pydantic tfl api

I originally used TfL-python-api by @dhilmathy but that verision depends on the msrest package, which has been deprecated for 2+ years. I have created this package to replace it, using pydantic and requests.

This API returns data from the TfL API in a more pythonic way, using pydantic models. It's a thin wrapper around the TfL API, so you can use the TfL API documentation to see what data is available.

Installation

pip install pydantic-tfl-api

or

poetry add pydantic-tfl-api

Usage

Uses Pydantic so you can use the model_dump_json() method to fully expand all the objects in the result. See Pydantic documentation for more help.

You can obtain an API key from your profile page on the API portal although you only need this if doing more than a dozen or so requests per minute.

from pydantic_tfl_api.client import Client

token = "your_secret_token from https://api-portal.tfl.gov.uk/profile"

client = Client(token)
print (client.get_line_meta_modes())
print (client.get_lines(mode="bus")[0].model_dump_json())
print (client.get_lines(line_id="victoria")[0].model_dump_json())
print (client.get_route_by_line_id_with_direction(line_id="northern", direction="all").model_dump_json())

Class structure

The Pydantic classes are in the tfl.models module. The tfl.client module contains the Client class, which is the main class you will use to interact with the API.

Pydantic models are used to represent the data returned by the TfL API. There is a circular reference in the TfL API, so I handled this in the StopPoint model to load the Line model only after StopPoint is fully loaded.

The following objects represent responses from the TfL API, and are therefore returned by the Client class methods - either individually or as an array of objects:

• StopPoint
• Mode
• Line
• RouteSequence
• Disruption
• StopPointsResponse
• Prediction

These objects contains two properties content_expires and shared_expires, which are the calculated expiry based on the HTTP response timestamp and the maxage/s-maxage header respectively. You can use these to calculate the time to live of the object, and to determine if the object is still valid - for example if implementing caching.

Here's a Mermaid visualisation of the Pydantic models (or view online):

classDiagram
    direction LR
    class AffectedRoute
    class RouteSectionNaptanEntrySequence
    AffectedRoute ..> RouteSectionNaptanEntrySequence

    class PredictionTiming
    class Prediction
    Prediction ..> PredictionTiming 

    class Crowding
    class PassengerFlow
    class TrainLoading
    Crowding ..> PassengerFlow
    Crowding ..> TrainLoading

    class Identifier
    Identifier ..> Crowding

    class OrderedRoute
    class RouteSequence
    class MatchedStop
    RouteSequence ..> MatchedStop
    RouteSequence ..> OrderedRoute

    class ApiError
    class Mode


    class Line
    Line ..> Disruption
    Line ..> LineStatus
    Line ..> RouteSection
    Line ..> ServiceType
    Line ..> Crowding

    class RouteSection
    class ServiceType
    class ValidityPeriod
    class LineStatus
    class Disruption

    class StopPoint
    class StopPointsResponse
    class AdditionalProperties
    class StopPointsResponse
    class LineModeGroup
    class LineGroup



    LineStatus ..> ValidityPeriod
    LineStatus ..> Disruption

    Disruption ..> AffectedRoute
    MatchedStop ..> Identifier 

    RouteSectionNaptanEntrySequence ..> StopPoint

    StopPoint ..> LineGroup
    StopPoint ..> LineModeGroup
    StopPoint ..> AdditionalProperties
    StopPoint ..> Line
    StopPointsResponse ..> StopPoint

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Development environment

The devcontainer is set up to use the poetry package manager. You can use the poetry commands to manage the environment. The poetry.lock file is checked in, so you can use poetry install --with dev --no-interaction --sync --no-root to install the dependencies (which the devcontainer does on the postCreateCommand command).