Getting started

This page contains instructions for software developers who want to get started with usage and development of the Dish Leaf Node.


Detailed information on how the SKA Software development community works is available at the SKA software developer portal. There you will find guidelines, policies, standards and a range of other documentation.

Set up your development environment

This project is structured to use k8s for development and testing so that the build environment, test environment and test results are all completely reproducible and are independent of host environment. It uses make to provide a consistent UI (run make help for targets documentation).

Install minikube

You will need to install minikube or equivalent k8s installation in order to set up your test environment. You can follow the instruction here:

git clone
cd deploy-minikube
make all
eval $(minikube docker-env)

Please note that the command `eval $(minikube docker-env)` will point your local docker client at the docker-in-docker for minikube. Use this only for building the docker image and another shell for other work.

How to Use

Clone this repo:

git clone
cd ska-tmc-dishleafnode

Install dependencies

apt update
apt install -y curl git build-essential libboost-python-dev libtango-dev
curl -sSL | python3 -
source $HOME/.poetry/env
Please note that:
  • the libtango-dev will install an old version of the TANGO-controls framework (9.2.5);

  • the best way to get the framework is compiling it (instructions can be found here);

  • the above script has been tested with Ubuntu 20.04.

During this step, `libtango-dev` instalation can ask for the Tango Server IP:PORT. Just accept the default proposed value.

Install python requirements for linting and unit testing:

$ poetry install

Activate the poetry environment:

$ source $(poetry env info --path)/bin/activate

Follow the steps till installation of dependencies then run below command:

$ virtualenv cn_venv
$ source cn_venv/bin/activate
$ make requirements

Run python-test:

$ make python-test
PyTango 9.3.3 (9, 3, 3)
PyTango compiled with:
Python : 3.8.5
Numpy  : 0.0.0 ## output generated from a WSL windows machine
Tango  : 9.2.5
Boost  : 1.71.0

PyTango runtime is:
Python : 3.8.5
Numpy  : None
Tango  : 9.2.5

PyTango running on:
uname_result(system='Linux', node='LAPTOP-5LBGJH83', release='4.19.128-microsoft-standard', version='#1 SMP Tue Jun 23 12:58:10 UTC 2020', machine='x86_64', processor='x86_64')

============================= test session starts ==============================
platform linux -- Python 3.8.5, pytest-5.4.3, py-1.10.0, pluggy-0.13.1 -- /home/

--------------------------------- JSON report ----------------------------------
JSON report written to: build/reports/report.json (165946 bytes)

----------- coverage: platform linux, python 3.8.5-final-0 -----------
Coverage HTML written to dir build/htmlcov
Coverage XML written to file build/reports/code-coverage.xml

======================== 48 passed, 5 deselected in 42.42s ========================

Formatting the code:

$ make python-format
Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)

Python linting:

$ make python-lint
Your code has been rated at 10.00/10 (previous run: 10.00/10, +0.00)