Crocodile -- Interferometry Imaging Algorithm Reference Library

This is a project to create a reference code in NumPy for somewhat simplified aperture synthesis imaging. Check the AW-gridding kernel work description for information about kernel prototyping efforts based on this repository.

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

Motivation

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

We are using GitHub's large file storage (LFS) to store data files. To pupluate the data/ directory you will need to have it installed and activated for this repository. So for example:

    $ git lfs install
    $ git lfs pull

After git-lfs has finished downloading, the required files should now appear in the data/ directory.

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.