49-51

.. code-block:: Python

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








.. GENERATED FROM PYTHON SOURCE LINES 52-54

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 54-59

.. code-block:: Python


    data = Transit(project)

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








.. GENERATED FROM PYTHON SOURCE LINES 60-63

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 63-75

.. 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]
                                                                      

    Loading stops (Step: 2/12)                        :   0%|          | 0/78 [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,  1.62it/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]
                                                                                         

    Saving trips (Step: 11/12)                        :   0%|          | 0/360 [00:00<?, ?it/s]
                                                                                           

    Saving links (Step: 11/12)                        :   0%|          | 0/78 [00:00<?, ?it/s]
                                                                                          

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    Saving stops (Step: 12/12)                        :  81%|████████  | 63/78 [00:01<00:00, 46.39it/s]
    Saving stops (Step: 12/12)                        :  87%|████████▋ | 68/78 [00:01<00:00, 46.36it/s]
    Saving stops (Step: 12/12)                        :  94%|█████████▎| 73/78 [00:01<00:00, 46.41it/s]
    Saving stops (Step: 12/12)                        : 100%|██████████| 78/78 [00:01<00:00, 46.61it/s]



.. GENERATED FROM PYTHON SOURCE LINES 76-77

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

.. GENERATED FROM PYTHON SOURCE LINES 77-81

.. code-block:: Python

    with project.transit_connection as conn:
        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 82-83

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

.. GENERATED FROM PYTHON SOURCE LINES 83-86

.. 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 87-88

And plot out data!

.. GENERATED FROM PYTHON SOURCE LINES 88-95

.. 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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    <div class="output_subarea output_html rendered_html output_result">
    <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><span style="color:#565656">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc="&lt;!DOCTYPE html&gt;
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    &lt;body&gt;
    
    
                &lt;div class=&quot;folium-map&quot; id=&quot;map_c589d7665dfdd2f1017f05842af108c7&quot; &gt;&lt;/div&gt;
        
    &lt;/body&gt;
    &lt;script&gt;
    
    
                var map_c589d7665dfdd2f1017f05842af108c7 = L.map(
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                    style: geo_json_116df9816a4ed318d5720c8afacd62c0_styler,
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        geo_json_116df9816a4ed318d5720c8afacd62c0.bindTooltip(
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            }
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                geo_json_116df9816a4ed318d5720c8afacd62c0.addTo(map_c589d7665dfdd2f1017f05842af108c7);
        
    
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                    },
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                    style: geo_json_c61283e25dfbf2f2dca244c5ad60c135_styler,
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.. GENERATED FROM PYTHON SOURCE LINES 96-97

.. code-block:: Python

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.. _sphx_glr_download__auto_examples_public_transport_plot_import_gtfs.py:

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