Installation

You can use EAGLE online or run it locally.

Online: https://eagle.icrar.org
Local: Docker (recommended) or a non-Docker setup

Docker Installation (Recommended)

Docker is the fastest way to run EAGLE locally.

Available image targets:

deployment (dep)
development (dev)
slim (slim)

Deployment image

Build and run:

git clone https://github.com/ICRAR/EAGLE.git
cd EAGLE
./build_eagle.sh dep
./run_eagle.sh dep

Open:

http://localhost:8888

Stop:

stop_eagle.sh dep

Development image

Use this mode while editing and testing:

./build_eagle.sh dev
./run_eagle.sh dev

Notes:

Runs in the foreground.
Watches TypeScript source changes.
Changes under static/ are reflected immediately.
Stop with CTRL+C.

If backend changes are not picked up, restart gunicorn:

docker/restart_gunicorn.sh

Slim image

Used mainly for release packaging and smaller image size. It uses SlimToolkit.

Non-Docker Installation for Local Development

Use this path for local debugging without Docker.

Setup and dependencies

Clone the repository:

git clone https://github.com/ICRAR/EAGLE
cd EAGLE

Install Node.js LTS from https://nodejs.org. On Linux, you can also use:

sudo apt install npm

Install TypeScript globally:

sudo npm install -g typescript

Install JavaScript dependencies:

npm install

Compile TypeScript:

tsc

Or use watch mode while developing:

tsc -w

Create and activate a Python environment (example using pyenv):

pyenv virtualenv -p python3.8 eagle
pyenv activate eagle

Install EAGLE:

pip install .

Run local server

Start EAGLE:

eagleServer -t /tmp

Optional debug mode:

eagleServer -t /tmp --debug

Schema Type Generation

The TypeScript snapshot used for typed V4 JSON loading is generated from the canonical schema in static/lg.graph.v4.schema. If you change that schema, regenerate the snapshot from the repository root with:

npm run generate:lgGraphV4Schema

To verify that the generated snapshot matches the canonical schema, run:

npm run check:lgGraphV4Schema