def test_vis_timeslice_iterator(self):
self.actualSetUp()
nchunks = len(list(vis_timeslice_iter(self.vis)))
log.debug('Found %d chunks' % (nchunks))
assert nchunks > 1
for chunk, rows in enumerate(vis_timeslice_iter(self.vis)):
visslice = create_visibility_from_rows(self.vis, rows)
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
assert numpy.sum(rows) < self.vis.nvis
def test_vis_timeslice_iterator(self):
self.actualSetUp()
nchunks = len(list(vis_timeslice_iter(self.vis)))
log.debug('Found %d chunks' % (nchunks))
assert nchunks > 1
total_rows = 0
for chunk, rows in enumerate(vis_timeslice_iter(self.vis)):
visslice = create_visibility_from_rows(self.vis, rows)
total_rows += visslice.nvis
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
assert numpy.sum(rows) < self.vis.nvis
assert total_rows == self.vis.nvis, "Total rows iterated %d, Original rows %d" % (
total_rows, self.vis.nvis)
def test_vis_timeslice_iterator_timeslice(self):
self.actualSetUp()
for chunk, rows in enumerate(
vis_timeslice_iter(self.vis, timeslice=65.0)):
visslice = create_visibility_from_rows(self.vis, rows)
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
assert numpy.sum(rows) < self.vis.nvis
def test_coalesce_decoalesce_with_iter(self):
for rows in vis_timeslice_iter(self.blockvis):
visslice = create_visibility_from_rows(self.blockvis, rows)
cvisslice = convert_blockvisibility_to_visibility(visslice)
assert numpy.min(cvisslice.frequency) == numpy.min(self.frequency)
assert numpy.min(cvisslice.frequency) > 0.0
dvisslice = decoalesce_visibility(cvisslice)
assert dvisslice.nvis == visslice.nvis
def apply_gaintable(vis: BlockVisibility,
gt: GainTable,
inverse=False,
**kwargs) -> BlockVisibility:
"""Apply a gain table to a block visibility
The corrected visibility is::
V_corrected = {g_i * g_j^*}^-1 V_obs
If the visibility data are polarised e.g. polarisation_frame("linear") then the inverse operator
represents an actual inverse of the gains.
:param vis: Visibility to have gains applied
:param gt: Gaintable to be applied
:param inverse: Apply the inverse (default=False)
:return: input vis with gains applied
"""
assert type(
vis) is BlockVisibility, "vis is not a BlockVisibility: %r" % vis
assert type(gt) is GainTable, "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
if inverse:
log.debug('apply_gaintable: Apply inverse gaintable')
else:
log.debug('apply_gaintable: Apply gaintable')
# 按照time对vis进行切片
for chunk, rows in enumerate(vis_timeslice_iter(vis)):
vistime = numpy.average(vis.time[rows])
integration_time = numpy.average(vis.integration_time[rows])
gaintable_rows = abs(gt.time - vistime) < integration_time / 2.0
# Lookup the gain for this set of visibilities
gain = gt.data['gain'][gaintable_rows]
# The shape of the mueller matrix is
ntimes, nant, nchan, nrec, _ = gain.shape
original = vis.vis[rows]
applied = copy.deepcopy(original)
for time in range(ntimes):
for a1 in range(vis.nants - 1):
for a2 in range(a1 + 1, vis.nants):
for chan in range(nchan):
mueller = numpy.kron(
gain[time, a1, chan, :, :],
numpy.conjugate(gain[time, a2, chan, :, :]))
if inverse:
mueller = numpy.linalg.inv(mueller)
applied[time, a2, a1, chan, :] = numpy.matmul(
mueller, original[time, a2, a1, chan, :])
vis.data['vis'][rows] = applied
return vis
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_vis_timeslice_iterator_timeslice(self):
self.actualSetUp()
total_rows = 0
for chunk, rows in enumerate(
vis_timeslice_iter(self.vis, timeslice=65.0)):
visslice = create_visibility_from_rows(self.vis, rows)
if visslice is not None:
total_rows += visslice.nvis
assert visslice.vis[0].real == visslice.time[0]
assert len(rows)
assert numpy.sum(rows) < self.vis.nvis
assert total_rows == self.vis.nvis, "Total rows iterated %d, Original rows %d" % (
total_rows, self.vis.nvis)
def apply_gaintable(vis: BlockVisibility,
gt: GainTable,
inverse=False,
**kwargs) -> BlockVisibility:
"""Apply a gain table to a block visibility
The corrected visibility is::
V_corrected = {g_i * g_j^*}^-1 V_obs
If the visibility data are polarised e.g. polarisation_frame("linear") then the inverse operator
represents an actual inverse of the gains.
:param vis: Visibility to have gains applied
:param gt: Gaintable to be applied
:param inverse: Apply the inverse (default=False)
:return: input vis with gains applied
"""
assert isinstance(
vis, BlockVisibility), "vis is not a BlockVisibility: %r" % vis
assert isinstance(gt, GainTable), "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
if inverse:
log.debug('apply_gaintable: Apply inverse gaintable')
else:
log.debug('apply_gaintable: Apply gaintable')
is_scalar = gt.gain.shape[-2:] == (1, 1)
if is_scalar:
log.debug('apply_gaintable: scalar gains')
for chunk, rows in enumerate(vis_timeslice_iter(vis, **kwargs)):
if numpy.sum(rows) > 0:
vistime = numpy.average(vis.time[rows])
gaintable_rows = abs(gt.time - vistime) < gt.interval / 2.0
# Lookup the gain for this set of visibilities
gain = gt.data['gain'][gaintable_rows]
# The shape of the mueller matrix is
ntimes, nant, nchan, nrec, _ = gain.shape
original = vis.vis[rows]
applied = copy.deepcopy(original)
for time in range(ntimes):
for a1 in range(vis.nants - 1):
for a2 in range(a1 + 1, vis.nants):
if is_scalar:
smueller = gain[time, a1, :, 0] * numpy.conjugate(
gain[time, a2, :, 0])
if inverse:
if numpy.abs(smueller).all() > 0.0:
applied[time, a2, a1, :,
0][..., numpy.newaxis] = original[
time, a2, a1, :,
0][...,
numpy.newaxis] / smueller
else:
applied[time, a2, a1, :,
0][..., numpy.newaxis] = original[
time, a2, a1, :,
0][..., numpy.newaxis] * smueller
else:
for chan in range(nchan):
mueller = numpy.kron(
gain[time, a1, chan, :, :],
numpy.conjugate(gain[time, a2,
chan, :, :]))
if inverse:
# If the Mueller is singular, ignore it
try:
mueller = numpy.linalg.inv(mueller)
applied[time, a2, a1,
chan, :] = numpy.matmul(
mueller,
original[time, a2, a1,
chan, :])
except numpy.linalg.linalg.LinAlgError:
applied[time, a2, a1,
chan, :] = original[time, a2,
a1,
chan, :]
else:
applied[time, a2, a1,
chan, :] = numpy.matmul(
mueller, original[time, a2, a1,
chan, :])
vis.data['vis'][rows] = applied
return vis
def test_vis_timeslice_iterator_single(self):
self.actualSetUp(times=numpy.zeros([1]))
nchunks = len(list(vis_timeslice_iter(self.vis)))
log.debug('Found %d chunks' % (nchunks))
for chunk, rows in enumerate(vis_timeslice_iter(self.vis)):
assert len(rows)
def solve_gaintable(vis: BlockVisibility,
modelvis: BlockVisibility,
phase_only=True,
niter=30,
tol=1e-8,
crosspol=False,
**kwargs) -> GainTable:
"""Solve a gain table to a block visibility
: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
:returns: GainTable containing solution
"""
assert type(
vis) is BlockVisibility, "vis is not a BlockVisibility: %r" % vis
assert type(
modelvis
) is BlockVisibility, "modelvis is not a BlockVisibility: %r" % vis
if phase_only:
log.info('solve_gaintable: Solving for phase only')
else:
log.info('solve_gaintable: Solving for complex gain')
gt = create_gaintable_from_blockvisibility(vis)
for chunk, rows in enumerate(vis_timeslice_iter(vis)):
x, xwt = remove_model(vis.vis[rows],
vis.weight[rows],
modelvis.vis[rows],
isscalar=vis.polarisation_frame.npol == 1,
crosspol=crosspol)
# Now average over time, chan. The axes of x are time, antenna2, antenna1, chan, pol
xave = numpy.average(x, axis=0)
xwtAve = numpy.average(xwt, axis=0)
mask = xwtAve <= 0.0
xave[mask] = 0.0
gainshape = gt.data['gain'][chunk, ...].shape
if vis.polarisation_frame.npol > 1:
if crosspol:
gt.data['gain'][chunk, ...], gt.data['weight'][chunk, ...], gt.data['residual'][chunk, ...] = \
solve_antenna_gains_itsubs_matrix(gainshape, xave, xwtAve, phase_only=phase_only, niter=niter,
tol=tol)
else:
gt.data['gain'][chunk, ...], gt.data['weight'][chunk, ...], gt.data['residual'][chunk, ...] = \
solve_antenna_gains_itsubs_vector(gainshape, xave, xwtAve, phase_only=phase_only, niter=niter,
tol=tol)
else:
gt.data['gain'][chunk, ...], gt.data['weight'][chunk, ...], gt.data['residual'][chunk, ...] = \
solve_antenna_gains_itsubs_scalar(gainshape, xave, xwtAve, phase_only=phase_only, niter=niter,
tol=tol)
assert type(gt) is GainTable, "gt is not a GainTable: %r" % 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 isinstance(vis, BlockVisibility), vis
assert isinstance(modelvis,
BlockVisibility) or type(modelvis) is not None, vis
if phase_only:
log.info('solve_gaintable: Solving for phase only')
else:
log.info('solve_gaintable: Solving for complex gain')
gt = create_gaintable_from_blockvisibility(vis)
for chunk, rows in enumerate(vis_timeslice_iter(vis, **kwargs)):
subvis = create_visibility_from_rows(vis, rows)
if modelvis is not None:
model_subvis = create_visibility_from_rows(modelvis, 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,
chunk,
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
