bluebird-dt 0.1.0

A simulation and AI development framework for air traffic control.

The bluebird_dt digital twin.

The python package bluebird_dt encodes a digital twin of an airspace, including classes that represent:

• The geometry of the airspace - Sectors, Volumes, Airways, Fixes, ...
• Aircraft, with properties such as location, heading, flight level, ...
• Predictors, to model how the aircraft parameters evolve with the simulation.
• Action, defining the schema by which agents can interact with the simulation.
• Infrastructure classes such as Simulator, ScenarioManagers, EventHandlers, logger, to allow the user to define and run simulated ATC scenarios.

The user will typically instantiate a Simulator, providing a scenario category and a scenario name. The Simulator instance can then evolve through finite time steps (typically 6 seconds), and at each step it can receive Actions, and provide an Environment, which contains the full instantaneous representation of the simulation state. This basic loop is demonstrated in the example notebook simulation.ipynb.

Quickstart

Prerequisites

We recommend using the uv, package manager to install and run bluebird_dt. Installation instructions for UV are available here.

Installation

Clone the BluebirdATC repository, and cd to this directory:

git clone https://github.com/project-bluebird/BluebirdATC
cd BluebirdATC/bluebird-dt

You can then install all the dependencies by running the command

uv sync

(though they will anyway be installed when needed if you do uv run before any other command, e.g. uv run python).

Documentation

The online documentation for the bluebird_dt package can be found by running the command:

../scripts/docs-serve

from this directory, then navigating a web browser to http://localhost:8010.

Notebooks

Various examples of using the bluebird_dt package can be found in the form of Jupyter notebooks in the [examples] directory. To run these, type:

uv run jupyter notebook

then navigating a web browser to http://localhost:8888 (if it doesn't open automatically).

Running the digital twin as a server.

A FastApi app is included, allowing the simulation to be run as a server, with the user (or an agent) interacting via a REST API. For information on this, see here.