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 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_serial_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_serial_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_serial_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 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)
pol_frame = PolarisationFrame("stokesIQUV")
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_subimage(dirty, psf):
assert isinstance(dirty, Image)
assert isinstance(psf, Image)
comp = deconvolve_cube(dirty, psf, **kwargs)
return comp[0]
bvt.configuration.diameter[...] = 35.0
vt = convert_blockvisibility_to_visibility(bvt)
vt = convert_visibility_to_stokes(vt)
cellsize = 20.0 * numpy.pi / (180.0 * 3600.0)
npixel = 512
model = create_image_from_visibility(vt, cellsize=cellsize, npixel=npixel,
polarisation_frame=PolarisationFrame('stokesIQUV'))
dirty, sumwt = invert_list_serial_workflow([vt], [model], context='2d')[0]
psf, sumwt = invert_list_serial_workflow([vt], [model], context='2d', dopsf=True)[0]
export_image_to_fits(dirty, '%s/compare_imaging_sim2_dirty.fits' % (results_dir))
export_image_to_fits(psf, '%s/compare_imaging_sim2_psf.fits' % (results_dir))
# Deconvolve using clean
comp, residual = deconvolve_cube(dirty, psf, niter=10000, threshold=0.001, fractional_threshold=0.001,
window_shape='quarter', gain=0.7, scales=[0, 3, 10, 30])
restored = restore_cube(comp, psf, residual)
export_image_to_fits(restored, '%s/compare_imaging_sim2_restored.fits' % (results_dir))
export_image_to_fits(residual, '%s/compare_imaging_sim2_residual.fits' % (results_dir))
qa = qa_image(restored)
assert numpy.abs(qa.data['max'] - 1.006140596404203) < 1e-7, qa
assert numpy.abs(qa.data['maxabs'] - 1.006140596404203) < 1e-7, qa
assert numpy.abs(qa.data['min'] + 0.23890808520954754) < 1e-7, qa
assert numpy.abs(qa.data['rms'] - 0.007366519782047875) < 1e-7, qa
assert numpy.abs(qa.data['medianabs'] - 0.0005590537883509844) < 1e-7, qa
except ModuleNotFoundError as error:
print(error.__class__.__name__ + ": " + error.msg)
pass