A Pydantic-based wrapper for the TfL Unified API https://api.tfl.gov.uk/. Not associated with or endorsed by TfL.
Project description
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
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).
Project details
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