Jack-knife

jackknifyis a Python-based package that jackknifes ALMA visibilities to create noise realizations from the observations.

Methodology

Jackknifing is a simplistic but effective tool to quantify the underlying noise distribution of any type of data set. This tool specifically is implemented for interferometric data. jackknify separates half the visibilities randomly in two subsets, then multiplies one half with -1 so that when the data is binned or imaged, any signal present in the data is averaged out. This creates observation-specific noise realization of the data, which can be used to for instance, sample the likelihood a false detection.

The full methodology can be found here and in an upcoming paper, which is still in preparation.

Installation

jackknify itself can be installed through

pip install jackknify

or alternatively

python -m pip install git+https://github.com/Joshiwavm/jackknify

or from the source

git clone https://github.com/Joshiwavm/jackknify
cd jackknify
pip install -e .

Dependancies

jackknify uses casatask and casatools to interface with CASA measurements. casatask and casatools requires casadata to load. Sadly, this is a ~350 MB sized file. Further, when performing line searches, we make use of the package interferopy, which is a Python-based package for common tasks used in the observational radio/mm interferometry data analysis.

Mac

If you want to run jackknify on a Mac with an Apple Silicon chip, run it in a Rosetta terminal. To open a Rosetta session in your terminal, run:

/usr/bin/arch -x86_64 /bin/zsh --login

Documentation

For your convenience, there are notebooks on how to run jackknify. You can find them in the docs/notebooks folder. Also, check out the documentation here.