CTTS

note: this is alpha software; apis may change quickly, and quality of the brightness prediction is still being ironed out

CTTS is an open source application for reading sky brightness all over the earth's landmass, without a sensor.

about

This project is motivated by wanting an answer to the following question:

how much better are the stars in the Atacama than in my backyard?

It would be infeasible to have sensors everywhere you would want a sky brightness measurement, but one path is to do inference of this value.

The approach this project takes is:

• use pytorch to capture the relationship between various factors and sky brightness within a public dataset
• put that behind an gRPC API
• discretize the world using H3
• continuously get predicted sky brightness for those H3 cells from the gRPC API

todos

• update open meteo data while app is running
• create typer app to manage brightness model
• brightness prediction cycle can push alerts

running with docker

• git clone
• cd into the repo
• run the following:

# create the volume for weather data
docker volume create --name open-meteo-data

# get latest data into the above volume
./update-open-meteo-data.sh

# build and run the containers
docker compose up --build

After rabbitmq starts up, the producer and consumer containers will start up, at which point you should see output like this:

producer-1   | 2024-12-21 17:08:55,237 [INFO] publishing {'uuid': '0cdacdcb-dcf3-4d5c-9e60-94d397d89840', 'lat': 69.66345294982115, 'lon': -30.968044606549025, 'h3_id': '8007fffffffffff', 'mpsas': 24.703824996948242, 'timestamp_utc': '2024-12-21T17:08:55.236185+00:00'} to brightness.prediction
producer-1   | 2024-12-21 17:08:55,355 [INFO] publishing {'uuid': 'f16a7b7c-039d-44d6-b764-fc37fadad1b7', 'lat': 26.80710329336693, 'lon': 109.167486033384, 'h3_id': '8041fffffffffff', 'mpsas': 10.82265853881836, 'timestamp_utc': '2024-12-21T17:08:55.354661+00:00'} to brightness.prediction
producer-1   | 2024-12-21 17:08:55,356 [INFO] publishing {'start_time_utc': '2024-12-21T17:08:34.174937+00:00', 'end_time_utc': '2024-12-21T17:08:55.356353+00:00', 'duration_s': 21} to brightness.cycle
producer-1   | 2024-12-21 17:08:55,502 [INFO] publishing {'uuid': 'bc236db7-dd78-43cb-925b-78ea7c777f5e', 'lat': 16.702868303031234, 'lon': -13.374845104752373, 'h3_id': '8055fffffffffff', 'mpsas': 6.5024333000183105, 'timestamp_utc': '2024-12-21T17:08:55.501490+00:00'} to brightness.prediction
consumer-1   | 2024-12-21 17:08:55,507 [INFO] cycle completed with max observation {'uuid': '0fbfe7cd-4b49-49b3-9c51-b5560706a2d8', 'lat': -69.66345294982115, 'lon': 149.03195539345094, 'h3_id': '80edfffffffffff', 'mpsas': 28.068134307861328, 'timestamp_utc': datetime.datetime(2024, 12, 21, 17, 8, 53, 2272, tzinfo=datetime.timezone.utc)}

The above output means:

1. the producer container is publishing the brightness readings it is getting from the api container

2. the consumer container has determined which reading made during the last cycle through H3 cells had the highest brightness (mpsas is the measure of brightness spread over a square arcsecond of sky, where higher means darker sky with more stars visible)

changing the resolution

By default, the producer runs over all resolution 0 cells, but this can be adjusted by setting the environment of the producer container in docker-compose.yaml:

producer:
  build: ./pp
  environment:
    RESOLUTION: 0

documentation

• how to write your own client for the sky brightness gRPC api

licensing

This project is licensed under the AGPL-3.0 license.