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

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

.. _sphx_glr__auto_examples_plot_general_example.py:


.. _example_matching_generic_model:

Matching with a generic network
==================================

.. GENERATED FROM PYTHON SOURCE LINES 11-23

.. code-block:: Python

    import uuid
    from os.path import join
    from tempfile import gettempdir

    import geopandas as gpd
    import numpy as np
    from aequilibrae.utils.create_example import create_example
    from aequilibrae.paths import Graph

    from mapmatcher import MapMatcher
    from mapmatcher.examples import nauru_data








.. GENERATED FROM PYTHON SOURCE LINES 25-30

.. code-block:: Python

    nauru_gps = nauru_data()

    # Let's see if the data has all the fields we need
    nauru_gps.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>x</th>
          <th>y</th>
          <th>timestamp</th>
          <th>gps_fix_id</th>
          <th>vehicle_unique_id</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
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          <td>2018-05-09 18:14:05</td>
          <td>2</td>
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          <td>POINT (166.94342 -0.54677)</td>
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          <td>166.943201</td>
          <td>-0.547001</td>
          <td>2018-05-09 18:14:08</td>
          <td>3</td>
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          <td>2018-05-09 18:14:39</td>
          <td>4</td>
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          <td>POINT (166.94096 -0.5497)</td>
        </tr>
        <tr>
          <th>4</th>
          <td>166.940674</td>
          <td>-0.550305</td>
          <td>2018-05-09 18:14:45</td>
          <td>5</td>
          <td>2</td>
          <td>POINT (166.94067 -0.5503)</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 31-32

Since it does not, let's fix it

.. GENERATED FROM PYTHON SOURCE LINES 32-34

.. code-block:: Python

    nauru_gps.rename(columns={"x": "longitude", "y": "latitude", "vehicle_unique_id": "trace_id"}, inplace=True)








.. GENERATED FROM PYTHON SOURCE LINES 35-36

Let's get a Nauru example from AequilibraE and extract the link network from it

.. GENERATED FROM PYTHON SOURCE LINES 36-43

.. code-block:: Python

    project = create_example(join(gettempdir(), uuid.uuid4().hex), "nauru")

    sql = "SELECT link_id, a_node, b_node, direction, distance, Hex(ST_AsBinary(geometry)) as geom FROM links"
    gdf = gpd.GeoDataFrame.from_postgis(sql, project.conn, geom_col="geom", crs=4326)
    gdf.head()







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>link_id</th>
          <th>a_node</th>
          <th>b_node</th>
          <th>direction</th>
          <th>distance</th>
          <th>geom</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
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          <td>2</td>
          <td>1</td>
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          <td>LINESTRING (166.93718 -0.52822, 166.93689 -0.5...</td>
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          <td>2</td>
          <td>4</td>
          <td>3</td>
          <td>0</td>
          <td>97.588811</td>
          <td>LINESTRING (166.9364 -0.50389, 166.93625 -0.50...</td>
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        <tr>
          <th>2</th>
          <td>3</td>
          <td>5</td>
          <td>4</td>
          <td>0</td>
          <td>44.419756</td>
          <td>LINESTRING (166.93604 -0.50376, 166.9363 -0.50...</td>
        </tr>
        <tr>
          <th>3</th>
          <td>4</td>
          <td>4</td>
          <td>6</td>
          <td>0</td>
          <td>34.209608</td>
          <td>LINESTRING (166.9364 -0.50389, 166.93659 -0.50...</td>
        </tr>
        <tr>
          <th>4</th>
          <td>5</td>
          <td>6</td>
          <td>7</td>
          <td>0</td>
          <td>14.783125</td>
          <td>LINESTRING (166.93669 -0.504, 166.93678 -0.50409)</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 44-47

Let's build a graph with the network.
For that, you need ta few key fields in the network: [link_id, a_node, b_node, direction]
You also need a cost field for your graph, which is *distance* in our case

.. GENERATED FROM PYTHON SOURCE LINES 47-60

.. code-block:: Python



    g = Graph()
    g.cost = gdf["distance"].to_numpy()

    g.network = gdf

    # Let's say nodes 1 through 133 are centroids
    g.prepare_graph(centroids=np.arange(133) + 1)
    g.set_graph("distance")
    g.set_skimming(["distance"])
    g.set_blocked_centroid_flows(False)








.. GENERATED FROM PYTHON SOURCE LINES 61-62

let's build the map-matcher object and run it!

.. GENERATED FROM PYTHON SOURCE LINES 62-69

.. code-block:: Python

    mmatcher = MapMatcher()
    mmatcher.load_network(graph=g, links=gdf)
    mmatcher.load_gps_traces(nauru_gps)

    # Let's run it single-threaded
    mmatcher.map_match(parallel_threads=1)





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

 .. code-block:: none


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    Map matching trips:   0%|          | 0/100 [00:00<?, ?it/s]
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    Map matching trips:  95%|█████████▌| 95/100 [00:08<00:00, 11.74it/s]
    Map matching trips: 100%|██████████| 100/100 [00:08<00:00, 11.46it/s]




.. GENERATED FROM PYTHON SOURCE LINES 70-74

.. code-block:: Python

    for trip in mmatcher.trips:
        if trip.success:
            break








.. GENERATED FROM PYTHON SOURCE LINES 75-79

.. code-block:: Python

    import folium
    import geopandas as gpd
    import numpy as np








.. GENERATED FROM PYTHON SOURCE LINES 80-81

Create a geometry list from the GeoDataFrame

.. GENERATED FROM PYTHON SOURCE LINES 81-114

.. code-block:: Python

    trace = trip.trace.to_crs(4326)
    geo_df_list = [[point.xy[1][0], point.xy[0][0], ts] for point, ts in zip(trace.geometry, trace.timestamp)]

    result_layer = folium.FeatureGroup("Map-match result")
    trace_layer = folium.FeatureGroup("GPS traces")

    # Iterate through list and add a marker for each GPS ping.
    i = 0
    for lat, lng, ts in geo_df_list:
        trace_layer.add_child(
            folium.CircleMarker(
                location=[lat, lng],
                radius=1,
                fill=True,  # Set fill to True
                color="black",
                tooltip=str(ts),
                fill_opacity=1.0,
            )
        )

    gdf = gpd.GeoDataFrame({"d": [1]}, geometry=[trip.path_shape], crs=3857).to_crs(4326).explode(ignore_index=True)
    for _, rec in gdf.iterrows():
        coords = rec.geometry.xy
        coordinates = [[y, x] for x, y in zip(coords[0], coords[1])]
        folium.PolyLine(coordinates, weight=5, color="red").add_to(result_layer)

    map = folium.Map(location=[np.mean(coords[1]), np.mean(coords[0])], tiles="OpenStreetMap", zoom_start=15)

    result_layer.add_to(map)
    trace_layer.add_to(map)
    folium.LayerControl(position="bottomright").add_to(map)
    map






.. raw:: html

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                tile_layer_018b6c5246e8087b9fdc056fddb3c8e6.addTo(map_b0c8478ebd17a3272c436c2c13f0b732);
        
    
                var feature_group_bfc4651ed6670b89123e5eabcf0fbbc0 = L.featureGroup(
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                circle_marker_3d76dec0eb59eafe300f0e945a145ce0.bindTooltip(
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                circle_marker_126ff67fa861fcf9ffec934855784fea.bindTooltip(
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                         2018-05-09 02:39:12
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                circle_marker_fb9a6646d41b2dceb35c952fcc96bfcd.bindTooltip(
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                         2018-05-09 02:39:13
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                circle_marker_4d6e5bde251736ef8a302ad3189f82bd.bindTooltip(
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                circle_marker_f372ccf3f8b615ffd251b280056755a7.bindTooltip(
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                circle_marker_326307ac8b7f03ffb1d824a160d037cb.bindTooltip(
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                circle_marker_3e00cd7c13741a77f92d1f635b099697.bindTooltip(
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                circle_marker_dd5b7b2bb961d9276b2fd998e9b2e51c.bindTooltip(
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                         2018-05-09 02:39:32
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                circle_marker_dabff136459dbac7126e8360248bd668.bindTooltip(
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                         2018-05-09 02:39:39
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                circle_marker_6193c23bc78cc8790c2c47807041792f.bindTooltip(
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                         2018-05-09 02:39:43
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                var circle_marker_ac3a0883b5ac937e5c24ad248d33ccc5 = L.circleMarker(
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                circle_marker_ac3a0883b5ac937e5c24ad248d33ccc5.bindTooltip(
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                         2018-05-09 02:39:49
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                var circle_marker_68853b956aa87191be24d188c29c0a9f = L.circleMarker(
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                circle_marker_68853b956aa87191be24d188c29c0a9f.bindTooltip(
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                         2018-05-09 02:40:05
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                var circle_marker_6e56859f1ad1ee27683e0014ade386c0 = L.circleMarker(
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                circle_marker_6e56859f1ad1ee27683e0014ade386c0.bindTooltip(
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                         2018-05-09 02:40:20
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                var circle_marker_037243a1915e72a5987bba442b694b4b = L.circleMarker(
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                circle_marker_037243a1915e72a5987bba442b694b4b.bindTooltip(
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                         2018-05-09 02:40:23
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                var circle_marker_7db4c55294fedac1167a58f3ce2a6907 = L.circleMarker(
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                circle_marker_7db4c55294fedac1167a58f3ce2a6907.bindTooltip(
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                         2018-05-09 02:40:24
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                var circle_marker_ebbe4afe786cc913bb754d28f5da5e8e = L.circleMarker(
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                circle_marker_ebbe4afe786cc913bb754d28f5da5e8e.bindTooltip(
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                         2018-05-09 02:40:29
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                var circle_marker_805f8f8190bf0291809120cfcc9d25ff = L.circleMarker(
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                circle_marker_805f8f8190bf0291809120cfcc9d25ff.bindTooltip(
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                         2018-05-09 02:40:30
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                var circle_marker_ea468382e6c30f6c73be9c248c80134c = L.circleMarker(
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                circle_marker_ea468382e6c30f6c73be9c248c80134c.bindTooltip(
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                         2018-05-09 02:40:34
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                var circle_marker_47edc547ebfc73230e21791bc9aab4b2 = L.circleMarker(
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                circle_marker_47edc547ebfc73230e21791bc9aab4b2.bindTooltip(
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                         2018-05-09 02:40:38
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                circle_marker_8fc73ad7f7c7c1a46d712379291c77b6.bindTooltip(
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                var circle_marker_117ac060f60c9ccc3e09d2296d3ade34 = L.circleMarker(
                    [-0.5220486251411643, 166.91816312459437],
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                circle_marker_117ac060f60c9ccc3e09d2296d3ade34.bindTooltip(
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                    {&quot;sticky&quot;: true}
                );
        
    
                feature_group_205cc00e43e4ee0ec59ff6a45c1a2e15.addTo(map_b0c8478ebd17a3272c436c2c13f0b732);
        
    
                var layer_control_3462cbedb61ba683172a74f97f607193_layers = {
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                };
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    &lt;/script&gt;
    &lt;/html&gt;" style="position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 115-116

.. code-block:: Python

    trip.result





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
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    <style scoped>
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          <th>geometry</th>
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    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 11.303 seconds)


.. _sphx_glr_download__auto_examples_plot_general_example.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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