Many radio-astronomers repeat the process of writing different scripts for self-calibration depending on their datasets. This repository holds an object-oriented Framework for self-calibration of radio-interferometric datasets that will help radio astronomers to minimize the tedious work of writing self-calibration scripts once again. The idea is to call just one main Python script that will run an imager (tclean, wsclean, gpuvmem, rascil, etc.) and one or multiple self-calibration objects (phase, amplitude, amplitude-phase) having the self-calibrated dataset as a result.
Python == 3.8- Check CASA pip current version requirements here.
- Check the
requirements.txtfile.
pip install snow
pip install -U git+https://github.com/miguelcarcamov/snow
git clone https://github.com/miguelcarcamov/snow cd snow pip install .git clone https://github.com/miguelcarcamov/snow cd snow pip install -e .docker pull ghcr.io/miguelcarcamov/snow:latest# Import the modules that you want to use import sys from snow.selfcalibration import Phasecal, AmpPhasecal from snow.imaging import Tclean if __name__ == '__main__': # This step is up to you, and option to capture your arguments from terminal is using sys.argv visfile = sys.argv[3] output = sys.argv[4] want_plot = eval(sys.argv[5]) # Table for automasking on long or short baselines can be found here: https://casaguides.nrao.edu/index.php/Automasking_Guide # The default clean object will use automasking values for short baselines # In this case we will use automasking values for long baselines # Create different imagers with different thresholds (this is optional, you can create just one) clean_imager_phs = Tclean(inputvis=visfile, output=output, niter=100, M=1024, N=1024, cell="0.005arcsec", stokes="I", datacolumn="corrected", robust=0.5, specmode="mfs", deconvolver="hogbom", gridder="standard", savemodel=True, usemask='auto-multithresh', threshold="0.1mJy", sidelobethreshold=3.0, noisethreshold=5.0, minbeamfrac=0.3, lownoisethreshold=1.5, negativethreshold=0.0, interactive=True) clean_imager_ampphs = Tclean(inputvis=visfile, output=output, niter=100, M=1024, N=1024, cell="0.005arcsec", stokes="I", datacolumn="corrected", robust=0.5, specmode="mfs", deconvolver="hogbom", gridder="standard", savemodel=True, usemask='auto-multithresh', threshold="0.025mJy", sidelobethreshold=3.0, noisethreshold=5.0, minbeamfrac=0.3, lownoisethreshold=1.5, negativethreshold=0.0, interactive=True) # This is a dictionary with shared variables between self-cal objects shared_vars_dict = {'visfile': visfile, 'minblperant': 6, 'refant': "DA51", 'spwmap': [ 0, 0, 0, 0], 'gaintype': 'T', 'want_plot': want_plot} # Create your solution intervals solint_phs = ['inf', '600s'] solint_ap = ['inf'] # Create your phasecal object phscal = Phasecal(minsnr=3.0, solint=solint_phs, combine="spw", imager=clean_imager_phs, **shared_vars_dict) # Run it! phscal.run() # If we are happy with the result of the only-phase self-cal we can end the code here, if not... # Create the amplitude-phase self-cal object apcal = AmpPhasecal(minsnr=3.0, solint=solint_ap, combine="", previous_selfcal=phscal, imager=clean_imager_ampphs, **shared_vars_dict) # Run it apcal.run() # Get your splitted final MS apcal.selfcal_output(overwrite=True)Then you can simply run the main script using python yourscript.py <arguments>
