def deconvolve(dirty, psf, model, facet, gthreshold, msk=None):
if prefix == '':
lprefix = "subimage %d" % facet
else:
lprefix = "%s, subimage %d" % (prefix, facet)
if nmoment > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(
moment0.data[0, ...])) / dirty.data.shape[0]
else:
ref_chan = dirty.data.shape[0] // 2
this_peak = numpy.max(numpy.abs(dirty.data[ref_chan, ...]))
if this_peak > 1.1 * gthreshold:
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty,
psf,
prefix=lprefix,
mask=msk,
**kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
return result
else:
return copy_image(model)
def threshold_list(imagelist, threshold, fractional_threshold, use_moment0=True, prefix=''):
""" Find actual threshold for list of results, optionally using moment 0
:param imagelist:
:param threshold: Absolute threshold
:param fractional_threshold: Fractional threshold
:param use_moment0: Use moment 0 for threshold
:return:
"""
peak = 0.0
for i, result in enumerate(imagelist):
if use_moment0:
moments = calculate_image_frequency_moments(result)
this_peak = numpy.max(numpy.abs(moments.data[0, ...] / result.shape[0]))
peak = max(peak, this_peak)
log.info("threshold_list: using moment 0, sub_image %d, peak = %f," % (i, this_peak))
else:
ref_chan = result.data.shape[0] // 2
this_peak = numpy.max(numpy.abs(result.data[ref_chan]))
peak = max(peak, this_peak)
log.info("threshold_list: using refchan %d , sub_image %d, peak = %f," % (ref_chan, i, this_peak))
actual = max(peak * fractional_threshold, threshold)
if use_moment0:
log.info("threshold_list %s: Global peak in moment 0 = %.6f, sub-image clean threshold will be %.6f" % (prefix,
peak,
actual))
else:
log.info("threshold_list %s: Global peak = %.6f, sub-image clean threshold will be %.6f" % (prefix, peak,
actual))
return actual