def make_e(vis, calskymodel, evis_all):
# Return the estep for a given skymodel
evis = copy_visibility(vis)
tvis = copy_visibility(vis, zero=True)
tvis = predict_skycomponent_visibility(tvis, calskymodel[0].components)
tvis = apply_gaintable(tvis, calskymodel[1])
# E step is the data model for a window plus the difference between the observed data
# and the summed data models or, put another way, its the observed data minus the
# summed visibility for all other windows
evis.data['vis'][...] = tvis.data['vis'][...] + vis.data['vis'][
...] - evis_all.data['vis'][...]
return evis
def zerovis(vis):
if vis is not None:
zerovis = copy_visibility(vis)
zerovis.data['vis'][...] = 0.0
return zerovis
else:
return None
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=[100.0, 50.0, -10.0, 40.0]):
self.actualSetup(spf, dpf, f=f)
gt = create_gaintable_from_blockvisibility(self.vis)
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))
def peel_skycomponent_blockvisibility(vis: BlockVisibility, sc: Skycomponent, remove=True) -> \
BlockVisibility:
""" Peel a collection of components.
Sequentially solve the gain towards each Skycomponent and optionally remove from the visibility.
:param params:
:param vis: Visibility to be processed
:param sc: Skycomponent or list of Skycomponents
:returns: subtracted visibility and list of GainTables
"""
assert type(
vis) is BlockVisibility, "vis is not a BlockVisibility: %r" % vis
if not isinstance(sc, collections.Iterable):
sc = [sc]
gtlist = []
for comp in sc:
assert comp.shape == 'Point', "Cannot handle shape %s" % comp.shape
modelvis = copy_visibility(vis)
modelvis = predict_skycomponent_blockvisibility(modelvis, comp)
gt = solve_gaintable(vis, modelvis, phase_only=False)
modelvis = apply_gaintable(modelvis, gt)
if remove:
vis.data['vis'] -= modelvis.data['vis']
gtlist.append(gt)
return vis, gtlist
def decoalesce_visibility(vis, overwrite=False):
""" Decoalesce the visibilities to the original values (opposite of coalesce_visibility)
This relies upon the block vis and the index being part of the vis.
'uv': Needs the original image used in coalesce_visibility
'tb': Needs the index generated by coalesce_visibility
:param vis: (Coalesced visibility)
:returns: BlockVisibility with vis and weight columns overwritten
"""
assert type(vis) is Visibility, "vis is not a Visibility: %r" % vis
assert type(vis.blockvis) is BlockVisibility, "No blockvisibility in vis"
assert vis.cindex is not None, "No reverse index in Visibility"
if overwrite:
log.debug('decoalesce_visibility: Created new Visibility for decoalesced data')
decomp_vis = copy_visibility(vis.blockvis)
else:
log.debug('decoalesce_visibility: Filled decoalesced data into template')
decomp_vis = vis.blockvis
vshape = decomp_vis.data['vis'].shape
npol = vshape[-1]
dvis = numpy.zeros(vshape, dtype='complex')
assert numpy.max(vis.cindex) < dvis.size
for i in range(dvis.size // npol):
decomp_vis.data['vis'].flat[i:i + npol] = vis.data['vis'][vis.cindex[i]]
log.debug('decoalesce_visibility: Coalesced %s, decoalesced %s' % (vis_summary(vis), vis_summary(decomp_vis)))
return decomp_vis
def skymodel_cal_e_step(vis: BlockVisibility, evis_all: BlockVisibility, calskymodel, **kwargs):
"""Calculates E step in equation A12
This is the data model for this window plus the difference between observed data and summed data models
:param evis_all: Sum data models
:param csm: csm element being fit
:param kwargs:
:return: Data model (i.e. visibility) for this csm
"""
evis = copy_visibility(evis_all)
tvis = copy_visibility(vis, zero=True)
tvis = predict_skycomponent_visibility(tvis, calskymodel[0].components)
tvis = apply_gaintable(tvis, calskymodel[1])
evis.data['vis'][...] = tvis.data['vis'][...] + vis.data['vis'][...] - evis_all.data['vis'][...]
return evis
def sagecal_fit_gaintable(evis, theta, gain=0.1, niter=3, tol=1e-3, **kwargs):
"""Fit a gaintable to a visibility i.e. A13
This is the update to the gain part of the window
:param evis:
:param theta:
:param kwargs:
:return:
"""
previous_gt = copy_gaintable(theta[1])
gt = copy_gaintable(theta[1])
model_vis = copy_visibility(evis, zero=True)
model_vis = predict_skycomponent_visibility(model_vis, theta[0])
gt = solve_gaintable(evis,
model_vis,
gt=gt,
niter=niter,
phase_only=True,
gain=0.5,
tol=1e-4,
**kwargs)
gt.data['gain'][...] = gain * gt.data['gain'][...] + \
(1 - gain) * previous_gt.data['gain'][...]
gt.data['gain'][...] /= numpy.abs(previous_gt.data['gain'][...])
return gt
def calibrate_visibility(vt: Visibility,
model=None,
components=None,
predict=predict_2d,
**kwargs):
""" calibrate Visibility with respect to model and optionally components
:param vt: Visibility
:param model: Model image
:param components: Sky components
:returns: Calibrated visibility
"""
assert model is not None or components is not None, "calibration requires a model or skycomponents"
vtpred = copy_visibility(vt, zero=True)
if model is not None:
vtpred = predict(vtpred, model, **kwargs)
if components is not None:
vtpred = predict_skycomponent_visibility(vtpred, components)
else:
vtpred = predict_skycomponent_visibility(vtpred, components)
bvt = decoalesce_visibility(vt)
bvtpred = decoalesce_visibility(vtpred)
gt = solve_gaintable(bvt, bvtpred, **kwargs)
bvt = apply_gaintable(bvt, gt, **kwargs)
return convert_blockvisibility_to_visibility(bvt)[0]
def subtract_vis(vis, model_vis):
if vis is not None and model_vis is not None:
assert vis.vis.shape == model_vis.vis.shape
subvis = copy_visibility(vis)
subvis.data['vis'][...] -= model_vis.data['vis'][...]
return subvis
else:
return None
def predict_facets_and_accumulate(vis, model, **kwargs):
if vis is not None:
predicted = copy_visibility(vis)
predicted = predict(predicted, model, **kwargs)
vis.data['vis'] += predicted.data['vis']
return vis
else:
return None
def predict_and_sum_wstack(vis, model, **kwargs):
if vis is not None:
predicted = copy_visibility(vis)
predicted = predict(predicted, model, **kwargs)
predicted.data['vis'] = vis.data['vis'] + predicted.data['vis']
return predicted
else:
return None
def skymodel_cal_e_all(vis: BlockVisibility, calskymodels):
"""Calculates E step in equation A12
This is the sum of the data models over all skymodel
:param vis: Visibility
:param csm: List of (skymodel, gaintable) tuples
:param kwargs:
:return: Sum of data models (i.e. a visibility)
"""
evis = copy_visibility(vis, zero=True)
tvis = copy_visibility(vis, zero=True)
for csm in calskymodels:
tvis.data['vis'][...] = 0.0
tvis = predict_skycomponent_visibility(tvis, csm[0].components)
tvis = apply_gaintable(tvis, csm[1])
evis.data['vis'][...] += tvis.data['vis'][...]
return evis
def sagecal_e_all(vis: BlockVisibility, thetas, **kwargs):
"""Calculates E step in equation A12
This is the sum of the data models over all windows
:param vis:
:param thetas:
:param kwargs:
:return:
"""
evis = copy_visibility(vis, zero=True)
tvis = copy_visibility(vis, zero=True)
for i, theta in enumerate(thetas):
tvis.data['vis'][...] = 0.0
tvis = predict_skycomponent_visibility(tvis, theta[0])
tvis = apply_gaintable(tvis, theta[1])
evis.data['vis'][...] += tvis.data['vis'][...]
return evis
def selfcal_single(vis, model, **kwargs):
if vis is not None:
predicted = copy_visibility(vis)
predicted = predict(predicted, model, **kwargs)
gtsol = solve_gaintable(vis, predicted, **kwargs)
vis = apply_gaintable(vis, gtsol, inverse=True, **kwargs)
return vis
else:
return None
def test_create_gaintable_from_visibility(self):
for spf, dpf in [('stokesIQUV', 'linear'), ('stokesIQUV', 'circular')]:
self.actualSetup(spf, dpf)
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=0.1)
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt)
assert numpy.max(numpy.abs(vis.vis - original.vis)) > 0.0
def test_solve_gaintable_scalar(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)
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)
def test_apply_gaintable_and_inverse_both(self):
for spf, dpf in [('stokesIQUV', 'linear'), ('stokesIQUV', 'circular')]:
self.actualSetup(spf, dpf)
gt = create_gaintable_from_blockvisibility(self.vis)
log.info("Created gain table: %s" % (gaintable_summary(gt)))
gt = simulate_gaintable(gt, phase_error=0.1, amplitude_error=0.1)
original = copy_visibility(self.vis)
vis = apply_gaintable(self.vis, gt)
vis = apply_gaintable(self.vis, gt, inverse=True)
error = numpy.max(numpy.abs(vis.vis - original.vis))
assert error < 1e-12, "Error = %s" % (error)
def sagecal_e_step(vis: BlockVisibility,
evis_all: BlockVisibility,
theta,
beta=1.0,
**kwargs):
"""Calculates E step in equation A12
This is the data model for this window plus the difference between observed data and summed data models
:param vis:
:param theta:
:param kwargs:
:return:
"""
evis = copy_visibility(evis_all)
tvis = copy_visibility(vis, zero=True)
tvis = predict_skycomponent_visibility(tvis, theta[0])
tvis = apply_gaintable(tvis, theta[1])
evis.data['vis'][...] = tvis.data['vis'][...] + \
beta * (vis.data['vis'][...] - evis_all.data['vis'][...])
return evis
def sagecal_solve(vis,
components,
niter=10,
tol=1e-8,
gain=0.25,
callback=None,
**kwargs):
""" Solve
Solve by iterating, performing E step and M step.
:param vis:
:param components:
:param gaintables:
:param kwargs:
:return: The individual data models and the residual visibility
"""
thetas = create_sagecal_thetas(vis, components, **kwargs)
for iter in range(niter):
new_thetas = list()
evis_all = sagecal_e_all(vis, thetas, **kwargs)
print("Iteration %d" % (iter))
for window_index, theta in enumerate(thetas):
evis = sagecal_e_step(vis, evis_all, theta, gain=gain, **kwargs)
new_theta = sagecal_m_step(evis, theta, gain=gain, **kwargs)
new_thetas.append(new_theta)
if callback is not None:
callback(iter, thetas)
flux = new_theta[0].flux[0, 0]
qa = qa_gaintable(new_theta[1])
residual = qa.data['residual']
rms_phase = qa.data['rms-phase']
print("\t Window %d, flux %s, residual %.3f, rms phase %.3f" %
(window_index, str(flux), residual, rms_phase))
thetas = new_thetas
residual_vis = copy_visibility(vis)
final_vis = sagecal_e_all(vis, thetas, **kwargs)
residual_vis.data['vis'][
...] = vis.data['vis'][...] - final_vis.data['vis'][...]
return thetas, residual_vis
def skymodel_cal_fit_gaintable(evis, calskymodel, gain=0.1, niter=3, tol=1e-3, **kwargs):
"""Fit a gaintable to a visibility
This is the update to the gain part of the window
:param evis: Expected vis for this ssm
:param calskymodel: csm element being fit
:param gain: Gain in step
:param niter: Number of iterations
:param kwargs: Gaintable
"""
previous_gt = copy_gaintable(calskymodel[1])
gt = copy_gaintable(calskymodel[1])
model_vis = copy_visibility(evis, zero=True)
model_vis = predict_skycomponent_visibility(model_vis, calskymodel[0].components)
gt = solve_gaintable(evis, model_vis, gt=gt, niter=niter, phase_only=True, gain=0.5,
tol=1e-4, **kwargs)
gt.data['gain'][...] = gain * gt.data['gain'][...] + \
(1 - gain) * previous_gt.data['gain'][...]
gt.data['gain'][...] /= numpy.abs(previous_gt.data['gain'][...])
return gt
def skymodel_cal_solve(vis, skymodels, niter=10, tol=1e-8, gain=0.25, **kwargs):
""" Solve
Solve by iterating, performing E step and M step.
:param vis: Initial visibility
:param components: Initial components to be used
:param gaintables: Initial gain tables to be used
:param kwargs:
:return: The individual data models and the residual visibility
"""
calskymodels = initialise_skymodel_cal_skymodel(vis, skymodels=skymodels, **kwargs)
for iter in range(niter):
new_calskymodels = list()
evis_all = skymodel_cal_e_all(vis, calskymodels)
log.debug("skymodel_cal_solve: Iteration %d" % (iter))
for window_index, csm in enumerate(calskymodels):
evis = skymodel_cal_e_step(vis, evis_all, csm, gain=gain, **kwargs)
new_csm = skymodel_cal_m_step(evis, csm, **kwargs)
new_calskymodels.append((new_csm[0], new_csm[1]))
flux = new_csm[0].components[0].flux[0, 0]
qa = qa_gaintable(new_csm[1])
residual = qa.data['residual']
rms_phase = qa.data['rms-phase']
log.debug("skymodel_cal_solve:\t Window %d, flux %s, residual %.3f, rms phase %.3f" % (window_index,
str(flux), residual,
rms_phase))
calskymodels = [(copy_skymodel(csm[0]), copy_gaintable(csm[1])) for csm in new_calskymodels]
residual_vis = copy_visibility(vis)
final_vis = skymodel_cal_e_all(vis, calskymodels)
residual_vis.data['vis'][...] = vis.data['vis'][...] - final_vis.data['vis'][...]
return calskymodels, residual_vis
def predict_and_apply(ovis, calskymodel):
tvis = copy_visibility(ovis, zero=True)
tvis = predict_skycomponent_visibility(tvis, calskymodel[0].components)
tvis = apply_gaintable(tvis, calskymodel[1])
return tvis
def res_vis(vis, final_vis):
residual_vis = copy_visibility(vis)
residual_vis.data['vis'][
...] = vis.data['vis'][...] - final_vis.data['vis'][...]
return residual_vis
