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Scripts for data QA

Wrapper scripts for specific data QA should live at the top level. There are separate directories for scripts/code related to each type of data QA. E.g.

run_crosscalQA.py
crosscal
->ccal_plots.py

Procedure

1. Initial setup

   1. Log-in to Happili-01
   2. Open a screen session (optional, but recommended)
      • Example: screen -S dataqa
   3. Set environment for apercal (if not already done)
      • See https://github.com/apertif/apercal/wiki/Running-apercal-on-happili
   4. Run set_up_qa.py
      • Example: python set_up_qa.py <obs_id>
      • This will create the directory structure by default to /data/apertif/<TASKID>/qa

2. Run crosscal qa

   • Example: python run_ccal_plots <scannumber> <fluxcal> [-p --/path/to/write/images/too]

3. Run selfcal qa

4. Run continuum qa

   1. Run pybdsf (not yet fully de-bugged)
      • Example: python run_pybdsf_validation.py <obs_id>
      • The script creates a directory for each beam in /data/apertif/<TASKID>/qa/continuum/<beam>/pybdsf/. Otherwise, there would be too many files in a single directory.

5. Run line qa

6. Run mosaic qa

   1. Run mosaic

      • change obs_id in make_mosaic_image.py
      • python make_mosaic_image.py
      • This script will create a directory: /data/apertif//mosaic/ and place the mosaic image there (miriad image + fits image).

   2. Run pybdsf

      • Example: `python run_pybdsf_validation.py <obs_id> --mosaic_name="/path/to/mosaic.fits"
      • The script creates a directory in /data/apertif/<TASKID>/qa/mosaic/pybdsf/.

7. Put relevant information into Google-Doc