def test_deconvolve_mmclean_quadratic_psf(self):
self.comp, self.residual = deconvolve_cube(self.dirty,
self.psf,
niter=self.niter,
gain=0.1,
algorithm='mmclean',
scales=[0, 3, 10],
threshold=0.01,
nmoments=2,
findpeak='ARL',
fractional_threshold=0.01,
window=self.innerquarter,
psf_support=32)
export_image_to_fits(
self.comp,
"%s/test_deconvolve_mmclean_quadratic_psf-comp.fits" % self.dir)
export_image_to_fits(
self.residual,
"%s/test_deconvolve_mmclean_quadratic_psf-residual.fits" %
self.dir)
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(
self.cmodel,
"%s/test_deconvolve_mmclean_quadratic_psf-clean.fits" % self.dir)
assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_hogbom(self):
self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=10000, gain=0.1, algorithm='hogbom',
threshold=0.01)
export_image_to_fits(self.residual, "%s/test_deconvolve_hogbom-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(self.cmodel, "%s/test_deconvolve_hogbom-clean.fits" % (self.dir))
assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_msclean(self):
self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=1000, gain=0.7, algorithm='msclean',
scales=[0, 3, 10, 30], threshold=0.01)
export_image_to_fits(self.comp, "%s/test_deconvolve_msclean-comp.fits" % (self.dir))
export_image_to_fits(self.residual, "%s/test_deconvolve_msclean-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(self.cmodel, "%s/test_deconvolve_msclean-clean.fits" % (self.dir))
assert numpy.max(self.residual.data) < 1.2
def test_deconvolve_hogbom_subpsf(self):
self.comp, self.residual = deconvolve_cube(self.dirty, psf=self.psf, psf_support=200, window='quarter',
niter=10000, gain=0.1, algorithm='hogbom', threshold=0.01)
export_image_to_fits(self.residual, "%s/test_deconvolve_hogbom_subpsf-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(self.cmodel, "%s/test_deconvolve_hogbom_subpsf-clean.fits" % (self.dir))
assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.2
def test_deconvolve_mmclean_no_taylor_noscales(self):
self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=self.niter, gain=0.1,
algorithm='mmclean',
scales=[0], threshold=0.01, nmoment=1, findpeak='ARL',
fractional_threshold=0.01, window=self.innerquarter)
if self.persist: export_image_to_fits(self.comp, "%s/test_deconvolve_mmclean_notaylor_noscales-comp.fits" % self.dir)
if self.persist: export_image_to_fits(self.residual, "%s/test_deconvolve_mmclean_notaylor_noscales-residual.fits" % self.dir)
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
if self.persist: export_image_to_fits(self.cmodel, "%s/test_deconvolve_mmclean_notaylor_noscales-clean.fits" % self.dir)
assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_mmclean_no_taylor_edge(self):
self.comp, self.residual = deconvolve_cube(self.dirty, self.psf, niter=self.niter, gain=0.1,
algorithm='mmclean',
scales=[0, 3, 10], threshold=0.01, nmoment=1, findpeak='ARL',
fractional_threshold=0.01, window_shape='no_edge', window_edge=32)
export_image_to_fits(self.comp, "%s/test_deconvolve_mmclean_notaylor-comp.fits" % self.dir)
export_image_to_fits(self.residual, "%s/test_deconvolve_mmclean_notaylor-residual.fits" % self.dir)
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(self.cmodel, "%s/test_deconvolve_mmclean_notaylor-clean.fits" % self.dir)
assert numpy.max(self.residual.data) < 3.0
def test_deconvolve_msclean_subpsf(self):
self.comp, self.residual = deconvolve_cube(self.dirty, psf=self.psf, psf_support=200,
window=self.innerquarter, niter=1000, gain=0.7,
algorithm='msclean', scales=[0, 3, 10, 30], threshold=0.01)
export_image_to_fits(self.comp, "%s/test_deconvolve_msclean_subpsf-comp.fits" % (self.dir))
export_image_to_fits(self.residual, "%s/test_deconvolve_msclean_subpsf-residual.fits" % (self.dir))
self.cmodel = restore_cube(self.comp, self.psf, self.residual)
export_image_to_fits(self.cmodel, "%s/test_deconvolve_msclean_subpsf-clean.fits" % (self.dir))
assert numpy.max(self.residual.data[..., 56:456, 56:456]) < 1.0
def main():
dlg_string = os.environ['DLG_UID']
dlg_string = dlg_string[(dlg_string.rindex('_') + 1):len(dlg_string)]
dlg_uid = dlg_string.split('/')
Freq_Iteration = int(dlg_uid[1]) # derived from ID
Facet_Iteration = int(dlg_uid[2]) # derived from ID
vt = load(1)
phasecentre_array = [[+15, -45], [+15.2, -45], [+15, -44], [+14.8, -45],
[+15, -46]]
phasecentre = SkyCoord(ra=phasecentre_array[Facet_Iteration][0] * u.deg,
dec=phasecentre_array[Facet_Iteration][1] * u.deg,
frame='icrs',
equinox='J2000')
model = create_image_from_visibility(vt,
phasecentre=phasecentre,
cellsize=0.001,
npixel=256)
dirty, sumwt = invert_function(vt, model)
psf, sumwt = invert_function(vt, model, dopsf=True)
#show_image(dirty)
print("Max, min in dirty image = %.6f, %.6f, sumwt = %f" %
(dirty.data.max(), dirty.data.min(), sumwt))
print("Max, min in PSF = %.6f, %.6f, sumwt = %f" %
(psf.data.max(), psf.data.min(), sumwt))
export_image_to_fits(
dirty, '%s/imaging_dirty_%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
export_image_to_fits(
psf, '%s/imaging_psf_%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
# Deconvolve using clean
comp, residual = deconvolve_cube(dirty,
psf,
niter=1000,
threshold=0.001,
fracthresh=0.01,
window_shape='quarter',
gain=0.7,
scales=[0, 3, 10, 30])
restored = restore_cube(comp, psf, residual)
export_image_to_fits(
restored, '%s/imaging_clean%02d_%02d.fits' %
(results_dir, Freq_Iteration, Facet_Iteration))
dump(2, restored)
def solve_image_arlexecute(vis: Visibility, model: Image, components=None, context='2d', **kwargs) -> \
(Visibility, Image, Image):
"""Solve for image using deconvolve_cube and specified predict, invert
This is the same as a majorcycle/minorcycle algorithm. The components are removed prior to deconvolution.
See also arguments for predict, invert, deconvolve_cube functions.2d
:param vis:
:param model: Model image
:param predict: Predict function e.g. predict_2d, predict_wstack
:param invert: Invert function e.g. invert_2d, invert_wstack
:return: Visibility, model
"""
nmajor = get_parameter(kwargs, 'nmajor', 5)
thresh = get_parameter(kwargs, "threshold", 0.0)
log.info("solve_image_arlexecute: Performing %d major cycles" % nmajor)
# The model is added to each major cycle and then the visibilities are
# calculated from the full model
vispred = copy_visibility(vis, zero=True)
visres = copy_visibility(vis, zero=True)
vispred = predict_arlexecute(vispred, model, context=context, **kwargs)
if components is not None:
vispred = predict_skycomponent_visibility(vispred, components)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_arlexecute(visres, model, context=context, dopsf=False, **kwargs)
assert sumwt.any() > 0.0, "Sum of weights is zero"
psf, sumwt = invert_arlexecute(visres, model, context=context, dopsf=True, **kwargs)
assert sumwt.any() > 0.0, "Sum of weights is zero"
for i in range(nmajor):
log.info("solve_image_arlexecute: Start of major cycle %d" % i)
cc, res = deconvolve_cube(dirty, psf, **kwargs)
model.data += cc.data
vispred.data['vis'][...]=0.0
vispred = predict_arlexecute(vispred, model, context=context, **kwargs)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_arlexecute(visres, model, context=context, dopsf=False, **kwargs)
if numpy.abs(dirty.data).max() < 1.1 * thresh:
log.info("Reached stopping threshold %.6f Jy" % thresh)
break
log.info("solve_image_arlexecute: End of minor cycles")
log.info("solve_image_arlexecute: End of major cycles")
return visres, model, dirty
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_arlexecute %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_arlexecute %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_arlexecute %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]
def ical_serial(block_vis: BlockVisibility,
model: Image,
components=None,
context='2d',
controls=None,
**kwargs):
""" Post observation image, deconvolve, and self-calibrate
:param vis:
:param model: Model image
:param components: Initial components
:param context: Imaging context
:param controls: calibration controls dictionary
:return: model, residual, restored
"""
nmajor = get_parameter(kwargs, 'nmajor', 5)
log.info("ical_serial: Performing %d major cycles" % nmajor)
do_selfcal = get_parameter(kwargs, "do_selfcal", False)
if controls is None:
controls = create_calibration_controls(**kwargs)
# The model is added to each major cycle and then the visibilities are
# calculated from the full model
vis = convert_blockvisibility_to_visibility(block_vis)
block_vispred = copy_visibility(block_vis, zero=True)
vispred = convert_blockvisibility_to_visibility(block_vispred)
vispred.data['vis'][...] = 0.0
visres = copy_visibility(vispred)
vispred = predict_serial(vispred, model, context=context, **kwargs)
if components is not None:
vispred = predict_skycomponent_visibility(vispred, components)
if do_selfcal:
vis, gaintables = calibrate_function(vis,
vispred,
'TGB',
controls,
iteration=-1)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_serial(visres, model, context=context, **kwargs)
log.info("Maximum in residual image is %.6f" %
(numpy.max(numpy.abs(dirty.data))))
psf, sumwt = invert_serial(visres,
model,
dopsf=True,
context=context,
**kwargs)
thresh = get_parameter(kwargs, "threshold", 0.0)
for i in range(nmajor):
log.info("ical_serial: Start of major cycle %d of %d" % (i, nmajor))
cc, res = deconvolve_cube(dirty, psf, **kwargs)
model.data += cc.data
vispred.data['vis'][...] = 0.0
vispred = predict_serial(vispred, model, context=context, **kwargs)
if do_selfcal:
vis, gaintables = calibrate_function(vis,
vispred,
'TGB',
controls,
iteration=i)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert_serial(visres, model, context=context, **kwargs)
log.info("Maximum in residual image is %s" %
(numpy.max(numpy.abs(dirty.data))))
if numpy.abs(dirty.data).max() < 1.1 * thresh:
log.info("ical_serial: Reached stopping threshold %.6f Jy" %
thresh)
break
log.info("ical_serial: End of major cycle")
log.info("ical_serial: End of major cycles")
restored = restore_cube(model, psf, dirty, **kwargs)
return model, dirty, restored
