A collection of python scripts to create high time resolution light curves from calibrated interferometric data sets. These scripts run in the Common Astronomy Software Application (CASA) and have been tested with VLA, SMA and NOEMA data sets.

• VLA: Can be downloaded from archive in CASA format.
• SMA: Can be calibrated in CASA. Follow instructions here for reduction details and details on how to convert to a CASA MS data set for use by these scripts
• NOEMA: Must be calibrated beforehand. Follow instructions here to convert to a CASA MS data set for use by these scripts.

• CASA (get it here, scripts tested with version 4.6.0)
• casa-python executable wrapper (get it here)
  • use this to install these python packages within CASA python
    • jdcal (casa-pip install jdcal)
    • astropy (casa-pip install astropy)
    • astroML (casa-pip install astroML)
• uvmultifit (get it here)
  • this package also needs gsl libraries to build (get them here)
• analysisUtils (get it here)
• (optional) aegean (get it here)
  • this package also needs lmfit-0.7.4 (get it here, install with casa-pip install git+git://github.com/lmfit/lmfit-py.git@0.7.4)

1. Primary script:
   • casa_timing_script.py: intended to be run within CASA. This is the script that does all the hard work.
     • All parameters need to be carefully considered and changed for each new data set.
     • If you have a complicated field with other sources it is recommended that you use your own mask file (with clean boxes around bright sources; mask_option='file') for cleaning, or run object detection, which will create a mask file with a boxed region around each detected source (mask_option='aegean').
     • Included in casa_timing_script.py is the option to run basic variability analysis:
       • Calculate weighted mean and do a chi^2 with a constant flux model,
       • Calculate excess variance (Vaughn et al. 2003; Bell et al., 2015),
       • Calculate fractional RMS (Vaughn et al. 2003; Bell et al., 2015), and
       • Make power spectrum using generalized lomb-periodogram (Zechmeister and Kurster, 2009)
2. Other scripts:
   • Aegean_ObjDet.py: object detection algorithm.
     • Integrated into casa_timing_script.py.
     • Script can also be run on its own, with a fits image as input. Output is a labelled image and region files (DS9 and CASA format) of detected sources.
   • utils.py: module of tools used in casa_timing_script.py.
   • download_data_AWS.py: downloads data from an AWS bucket.
   • upload_data_AWS.py: uploads data to an AWS bucket.
   • remove_bucket_AWS.py: removes a bucket from AWS.
   • sim.py: creates a simulated CASA MS of time-variable source for testing using CASA's simulation toolbox.

• All input parameters for casa_timing_script.py are set in the parameter file (param.txt)
• A complete description of each parameter is provided in param_description.txt.

Support from the SKA/AWS AstroCompute in the Cloud Program