deconvolve_cube
- ska_sdp_func_python.image.deconvolution.deconvolve_cube(dirty: ~ska_sdp_datamodels.image.image_model.Image, psf: ~ska_sdp_datamodels.image.image_model.Image, sensitivity: ~ska_sdp_datamodels.image.image_model.Image = None, prefix='', **kwargs) -> (<class 'ska_sdp_datamodels.image.image_model.Image'>, <class 'ska_sdp_datamodels.image.image_model.Image'>)[source]
Clean using a variety of algorithms.
The algorithms available are:
hogbom: Hogbom CLEAN See: Hogbom CLEAN A&A Suppl, 15, 417, (1974)
hogbom-complex: Complex Hogbom CLEAN of stokesIQUV image
msclean: MultiScale CLEAN See: Cornwell, T.J., Multiscale CLEAN (IEEE Journal of Selected Topics in Sig Proc, 2008 vol. 2 pp. 793-801)
mfsmsclean, msmfsclean, mmclean: MultiScale Multi-Frequency See: U. Rau and T. J. Cornwell, “A multi-scale multi-frequency deconvolution algorithm for synthesis imaging in radio interferometry,” A&A 532, A71 (2011).
For example:
comp, residual = deconvolve_cube(dirty, psf, niter=1000, gain=0.7, algorithm='msclean', scales=[0, 3, 10, 30], threshold=0.01)
For the MFS clean, the psf must have number of channels >= 2 * nmoment.
- Parameters:
dirty – Image dirty image
psf – Image Point Spread Function
sensitivity – Sensitivity image (i.e. inverse noise level)
prefix – Informational message for logging
window_shape – Window image (Bool) - clean where True
mask – Window in the form of an image, overrides window_shape
algorithm – Cleaning algorithm: ‘msclean’|’hogbom’|’hogbom-complex’|’mfsmsclean’
gain – loop gain (float) 0.7
threshold – Clean threshold (0.0)
fractional_threshold – Fractional threshold (0.01)
scales – Scales (in pixels) for multiscale ([0, 3, 10, 30])
nmoment – Number of frequency moments (default 3)
findpeak – Method of finding peak in mfsclean: ‘Algorithm1’|’ASKAPSoft’|’CASA’|’RASCIL’, Default is RASCIL.
- Returns:
component image, residual image