def test_weighting(self):
self.actualSetUp()
vis, density, densitygrid = weight_visibility(self.componentvis,
self.model,
weighting='uniform')
assert vis.nvis == self.componentvis.nvis
assert len(density) == vis.nvis
assert numpy.std(vis.imaging_weight) > 0.0
assert densitygrid.data.shape == self.model.data.shape
vis, density, densitygrid = weight_visibility(self.componentvis,
self.model,
weighting='natural')
assert density is None
assert densitygrid is None
def test_skymodel_solve_fixed(self):
self.actualSetup(ntimes=1, doiso=True, fixed=True)
calskymodel, residual_vis = calskymodel_solve(self.vis,
self.skymodels,
niter=30,
gain=0.25)
# Check that the components are unchanged
calskymodel_skycomponents = list()
for sm in [csm[0] for csm in calskymodel]:
for comp in sm.components:
calskymodel_skycomponents.append(comp)
recovered_components = find_skycomponent_matches(
calskymodel_skycomponents, self.components, 1e-5)
for p in recovered_components:
assert numpy.abs(calskymodel_skycomponents[p[0]].flux[0, 0] -
self.components[p[1]].flux[0, 0]) < 1e-15
assert calskymodel_skycomponents[p[0]].direction.separation(
self.components[p[1]].direction).rad < 1e-15
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_function(residual_vis, self.beam, context='2d')
export_image_to_fits(
dirty, "%s/test_skymodel-final-iso-residual.fits" % self.dir)
qa = qa_image(dirty)
assert qa.data['rms'] < 3.4e-3, qa
def test_tapering_Gaussian(self):
self.actualSetUp()
size_required = 0.003542
self.componentvis, _, _ = weight_visibility(self.componentvis,
self.model,
algoritm='uniform')
self.componentvis = taper_visibility_gaussian(self.componentvis,
beam=size_required)
psf, sumwt = invert_2d(self.componentvis, self.model, dopsf=True)
export_image_to_fits(
psf, '%s/test_weighting_gaussian_taper_psf.fits' % self.dir)
xfr = fft_image(psf)
xfr.data = xfr.data.real.astype('float')
export_image_to_fits(
xfr, '%s/test_weighting_gaussian_taper_xfr.fits' % self.dir)
npixel = psf.data.shape[3]
sl = slice(npixel // 2 - 7, npixel // 2 + 8)
fit = fit_2dgaussian(psf.data[0, 0, sl, sl])
if fit.x_stddev <= 0.0 or fit.y_stddev <= 0.0:
raise ValueError('Error in fitting to psf')
# fit_2dgaussian returns sqrt of variance. We need to convert that to FWHM.
# https://en.wikipedia.org/wiki/Full_width_at_half_maximum
scale_factor = numpy.sqrt(8 * numpy.log(2.0))
size = numpy.sqrt(fit.x_stddev * fit.y_stddev) * scale_factor
# Now we need to convert to radians
size *= numpy.pi * self.model.wcs.wcs.cdelt[1] / 180.0
# Very impressive! Desired 0.01 Acheived 0.0100006250829
assert numpy.abs(size - size_required) < 0.001 * size_required, \
"Fit should be %f, actually is %f" % (size_required, size)
def test_convert_weight(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
model = create_image_from_visibility(vis=cvis, npixel=256, cellsize=0.001, phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
cvis = weight_visibility(cvis, model)
assert numpy.mean(cvis.data['imaging_weight']) < 1.0
assert numpy.std(cvis.data['imaging_weight']) > 0.0
dvis = decoalesce_visibility(cvis)
assert numpy.mean(dvis.data['imaging_weight']) < 1.0
assert numpy.std(dvis.data['imaging_weight']) > 0.0
assert dvis.nvis == self.blockvis.nvis
def weight_vis(vis, model):
if vis is not None:
if model is not None:
vis, _, _ = weight_visibility(vis,
model,
weighting=weighting,
**kwargs)
return vis
else:
return None
else:
return None
def test_convert_weight(self):
cvis = convert_blockvisibility_to_visibility(self.blockvis)
cvis.data['imaging_weight'] = 10.0
model = create_image_from_visibility(
vis=cvis,
npixel=256,
cellsize=0.001,
phasecentre=self.phasecentre,
polarisation_frame=PolarisationFrame('stokesI'))
cvis = weight_visibility(cvis, model)
dvis = decoalesce_visibility(cvis, overwrite=True)
assert numpy.max(dvis.data['imaging_weight'] - 10.0) < 1e-7
assert dvis.nvis == self.blockvis.nvis
def test_tapering_Tukey(self):
self.actualSetUp()
self.componentvis, _, _ = weight_visibility(self.componentvis,
self.model,
algoritm='uniform')
self.componentvis = taper_visibility_tukey(self.componentvis,
tukey=1.0)
psf, sumwt = invert_2d(self.componentvis, self.model, dopsf=True)
export_image_to_fits(
psf, '%s/test_weighting_tukey_taper_psf.fits' % self.dir)
xfr = fft_image(psf)
xfr.data = xfr.data.real.astype('float')
export_image_to_fits(
xfr, '%s/test_weighting_tukey_taper_xfr.fits' % self.dir)
def test_skymodel_solve_noiso(self):
self.actualSetup(ntimes=1, doiso=False)
calskymodel, residual_vis = calskymodel_solve(self.vis,
self.skymodels,
niter=30,
gain=0.25)
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_function(residual_vis, self.beam, context='2d')
export_image_to_fits(
dirty, "%s/test_skymodel-final-noiso-residual.fits" % self.dir)
qa = qa_image(dirty)
assert qa.data['rms'] < 3.8e-3, qa
def test_modelpartition_solve_arlexecute(self):
self.actualSetup(doiso=True)
self.skymodel_list = [arlexecute.execute(SkyModel, nout=1)(components=[cm])
for cm in self.components]
modelpartition_list = solve_modelpartition_arlexecute(self.vis, skymodel_list=self.skymodel_list, niter=30,
gain=0.25)
skymodel, residual_vis = arlexecute.compute(modelpartition_list, sync=True)
residual_vis = convert_blockvisibility_to_visibility(residual_vis)
residual_vis, _, _ = weight_visibility(residual_vis, self.beam)
dirty, sumwt = invert_arlexecute(residual_vis, self.beam, context='2d')
export_image_to_fits(dirty, "%s/test_modelpartition-%s-final-iso-residual.fits" % (self.dir, arlexecute.type()))
qa = qa_image(dirty)
assert qa.data['rms'] < 3.2e-3, qa
def image_2d(vis, npixel_advice, cell_advice, channel, results_dir):
"""Do 2D imaging of visibility data.
Args:
vis (obj): ARL visibility data.
npixel_advice (float): number of pixels in output image.
cell_advice (float): cellsize in output image.
channel (int): channel number to be imaged (affects output filename).
results_dir (str): directory to save results.
Returns:
dirty: dirty image.
psf: image of psf.
"""
try:
vis_slices = len(np.unique(vis.time))
print("There are %d timeslices" % vis_slices)
# Obtain advice on w-proj parameters:
advice = advise_wide_field(vis)
# Create a model image:
model = create_image_from_visibility(vis, cellsize=cell_advice, npixel=npixel_advice, phasecentre=vis.phasecentre, polarisation_frame=PolarisationFrame('stokesIQUV'))
# Weight the visibilities:
vis, _, _ = weight_visibility(vis, model)
# Create a dirty image:
dirty, sumwt = invert_serial(vis, model, context='2d', vis_slices=1, padding=2)
# Create the psf:
psf, sumwt = invert_serial(vis, model, dopsf=True, context='2d', vis_slices=1, padding=2)
# Save to disk:
export_image_to_fits(dirty, '%s/imaging_dirty_WStack-%s.fits'
% (results_dir, channel))
export_image_to_fits(psf, '%s/imaging_psf_WStack-%s.fits'
% (results_dir, channel))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
return dirty, psf
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
]
