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,