def test_mpccal_ICAL_onesource(self):
self.actualSetup(nsources=1, nvoronoi=1)
model = create_empty_image_like(self.theta_list[0].image)
if rsexecute.using_dask:
progress = None
else:
progress = self.progress
future_vis = rsexecute.scatter(self.all_skymodel_noniso_vis)
future_model = rsexecute.scatter(model)
future_theta_list = rsexecute.scatter(self.theta_list)
result = mpccal_skymodel_list_rsexecute_workflow(future_vis, future_model, future_theta_list,
mpccal_progress=progress,
nmajor=5,
context='2d',
algorithm='hogbom',
scales=[0, 3, 10],
fractional_threshold=0.15, threshold=0.05,
gain=0.1, niter=1000, psf_support=256,
deconvolve_facets=8, deconvolve_overlap=16,
deconvolve_taper='tukey')
(self.theta_list, residual) = rsexecute.compute(result, sync=True)
combined_model = calculate_skymodel_equivalent_image(self.theta_list)
psf_obs = invert_list_rsexecute_workflow([self.all_skymodel_noniso_vis], [model], context='2d', dopsf=True)
result = restore_list_rsexecute_workflow([combined_model], psf_obs, [(residual, 0.0)])
result = rsexecute.compute(result, sync=True)
if self.persist: export_image_to_fits(residual,
rascil_path('test_results/test_mpccal_ical_onesource_residual.fits'))
if self.persist: export_image_to_fits(result[0],
rascil_path('test_results/test_mpccal_ical_onesource_restored.fits'))
if self.persist: export_image_to_fits(combined_model,
rascil_path('test_results/test_mpccal_ical_onesource_deconvolved.fits'))
recovered_mpccal_components = find_skycomponents(result[0], fwhm=2, threshold=0.32, npixels=12)
def max_flux(elem):
return numpy.max(elem.flux)
recovered_mpccal_components = sorted(recovered_mpccal_components, key=max_flux, reverse=True)
assert recovered_mpccal_components[0].name == 'Segment 0', recovered_mpccal_components[0].name
assert numpy.abs(recovered_mpccal_components[0].flux[0, 0] - 1.138095494391862) < 1e-6, \
recovered_mpccal_components[0].flux[0, 0]
newscreen = create_empty_image_like(self.screen)
gaintables = [th.gaintable for th in self.theta_list]
newscreen, weights = grid_gaintable_to_screen(self.all_skymodel_noniso_blockvis, gaintables, newscreen)
if self.persist: export_image_to_fits(newscreen,
rascil_path('test_results/test_mpccal_ical_onesource_screen.fits'))
if self.persist: export_image_to_fits(weights,
rascil_path('test_results/test_mpccal_ical_onesource_screenweights.fits'))
rsexecute.close()
[(residual, 0.0)])
result = rsexecute.compute(result, sync=True)
ical_restored = result[0]
export_image_to_fits(
ical_restored,
rascil_path('test_results/low-sims-mpc-ical-restored_%.1frmax.fits' %
rmax))
#######################################################################################################
# Now set up the skymodels for MPCCAL. We find the brightest components in the ICAL image, remove
# sources that are too close to another stronger source, and then use these to set up
# a Voronoi tesselation to define the skymodel masks
ical_components = find_skycomponents(ical_restored,
fwhm=2,
threshold=args.finding_threshold,
npixels=12)
for comp in all_components[:args.ninitial]:
ical_components.append(comp)
# ### Remove weaker of components that are too close (0.02 rad)
idx, ical_components = remove_neighbouring_components(
ical_components, 0.02)
ical_components = sorted(ical_components,
key=lambda comp: numpy.max(comp.flux),
reverse=True)
print("Voronoi decomposition based on %d point sources" %
len(ical_components))
print(qa_image(ical_restored, context='ICAL restored image'))
show_image(ical_restored,
