deconvolution-example.py

#! /usr/bin/python

# run this e.g. with:
# wsclean -niter 10000 -save-first-residual -auto-threshold 3 -mgain 0.8 -interval 10 11 -size 1024 1024 -scale 1amin -python-deconvolution ~/projects/wsclean/scripts/deconvolution-example.py 1052736496-averaged.ms/

import numpy

def deconvolve(residual, model, psf, meta):
    nchan=residual.shape[0]
    npol=residual.shape[1]
    height=residual.shape[2]
    width=residual.shape[3]
    print("Starting Python deconvolve() function for "+str(width) + " x " + str(height) \
          + " x " + str(npol) + " x " + str(nchan) + " dataset")
    
    # residual and model are a numpy arrays with dimensions nchan x npol x height x width
    # psf is a numpy arrays with dimensions nchan x height x width
    
    # This file demonstrates a very simple deconvolution strategy, which doesn't
    # support multiple channels or polarizations:
    if nchan!=1 or npol!=1:
        raise NotImplementedError('nchan and npol must be one')
    
    # meta contains several useful meta data:
    # meta.channels is an array, each element has properties 'frequency' and 'weight'
    # meta.square_joined_channels: boolean, true if joined channels are to be squared.
    # meta.spectral_fitter is an object that knows how the user requested spectral fitting,
    # and is also aware of the channel frequencies / weights. It
    # has methods 'fit' and 'fit_and_evaluate'. Example:
    # Furthermore, the meta class has properties major_iter_threshold, final_threshold,
    # mgain, iteration_number and max_iterations. These are demonstrated below.
    # iteration_number can be modified, and will keep its value when deconvolve()
    # is called again.
    
    # values = numpy.zeros(nchan, dtype=numpy.float64)
    # coefficients = meta.spectral_fitter.fit(values)
    # or:
    # values = meta.spectral_fitter.fit_and_evaluate(values)
    
    # find the largest peak
    peak_index = numpy.unravel_index(numpy.argmax(residual), residual.shape)
    peak_value = residual[peak_index]
    print peak_value
    print peak_index
    
    mgain_threshold = peak_value * (1.0-meta.mgain)
    first_threshold = max(meta.major_iter_threshold, meta.final_threshold, mgain_threshold)
    
    print("Starting iteration " + str(meta.iteration_number) + ", peak=" + str(peak_value) \
          + ", first threshold=" + str(first_threshold))
    while peak_value > first_threshold and meta.iteration_number < meta.max_iterations:
    
        model[peak_index] += peak_value
	
        psf_shift = (peak_index[2]+height//2, peak_index[3]+width//2)
        print psf_shift
        # In Python 3, roll can be applied on both axes at once, but on Dawn that results in an error
        residual = residual - peak_value * numpy.roll( numpy.roll( psf, psf_shift[0], axis=1 ) , psf_shift[1], axis=2 )

        peak_index = numpy.unravel_index(numpy.argmax(residual), residual.shape)
        peak_value = residual[peak_index]
        
        meta.iteration_number = meta.iteration_number+1

    print("Stopped after iteration " + str(meta.iteration_number) + ", peak=" + str(peak_value))

    # Fill a dictionary with values that wsclean expects:
    result = dict()
    result['residual'] = residual
    result['model'] = model
    result['level'] = peak_value
    result['continue'] = peak_value > meta.final_threshold and meta.iteration_number < meta.max_iterations
    
    print("Finished deconvolve()" )
    return result