def ift_ical_sm(v, sm, g):
assert isinstance(v, Visibility) or isinstance(v, BlockVisibility), v
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
if docal and isinstance(sm.gaintable, GainTable):
if isinstance(v, Visibility):
bv = convert_visibility_to_blockvisibility(v)
bv = apply_gaintable(bv, sm.gaintable)
v = convert_blockvisibility_to_visibility(bv)
else:
v = apply_gaintable(v, sm.gaintable)
result = invert_list_serial_workflow([v], [sm.image],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
if isinstance(sm.mask, Image):
result[0].data *= sm.mask.data
return result
def ft_cal_sm(ov, sm, g):
assert isinstance(ov, Visibility) or isinstance(ov,
BlockVisibility), ov
assert isinstance(sm, SkyModel), sm
if g is not None:
assert len(g) == 2, g
assert isinstance(g[0], Image), g[0]
assert isinstance(g[1], ConvolutionFunction), g[1]
v = copy_visibility(ov)
v.data['vis'][...] = 0.0 + 0.0j
if len(sm.components) > 0:
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
v = predict_skycomponent_visibility(v, comps)
else:
v = predict_skycomponent_visibility(v, sm.components)
if isinstance(sm.image, Image):
if numpy.max(numpy.abs(sm.image.data)) > 0.0:
if isinstance(sm.mask, Image):
model = copy_image(sm.image)
model.data *= sm.mask.data
else:
model = sm.image
v = predict_list_serial_workflow([v], [model],
context=context,
vis_slices=vis_slices,
facets=facets,
gcfcf=[g],
**kwargs)[0]
if docal and isinstance(sm.gaintable, GainTable):
if isinstance(ov, Visibility):
bv = convert_visibility_to_blockvisibility(v)
bv = apply_gaintable(bv, sm.gaintable, inverse=True)
v = convert_blockvisibility_to_visibility(bv)
else:
v = apply_gaintable(v, sm.gaintable, inverse=True)
return v
def corrupt_vis(vis, gt, **kwargs):
if isinstance(vis, Visibility):
bv = convert_visibility_to_blockvisibility(vis)
else:
bv = vis
if gt is None:
gt = create_gaintable_from_blockvisibility(bv, **kwargs)
gt = simulate_gaintable(gt, **kwargs)
bv = apply_gaintable(bv, gt)
if isinstance(vis, Visibility):
return convert_blockvisibility_to_visibility(bv)
else:
return bv
def test_calibrate_G_function(self):
self.actualSetup('stokesIQUV', 'linear', f=[100.0, 0.0, 0.0, 50.0])
# Prepare the corrupted visibility data_models
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)
original = copy_visibility(self.vis)
self.vis = apply_gaintable(self.vis, gt)
# Now get the control dictionary and calibrate
controls = create_calibration_controls()
controls['G']['first_selfcal'] = 0
calibrated_vis, gaintables = calibrate_chain(self.vis,
original,
calibration_context='G',
controls=controls)
residual = numpy.max(gaintables['G'].residual)
assert residual < 1e-8, "Max T residual = %s" % residual
def ft_cal_sm(obv, sm):
assert isinstance(obv, BlockVisibility), obv
bv = copy_visibility(obv)
bv.data['vis'][...] = 0.0 + 0.0j
assert len(sm.components) > 0
if isinstance(sm.mask, Image):
comps = copy_skycomponent(sm.components)
comps = apply_beam_to_skycomponent(comps, sm.mask)
bv = predict_skycomponent_visibility(bv, comps)
else:
bv = predict_skycomponent_visibility(bv, sm.components)
if docal and isinstance(sm.gaintable, GainTable):
bv = apply_gaintable(bv, sm.gaintable, inverse=True)
return bv
def selfcal_convert(error_bvis, no_error_bvis):
if selfcal:
gt = solve_gaintable(error_bvis,
no_error_bvis,
gt=None,
phase_only=True,
niter=30,
tol=1e-8,
crosspol=False,
normalise_gains=True,
**kwargs)
error_bvis = apply_gaintable(error_bvis, gt)
if residual:
error_bvis.data[
'vis'] = error_bvis.data['vis'] - no_error_bvis.data['vis']
if context != "ng":
error_vis = convert_blockvisibility_to_visibility(error_bvis)
return error_vis
else:
return error_bvis
