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

sim-target

Help function

sim-target --help

Configuration file based simulations and analysis

tabascal now includes the facility to define a simulation using a YaML configuration file. There is a general command line interface to run these simulations allowing one to change certain parameters on the file as well as in the configuration file. All input data is copied into the output simulation directory to allow one to run an identical simulation with ease. Inside tabascal/examples/yaml_obs are a set of config files to get you started. There are also example data files which are used for including predefined astronomical and rfi models. They are all csv files with file extensions to help distinguish them. To run a simulation of a target field with 100 randomly distributed point sources (just like is shown above) simply run

sim-vis -c target_obs.yaml

Again, as before, you can run the help function to see what other command line options there are.

sim-vis -h

Downstream analysis such as flagging, RFI subtraction, imaging, and source extraction can be performed through such configuration files as well. This is currently still in development where the tabascal RFI subtraction algorithm itself is not yet publically available. However, a full end to end analysis pipeline is available. Individual portions can be accessed through the command line scripts: flag-data, image, and src-extract, with example configs in tabascal/analysis/yaml_configs/target. All three of these can be perfomed in a single command line script by using extract. See the help documentation of these scripts for further details.

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},
}