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IDIA pipelines

The IDIA MeerKAT Pipeline

The IDIA MeerKAT pipeline is a radio interferometric calibration pipeline designed to process MeerKAT data. It is under regular development, and so far implements cross-calibration, selfcal-calibration, and science imaging. Please report any issues you find in the GitHub issues.

Requirements

This pipeline is designed to run on the Ilifu cluster, making use of SLURM and MPICASA. For other uses, please contact the authors. Currently, use of the pipeline requires access to the Ilifu cloud infrastructure. You can request access using the following form.

Quick Start

Note: It is not necessary to copy the raw data (i.e. the MS) to your working directory. The first step of the pipeline does this for you by creating an MMS or MS, and does not attempt to manipulate the raw data (e.g. stored in /idia/projects - see data format).

1. Setup the pipeline in your environment

In order to use the processMeerKAT.py script, source the setup.sh file, which can be done on ilifu as

    source /idia/software/pipelines/master/setup.sh

which will add the correct paths to your $PATH and $PYTHONPATH in order to correctly use the pipeline. You could consider adding this to your ~/.profile or ~/.bashrc for future use.

2. Build a config file:

a. For continuum/spectral line processing :

    processMeerKAT.py -B -C myconfig.txt -M mydata.ms

b. For polarization processing :

    processMeerKAT.py -B -C myconfig.txt -M mydata.ms -P

c. Including self-calibration :

    processMeerKAT.py -B -C myconfig.txt -M mydata.ms -2

d. Including science imaging :

    processMeerKAT.py -B -C myconfig.txt -M mydata.ms -I

This defines several variables that are read by the pipeline while calibrating the data, as well as requesting resources on the cluster. The config file parameters are described by in-line comments in the config file itself wherever possible. The [-P --dopol] option can be used in conjunction with the [-2 --do2GC] and [-I --science_image] options to enable polarization calibration as well as self-calibration and science imaging.

3. To run the pipeline:

    processMeerKAT.py -R -C myconfig.txt

This will create submit_pipeline.sh, which you can then run with ./submit_pipeline.sh to submit all pipeline jobs to the SLURM queue.

Other convenience scripts are also created that allow you to monitor and (if necessary) kill the jobs.

  • summary.sh provides a brief overview of the status of the jobs in the pipeline
  • findErrors.sh checks the log files for commonly reported errors (after the jobs have run)
  • killJobs.sh kills all the jobs from the current run of the pipeline, ignoring any other (unrelated) jobs you might have running.
  • cleanup.sh wipes all the intermediate data products created by the pipeline. This is intended to be launched after the pipeline has run and the output is verified to be good.

For help, run processMeerKAT.py -h, which provides a brief description of all the command line options.

Using multiple spectral windows (new in v1.1)

Starting with v1.1 of the processMeerKAT pipeline, the default behaviour is to split up the MeerKAT band into several spectral windows (SPWs), and process each concurrently. This results in a few major usability changes as outlined below:

  1. Calibration output : Since the calibration is performed independently per SPW, all the output specific to that SPW is within its own directory. Output such as the calibration tables, logs, plots etc. per SPW can be found within each SPW directory.

  2. Logs in the top level directory : Logs in the top level directory (i.e., the directory where the pipeline was launched) correspond to the scripts in the precal_scripts and postcal_scripts variables in the config file. These scripts are run from the top level before and after calibration respectively. By default these correspond to the scripts to calculate the reference antenna (if enabled), partition the data into SPWs, and concat the individual SPWs back into a single MS/MMS.

More detailed information about SPW splitting is found here.

The documentation can be accessed on the pipelines website, or on the Github wiki.