def test_solve_gaintable_stokesI_repeat_apply(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)
gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=200)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
self.vis = apply_gaintable(self.vis, gtsol)
gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=1)
self.vis = apply_gaintable(self.vis, gtsol)
residual = numpy.max(gtsol.residual)
assert residual < 3e-8, "Max residual = %s" % (residual)
assert numpy.max(numpy.abs(gtsol.gain - 1.0)) < 1e-8
def solve_calibrate_chain(vis, model_vis, calibration_context='T', controls=None, iteration=0, tol=1e-6, **kwargs):
""" Calibrate using algorithm specified by calibration_context
The context string can denote a sequence of calibrations e.g. TGB with different timescales.
:param vis:
:param model_vis:
:param calibration_context: calibration contexts in order of correction e.g. 'TGB'
:param controls: controls dictionary, modified as necessary
:param iteration: Iteration number to be compared to the 'first_selfcal' field.
:param kwargs:
:return: Calibrated data_models, dict(gaintables)
"""
gaintables = {}
if controls is None:
controls = create_calibration_controls()
isVis = isinstance(vis, Visibility)
if isVis:
avis = convert_visibility_to_blockvisibility(vis)
else:
avis = vis
isMVis = isinstance(model_vis, Visibility)
if isMVis:
amvis = convert_visibility_to_blockvisibility(model_vis)
else:
amvis = model_vis
assert isinstance(avis, BlockVisibility), avis
assert amvis.__repr__() != avis.__repr__(), "Vis and model vis are the same object: convert problem"
# Always return a gain table, even if null
for c in calibration_context:
gaintables[c] = \
create_gaintable_from_blockvisibility(avis, timeslice=controls[c]['timeslice'])
if iteration >= controls[c]['first_selfcal']:
if numpy.max(numpy.abs(vis.weight)) > 0.0 and (amvis is None or numpy.max(numpy.abs(amvis.vis)) > 0.0):
gaintables[c] = solve_gaintable(avis, amvis,
timeslice=controls[c]['timeslice'],
phase_only=controls[c]['phase_only'],
crosspol=controls[c]['shape'] == 'matrix',
tol=tol)
log.debug('calibrate_chain: Jones matrix %s, iteration %d' % (c, iteration))
log.debug(qa_gaintable(gaintables[c], context='Jones matrix %s, iteration %d' % (c, iteration)))
avis = apply_gaintable(avis, gaintables[c], inverse=True, timeslice=controls[c]['timeslice'])
else:
log.debug('calibrate_chain: Jones matrix %s not solved, iteration %d' % (c, iteration))
else:
log.debug('calibrate_chain: Jones matrix %s not solved, iteration %d' % (c, iteration))
return gaintables
def test_solve_gaintable_stokesI_small_n_large_t(self):
# Select only 6 stations
self.actualSetup('stokesI', 'stokesI', f=[100.0], ntimes=4000, rmax=83)
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)
gt.data['gain'] = gt.gain[1, ...]
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
gtsol = solve_gaintable(self.vis, original, phase_only=True, niter=200)
self.vis = apply_gaintable(self.vis, gtsol)
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_solve_gaintable_stokesI_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,
damping=0.5)
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 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,
timeslice='auto'):
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,
timeslice=timeslice)
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 calibrate_chain(vis,
model_vis,
gaintables=None,
calibration_context='T',
controls=None,
iteration=0,
tol=1e-8,
**kwargs):
""" Calibrate using algorithm specified by calibration_context
The context string can denote a sequence of calibrations e.g. TGB with different timescales.
:param vis:
:param model_vis:
:param calibration_context: calibration contexts in order of correction e.g. 'TGB'
:param controls: controls dictionary, modified as necessary
:param iteration: Iteration number to be compared to the 'first_selfcal' field.
:param kwargs:
:return: Calibrated data_models, dict(gaintables)
"""
if controls is None:
controls = create_calibration_controls()
# Check to see if changes are required
changes = False
for c in calibration_context:
if iteration >= controls[c]['first_selfcal']:
changes = True
if changes:
isVis = isinstance(vis, Visibility)
if isVis:
avis = convert_visibility_to_blockvisibility(vis)
else:
avis = vis
isMVis = isinstance(model_vis, Visibility)
if isMVis:
amvis = convert_visibility_to_blockvisibility(model_vis)
else:
amvis = model_vis
assert isinstance(avis, BlockVisibility), avis
if gaintables is None:
gaintables = dict()
for c in calibration_context:
if iteration >= controls[c]['first_selfcal']:
if c not in gaintables.keys():
log.info("Creating new {} gaintable".format(c))
gaintables[c] = \
create_gaintable_from_blockvisibility(avis,
timeslice=controls[c]['timeslice'])
gaintables[c] = solve_gaintable(
avis,
amvis,
gt=gaintables[c],
timeslice=controls[c]['timeslice'],
phase_only=controls[c]['phase_only'],
crosspol=controls[c]['shape'] == 'matrix',
tol=tol)
log.debug('calibrate_chain: Jones matrix %s, iteration %d' %
(c, iteration))
log.debug(
qa_gaintable(gaintables[c],
context='Jones matrix %s, iteration %d' %
(c, iteration)))
avis = apply_gaintable(avis,
gaintables[c],
inverse=True,
timeslice=controls[c]['timeslice'])
else:
log.debug(
'calibrate_chain: Jones matrix %s not solved, iteration %d'
% (c, iteration))
if isVis:
return convert_blockvisibility_to_visibility(avis), gaintables
else:
return avis, gaintables
else:
return vis, gaintables
