def test_divide_visibility(self):
self.vis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
self.vis.data['vis'][..., :] = [2.0 + 0.0j, 0.0j, 0.0j, 2.0 + 0.0j]
self.othervis = create_blockvisibility(
self.lowcore,
self.times,
self.frequency,
channel_bandwidth=self.channel_bandwidth,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame("stokesIQUV"))
self.othervis.data['vis'][..., :] = [
1.0 + 0.0j, 0.0j, 0.0j, 1.0 + 0.0j
]
self.ratiovis = divide_visibility(self.vis, self.othervis)
assert self.ratiovis.nvis == self.vis.nvis
assert numpy.max(numpy.abs(self.ratiovis.vis)) == 2.0, numpy.max(
numpy.abs(self.ratiovis.vis))
def test_solve_gaintable_stokesI_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=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 solve_gaintable(vis: BlockVisibility,
modelvis: BlockVisibility = None,
gt=None,
phase_only=True,
niter=30,
tol=1e-8,
crosspol=False,
normalise_gains=True,
**kwargs) -> GainTable:
"""Solve a gain table by fitting an observed visibility to a model visibility
If modelvis is None, a point source model is assumed.
:param vis: BlockVisibility containing the observed data_models
:param modelvis: BlockVisibility containing the visibility predicted by a model
:param gt: Existing gaintable
:param phase_only: Solve only for the phases (default=True)
:param niter: Number of iterations (default 30)
:param tol: Iteration stops when the fractional change in the gain solution is below this tolerance
:param crosspol: Do solutions including cross polarisations i.e. XY, YX or RL, LR
:return: GainTable containing solution
"""
assert isinstance(vis, BlockVisibility), vis
if modelvis is not None:
assert isinstance(modelvis, BlockVisibility), modelvis
assert numpy.max(numpy.abs(
modelvis.vis)) > 0.0, "Model visibility is zero"
if phase_only:
log.debug('solve_gaintable: Solving for phase only')
else:
log.debug('solve_gaintable: Solving for complex gain')
if gt is None:
log.debug("solve_gaintable: creating new gaintable")
gt = create_gaintable_from_blockvisibility(vis, **kwargs)
else:
log.debug("solve_gaintable: starting from existing gaintable")
if modelvis is not None:
pointvis = divide_visibility(vis, modelvis)
else:
pointvis = vis
for row in range(gt.ntimes):
vis_rows = numpy.abs(vis.time - gt.time[row]) < gt.interval[row] / 2.0
if numpy.sum(vis_rows) > 0:
x = numpy.sum(
(pointvis.vis[vis_rows] * pointvis.weight[vis_rows]) *
(1 - pointvis.flags[vis_rows]),
axis=0)
xwt = numpy.sum(pointvis.weight[vis_rows] *
(1 - pointvis.flags[vis_rows]),
axis=0)
mask = numpy.abs(xwt) > 0.0
if numpy.sum(mask) > 0:
x_shape = x.shape
x[mask] = x[mask] / xwt[mask]
x[~mask] = 0.0
xwt[mask] = xwt[mask] / numpy.max(xwt[mask])
x = x.reshape(x_shape)
if vis.npol == 1:
gt.data['gain'][row, ...], gt.data['weight'][row, ...], gt.data['residual'][row, ...] = \
solve_antenna_gains_itsubs_scalar(gt.data['gain'][row, ...],
gt.data['weight'][row, ...],
x, xwt, phase_only=phase_only, niter=niter,
tol=tol)
elif vis.npol == 2:
gt.data['gain'][row, ...], gt.data['weight'][row, ...], gt.data['residual'][row, ...] = \
solve_antenna_gains_itsubs_nocrossdata(gt.data['gain'][row, ...], gt.data['weight'][row, ...],
x, xwt, phase_only=phase_only, niter=niter,
tol=tol)
elif vis.npol == 4:
if crosspol:
gt.data['gain'][row, ...], gt.data['weight'][row, ...], gt.data['residual'][row, ...] = \
solve_antenna_gains_itsubs_matrix(gt.data['gain'][row, ...], gt.data['weight'][row, ...],
x, xwt, phase_only=phase_only, niter=niter,
tol=tol)
else:
gt.data['gain'][row, ...], gt.data['weight'][row, ...], gt.data['residual'][row, ...] = \
solve_antenna_gains_itsubs_vector(gt.data['gain'][row, ...], gt.data['weight'][row, ...],
x, xwt, phase_only=phase_only, niter=niter,
tol=tol)
else:
gt.data['gain'][row, ...], gt.data['weight'][row, ...], \
gt.data['residual'][row, ...] = \
solve_antenna_gains_itsubs_scalar(gt.data['gain'][row, ...],
gt.data['weight'][row, ...],
x, xwt, phase_only=phase_only, niter=niter,
tol=tol)
if normalise_gains and not phase_only:
gabs = numpy.average(numpy.abs(gt.data['gain'][row]))
gt.data['gain'][row] /= gabs
else:
gt.data['gain'][row, ...] = 1.0 + 0.0j
gt.data['weight'][row, ...] = 0.0
gt.data['residual'][row, ...] = 0.0
else:
log.warning("Gaintable {0}, vis time mismatch {1}".format(
gt.time, vis.time))
assert isinstance(gt, GainTable), "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
return gt
def solve_gaintable(vis: BlockVisibility,
modelvis: BlockVisibility = None,
gt=None,
phase_only=True,
niter=30,
tol=1e-8,
crosspol=False,
normalise_gains=True,
**kwargs) -> GainTable:
"""Solve a gain table by fitting an observed visibility to a model visibility
If modelvis is None, a point source model is assumed.
:param vis: BlockVisibility containing the observed data_models
:param modelvis: BlockVisibility containing the visibility predicted by a model
:param gt: Existing gaintable
:param phase_only: Solve only for the phases (default=True)
:param niter: Number of iterations (default 30)
:param tol: Iteration stops when the fractional change in the gain solution is below this tolerance
:param crosspol: Do solutions including cross polarisations i.e. XY, YX or RL, LR
:return: GainTable containing solution
"""
assert isinstance(vis, BlockVisibility), vis
if modelvis is not None:
assert isinstance(modelvis, BlockVisibility), modelvis
assert numpy.max(numpy.abs(
modelvis.vis)) > 0.0, "Model visibility is zero"
if phase_only:
log.debug('solve_gaintable: Solving for phase only')
else:
log.debug('solve_gaintable: Solving for complex gain')
if gt is None:
log.debug("solve_gaintable: creating new gaintable")
gt = create_gaintable_from_blockvisibility(vis, **kwargs)
else:
log.debug("solve_gaintable: starting from existing gaintable")
for row in range(gt.ntimes):
vis_rows = numpy.abs(vis.time - gt.time[row]) < gt.interval[row] / 2.0
if numpy.sum(vis_rows) > 0:
subvis = create_visibility_from_rows(vis, vis_rows)
if modelvis is not None:
model_subvis = create_visibility_from_rows(modelvis, vis_rows)
pointvis = divide_visibility(subvis, model_subvis)
x = numpy.sum(pointvis.vis * pointvis.weight, axis=0)
xwt = numpy.sum(pointvis.weight, axis=0)
else:
x = numpy.sum(subvis.vis * subvis.weight, axis=0)
xwt = numpy.sum(subvis.weight, axis=0)
mask = numpy.abs(xwt) > 0.0
x_shape = x.shape
x[mask] = x[mask] / xwt[mask]
x[~mask] = 0.0
x = x.reshape(x_shape)
gt = solve_from_X(gt,
x,
xwt,
row,
crosspol,
niter,
phase_only,
tol,
npol=vis.polarisation_frame.npol)
if normalise_gains and not phase_only:
gabs = numpy.average(numpy.abs(gt.data['gain'][row]))
gt.data['gain'][row] /= gabs
assert isinstance(gt, GainTable), "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
return gt
