joaofig

def generate_triples(hex_list: list[int]) -> list[(int,int,int)]:
    triples = []
    if len(hex_list) > 2:
        for i in range(len(hex_list) - 2):
            t0, t1, t2 = hex_list[i:i + 3]
            triples.append((t0, t1, t2))
    return triples

joaofig

from valhalla import Actor, get_config

config = get_config(tile_extract='./valhalla/custom_files/valhalla_tiles.tar', 
                    verbose=True)
actor = Actor(config)

joaofig

pip install pyvalhalla

joaofig

docker run -it --rm --name valhalla_gis-ops -p 8002:8002 \
-v $PWD/valhalla/custom_files:/custom_files \
-e tile_urls=http://download.geofabrik.de/north-america/us/michigan-latest.osm.pbf \
-e serve_tiles=True -e build_admins=True \
docker.pkg.github.com/gis-ops/docker-valhalla/valhalla:3.3.0

joaofig

docker login docker.pkg.github.com
docker pull docker.pkg.github.com/gis-ops/docker-valhalla/valhalla:3.3.0

joaofig

def insert_geometry(traj_id: int,
                    geometry: str) -> None:
    db = TrajDb()
    sql = "insert into traj_match (traj_id, geometry) values (?, ?)"
    db.execute_sql(sql, [traj_id, geometry])

joaofig

def main():
    tiles = './valhalla/custom_files/valhalla_tiles.tar'
    config = get_config(tile_extract=tiles, verbose=True)

    max_traj_id = get_max_traj_id()
    max_match = get_max_match()

    for traj_id in range(max_match + 1, max_traj_id + 1):
        actor = Actor(config)

joaofig

def load_trajectory_points(traj_id):
    db = EVedDb()

    sql = f"""
    select     distinct
               s.latitude as lat
    ,          s.longitude as lon
    ,          min(s.time_stamp) / 1000 as time
    from       signal s
    inner join trajectory t on s.vehicle_id = t.vehicle_id and s.trip_id = t.trip_id

joaofig

def map_match(actor: Actor,
              df: pd.DataFrame) -> str:
    param = {
        "use_timestamps": True,
        "shortest": True,
        "shape_match": "walk_or_snap",
        "shape": df.to_dict(orient='records'),
        "costing": "auto",
        "format": "osrm",
        "directions_options": {

joaofig

def get_matching_links(self, exclude_self=True):
    df = load_matching_links(self.traj_id)

    if exclude_self:
        df = df[df["traj_id"] != self.traj_id]

    links = np.unique(df["link_id"].values)
    return links