python-lametro-api¶

Installation¶

Provided that you have pip installed, you can install the library like so:

$ pip install python-lametro-api

Creating a client¶

Before you can interact with Metro’s data, you first must import the library and initialize a client to talk with the site on your behalf.

>>> from la_metro import LAMetro
>>> client = LAMetro()

Retrieve a bus stop and get predictions for incoming buses¶

>>> obj = client.bus.stops.get(6033)
>>> obj
<BusStop: Santa Monica / Vermont>
>>> obj.predictions
[<BusPrediction: Santa Monica / Vermont (4)>, <BusPrediction: Santa Monica / Vermont (4)>]

Retrieve a bus route and get the location of all stops and vehicles¶

>>> obj = client.bus.routes.get(704)
>>> obj
<BusRoute: 704>
>>> obj.stops
[<BusStop: 2nd / Santa Monica>, <BusStop: Ocean / Santa Monica>, <BusStop: Santa Monica / 4th>, <BusStop: Santa Monica / Lincoln>, <BusStop: Santa Monica / 20th>, <BusStop: Santa Monica / 26th>, <BusStop: Santa Monica / Bundy>, <BusStop: Santa Monica / Barrington>, <BusStop: Santa Monica / Sepulveda>, <BusStop: Santa Monica / Westwood>, <BusStop: Santa Monica / Beverly Glen>, <BusStop: Santa Monica / Ave Of The Stars>, <BusStop: Santa Monica / Wilshire>, <BusStop: Santa Monica / Canon>, <BusStop: Santa Monica / San Vicente>, <BusStop: Santa Monica / La Cienega>, <BusStop: Santa Monica / Sweetzer>, <BusStop: Santa Monica / Fairfax>, <BusStop: Santa Monica / La Brea>, <BusStop: Santa Monica / Highland>, <BusStop: Santa Monica / Vine>, <BusStop: Santa Monica / Western>, <BusStop: Santa Monica / Normandie>, <BusStop: Santa Monica / Vermont>, <BusStop: Sunset / Sanborn>, <BusStop: Sunset / Parkman>, <BusStop: Sunset / Alvarado>, <BusStop: Sunset / Echo Park>, <BusStop: Sunset / Figueroa>, <BusStop: Cesar E Chavez / Grand>, <BusStop: Cesar E Chavez / Spring>, <BusStop: Vignes / Cesar E Chavez>, <BusStop: Terminal 31>]
>>> obj.vehicles
[<BusVehicle: 9364>, <BusVehicle: 9376>, <BusVehicle: 9391>, <BusVehicle: 9380>, <BusVehicle: 9390>, <BusVehicle: 9399>, <BusVehicle: 9373>, <BusVehicle: 9372>, <BusVehicle: 9371>]

Get the location of vehicles¶

Here’s how you can get all vehicles:

>>> obj_list = client.bus.vehicles.all()
>>> len(obj_list)
392
>>> obj_list[0]
<BusVehicle: 7433>

And here’s how to get a single one:

>>> obj = client.bus.vehicles.get(7433)
>>> obj.latitude, obj.longitude
(34.047089, -118.282776)
# Also available with some other mappable attributes
>>> obj.y, obj.x
(34.047089, -118.282776)
>>> obj.wkt
POINT(-118.282776 34.047089)
>>> obj.geojson
{"type": "Point", "coordinates": [-118.282776, 34.047089]}

Stops¶

client.bus.stops.get(id)¶

Return the stop with the provided Metro identifer.

>>> from la_metro import LAMetro
>>> client = LAMetro()
>>> client.bus.stops.get(6033)
<BusStop: Santa Monica / Vermont>

stop_obj.id¶

The identifier in the Metro system

stop_obj.name¶

The name of the bus stop

stop_obj.latitute¶

The y coordinate of the stop’s location

stop_obj.longitude¶

The x coordinate of the stop’s location

stop_obj.y¶

Alias to the latitude of the stop’s location

stop_obj.x¶

Alias to the longitude of the stop’s location

stop_obj.wkt¶

The stop’s location in Well-Known Text format

stop_obj.geojson¶

The stop’s location in GeoJSON format

stop_obj.messages¶

Returns an messages Metro has left for users of this bus stop. This can contain information about service problems and delays.

stop_obj.predictions¶

Returns a list of predictions that guess when busses will next arrive at this stop.

stop_obj.routes¶

Returns a list of the routes that connect with this bus route.

Routes¶

client.bus.routes.all()¶

Return all routes in the Metro system

>>> from la_metro import LAMetro
>>> client = LAMetro()
>>> client.bus.routes.all()
[<BusRoute: 2>, <BusRoute: 4>, <BusRoute: 10>, <BusRoute: 14>, <BusRoute: 16>, <BusRoute: 18>, <BusRoute: 20>, <BusRoute: 26>, <BusRoute: 28>, <BusRoute: 30> ...]

client.bus.routes.get(id)¶

Return the route with the provided Metro identifer.

>>> from la_metro import LAMetro
>>> client = LAMetro()
>>> client.bus.routes.get(704)
<BusRoute: 704>

route_obj.id¶

The identifier in the Metro system

route_obj.name¶

The name of the bus route

route_obj.runs¶

Returns a list of the runs on this bus route.

route_obj.stops¶

Returns a list of the stops on this bus route in their proper order.

route_obj.vehicles¶

Returns a list of the vehicles on this bus route with their latest positions.

Vehicles¶

client.bus.vehicles.all()¶

Return all vehicles out in the Metro system

>>> from la_metro import LAMetro
>>> client = LAMetro()
[<BusVehicle: 3129>, <BusVehicle: 6735>, <BusVehicle: 7433>, <BusVehicle: 6729>, <BusVehicle: 9270>, <BusVehicle: 6758>, <BusVehicle: 7071>, <BusVehicle: 9306>, <BusVehicle: 8206>, <BusVehicle: 8314> ...]

client.bus.vehicles.get(id)¶

Return the vehicle with the provided Metro identifer.

>>> from la_metro import LAMetro
>>> client = LAMetro()
>>> client.bus.vehicles.get(7433)
<BusVehicle: 7433>

vehicle_obj.id¶

The identifier in the Metro system

vehicle_obj.seconds_since_report¶

The time since the data on this vehicle was last updated

vehicle_obj.is_predictable¶

The boolean indicator related to whether or not the busses arrival time can be predicted that I do not understand

vehicle_obj.id

The identifier in the Metro system

vehicle_obj.latitute¶

The y coordinate of the vehicle’s location

vehicle_obj.longitude¶

The x coordinate of the vehicle’s location

vehicle_obj.y¶

Alias to the latitude of the vehicle’s location

vehicle_obj.x¶

Alias to the longitude of the vehicle’s location

vehicle_obj.wkt¶

The vehicle’s location in Well-Known Text format

vehicle_obj.geojson¶

The vehicle’s location in GeoJSON format

vehicle_obj.heading¶

vehicle_obj.route¶

The route the vehicle is on.

vehicle_obj.run¶

The run the vehicle is on.

Runs¶

run_obj.id¶

The identifier in the Metro system

run_obj.name¶

The name of the bus run

run_obj.direction¶

The direction the run is going along the route

run_obj.route¶

The route the run is on.

Predictions¶

prediction_obj.stop¶

The stop this prediction is estimating an arrival for

prediction_obj.route¶

The route the prediction is estimating an arrival for

prediction_obj.run¶

The run the prediction is estimating an arrival for

prediction_obj.minutes¶

The estimated arrival time in minutes

prediction_obj.seconds¶

The estimated arrival time in seconds

prediction_obj.is_departing¶

A boolean indicator I do not understand

0.2¶

• Improved test coverage
• Python 3 fixes
• Automated coverage testing with coveralls.io
• PEP8 and PyFlakes compilance and testing

0.1.4¶

• Alternative GIS formats for longitude and latitude
• Python 3.3 support
• Removed unneeded dependencies
• Travis CI integration

0.1¶

• A rough client that pulls bus data on stops, routes and vehicles.