def test_crosssubtract_datamodel(self): self.actualSetUp(zerow=True) skymodel_vislist = predict_skymodel_list_serial_workflow( self.vis_list[0], self.skymodel_list, context='2d', docal=True) vobs = sum_predict_results(skymodel_vislist) skymodel_vislist = crosssubtract_datamodels_skymodel_list_serial_workflow( vobs, skymodel_vislist) result_skymodel = [ SkyModel(components=None, image=self.skymodel_list[-1].image) for v in skymodel_vislist ] results = invert_skymodel_list_serial_workflow(skymodel_vislist, result_skymodel, context='2d', docal=True) assert numpy.max(numpy.abs(results[0][0].data)) > 0.0 assert numpy.max(numpy.abs(results[0][1])) > 0.0 if self.plot: import matplotlib.pyplot as plt from wrappers.serial.image.operations import show_image show_image(results[0][0], title='Dirty image after cross-subtraction', vmax=0.1, vmin=-0.01) plt.show()
def test_predict(self): self.actualSetUp(zerow=True) self.skymodel_list = [ create_low_test_skymodel_from_gleam( npixel=self.npixel, cellsize=self.cellsize, frequency=[self.frequency[f]], phasecentre=self.phasecentre, polarisation_frame=PolarisationFrame("stokesI"), flux_limit=0.3, flux_threshold=0.3, flux_max=5.0) for f, freq in enumerate(self.frequency) ] assert isinstance(self.skymodel_list[0].images[0], Image), self.skymodel_list[0].images[0] assert isinstance(self.skymodel_list[0].components[0], Skycomponent), self.skymodel_list[0].components[0] assert len(self.skymodel_list[0].components) == 119, len( self.skymodel_list[0].components) assert len(self.skymodel_list[0].images) == 1, len( self.skymodel_list[0].images) assert numpy.max(numpy.abs( self.skymodel_list[0].images[0].data)) > 0.0, "Image is empty" skymodel_vislist = predict_skymodel_list_serial_workflow( self.vis_list, self.skymodel_list, context='2d') assert numpy.max(numpy.abs(skymodel_vislist[0].vis)) > 0.0
def test_predictcal(self): self.actualSetUp(zerow=True) skymodel_vislist = predict_skymodel_list_serial_workflow( self.vis_list[0], self.skymodel_list, context='2d', docal=True) vobs = sum_predict_results(skymodel_vislist) if self.plot: def plotvis(i, v): import matplotlib.pyplot as plt uvr = numpy.hypot(v.u, v.v) amp = numpy.abs(v.vis[:, 0]) plt.plot(uvr, amp, '.') plt.title(str(i)) plt.show() plotvis(0, vobs)
def trial_case(results, seed=180555, context='wstack', nworkers=8, threads_per_worker=1, memory=8, processes=True, order='frequency', nfreqwin=7, ntimes=3, rmax=750.0, facets=1, wprojection_planes=1, use_dask=True, use_serial_imaging=True, flux_limit=0.3, nmajor=5, dft_threshold=1.0, use_serial_clean=True, write_fits=False): """ Single trial for performance-timings Simulates visibilities from GLEAM including phase errors Makes dirty image and PSF Runs ICAL pipeline The results are in a dictionary: 'context': input - a string describing concisely the purpose of the test 'time overall', overall execution time (s) 'time predict', time to execute GLEAM prediction graph 'time invert', time to make dirty image 'time invert graph', time to make dirty image graph 'time ICAL graph', time to create ICAL graph 'time ICAL', time to execute ICAL graph 'context', type of imaging e.g. 'wstack' 'nworkers', number of workers to create 'threads_per_worker', 'nnodes', Number of nodes, 'processes', 'order', Ordering of data_models 'nfreqwin', Number of frequency windows in simulation 'ntimes', Number of hour angles in simulation 'rmax', Maximum radius of stations used in simulation (m) 'facets', Number of facets in deconvolution and imaging 'wprojection_planes', Number of wprojection planes 'vis_slices', Number of visibility slices (per Visibbility) 'npixel', Number of pixels in image 'cellsize', Cellsize in radians 'seed', Random number seed 'dirty_max', Maximum in dirty image 'dirty_min', Minimum in dirty image 'restored_max', 'restored_min', 'deconvolved_max', 'deconvolved_min', 'residual_max', 'residual_min', 'git_info', GIT hash (not definitive since local mods are possible) :param results: Initial state :param seed: Random number seed (used in gain simulations) :param context: imaging context :param context: Type of context: '2d'|'timeslice'|'wstack' :param nworkers: Number of dask workers to use :param threads_per_worker: Number of threads per worker :param processes: Use processes instead of threads 'processes'|'threads' :param order: See simulate_list_list_arlexecute_workflow_workflowkflow :param nfreqwin: See simulate_list_list_arlexecute_workflow_workflowkflow :param ntimes: See simulate_list_list_arlexecute_workflow_workflowkflow :param rmax: See simulate_list_list_arlexecute_workflow_workflowkflow :param facets: Number of facets to use :param wprojection_planes: Number of wprojection planes to use :param use_dask: Use dask or immediate evaluation :return: results dictionary """ # Initialise MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() def init_logging(): logging.basicConfig( filename='pipelines_mpi_timings.log', filemode='w', format='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s', datefmt='%H:%M:%S', level=logging.INFO) init_logging() log = logging.getLogger() # Initialise logging on the workers. This appears to only work using the process scheduler. def lprint(*args): log.info(*args) print(*args) lprint("Starting pipelines_mpi_timings") numpy.random.seed(seed) results['seed'] = seed start_all = time.time() results['context'] = context results['hostname'] = socket.gethostname() results['git_hash'] = git_hash() results['epoch'] = time.strftime("%Y-%m-%d %H:%M:%S") lprint("Context is %s" % context) results['nworkers'] = nworkers results['threads_per_worker'] = threads_per_worker results['processes'] = processes results['memory'] = memory results['order'] = order results['nfreqwin'] = nfreqwin results['ntimes'] = ntimes results['rmax'] = rmax results['facets'] = facets results['wprojection_planes'] = wprojection_planes results['dft threshold'] = dft_threshold results['use_dask'] = use_dask lprint("At start, configuration is:") lprint(sort_dict(results)) # Parameters determining scale of simulation. frequency = numpy.linspace(1.0e8, 1.2e8, nfreqwin) centre = nfreqwin // 2 if nfreqwin > 1: channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]]) else: channel_bandwidth = numpy.array([1e6]) times = numpy.linspace(-numpy.pi / 4.0, numpy.pi / 4.0, ntimes) phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-40.0 * u.deg, frame='icrs', equinox='J2000') lprint("****** Visibility creation ******") # Create the empty BlockVisibility's and persist these on the cluster if rank == 0: tmp_bvis_list = simulate_list_serial_workflow( 'LOWBD2', frequency=frequency, channel_bandwidth=channel_bandwidth, times=times, phasecentre=phasecentre, order=order, format='blockvis', rmax=rmax) vis_list = [ convert_blockvisibility_to_visibility(bv) for bv in tmp_bvis_list ] else: vis_list = list() #if rank==0: # import matplotlib.pyplot as plt # plt.clf() # plt.hist(vis_list[0].w, bins=100) # plt.title('Histogram of w samples: rms=%.1f (wavelengths)' % numpy.std(vis_list[0].w)) # plt.xlabel('W (wavelengths)') # plt.show() # plt.clf() # plt.hist(vis_list[0].uvdist, bins=100) # plt.title('Histogram of uvdistance samples') # plt.xlabel('UV Distance (wavelengths)') # plt.show() sub_vis_list = numpy.array_split(vis_list, size) sub_vis_list = comm.scatter(sub_vis_list, root=0) #NOTE: sub_vis_list is scattered (future_vis_list in arlexecute) # Find the best imaging parameters but don't bring the vis_list back here print("****** Finding wide field parameters ******") sub_advice = [ advise_wide_field(v, guard_band_image=6.0, delA=0.1, facets=facets, wprojection_planes=wprojection_planes, oversampling_synthesised_beam=4.0) for v in sub_vis_list ] # MONTSE: This does not make sense cause they all compute advice but only # the last one is used. Confirm that this is what the dask version does!!! advice_list = comm.gather(sub_advice, root=0) if rank == 0: advice = numpy.concatenate(advice_list)[-1] else: advice = None advice = comm.bcast(advice, root=0) # Deconvolution via sub-images requires 2^n npixel = advice['npixels2'] results['npixel'] = npixel cellsize = advice['cellsize'] results['cellsize'] = cellsize lprint("Image will have %d by %d pixels, cellsize = %.6f rad" % (npixel, npixel, cellsize)) # NOTE: frequency and channel_bandwidth are replicated sub_frequency = numpy.array_split(frequency, size) sub_channel_bandwidth = numpy.array_split(channel_bandwidth, size) # Create an empty model image tmp_model_list = [ create_image(npixel=npixel, cellsize=cellsize, frequency=[sub_frequency[rank][f]], channel_bandwidth=[sub_channel_bandwidth[rank][f]], phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI")) for f, freq in enumerate(sub_frequency[rank]) ] model_list = comm.gather(tmp_model_list, root=0) if rank == 0: model_list = numpy.concatenate(model_list).tolist() else: model_list = list() print(type(model_list)) assert isinstance(model_list, list), model_list # NOTE: tmp_model_list is the scattered model_list # NOTE: template_model and gcfcf are replicated lprint("****** Setting up imaging parameters ******") # Now set up the imaging parameters template_model = create_image( npixel=npixel, cellsize=cellsize, frequency=[frequency[centre]], phasecentre=phasecentre, channel_bandwidth=[channel_bandwidth[centre]], polarisation_frame=PolarisationFrame("stokesI")) gcfcf = [create_pswf_convolutionfunction(template_model)] if context == 'timeslice': vis_slices = ntimes lprint("Using timeslice with %d slices" % vis_slices) elif context == '2d': vis_slices = 1 elif context == "wprojection": wstep = advice['wstep'] nw = advice['wprojection_planes'] vis_slices = 1 support = advice['nwpixels'] results['wprojection_planes'] = nw lprint("****** Starting W projection kernel creation ******") lprint("Using wprojection with %d planes with wstep %.1f wavelengths" % (nw, wstep)) lprint("Support of wprojection = %d pixels" % support) gcfcf = [ create_awterm_convolutionfunction(template_model, nw=nw, wstep=wstep, oversampling=4, support=support, use_aaf=True) ] lprint("Size of W projection gcf, cf = %.2E bytes" % get_size(gcfcf)) else: context = 'wstack' vis_slices = advice['vis_slices'] lprint("Using wstack with %d slices" % vis_slices) results['vis_slices'] = vis_slices # Make a skymodel from gleam, with bright sources as components and weak sources in an image lprint("****** Starting GLEAM skymodel creation ******") future_skymodel_list = [ create_low_test_skymodel_from_gleam( npixel=npixel, cellsize=cellsize, frequency=[sub_frequency[rank][f]], phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI"), flux_limit=flux_limit, flux_threshold=dft_threshold, flux_max=5.0) for f, freq in enumerate(sub_frequency[rank]) ] # We use predict_skymodel so that we can use skycomponents as well as images lprint("****** Starting GLEAM skymodel prediction ******") #NOTE: future_skymodel_list is a subset of skymodel_list which has not been #gathered sub_predicted_vis_list = [ predict_skymodel_list_serial_workflow(sub_vis_list[f], [future_skymodel_list[f]], context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf)[0] for f, freq in enumerate(sub_frequency[rank]) ] predicted_vis_list = comm.gather(sub_predicted_vis_list, root=0) if rank == 0: predicted_vis_list = numpy.concatenate(predicted_vis_list) else: predicted_vis_list = list() #NOTE: sub_predicted_vis_list is predicted_vis_list scattered # Corrupt the visibility for the GLEAM model lprint("****** Visibility corruption ******") tmp_corrupted_vis_list = corrupt_list_serial_workflow( sub_predicted_vis_list, phase_error=1.0, seed=seed) lprint("****** Weighting and tapering ******") tmp_corrupted_vis_list = weight_list_serial_workflow( tmp_corrupted_vis_list, tmp_model_list) tmp_corrupted_vis_list = taper_list_serial_workflow( tmp_corrupted_vis_list, 0.003 * 750.0 / rmax) corrupted_vis_list = comm.gather(tmp_corrupted_vis_list, root=0) if rank == 0: corrupted_vis_list = numpy.concatenate(corrupted_vis_list) else: corrupted_vis_list = list() #future_corrupted_vis_list = arlexecute.scatter(corrupted_vis_list) # At this point the only futures are of scatter'ed data so no repeated calculations should be # incurred. lprint("****** Starting dirty image calculation ******") start = time.time() dirty_list = invert_list_mpi_workflow(corrupted_vis_list, model_list, vis_slices=vis_slices, context=context, facets=facets, use_serial_invert=use_serial_imaging, gcfcf=gcfcf) end = time.time() results['size invert graph'] = get_size(dirty_list) lprint('Size of dirty graph is %.3E bytes' % (results['size invert graph'])) results['time invert graph'] = 0.0 results['time invert'] = end - start lprint("Dirty image invert took %.3f seconds" % (end - start)) if rank == 0: dirty, sumwt = dirty_list[centre] lprint("Maximum in dirty image is %f, sumwt is %s" % (numpy.max(numpy.abs(dirty.data)), str(sumwt))) qa = qa_image(dirty) results['dirty_max'] = qa.data['max'] results['dirty_min'] = qa.data['min'] if write_fits and rank == 0: export_image_to_fits( dirty, "pipelines_arlexecute_timings-%s-dirty.fits" % context) lprint("****** Starting prediction ******") start = time.time() result = predict_list_mpi_workflow(corrupted_vis_list, model_list, vis_slices=vis_slices, context=context, facets=facets, use_serial_predict=use_serial_imaging, gcfcf=gcfcf) # arlexecute.client.cancel(tmp_vis_list) end = time.time() results['time predict'] = end - start lprint("Predict took %.3f seconds" % (end - start)) # Create the ICAL pipeline to run major cycles, starting selfcal at cycle 1. A global solution across all # frequencies (i.e. Visibilities) is performed. print("Using subimage clean") deconvolve_facets = 8 deconvolve_overlap = 16 deconvolve_taper = 'tukey' lprint("****** Starting ICAL graph creation ******") controls = create_calibration_controls() controls['T']['first_selfcal'] = 1 controls['T']['timescale'] = 'auto' start = time.time() ical_list = ical_list_mpi_workflow(corrupted_vis_list, model_imagelist=model_list, context=context, vis_slices=vis_slices, scales=[0, 3, 10], algorithm='mmclean', nmoment=3, niter=1000, fractional_threshold=0.1, threshold=0.01, nmajor=nmajor, gain=0.25, psf_support=64, deconvolve_facets=deconvolve_facets, deconvolve_overlap=deconvolve_overlap, deconvolve_taper=deconvolve_taper, timeslice='auto', global_solution=True, do_selfcal=True, calibration_context='T', controls=controls, use_serial_predict=use_serial_imaging, use_serial_invert=use_serial_imaging, use_serial_clean=use_serial_clean, gcfcf=gcfcf) end = time.time() results['size ICAL graph'] = get_size(ical_list) lprint('Size of ICAL graph is %.3E bytes' % results['size ICAL graph']) results['time ICAL graph'] = 0.0 # Execute the graph lprint("****** Executing ICAL graph ******") deconvolved, residual, restored, gaintables = ical_list results['time ICAL'] = end - start lprint("ICAL graph execution took %.3f seconds" % (end - start)) if rank == 0: qa = qa_image(deconvolved[centre]) results['deconvolved_max'] = qa.data['max'] results['deconvolved_min'] = qa.data['min'] deconvolved_cube = image_gather_channels(deconvolved) if write_fits: export_image_to_fits( deconvolved_cube, "pipelines_arlexecute_timings-%s-ical_deconvolved.fits" % context) qa = qa_image(residual[centre][0]) results['residual_max'] = qa.data['max'] results['residual_min'] = qa.data['min'] residual_cube = remove_sumwt(residual) residual_cube = image_gather_channels(residual_cube) if write_fits: export_image_to_fits( residual_cube, "pipelines_arlexecute_timings-%s-ical_residual.fits" % context) qa = qa_image(restored[centre]) results['restored_max'] = qa.data['max'] results['restored_min'] = qa.data['min'] restored_cube = image_gather_channels(restored) if write_fits: export_image_to_fits( restored_cube, "pipelines_arlexecute_timings-%s-ical_restored.fits" % context) # end_all = time.time() results['time overall'] = end_all - start_all lprint("At end, results are:") results = sort_dict(results) lprint(results) return results