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_calibrate_function(self): self.actualSetup('stokesI', 'stokesI', f=[100.0]) # Prepare the corrupted visibility data_models 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=0.1) bgt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.01) original = copy_visibility(self.vis) self.vis = apply_gaintable(self.vis, bgt, vis_slices=1) self.vis = apply_gaintable(self.vis, gt, vis_slices=None) # Now get the control dictionary and calibrate controls = create_calibration_controls() controls['T']['first_selfcal'] = 0 controls['B']['first_selfcal'] = 0 calibrated_vis, gaintables = calibrate_function( self.vis, original, calibration_context='TB', controls=controls) residual = numpy.max(gaintables['T'].residual) assert residual < 3e-2, "Max T residual = %s" % (residual) residual = numpy.max(gaintables['B'].residual) assert residual < 6e-5, "Max B residual = %s" % (residual)
def test_solve_gaintable_scalar_pointsource(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=10.0, amplitude_error=0.0) original = copy_visibility(self.vis) self.vis = apply_gaintable(self.vis, gt) point_vis = divide_visibility(self.vis, original) gtsol = solve_gaintable(point_vis, 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 test_apply_gaintable_only(self): for spf, dpf in [('stokesI', 'stokesI'), ('stokesIQUV', 'linear'), ('stokesIQUV', 'circular')]: self.actualSetup(spf, dpf) gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto') log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.01) original = copy_visibility(self.vis) vis = apply_gaintable(self.vis, gt) error = numpy.max(numpy.abs(vis.vis - original.vis)) assert error > 10.0, "Error = %f" % (error)
def test_create_gaintable_from_visibility_interval(self): for timeslice in [10.0, 'auto', 1e5]: for spf, dpf in [('stokesIQUV', 'linear')]: self.actualSetup(spf, dpf) gt = create_gaintable_from_blockvisibility(self.vis, timeslice=timeslice) log.info("Created gain table: %s" % (gaintable_summary(gt))) gt = simulate_gaintable(gt, phase_error=1.0) original = copy_visibility(self.vis) vis = apply_gaintable(self.vis, gt) assert numpy.max(numpy.abs(original.vis)) > 0.0 assert numpy.max(numpy.abs(vis.vis)) > 0.0 assert numpy.max(numpy.abs(vis.vis - original.vis)) > 0.0
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 test_readwritegaintable(self): self.vis = create_blockvisibility( self.lowcore, self.times, self.frequency, channel_bandwidth=self.channel_bandwidth, phasecentre=self.phasecentre, polarisation_frame=PolarisationFrame("linear"), weight=1.0) gt = create_gaintable_from_blockvisibility(self.vis, timeslice='auto') gt = simulate_gaintable(gt, phase_error=1.0, amplitude_error=0.1) export_gaintable_to_hdf5(gt, '%s/test_gaintable.hdf' % self.dir) newgt = import_gaintable_from_hdf5('%s/test_gaintable.hdf' % self.dir) assert gt.data.shape == newgt.data.shape assert numpy.max(numpy.abs(gt.gain - newgt.gain)) < 1e-15
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 ]
context='wstack') #print("np.sum(predicted_vis.data): ", numpy.sum(predicted_vis.data['vis'])) block_vis = convert_visibility_to_blockvisibility(predicted_vis) #print("np.sum(block_vis.data): ", numpy.sum(block_vis.data['vis'])) #print("nchan npol nants ", block_vis.nchan, block_vis.npol, block_vis.nants) #print("uvw", block_vis.uvw, numpy.sum(block_vis.uvw)) #print("vis", block_vis.vis, numpy.sum(block_vis.vis)) #print("weight", block_vis.weight, numpy.sum(block_vis.weight)) #print("time", block_vis.time, numpy.sum(block_vis.time)) #print("integration_time", block_vis.integration_time, numpy.sum(block_vis.integration_time)) #print("nvis, size", block_vis.nvis, block_vis.size()) gt = create_gaintable_from_blockvisibility(block_vis) #print("np.sum(gt.data): ", numpy.sum(gt.data['gain'])) gt = simulate_gaintable(gt, phase_error=1.0) #print("np.sum(gt.data): ", numpy.sum(gt.data['gain'])) blockvis = apply_gaintable(block_vis, gt) #print("np.sum(blockvis.data): ", numpy.sum(blockvis.data['vis'])) model = create_image_from_visibility( block_vis, npixel=npixel, frequency=[numpy.average(frequency)], nchan=1, channel_bandwidth=[numpy.sum(channel_bandwidth)], cellsize=cellsize, phasecentre=phasecentre) #print("model sum, min, max, shape: ", numpy.sum(model.data), numpy.amin(model.data), numpy.amax(model.data), model.shape)
def ingest_visibility(self, freq=None, chan_width=None, times=None, add_errors=False, block=True, bandpass=False): if freq is None: freq = [1e8] if chan_width is None: chan_width = [1e6] if times is None: times = (numpy.pi / 12.0) * numpy.linspace(-3.0, 3.0, 5) lowcore = create_named_configuration('LOWBD2', rmax=750.0) frequency = numpy.array(freq) channel_bandwidth = numpy.array(chan_width) phasecentre = SkyCoord(ra=+180.0 * u.deg, dec=-60.0 * u.deg, frame='icrs', equinox='J2000') if block: vt = create_blockvisibility( lowcore, times, frequency, channel_bandwidth=channel_bandwidth, weight=1.0, phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI")) else: vt = create_visibility( lowcore, times, frequency, channel_bandwidth=channel_bandwidth, weight=1.0, phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI")) cellsize = 0.001 model = create_image_from_visibility( vt, npixel=self.npixel, cellsize=cellsize, npol=1, frequency=frequency, phasecentre=phasecentre, polarisation_frame=PolarisationFrame("stokesI")) nchan = len(self.frequency) flux = numpy.array(nchan * [[100.0]]) facets = 4 rpix = model.wcs.wcs.crpix - 1.0 spacing_pixels = self.npixel // facets centers = [-1.5, -0.5, 0.5, 1.5] comps = list() for iy in centers: for ix in centers: p = int(round(rpix[0] + ix * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[0]))), \ int(round(rpix[1] + iy * spacing_pixels * numpy.sign(model.wcs.wcs.cdelt[1]))) sc = pixel_to_skycoord(p[0], p[1], model.wcs, origin=1) comp = create_skycomponent( direction=sc, flux=flux, frequency=frequency, polarisation_frame=PolarisationFrame("stokesI")) comps.append(comp) if block: predict_skycomponent_visibility(vt, comps) else: predict_skycomponent_visibility(vt, comps) insert_skycomponent(model, comps) self.comps = comps self.model = copy_image(model) self.empty_model = create_empty_image_like(model) export_image_to_fits( model, '%s/test_pipeline_functions_model.fits' % (self.dir)) if add_errors: # These will be the same for all calls numpy.random.seed(180555) gt = create_gaintable_from_blockvisibility(vt) gt = simulate_gaintable(gt, phase_error=1.0, amplitude_error=0.0) vt = apply_gaintable(vt, gt) if bandpass: bgt = create_gaintable_from_blockvisibility(vt, timeslice=1e5) bgt = simulate_gaintable(bgt, phase_error=0.01, amplitude_error=0.01, smooth_channels=4) vt = apply_gaintable(vt, bgt) return vt