def core_solve(self, spf, dpf, phase_error=0.1, amplitude_error=0.0, leakage=0.0, phase_only=True, niter=200, crosspol=False, residual_tol=1e-6, f=None, vnchan=3): if f is None: f = [100.0, 50.0, -10.0, 40.0] self.actualSetup(spf, dpf, f=f, vnchan=vnchan) gt = create_gaintable_from_blockvisibility(self.vis) log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=phase_error, amplitude_error=amplitude_error, leakage=leakage) original = copy_visibility(self.vis) vis = apply_gaintable(self.vis, gt) gtsol = solve_gaintable(self.vis, original, phase_only=phase_only, niter=niter, crosspol=crosspol, tol=1e-6) vis = apply_gaintable(vis, gtsol, inverse=True) residual = numpy.max(gtsol.residual) assert residual < residual_tol, "%s %s Max residual = %s" % (spf, dpf, residual) log.debug(qa_gaintable(gt)) assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def calskymodel_fit_gaintable(evis, calskymodel, gain=0.1, niter=3, tol=1e-3, **kwargs): """Fit a gaintable to a visibility This is the update to the gain part of the window :param evis: Expected vis for this ssm :param calskymodel: csm element being fit :param gain: Gain in step :param niter: Number of iterations :param kwargs: Gaintable """ previous_gt = copy_gaintable(calskymodel[1]) gt = copy_gaintable(calskymodel[1]) model_vis = copy_visibility(evis, zero=True) model_vis = predict_skymodel_visibility(model_vis, calskymodel[0]) gt = solve_gaintable(evis, model_vis, gt=gt, niter=niter, phase_only=True, gain=0.5, tol=1e-4, **kwargs) gt.data['gain'][...] = gain * gt.data['gain'][...] + ( 1 - gain) * previous_gt.data['gain'][...] gt.data['gain'][...] /= numpy.abs(previous_gt.data['gain'][...]) return gt
def test_solve_gaintable_scalar_bandpass(self): self.actualSetup('stokesI', 'stokesI', f=[100.0], vnchan=128) gt = create_gaintable_from_blockvisibility(self.vis) log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=10.0, amplitude_error=0.01, smooth_channels=8) original = copy_visibility(self.vis) self.vis = apply_gaintable(self.vis, gt) gtsol = solve_gaintable(self.vis, original, phase_only=False, niter=200) residual = numpy.max(gtsol.residual) assert residual < 3e-8, "Max residual = %s" % (residual) assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_solve_gaintable_scalar_timeslice(self): self.actualSetup('stokesI', 'stokesI', f=[100.0], ntimes=10) gt = create_gaintable_from_blockvisibility(self.vis, timeslice=120.0) log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=10.0, amplitude_error=0.0) original = copy_visibility(self.vis) self.vis = apply_gaintable(self.vis, gt) gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=200) residual = numpy.max(gtsol.residual) assert residual < 3e-8, "Max residual = %s" % (residual) assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def test_solve_gaintable_scalar_normalise(self): self.actualSetup('stokesI', 'stokesI', f=[100.0]) gt = create_gaintable_from_blockvisibility(self.vis) log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=0.0, amplitude_error=0.1) gt.data['gain'] *= 2.0 original = copy_visibility(self.vis) self.vis = apply_gaintable(self.vis, gt) gtsol = solve_gaintable(self.vis, original, phase_only=False, niter=200, normalise_gains=True) residual = numpy.max(gtsol.residual) assert residual < 3e-8, "Max residual = %s" % (residual) assert numpy.max(numpy.abs(gtsol.gain - 1.0)) > 0.1
def rcal(vis: BlockVisibility, components, **kwargs) -> GainTable: """ Real-time calibration pipeline. Reads visibilities through a BlockVisibility iterator, calculates model visibilities according to a component-based sky model, and performs calibration solution, writing a gaintable for each chunk of visibilities. :param vis: Visibility or Union(Visibility, Iterable) :param components: Component-based sky model :param kwargs: Parameters :return: gaintable """ if not isinstance(vis, collections.Iterable): vis = [vis] for ichunk, vischunk in enumerate(vis): vispred = copy_visibility(vischunk, zero=True) vispred = predict_skycomponent_visibility(vispred, components) gt = solve_gaintable(vischunk, vispred, **kwargs) yield gt
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_function(self.model_vis, self.beam, context='2d') export_image_to_fits(self.dirty_model, "%s/test_skymodel-model_dirty.fits" % self.dir) lvis = convert_blockvisibility_to_visibility(self.vis) lvis, _, _ = weight_visibility(lvis, self.beam) dirty, sumwt = invert_function(lvis, self.beam, context='2d') if doiso: export_image_to_fits( dirty, "%s/test_skymodel-initial-iso-residual.fits" % self.dir) else: export_image_to_fits( dirty, "%s/test_skymodel-initial-noiso-residual.fits" % self.dir) self.skymodels = [ SkyModel(components=[cm], fixed=fixed) for cm in self.components ]