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version 0.0.2 (Tested on python3.10)

New in v.0.0.2:
[1] Moved from KML to GeoJSON. It's simplier and faster to parse.
[2] Intermediate .csv files removed from using
[3] Add DataGrabbler class. It unifies DataFrame build process 
[4] Navigator class refactored from main() to its own utils.navigator.py file
[5] Now we use general classifier based in utils.grabbler.py

Updates in v.0.0.2:
[1] point_base got new structure: [id, geometry, Tag, Description]
[2] rails.get_closest_line(points) ready for active using (updating)
[3] osm_router now in helpers. Just for test some tools/features.
[4] _init_kd_tree got its own method in Navigator()

Open Questions:
[1] How to work with polygons. Tool for fast routing.
[2] How to make OSM_routing faster. Multiprocess optimizations.
[3] Own rail LineString -> MultiDiGraph.
[4] Web map with route streaming.
[5] SharedMemory stability - need tests.
Install: 

git clone https://github.com/ignatpenshin/navigation_engine.git
cd navigator
pip install -r requirements.txt

Run: 

python main.py

Usage doc:

  1. main.py will create Navigator class instance and run Navigator.tracking_info() it will emulate train-motion data and build kd-tree for 5 closest points:

  2. closest points are shared with utils.shared_memory.RamBox object so, you can use it that way:

    from utils.shared_memory import RamBox
from time import sleep

data = RamBox(name = "point_routing", role = "r")

while True:
    s = data.read()
    if type(s) == list:
        print(s)
        sleep(1)
    continue

    shared data contains next info (for example):

    data: list[dict[str, int|float|str|tuple[float, float]]]

data = [{'Pose': (37.419485, 55.727001), 'Speed': 13.9, 'Heading': 271.27}, 
        {'id': 341, 'coords': (37.415335, 55.726974),'Tag': "path", 'Description': None,  'direct_m': 260.74}, 
        {'id': 696, 'coords': (37.41496, 55.726992), 'Tag': "platform", 'Description': None, 'direct_m': 284.32}, 
        {'id': 697, 'coords': (37.414956, 55.726977),'Tag': "crossing", 'Description': None,  'direct_m': 284.58}, 
        {'id': 117, 'coords': (37.41485, 55.727057), 'Tag': "tongue", 'Description': None, 'direct_m': 291.3}, 
        {'id': 343, 'coords': (37.414614, 55.726994),'Tag': "station", 'Description': "Усово",  'direct_m': 306.06}
       ]

                ### Data Description ###

# data[0]   -> Train:           Pose (lon, lat: WGS-84), 
#                               Speed (km/h), 
#                               Heading (azimuth to North in degrees)

# data[1:6] -> Point info dict: id (point index from DB), 
#                               coords (lon, lat: WGS-84)
#                               Tag (type: str from classifier)
#                               Description (Optional[str] augmented info)
#                               direct_m (distance to point in meters)

  3. You can check

    python test_shared.py

    to make sure the system is working.

  4. By the way: Sometimes there is no detected points around. So you will take only train coords in shared data list.