def deconvolve(dirty, psf, model, facet, gthreshold, msk=None):
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %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 load_invert_and_deconvolve(c):
v1 = create_visibility_from_ms(input_vis[0], channum=[c])[0]
v2 = create_visibility_from_ms(input_vis[1], channum=[c])[0]
vf = append_visibility(v1, v2)
vf = convert_visibility_to_stokes(vf)
vf.configuration.diameter[...] = 35.0
rows = vis_select_uvrange(vf, 0.0, uvmax=uvmax)
v = create_visibility_from_rows(vf, rows)
m = create_image_from_visibility(v, npixel=npixel, cellsize=cellsize,
polarisation_frame=pol_frame)
if context == '2d':
d, sumwt = invert_2d(v, m, dopsf=False)
p, sumwt = invert_2d(v, m, dopsf=True)
else:
d, sumwt = invert_list_serial_workflow([v], [m], context=context, dopsf=False,
vis_slices=vis_slices)[0]
p, sumwt = invert_list_serial_workflow([v], [m], context=context, dopsf=True,
vis_slices=vis_slices)[0]
c, resid = deconvolve_cube(d, p, m, threshold=0.01, fracthresh=0.01, window_shape='quarter',
niter=100, gain=0.1, algorithm='hogbom-complex')
r = restore_cube(c, p, resid, psfwidth=psfwidth)
return r
def deconvolve(d, p, m):
import time
c, resid = deconvolve_cube(d[0],
p[0],
m,
threshold=0.01,
fracthresh=0.01,
window_shape='quarter',
niter=100,
gain=0.1,
algorithm='hogbom-complex')
r = restore_cube(c, p[0], resid)
return r
def deconvolve(dirty, psf, model, facet, gthreshold):
import time
starttime = time.time()
if prefix == '':
lprefix = "facet %d" % facet
else:
lprefix = "%s, facet %d" % (prefix, facet)
nmoments = get_parameter(kwargs, "nmoments", 0)
if nmoments > 0:
moment0 = calculate_image_frequency_moments(dirty)
this_peak = numpy.max(numpy.abs(
moment0.data[0, ...])) / dirty.data.shape[0]
else:
this_peak = numpy.max(numpy.abs(dirty.data[0, ...]))
if this_peak > 1.1 * gthreshold:
log.info(
"deconvolve_list_arlexecute_workflow %s: cleaning - peak %.6f > 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
kwargs['threshold'] = gthreshold
result, _ = deconvolve_cube(dirty, psf, prefix=lprefix, **kwargs)
if result.data.shape[0] == model.data.shape[0]:
result.data += model.data
else:
log.warning(
"deconvolve_list_arlexecute_workflow %s: Initial model %s and clean result %s do not have the same shape"
% (lprefix, str(
model.data.shape[0]), str(result.data.shape[0])))
flux = numpy.sum(result.data[0, 0, ...])
log.info(
'### %s, %.6f, %.6f, True, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, flux, time.time() - starttime))
return result
else:
log.info(
"deconvolve_list_arlexecute_workflow %s: Not cleaning - peak %.6f <= 1.1 * threshold %.6f"
% (lprefix, this_peak, gthreshold))
log.info(
'### %s, %.6f, %.6f, False, %.3f # cycle, facet, peak, cleaned flux, clean, time?'
% (lprefix, this_peak, 0.0, time.time() - starttime))
return copy_image(model)
def deconvolve_subimage(dirty, psf):
assert isinstance(dirty, Image)
assert isinstance(psf, Image)
comp = deconvolve_cube(dirty, psf, **kwargs)
return comp[0]
