ska_sdp_spectral_line_imaging.data_procs.deconvolution.radler module
- ska_sdp_spectral_line_imaging.data_procs.deconvolution.radler.radler_deconvolve_channel(dirty_channel, psf, nx=None, ny=None, cell_size=None, algorithm='multiscale', niter=500, threshold=0.0001, gain=0.7, scales=None, **kwargs)[source]
Perform deconvolution using radler for a given channel data and returns restored model and the residual data
- Parameters:
dirty_channel (numpy.ndarray) -- Dirty channel data
psf (numpy.ndarray) -- PSF for the given channel
nx (int) -- Number of x pixels
ny (int) -- Number of y pixels
cell_size (float) -- Cell size of each pixel in the image
algorithm (str) -- Cleaning algorithm: 'multiscale'|'iuwt'|'more_sane'|'generic_clean'
niter (int) -- Maximum number of iterations
threshold (float) -- Clean threshold
gain (float) -- loop gain (float) 0.7
scales (List[int]) -- Scales (in pixels) for multiscale ([0, 3, 10, 30])
- Returns:
Tuple[numpy.ndarray, numpy.ndarray]
- ska_sdp_spectral_line_imaging.data_procs.deconvolution.radler.radler_deconvolve_cube(dirty, psf, **kwargs)[source]
Note: This documentation copied from ska_sdp_func_python.image.deconvolution.radler_deconvolve_list
Clean using the Radler module, using various algorithms.
The algorithms available are (see: https://radler.readthedocs.io/en/latest/tree/cpp/algorithms.html)
multiscale iuwt more_sane generic_clean
For example:
comp, res = radler_deconvolve_cube(dirty_image, psf_image, nx=256, ny=256, cell_size=1.0, niter=1000, gain=0.7, algorithm='multiscale', scales=[0, 3, 10, 30], threshold=0.01)