Usage Guide

The tubeulator interface to the TfL APIs always returns results in Pydantic data models:

Success

from tubeulator import fetch

response = fetch.stop_point.meta_modes()
coords = Matches[0].model_dump(include=["Lat","Lon"])

{'Lat': 51.52918, 'Lon': -0.132944}

As shown here, you can retrieve regular Python dicts with the model_dump() method, but data models do a lot more besides. Here their primary role is to ensure we always have valid data. Definitions for all data models in tubeulator are generated from the official TfL schemas.