Home

Welcome to the dataqa wiki!

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. Clone the repository to your home

      • Example: git clone https://github.com/apertif/dataqa.git

   4. Go to the cloned directory (e.g., /home/<user>/dataqa)

   5. Run set_up_qa.py

      • Example: python set_up_qa.py <obs_id>
      • This will create the directory structure by default to /home/<user>/qa_science_demo_2019/<obs_id>

   6. Set environment for apercal (if not already done)

      • See https://github.com/apertif/apercal/wiki/Running-apercal-on-happili

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.py <obs_id>
      • The script creates a directory for each beam in /home/<user>/qa_science_demo_2019/<obs_id>/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

   2. Run pybdsf

      • Example: `python run_pybdsf.py <obs_id> --mosaic_name="/path/to/mosaic.fits"
      • The script creates a directory in /home/<user>/qa_science_demo_2019/<obs_id>/mosaic/pybdsf/.

7. Put relevant information into Google-Doc