CUT Prototype INFRARED WRAPPER API

This API wraps the INFRARED.CITY service. It offers an OGC-API-Processes conform service, with geojson based inputs/outputs, while handling all communication with INFRARED.

In general this is a celery-based app using 3 containers each: api,worker, redis. The api container excepts requests and creates tasks in the redis db. Workers look for tasks in the redis db, calculate result and publish it in redis. Results (also cached ones) can be accessed via the api container that get’s the result from the redis db.

Wind-Comfort AND Sunlight-Hour Simulations

TILING

The maximum simulatable area of an INFRARED simulation is 500m500m . However results in the egdes of these areas are unreliable as the simulation is unaware of the neighboring building context. If the total extend of the submitted buildings geojson input is less than 500m500m the input gets simulated as 1 single tile. Larger areas are split into overlapping bboxes. A celery-group task is created and each bbox is simulated as an independent project at INFRARED. Results are merged upon collection.

Example for splitting the buildings input into multiple overlapping bboxes.

SUNLIGHT-HOURS

See SWAGGER documentation on /infrared/docs See process description on /processes/sunlight-hours

RUN SIMULATION @router.post("/processes/sunlight-hours/execution")

Inputs:

• {"buildings": buildings } with buildings as a geojson with "building_height" attribute. example buidlings: infrared_wrapper_api/models/jsons/buildings.json

WIND-COMFORT

See SWAGGER documentation on /infrared/docs See process description on /processes/sunlight-hours

RUN SIMULATION @router.post("/processes/sunlight-hours/execution")

Inputs:

• { "buildings": buildings, "wind_speed": int "wind_direction": int } with buildings as a geojson with "building_height" attribute. example buidlings: infrared_wrapper_api/models/jsons/buildings.json

Example result:

SUNLIGHT HOURS RESULTS

Returned as geojson with each feature having a attribute "value".

value	sunlight hours average
0.0	"< 1.2 h/day"
0.1	"2.4 h/day"
0.2	"3.6 h/day"
0.3	"4.8 h/day"
0.4	"6 h/day"
....
1.0	"12 h/day "

Wind Comfort Result

Returned as geojson with each feature having a attribute "value". The "wind comfort" service predicts a plane of Lawson Criteria categories, given an input wind direction and speed. The returned normalised values represent categories as seen in the following table:

value	lawson criteria category
0.0	"Sitting Long"
0.2	"Sitting Short"
0.4	"Walking Slow"
0.6	"Walking Fast"
0.8	"Uncomfortable"
1.0	"Dangerous"

Local Dev

Initial Setup

The CUT Prototype Infrared Wrapper API V2 is run on Docker, however it is still necessary to create a Python virtual environment to run tests and enable linting for pre-commit hooks. Run the following command to set up your environment:

$ make venv

This command will create a virtualenv, install all dependencies including pre-commit hooks and create a .env file based on ./.env.example.

After the command runs, make sure to adapt your .env file with secure secrets, etc. If your IDE does not activate your virtualenv automatically, run:

$ source .venv/bin/activate

Important

This repository uses Makefile to run commands, in case you can't use Make, just run the correspondent commands as in this file.

Running the API

To run the API:

$ make start

After the image is built and containers initialise, you can access the following in your browser:

Service	URL	Access
Swagger UI	http://0.0.0.0:8002/infrared/docs	Not password protected
Redoc	http://0.0.0.0:8002/infrared/redoc	Not password protected
OpenAPI	http://0.0.0.0:8002/infrared/openapi.json	Not password protected

Tests

To run the Docker container in interactive mode:

make test-it

Once the container terminal is available, to run tests:

pytest

To run tests only, without interactive mode:

make test-docker

Formating/ linting code

$ make fmt

$ make lint