def _define_forest(ns, parent=None, **kw):
if run_purr:
Timba.TDL.GUI.purr(mssel.msname + ".purrlog", [mssel.msname, '.'])
# create Purr pipe
global purrpipe
purrpipe = Purr.Pipe.Pipe(mssel.msname)
# get antennas from MS
ANTENNAS = mssel.get_antenna_set(list(range(1, 15)))
array = Meow.IfrArray(ns, ANTENNAS, mirror_uvw=False)
stas = array.stations()
# get phase centre from MS, setup observation
observation = Meow.Observation(ns,
phase_centre=mssel.get_phase_dir(),
linear=mssel.is_linear_pol(),
circular=mssel.is_circular_pol())
Meow.Context.set(array, observation)
# get active correlations from MS
Meow.Context.active_correlations = mssel.get_correlations()
# make spigot nodes
spigots = spigots0 = outputs = array.spigots(corr=mssel.get_corr_index())
# ...and an inspector for them
StdTrees.vis_inspector(ns.inspector('input'),
spigots,
bookmark="Inspect input visibilities")
inspectors = [ns.inspector('input')]
Bookmarks.make_node_folder("Input visibilities by baseline",
[spigots(p, q) for p, q in array.ifrs()],
sorted=True,
ncol=2,
nrow=2)
inspect_ifrs = array.ifrs()
if do_solve:
# filter solvable baselines by baseline length
solve_ifrs = []
antpos = mssel.ms_antenna_positions
if (min_baseline or max_baseline) and antpos is not None:
for (ip, p), (iq, q) in array.ifr_index():
baseline = math.sqrt(
((antpos[ip, :] - antpos[iq, :])**2).sum())
if (not min_baseline or baseline > min_baseline) and \
(not max_baseline or baseline < max_baseline):
solve_ifrs.append((p, q))
else:
solve_ifrs = array.ifrs()
inspect_ifrs = solve_ifrs
# make a predict tree using the MeqMaker
if do_solve or do_subtract:
predict = meqmaker.make_predict_tree(ns)
# make a ParmGroup and solve jobs for source parameters, if we have any
if do_solve:
parms = {}
for src in meqmaker.get_source_list(ns):
parms.update([(p.name, p) for p in src.get_solvables()])
if parms:
pg_src = ParmGroup.ParmGroup("source",
list(parms.values()),
table_name="sources.fmep",
individual=True,
bookmark=True)
# now make a solvejobs for the source
ParmGroup.SolveJob("cal_source", "Calibrate source model",
pg_src)
# make nodes to compute residuals
if do_subtract:
residuals = ns.residuals
for p, q in array.ifrs():
residuals(p, q) << spigots(p, q) - predict(p, q)
outputs = residuals
# and now we may need to correct the outputs
if do_correct:
if do_correct_sky:
srcs = meqmaker.get_source_list(ns)
sky_correct = srcs and srcs[0]
else:
sky_correct = None
outputs = meqmaker.correct_uv_data(ns,
outputs,
sky_correct=sky_correct,
inspect_ifrs=inspect_ifrs)
# make solve trees
if do_solve:
# inputs to the solver are based on calibration type
# if calibrating visibilities, feed them to condeq directly
if cal_type == CAL.VIS:
observed = spigots
model = predict
# else take ampl/phase component
else:
model = ns.model
observed = ns.observed
if cal_type == CAL.AMPL:
for p, q in array.ifrs():
observed(p, q) << Meq.Abs(spigots(p, q))
model(p, q) << Meq.Abs(predict(p, q))
elif cal_type == CAL.LOGAMPL:
for p, q in array.ifrs():
observed(p, q) << Meq.Log(Meq.Abs(spigots(p, q)))
model(p, q) << Meq.Log(Meq.Abs(predict(p, q)))
elif cal_type == CAL.PHASE:
for p, q in array.ifrs():
observed(p, q) << 0
model(p, q) << Meq.Abs(predict(p, q)) * Meq.FMod(
Meq.Arg(spigots(p, q)) - Meq.Arg(predict(p, q)),
2 * math.pi)
else:
raise ValueError("unknown cal_type setting: " + str(cal_type))
# make a solve tree
solve_tree = StdTrees.SolveTree(ns, model, solve_ifrs=solve_ifrs)
# the output of the sequencer is either the residuals or the spigots,
# according to what has been set above
outputs = solve_tree.sequencers(inputs=observed, outputs=outputs)
# make sinks and vdm.
# The list of inspectors must be supplied here
inspectors += meqmaker.get_inspectors() or []
StdTrees.make_sinks(ns, outputs, spigots=spigots0, post=inspectors)
Bookmarks.make_node_folder("Corrected/residual visibilities by baseline",
[outputs(p, q) for p, q in array.ifrs()],
sorted=True,
ncol=2,
nrow=2)
if not do_solve:
if do_subtract:
name = "Generate residuals"
comment = "Generated residual visibilities."
elif do_correct:
name = "Generate corrected data"
comment = "Generated corrected visibilities."
else:
name = None
if name:
# make a TDL job to runsthe tree
def run_tree(mqs, parent, **kw):
global tile_size
purrpipe.title("Calibrating").comment(comment)
mqs.execute(Meow.Context.vdm.name,
mssel.create_io_request(tile_size),
wait=False)
TDLRuntimeMenu(
name,
TDLOption(
'tile_size',
"Tile size, in timeslots", [10, 60, 120, 240],
more=int,
doc=
"""Input data is sliced by time, and processed in chunks (tiles) of
the indicated size. Larger tiles are faster, but use more memory."""
), TDLRuntimeJob(run_tree, name))
# very important -- insert meqmaker's runtime options properly
# this should come last, since runtime options may be built up during compilation.
TDLRuntimeOptions(*meqmaker.runtime_options(nest=False))
# insert solvejobs
if do_solve:
TDLRuntimeOptions(*ParmGroup.get_solvejob_options())
# finally, setup imaging options
imsel = mssel.imaging_selector(npix=512,
arcmin=meqmaker.estimate_image_size())
TDLRuntimeMenu("Make an image from this MS", *imsel.option_list())
# and close meqmaker -- this exports annotations, etc
meqmaker.close()
def _define_forest(ns,parent=None,**kw):
if run_purr:
Timba.TDL.GUI.purr(mssel.msname+".purrlog",[mssel.msname,'.']);
# create Purr pipe
global purrpipe;
purrpipe = Purr.Pipe.Pipe(mssel.msname);
# get antennas from MS
ANTENNAS = mssel.get_antenna_set(list(range(1,15)));
array = Meow.IfrArray(ns,ANTENNAS,mirror_uvw=False);
stas = array.stations();
# get phase centre from MS, setup observation
observation = Meow.Observation(ns,phase_centre=mssel.get_phase_dir(),
linear=mssel.is_linear_pol(),
circular=mssel.is_circular_pol());
Meow.Context.set(array,observation);
# get active correlations from MS
Meow.Context.active_correlations = mssel.get_correlations();
# make spigot nodes
spigots = spigots0 = outputs = array.spigots(corr=mssel.get_corr_index());
# ...and an inspector for them
StdTrees.vis_inspector(ns.inspector('input'),spigots,
bookmark="Inspect input visibilities");
inspectors = [ ns.inspector('input') ];
Bookmarks.make_node_folder("Input visibilities by baseline",
[ spigots(p,q) for p,q in array.ifrs() ],sorted=True,ncol=2,nrow=2);
inspect_ifrs = array.ifrs();
if do_solve:
# filter solvable baselines by baseline length
solve_ifrs = [];
antpos = mssel.ms_antenna_positions;
if (min_baseline or max_baseline) and antpos is not None:
for (ip,p),(iq,q) in array.ifr_index():
baseline = math.sqrt(((antpos[ip,:]-antpos[iq,:])**2).sum());
if (not min_baseline or baseline > min_baseline) and \
(not max_baseline or baseline < max_baseline):
solve_ifrs.append((p,q));
else:
solve_ifrs = array.ifrs();
inspect_ifrs = solve_ifrs;
# make a predict tree using the MeqMaker
if do_solve or do_subtract:
predict = meqmaker.make_predict_tree(ns);
# make a ParmGroup and solve jobs for source parameters, if we have any
if do_solve:
parms = {};
for src in meqmaker.get_source_list(ns):
parms.update([(p.name,p) for p in src.get_solvables()]);
if parms:
pg_src = ParmGroup.ParmGroup("source",list(parms.values()),
table_name="sources.fmep",
individual=True,bookmark=True);
# now make a solvejobs for the source
ParmGroup.SolveJob("cal_source","Calibrate source model",pg_src);
# make nodes to compute residuals
if do_subtract:
residuals = ns.residuals;
for p,q in array.ifrs():
residuals(p,q) << spigots(p,q) - predict(p,q);
outputs = residuals;
# and now we may need to correct the outputs
if do_correct:
if do_correct_sky:
srcs = meqmaker.get_source_list(ns);
sky_correct = srcs and srcs[0];
else:
sky_correct = None;
outputs = meqmaker.correct_uv_data(ns,outputs,sky_correct=sky_correct,inspect_ifrs=inspect_ifrs);
# make solve trees
if do_solve:
# inputs to the solver are based on calibration type
# if calibrating visibilities, feed them to condeq directly
if cal_type == CAL.VIS:
observed = spigots;
model = predict;
# else take ampl/phase component
else:
model = ns.model;
observed = ns.observed;
if cal_type == CAL.AMPL:
for p,q in array.ifrs():
observed(p,q) << Meq.Abs(spigots(p,q));
model(p,q) << Meq.Abs(predict(p,q));
elif cal_type == CAL.LOGAMPL:
for p,q in array.ifrs():
observed(p,q) << Meq.Log(Meq.Abs(spigots(p,q)));
model(p,q) << Meq.Log(Meq.Abs(predict(p,q)));
elif cal_type == CAL.PHASE:
for p,q in array.ifrs():
observed(p,q) << 0;
model(p,q) << Meq.Abs(predict(p,q))*Meq.FMod(Meq.Arg(spigots(p,q))-Meq.Arg(predict(p,q)),2*math.pi);
else:
raise ValueError("unknown cal_type setting: "+str(cal_type));
# make a solve tree
solve_tree = StdTrees.SolveTree(ns,model,solve_ifrs=solve_ifrs);
# the output of the sequencer is either the residuals or the spigots,
# according to what has been set above
outputs = solve_tree.sequencers(inputs=observed,outputs=outputs);
# make sinks and vdm.
# The list of inspectors must be supplied here
inspectors += meqmaker.get_inspectors() or [];
StdTrees.make_sinks(ns,outputs,spigots=spigots0,post=inspectors);
Bookmarks.make_node_folder("Corrected/residual visibilities by baseline",
[ outputs(p,q) for p,q in array.ifrs() ],sorted=True,ncol=2,nrow=2);
if not do_solve:
if do_subtract:
name = "Generate residuals";
comment = "Generated residual visibilities.";
elif do_correct:
name = "Generate corrected data";
comment = "Generated corrected visibilities.";
else:
name = None;
if name:
# make a TDL job to runsthe tree
def run_tree (mqs,parent,**kw):
global tile_size;
purrpipe.title("Calibrating").comment(comment);
mqs.execute(Meow.Context.vdm.name,mssel.create_io_request(tile_size),wait=False);
TDLRuntimeMenu(name,
TDLOption('tile_size',"Tile size, in timeslots",[10,60,120,240],more=int,
doc="""Input data is sliced by time, and processed in chunks (tiles) of
the indicated size. Larger tiles are faster, but use more memory."""),
TDLRuntimeJob(run_tree,name)
);
# very important -- insert meqmaker's runtime options properly
# this should come last, since runtime options may be built up during compilation.
TDLRuntimeOptions(*meqmaker.runtime_options(nest=False));
# insert solvejobs
if do_solve:
TDLRuntimeOptions(*ParmGroup.get_solvejob_options());
# finally, setup imaging options
imsel = mssel.imaging_selector(npix=512,arcmin=meqmaker.estimate_image_size());
TDLRuntimeMenu("Make an image from this MS",*imsel.option_list());
# and close meqmaker -- this exports annotations, etc
meqmaker.close();
