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)

Create a conda environment with all the dependencies including JAX with optional GPU support.

GPU Enabled

conda env create -n tab_env -f tabascal/env_gpu.yaml

or

CPU Only

conda env create -n tab_env -f tabascal/env_cpu.yaml

Then proceed to activate the conda environment and install tabascal

conda activate tab_env
pip install -e tabascal/

Pure pip install

Alternatively, you can install tabascal with pip alone inside an enivironment of your choice, again, with optional GPU support.

GPU Enabled

pip install -e ./tabascal/[gpu]

or

CPU Only

pip install -e ./tabascal/

GPU

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

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},
}