Crocodile -- Interferometry Imaging Algorithm Reference Library

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

Warning: The current code is an experimental proof-of-concept. More here soon.

Motivation

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 Crocodile algorithm reference library is designed to present 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.

Requirements

This library is built using Python 3.0. We use the following libraries:

• jupyter - for example notebooks
• numpy - for calculations
• matplotlib - for visualisation
• pyfits - for reading reference data

You will have to install these dependencies, either manually using your package manager of choice or using pip:

 $ pip install -r requirements.txt

Acquiring data

The data directory should hold various test data that can be used to test the functions of this library. However, this is not currently included in the repository, and must be downloaded and extracted separately.

You can either have this done automatically:

$ make -C data

or extract the package found at http://www.mrao.cam.ac.uk/~pw410/crocodile/data.tar.gz manually into the data folder.

Orientation

The content of this project is meant for learning and experimentation, not usage. If you are here to learn about the process of imaging, here is a quick guide to the project:

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

Running Notebooks

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

 $ jupyter notebook examples/notebooks/wkernel.ipynb

Building documentation

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.