def _checkcomponents(self, dirty, fluxthreshold=0.6, positionthreshold=1.0):
comps = find_skycomponents(dirty, fwhm=1.0, threshold=10 * fluxthreshold, npixels=5)
assert len(comps) == len(self.components), "Different number of components found: original %d, recovered %d" % \
(len(self.components), len(comps))
cellsize = abs(dirty.wcs.wcs.cdelt[0])
for comp in comps:
# Check for agreement in direction
ocomp, separation = find_nearest_skycomponent(comp.direction, self.components)
assert separation / cellsize < positionthreshold, "Component differs in position %.3f pixels" % \
separation / cellsize
def test_mpccal_MPCCAL_manysources_subimages(self):
self.actualSetup()
model = create_empty_image_like(self.theta_list[0].image)
if arlexecute.using_dask:
progress = None
else:
progress = self.progress
future_vis = arlexecute.scatter(self.all_skymodel_noniso_vis)
future_model = arlexecute.scatter(model)
future_theta_list = arlexecute.scatter(self.theta_list)
result = mpccal_skymodel_list_arlexecute_workflow(
future_vis,
future_model,
future_theta_list,
mpccal_progress=progress,
nmajor=5,
context='2d',
algorithm='hogbom',
scales=[0, 3, 10],
fractional_threshold=0.3,
threshold=0.2,
gain=0.1,
niter=1000,
psf_support=256,
deconvolve_facets=8,
deconvolve_overlap=8,
deconvolve_taper='tukey')
(self.theta_list, residual) = arlexecute.compute(result, sync=True)
combined_model = calculate_skymodel_equivalent_image(self.theta_list)
psf_obs = invert_list_arlexecute_workflow(
[self.all_skymodel_noniso_vis], [model], context='2d', dopsf=True)
result = restore_list_arlexecute_workflow([combined_model], psf_obs,
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
if self.persist:
export_image_to_fits(
residual,
arl_path('test_results/test_mpccal_no_edge_residual.fits'))
if self.persist:
export_image_to_fits(
result[0],
arl_path('test_results/test_mpccal_no_edge_restored.fits'))
if self.persist:
export_image_to_fits(
combined_model,
arl_path('test_results/test_mpccal_no_edge_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 8', recovered_mpccal_components[0].name
assert numpy.abs(recovered_mpccal_components[0].flux[0, 0] - 7.773751416364857) < 1e-7, \
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,
arl_path('test_results/test_mpccal_no_edge_screen.fits'))
if self.persist:
export_image_to_fits(
weights,
arl_path(
'test_results/test_mpccal_no_edge_screenweights.fits'))
arlexecute.close()
[(residual, 0.0)])
result = arlexecute.compute(result, sync=True)
ical_restored = result[0]
export_image_to_fits(
ical_restored,
arl_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,
