chrisfinlay / tabascal

TrAjectory BAsed RFI Subtraction and CALibration (TABASCAL) of radio interferometry data. A source to visibility model for astronomical and RFI sources including near-field effects. Visibility data is jointly calibrated with excision of specific RFI contamination.

tabascal

TrAjectory BAsed RFI Subtraction and CALibration (tabascal) of radio interferometry data. A source to visibility model for RFI sources including certain near-field effects. Visibility data is jointly calibrated and cleaned from specific RFI contamination by modelling the RFI signal in the visibilities.

tabascal is written in JAX and Dask and can therefore use GPUs and/or CPUs and be distributed across clusters of these compute units.

Installation

git clone https://github.com/chrisfinlay/tabascal.git

Conda Environment (Recommended)

conda env create -n tab_env -f tabascal/env.yaml
pip install -e tabascal/

GPU

To enable GPU compute you need the GPU version of jaxlib installed. The easiest way is using conda, otherwise, refer to the JAX installation documentation.

conda install jaxlib=*=*cuda* jax cuda-nvcc -c conda-forge -c nvidia

Simulate a contaminated MeerKAT observation

python tabascal/examples/target_observation.py

Help function

python tabascal/examples/target_observation.py --help

Documentation

https://tabascal.readthedocs.io/en/latest/

Citing tabascal

@ARTICLE{Finlay2023,
       author = {{Finlay}, Chris and {Bassett}, Bruce A. and {Kunz}, Martin and {Oozeer}, Nadeem},
        title = "{Trajectory-based RFI subtraction and calibration for radio interferometry}",
      journal = {\mnras},
         year = 2023,
        month = sep,
       volume = {524},
       number = {3},
        pages = {3231-3251},
          doi = {10.1093/mnras/stad1979},
archivePrefix = {arXiv},
       eprint = {2301.04188},
}