def solve_gaintable(vis: BlockVisibility, modelvis: BlockVisibility = None, gt=None, phase_only=True, niter=30,
tol=1e-8,
crosspol=False, **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
: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
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)
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, phase_only=True, niter=30, tol=1e-8,
crosspol=False, **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
:param modelvis: BlockVisibility containing the visibility predicted by a model
: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 type(vis) is BlockVisibility, "vis is not a BlockVisibility: %r" % vis
assert type(modelvis) is BlockVisibility or type(modelvis) is None, "modelvis is not None or a " \
"BlockVisibility: %r" % vis
if phase_only:
log.info('solve_gaintable: Solving for phase only')
else:
log.info('solve_gaintable: Solving for complex gain')
# 根据blockvisibility的元数据初始化一个gaintable
gt = create_gaintable_from_blockvisibility(vis)
for chunk, rows in enumerate(vis_timeslice_iter(vis, **kwargs)): # 对visibility按照time切片
# 切片好的visibility shape: [1,nant,nant,nchan,npol]
subvis = create_visibility_from_rows(vis, rows)
#若存在model
if modelvis is not None:
# model_visibility也要以相同的方式按time切片
model_subvis = create_visibility_from_rows(modelvis, rows)
# 两个vis相除计算得到新的block_vis，其中的元数据未改变，只有vis和weight发生了改变
pointvis = divide_visibility(subvis, model_subvis)
# 此处因为第0个axis是time轴，并且vis已经按照time分片，所以按照第0axis做了average以后值不发生变化
x = numpy.average(pointvis.vis, axis=0)
xwt = numpy.average(pointvis.weight, axis=0)
else:
x = numpy.average(subvis.vis, axis=0)
xwt = numpy.average(subvis.weight, axis=0)
# 将数据填入gaintable中
# solve 和 timeslot的分界线
gt = solve_from_X(gt, x, xwt, chunk, crosspol, niter, phase_only,
tol, npol=vis.polarisation_frame.npol)
assert type(gt) is GainTable, "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
return gt
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 divide_kernel(ixs):
vis, model_vis = ixs
return divide_visibility(vis, model_vis)