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,