Algorithm Reference Library

This is a project to create a reference code in NumPy for aperture synthesis imaging.

In many software packages, the only function specification is the application code itself. Although the underlying algorithm may be documented (e.g. published), the implementation tends to diverge overtime, making this method of documentation less effective.

The Algorithm Reference Library is designed to present calibration and imaging algorithms in a simple Python-based form. This is so that the implemented functions can be seen and understood without resorting to interpreting source code shaped by real-world concerns such as optimisations.

The actual excutable code may be accessed directly from the documentation.

• Install git and git-lfs if not already installed. Note that git-lfs is required for some data files

• 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 package::

  pip install -r requirements.txt

• Setup ARL::

  python setup.py install

• Get the data files form Git LFS::

  git-lfs pull

The prime focus of the ARL is on learning and experimentation, not usage. If you are here to learn about the process of imaging, here is a quick guide to the project:

• arl: The main Python source code
• examples: Usage examples, mainly using Jupyter notebooks.
• tests: Unit and regression tests
• docs: Complete documentation. Includes non-interactive output of examples.
• data: Data used

Jupyter notebooks end with .ipynb and can be run as follows from the command line:

 $ jupyter notebook examples/notebooks/imaging.ipynb

The last build documentation is at:

http://www.mrao.cam.ac.uk/projects/jenkins/algorithm-reference-library/docs/build/html/index.html

For building the documentation you will need Sphinx as well as Pandoc. This will extract docstrings from the crocodile source code, evaluate all notebooks and compose the result to form the documentation package.

You can build it as follows:

$ make -C docs [format]

Omit [format] to view a list of documentation formats that Sphinx can generate for you. You probaby want dirhtml.