def vsum(ov, mv): # Observed vis minus the sum of all predictions verr = copy_visibility(ov) for m in mv: verr.data['vis'] -= m.data['vis'] # Now add back each model in turn result = list() for m in mv: vr = copy_visibility(verr) vr.data['vis'] += m.data['vis'] result.append(vr) assert len(result) == len(mv) return result
def zero(vis): if vis is not None: zerovis = copy_visibility(vis) zerovis.data['vis'][...] = 0.0 return zerovis else: return None
def ft_cal_sm(ov, sm): assert isinstance(ov, Visibility), ov assert isinstance(sm, SkyModel), sm v = copy_visibility(ov) v.data['vis'][...] = 0.0 + 0.0j if len(sm.components) > 0: if isinstance(sm.mask, Image): comps = copy_skycomponent(sm.components) comps = apply_beam_to_skycomponent(comps, sm.mask) v = predict_skycomponent_visibility(v, comps) else: v = predict_skycomponent_visibility(v, sm.components) if isinstance(sm.image, Image): if numpy.max(numpy.abs(sm.image.data)) > 0.0: if isinstance(sm.mask, Image): model = copy_image(sm.image) model.data *= sm.mask.data else: model = sm.image v = predict_list_serial_workflow([v], [model], context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs)[0] if docal and isinstance(sm.gaintable, GainTable): bv = convert_visibility_to_blockvisibility(v) bv = apply_gaintable(bv, sm.gaintable, inverse=True) v = convert_blockvisibility_to_visibility(bv) return v
def subtract_vis(vis, model_vis): if vis is not None and model_vis is not None: assert vis.vis.shape == model_vis.vis.shape subvis = copy_visibility(vis) subvis.data['vis'][...] -= model_vis.data['vis'][...] return subvis else: return None
def actualSetUp(self, freqwin=1, block=True, dopol=False, zerow=False): self.npixel = 1024 self.low = create_named_configuration('LOWBD2', rmax=550.0) self.freqwin = freqwin self.blockvis_list = list() self.ntimes = 5 self.cellsize = 0.0005 # Choose the interval so that the maximum change in w is smallish integration_time = numpy.pi * (24 / (12 * 60)) self.times = numpy.linspace(-integration_time * (self.ntimes // 2), integration_time * (self.ntimes // 2), self.ntimes) if freqwin > 1: self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) self.channelwidth = numpy.array(freqwin * [self.frequency[1] - self.frequency[0]]) else: self.frequency = numpy.array([1.0e8]) self.channelwidth = numpy.array([4e7]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) self.phasecentre = SkyCoord(ra=+0.0 * u.deg, dec=-40.0 * u.deg, frame='icrs', equinox='J2000') self.blockvis_list = [arlexecute.execute(ingest_unittest_visibility)(self.low, [self.frequency[freqwin]], [self.channelwidth[freqwin]], self.times, self.vis_pol, self.phasecentre, block=block, zerow=zerow) for freqwin, _ in enumerate(self.frequency)] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) self.vis_list = [arlexecute.execute(convert_blockvisibility_to_visibility)(bv) for bv in self.blockvis_list] self.vis_list = arlexecute.compute(self.vis_list, sync=True) self.skymodel_list = [arlexecute.execute(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=0.6, flux_threshold=1.0, flux_max=5.0) for f, freq in enumerate(self.frequency)] self.skymodel_list = arlexecute.compute(self.skymodel_list, sync=True) assert isinstance(self.skymodel_list[0].image, Image), self.skymodel_list[0].image assert isinstance(self.skymodel_list[0].components[0], Skycomponent), self.skymodel_list[0].components[0] assert len(self.skymodel_list[0].components) == 19, len(self.skymodel_list[0].components) self.skymodel_list = expand_skymodel_by_skycomponents(self.skymodel_list[0]) assert len(self.skymodel_list) == 20, len(self.skymodel_list) assert numpy.max(numpy.abs(self.skymodel_list[-1].image.data)) > 0.0, "Image is empty" self.vis_list = [copy_visibility(self.vis_list[0], zero=True) for i, _ in enumerate(self.skymodel_list)]
def ft_ift_sm(ov, sm, g): assert isinstance(ov, Visibility) or isinstance(ov, BlockVisibility), ov assert isinstance(sm, SkyModel), sm if g is not None: assert len(g) == 2, g assert isinstance(g[0], Image), g[0] assert isinstance(g[1], ConvolutionFunction), g[1] v = copy_visibility(ov) v.data['vis'][...] = 0.0 + 0.0j if len(sm.components) > 0: if isinstance(sm.mask, Image): comps = copy_skycomponent(sm.components) comps = apply_beam_to_skycomponent(comps, sm.mask) v = predict_skycomponent_visibility(v, comps) else: v = predict_skycomponent_visibility(v, sm.components) if isinstance(sm.image, Image): if numpy.max(numpy.abs(sm.image.data)) > 0.0: if isinstance(sm.mask, Image): model = copy_image(sm.image) model.data *= sm.mask.data else: model = sm.image v = predict_list_serial_workflow([v], [model], context=context, vis_slices=vis_slices, facets=facets, gcfcf=[g], **kwargs)[0] assert isinstance(sm.image, Image), sm.image result = invert_list_serial_workflow([v], [sm.image], context=context, vis_slices=vis_slices, facets=facets, gcfcf=[g], **kwargs)[0] if isinstance(sm.mask, Image): result[0].data *= sm.mask.data return result
def ft_cal_sm(obv, sm): assert isinstance(obv, BlockVisibility), obv bv = copy_visibility(obv) bv.data['vis'][...] = 0.0 + 0.0j assert len(sm.components) > 0 if isinstance(sm.mask, Image): comps = copy_skycomponent(sm.components) comps = apply_beam_to_skycomponent(comps, sm.mask) bv = predict_skycomponent_visibility(bv, comps) else: bv = predict_skycomponent_visibility(bv, sm.components) if docal and isinstance(sm.gaintable, GainTable): bv = apply_gaintable(bv, sm.gaintable, inverse=True) return bv
def ft_ift_sm(ov, sm): assert isinstance(ov, Visibility), ov v = copy_visibility(ov) v.data['vis'][...] = 0.0 + 0.0j if len(sm.components) > 0: if isinstance(sm.mask, Image): comps = copy_skycomponent(sm.components) comps = apply_beam_to_skycomponent(comps, sm.mask) v = predict_skycomponent_visibility(v, comps) else: v = predict_skycomponent_visibility(v, sm.components) if isinstance(sm.image, Image): if numpy.max(numpy.abs(sm.image.data)) > 0.0: if isinstance(sm.mask, Image): model = copy_image(sm.image) model.data *= sm.mask.data else: model = sm.image v = predict_list_serial_workflow([v], [model], context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs)[0] assert isinstance(sm.image, Image), sm.image result = invert_list_serial_workflow([v], [sm.image], context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs)[0] if isinstance(sm.mask, Image): result[0].data *= sm.mask.data return result
def actualSetUp(self, nfreqwin=3, dospectral=True, dopol=False, amp_errors=None, phase_errors=None, zerow=True): if amp_errors is None: amp_errors = {'T': 0.0, 'G': 0.1} if phase_errors is None: phase_errors = {'T': 1.0, 'G': 0.0} self.npixel = 512 self.low = create_named_configuration('LOWBD2', rmax=750.0) self.freqwin = nfreqwin self.vis_list = list() self.ntimes = 1 self.times = numpy.linspace(-3.0, +3.0, self.ntimes) * numpy.pi / 12.0 self.frequency = numpy.linspace(0.8e8, 1.2e8, self.freqwin) if self.freqwin > 1: self.channelwidth = numpy.array( self.freqwin * [self.frequency[1] - self.frequency[0]]) else: self.channelwidth = numpy.array([1e6]) if dopol: self.vis_pol = PolarisationFrame('linear') self.image_pol = PolarisationFrame('stokesIQUV') f = numpy.array([100.0, 20.0, -10.0, 1.0]) else: self.vis_pol = PolarisationFrame('stokesI') self.image_pol = PolarisationFrame('stokesI') f = numpy.array([100.0]) if dospectral: flux = numpy.array( [f * numpy.power(freq / 1e8, -0.7) for freq in self.frequency]) else: flux = numpy.array([f]) self.phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') self.blockvis_list = [ arlexecute.execute(ingest_unittest_visibility, nout=1)(self.low, [self.frequency[i]], [self.channelwidth[i]], self.times, self.vis_pol, self.phasecentre, block=True, zerow=zerow) for i in range(nfreqwin) ] self.blockvis_list = arlexecute.compute(self.blockvis_list, sync=True) for v in self.blockvis_list: v.data['vis'][...] = 1.0 + 0.0j self.error_blockvis_list = [ arlexecute.execute(copy_visibility(v)) for v in self.blockvis_list ] gt = arlexecute.execute(create_gaintable_from_blockvisibility)( self.blockvis_list[0]) gt = arlexecute.execute(simulate_gaintable)(gt, phase_error=0.1, amplitude_error=0.0, smooth_channels=1, leakage=0.0, seed=180555) self.error_blockvis_list = [ arlexecute.execute(apply_gaintable)(self.error_blockvis_list[i], gt) for i in range(self.freqwin) ] self.error_blockvis_list = arlexecute.compute(self.error_blockvis_list, sync=True) assert numpy.max( numpy.abs(self.error_blockvis_list[0].vis - self.blockvis_list[0].vis)) > 0.0
def actualSetup(self, vnchan=1, doiso=True, ntimes=5, flux_limit=2.0, zerow=True, fixed=False): nfreqwin = vnchan rmax = 300.0 npixel = 512 cellsize = 0.001 frequency = numpy.linspace(0.8e8, 1.2e8, nfreqwin) if nfreqwin > 1: channel_bandwidth = numpy.array(nfreqwin * [frequency[1] - frequency[0]]) else: channel_bandwidth = [0.4e8] times = numpy.linspace(-numpy.pi / 3.0, numpy.pi / 3.0, ntimes) phasecentre = SkyCoord(ra=-60.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') lowcore = create_named_configuration('LOWBD2', rmax=rmax) block_vis = create_blockvisibility( lowcore, times, frequency=frequency, channel_bandwidth=channel_bandwidth, weight=1.0, phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI"), zerow=zerow) block_vis.data['uvw'][..., 2] = 0.0 self.beam = create_image_from_visibility( block_vis, npixel=npixel, frequency=[numpy.average(frequency)], nchan=nfreqwin, channel_bandwidth=[numpy.sum(channel_bandwidth)], cellsize=cellsize, phasecentre=phasecentre) self.components = create_low_test_skycomponents_from_gleam( flux_limit=flux_limit, phasecentre=phasecentre, frequency=frequency, polarisation_frame=PolarisationFrame('stokesI'), radius=npixel * cellsize) self.beam = create_low_test_beam(self.beam) self.components = apply_beam_to_skycomponent(self.components, self.beam, flux_limit=flux_limit) self.vis = copy_visibility(block_vis, zero=True) gt = create_gaintable_from_blockvisibility(block_vis, timeslice='auto') for i, sc in enumerate(self.components): if sc.flux[0, 0] > 10: sc.flux[...] /= 10.0 component_vis = copy_visibility(block_vis, zero=True) gt = simulate_gaintable(gt, amplitude_error=0.0, phase_error=0.1, seed=None) component_vis = predict_skycomponent_visibility(component_vis, sc) component_vis = apply_gaintable(component_vis, gt) self.vis.data['vis'][...] += component_vis.data['vis'][...] # Do an isoplanatic selfcal self.model_vis = copy_visibility(self.vis, zero=True) self.model_vis = predict_skycomponent_visibility( self.model_vis, self.components) if doiso: gt = solve_gaintable(self.vis, self.model_vis, phase_only=True, timeslice='auto') self.vis = apply_gaintable(self.vis, gt, inverse=True) self.model_vis = convert_blockvisibility_to_visibility(self.model_vis) self.model_vis, _, _ = weight_visibility(self.model_vis, self.beam) self.dirty_model, sumwt = invert_list_arlexecute_workflow( self.model_vis, self.beam, context='2d') export_image_to_fits( self.dirty_model, "%s/test_modelpartition-model_dirty.fits" % self.dir) lvis = convert_blockvisibility_to_visibility(self.vis) lvis, _, _ = weight_visibility(lvis, self.beam) dirty, sumwt = invert_list_arlexecute_workflow(lvis, self.beam, context='2d') if doiso: export_image_to_fits( dirty, "%s/test_modelpartition-initial-iso-residual.fits" % self.dir) else: export_image_to_fits( dirty, "%s/test_modelpartition-initial-noiso-residual.fits" % self.dir) self.skymodels = [ SkyModel(components=[cm], fixed=fixed) for cm in self.components ]
def vis_add(v1, v2): vout = copy_visibility(v1) vout.data['vis'] += v2.data['vis'] return vout
def ical_list_mpi_workflow(vis_list, model_imagelist, context, vis_slices=1, facets=1, gcfcf=None, calibration_context='TG', do_selfcal=True, comm=MPI.COMM_WORLD, **kwargs): """Create graph for ICAL pipeline :param vis_list: :param model_imagelist: :param context: imaging context e.g. '2d' :param calibration_context: Sequence of calibration steps e.g. TGB :param do_selfcal: Do the selfcalibration? :param kwargs: Parameters for functions in components :return: """ rank = comm.Get_rank() size = comm.Get_size() assert isinstance(model_imagelist, list), model_imagelist log.info( '%d: ical_list_mpi_workflow: vis_list len %d model_imagelist len %d' % (rank, len(vis_list), len(model_imagelist))) gt_list = list() if gcfcf is None: if rank == 0: gcfcf = [create_pswf_convolutionfunction(model_imagelist[0])] gcfcf = comm.bcast(gcfcf, root=0) psf_imagelist = invert_list_mpi_workflow(vis_list, model_imagelist, dopsf=True, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) model_vislist = [copy_visibility(v, zero=True) for v in vis_list] if do_selfcal: cal_vis_list = [copy_visibility(v) for v in vis_list] else: cal_vis_list = vis_list if do_selfcal: # Make the predicted visibilities, selfcalibrate against it correcting the gains, then # form the residual visibility, then make the residual image predicted_model_vislist = predict_list_mpi_workflow( model_vislist, model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) recal_vis_list, gt_list = calibrate_list_mpi_workflow( cal_vis_list, predicted_model_vislist, calibration_context=calibration_context, **kwargs) residual_vislist = subtract_list_mpi_workflow(recal_vis_list, predicted_model_vislist) residual_imagelist = invert_list_mpi_workflow(residual_vislist, model_imagelist, dopsf=True, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, iteration=0, **kwargs) else: # If we are not selfcalibrating it's much easier and we can avoid an unnecessary round of gather/scatter # for visibility partitioning such as timeslices and wstack. residual_imagelist = residual_list_mpi_workflow(cal_vis_list, model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) assert isinstance(model_imagelist, list), model_imagelist deconvolve_model_imagelist = deconvolve_list_mpi_workflow( residual_imagelist, psf_imagelist, model_imagelist, prefix='cycle 0', **kwargs) nmajor = get_parameter(kwargs, "nmajor", 5) if nmajor > 1: for cycle in range(nmajor): if do_selfcal: predicted_model_vislist = predict_list_mpi_workflow( model_vislist, deconvolve_model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) recal_vis_list, gt_list = calibrate_list_mpi_workflow( cal_vis_list, predicted_model_vislist, calibration_context=calibration_context, iteration=cycle, **kwargs) residual_vislist = subtract_list_mpi_workflow( recal_vis_list, model_vislist) residual_imagelist = invert_list_mpi_workflow( residual_vislist, model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) else: residual_imagelist = residual_list_mpi_workflow( cal_vis_list, deconvolve_model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) prefix = "cycle %d" % (cycle + 1) deconvolve_model_imagelist = deconvolve_list_mpi_workflow( residual_imagelist, psf_imagelist, deconvolve_model_imagelist, prefix=prefix, **kwargs) residual_imagelist = residual_list_mpi_workflow(cal_vis_list, deconvolve_model_imagelist, context=context, vis_slices=vis_slices, facets=facets, gcfcf=gcfcf, **kwargs) restore_imagelist = restore_list_mpi_workflow(deconvolve_model_imagelist, psf_imagelist, residual_imagelist) return (deconvolve_model_imagelist, residual_imagelist, restore_imagelist, gt_list)