Crystalball currently supports prediction from a WSClean list of delta and Gaussian components with (log-) polynomial spectral shape (https://sourceforge.net/p/wsclean/wiki/ComponentList) into the MODEL_DATA column of a measurement set. This is done largely based on code available in the https://github.com/ska-sa/codex-africanus library.
Installation in a virtual environment
Crystalball depends on python-casacore which builds from source. The dependencies mentioned at the following links must be installed in order for the build to succeed:
Create and activate a Python3 virtual environment
Pip install crystalball
pip install <path to crystalball>
Activate the virtual environment where you installed codex-africanus, xarray and xarray-ms (see above)
crystalball <file.ms> [-h] [-sm SKY_MODEL] [-rc ROW_CHUNKS] [-mc MODEL_CHUNKS] [-f FIELD] [-mf MEMORY_FRACTION] [-w REGION_FILE] [-po] [-ns NUM_BRIGHTEST_SOURCES] [-j NUM_WORKERS] [-o OUTPUT_COLUMN]