Applicability
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
virtualenv -p python3 <name-of-virtualenv>
(systems without a Python2 installation don’t even need the -p python3 specifier, but it doesn’t hurt.)
source <name-of-virtualenv>/bin/activate
Pip install crystalball
pip install <path to crystalball>
Run crystalball
Activate the virtual environment where you installed codex-africanus, xarray and xarray-ms (see above)
source <name-of-virtualenv>/bin/activate
Run crystalball
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]
5 maintainers
Metadata
5 maintainers
Release files for crystalball 0.4.3
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| crystalball-0.4.3.tar.gz | 17.6 kB | Without provenance | Details |
Release files / crystalball-0.4.3.tar.gz
| Download URL | crystalball-0.4.3.tar.gz |
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Security for crystalball 0.4.3
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