Quick start

Installation

Installation should be straightforward. We recommend the use of virtual environment. A prepackaged python system such as Anaconda https://www.anaconda.com is usually best as a base.

ARL requires python 3.6 or higher.

# Use git to make a local clone of the Github respository:

git clone https://github.com/SKA-ScienceDataProcessor/algorithm-reference-library

# Change into that directory:

cd algorithm-reference-library

# Install required python packages:

pip install -r requirements.txt

There may be some dependencies that require either conda (or brew install on a mac).

# Setup ARL:

python setup.py install

# Get the data files form Git LFS:

git-lfs pull

The README.md file contains much more information about installation.

Running notebooks

The best way to get familiar with ARL is via jupyter notebooks. For example:

jupyter-notebook imaging.ipynb

See the jupyter note books below:

• Imaging and deconvolution demonstration
• Demonstrate full circle wide field imaging
• Wide-field imaging demonstration
• Simple demonstration of the use of arlexecute
• Imaging and deconvolution demonstration
• Pipeline processing using arlexecute workflows.
• Set up and run a simple real-time calibration pipeline, RCAL.

In addition, there are other notebooks that are not built as part of this documentation.