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();
def _define_forest(ns):
# make pynodes, xyzcomponent for sources
ANTENNAS = mssel.get_antenna_set(list(range(1, 15)))
array = Meow.IfrArray(ns, ANTENNAS, mirror_uvw=False)
observation = Meow.Observation(ns)
Meow.Context.set(array, observation)
# make a predict tree using the MeqMaker
if do_solve or do_subtract or not do_not_simulate:
outputs = predict = meqmaker.make_tree(ns)
# make a list of selected corrs
selected_corrs = cal_corr.split(" ")
# make spigot nodes
if not do_not_simulate and do_add:
spigots = spigots0 = outputs = array.spigots()
sums = ns.sums
for p, q in array.ifrs():
sums(p, q) << spigots(p, q) + predict(p, q)
outputs = sums
# make spigot nodes
if do_not_simulate:
spigots = spigots0 = outputs = array.spigots()
# make nodes to compute residuals
# 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:
if src_name:
sky_correct = src_name
else:
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)
# make solve trees
if do_solve:
# extract selected correlations
if cal_corr != ALL_CORRS:
index = [CORR_INDICES[c] for c in selected_corrs]
for p, q in array.ifrs():
ns.sel_predict(p, q) << Meq.Selector(
predict(p, q), index=index, multi=True)
ns.sel_spigot(p, q) << Meq.Selector(
spigots(p, q), index=index, multi=True)
spigots = ns.sel_spigot
predict = ns.sel_predict
model = predict
observed = spigots
# make a solve tree
solve_tree = Meow.StdTrees.SolveTree(ns, model)
# 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)
# throw in a bit of noise
if not do_not_simulate and noise_stddev:
# make two complex noise terms per station (x/y)
noisedef = Meq.GaussNoise(stddev=noise_stddev)
noise_x = ns.sta_noise('x')
noise_y = ns.sta_noise('y')
for p in array.stations():
noise_x(p) << Meq.ToComplex(noisedef, noisedef)
noise_y(p) << Meq.ToComplex(noisedef, noisedef)
# now combine them into per-baseline noise matrices
for p, q in array.ifrs():
noise = ns.noise(p, q) << Meq.Matrix22(
noise_x(p) + noise_x(q),
noise_x(p) + noise_y(q),
noise_y(p) + noise_x(q),
noise_y(p) + noise_y(q))
ns.noisy_predict(p, q) << outputs(p, q) + noise
outputs = ns.noisy_predict
# make sinks and vdm.
# The list of inspectors comes in handy here
Meow.StdTrees.make_sinks(ns,
outputs,
spigots=None,
post=meqmaker.get_inspectors())
if not do_not_simulate:
# add simulate job
TDLRuntimeJob(job_simulate, "Simulate")
if do_not_simulate and not do_solve:
# add subtract or correct job
TDLRuntimeJob(job_subtract, "Subtract or Correct the data")
if do_not_simulate and do_solve:
pg_iono = ParmGroup.ParmGroup("Z_iono",
outputs.search(tags="solvable Z"),
table_name="iono.mep",
bookmark=4)
ParmGroup.SolveJob("cal_iono", "Calibrate Ionosphere parameters ",
pg_iono)
# 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));
# and insert all solvejobs
TDLRuntimeOptions(*ParmGroup.get_solvejob_options())
# finally, setup imaging options
imsel = mssel.imaging_selector(npix=512,
arcmin=meqmaker.estimate_image_size())
TDLRuntimeMenu("Imaging options", *imsel.option_list())
def _define_forest(ns):
#make pynodes, xyzcomponent for sources
ANTENNAS = mssel.get_antenna_set(range(1,15));
array = Meow.IfrArray(ns,ANTENNAS,mirror_uvw=False);
observation = Meow.Observation(ns);
Meow.Context.set(array,observation);
# make a predict tree using the MeqMaker
if do_solve or do_subtract or not do_not_simulate:
outputs=predict = meqmaker.make_tree(ns);
#make a list of selected corrs
selected_corrs = cal_corr.split(" ");
# make spigot nodes
if not do_not_simulate and do_add:
spigots = spigots0 = outputs = array.spigots();
sums = ns.sums;
for p,q in array.ifrs():
sums(p,q) << spigots(p,q) + predict(p,q);
outputs = sums;
# make spigot nodes
if do_not_simulate:
spigots = spigots0 = outputs = array.spigots();
# make nodes to compute residuals
# 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:
if src_name:
sky_correct = src_name;
else:
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);
# make solve trees
if do_solve:
# extract selected correlations
if cal_corr != ALL_CORRS:
index = [ CORR_INDICES[c] for c in selected_corrs ];
for p,q in array.ifrs():
ns.sel_predict(p,q) << Meq.Selector(predict(p,q),index=index,multi=True);
ns.sel_spigot(p,q) << Meq.Selector(spigots(p,q),index=index,multi=True);
spigots = ns.sel_spigot;
predict = ns.sel_predict;
model = predict;
observed = spigots;
# make a solve tree
solve_tree = Meow.StdTrees.SolveTree(ns,model);
# 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);
# throw in a bit of noise
if not do_not_simulate and noise_stddev:
# make two complex noise terms per station (x/y)
noisedef = Meq.GaussNoise(stddev=noise_stddev)
noise_x = ns.sta_noise('x');
noise_y = ns.sta_noise('y');
for p in array.stations():
noise_x(p) << Meq.ToComplex(noisedef,noisedef);
noise_y(p) << Meq.ToComplex(noisedef,noisedef);
# now combine them into per-baseline noise matrices
for p,q in array.ifrs():
noise = ns.noise(p,q) << Meq.Matrix22(
noise_x(p)+noise_x(q),noise_x(p)+noise_y(q),
noise_y(p)+noise_x(q),noise_y(p)+noise_y(q)
);
ns.noisy_predict(p,q) << outputs(p,q) + noise;
outputs = ns.noisy_predict;
# make sinks and vdm.
# The list of inspectors comes in handy here
Meow.StdTrees.make_sinks(ns,outputs,spigots=None,post=meqmaker.get_inspectors());
if not do_not_simulate:
#add simulate job
TDLRuntimeJob(job_simulate,"Simulate");
if do_not_simulate and not do_solve:
#add subtract or correct job
TDLRuntimeJob(job_subtract,"Subtract or Correct the data");
if do_not_simulate and do_solve:
pg_iono = ParmGroup.ParmGroup("Z_iono",
outputs.search(tags="solvable Z"),
table_name="iono.mep",bookmark=4);
ParmGroup.SolveJob("cal_iono","Calibrate Ionosphere parameters ",pg_iono);
# 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));
# and insert all solvejobs
TDLRuntimeOptions(*ParmGroup.get_solvejob_options());
# finally, setup imaging options
imsel = mssel.imaging_selector(npix=512,arcmin=meqmaker.estimate_image_size());
TDLRuntimeMenu("Imaging options",*imsel.option_list());
def _define_forest(ns, parent=None, **kw):
if not mssel.msname:
raise RuntimeError("MS not set")
if run_purr:
Timba.TDL.GUI.purr(mssel.msname + ".purrlog", [mssel.msname, '.'])
# create Purr pipe
global purrpipe
purrpipe = Purr.Pipe.Pipe(mssel.msname)
# setup contexts from MS
mssel.setup_observation_context(ns, prefer_baseline_uvw=True)
array = Meow.Context.array
# make spigot nodes for data
if do_solve or do_output not in [CORRUPTED_MODEL]:
mssel.enable_input_column(True)
spigots = spigots0 = outputs = array.spigots(
corr=mssel.get_corr_index())
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(spigots, "Input visibilities")
# add IFR-based errors, if any
spigots = meqmaker.apply_visibility_processing(ns, spigots)
else:
mssel.enable_input_column(False)
spigots = spigots0 = None
# make spigot nodes for model
corrupt_uvdata = model_spigots = None
if read_ms_model:
mssel.enable_model_column(True)
model_spigots = array.spigots(column="PREDICT",
corr=mssel.get_corr_index())
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(model_spigots,
"UV-model visibilities")
# if calibrating on (input-corrupt model), make corrupt model
if do_solve and cal_type == CAL.DIFF:
corrupt_uvdata = meqmaker.corrupt_uv_data(ns, model_spigots)
# if needed, then make a predict tree using the MeqMaker
if do_solve or do_output != CORRECTED_DATA:
if model_spigots and not corrupt_uvdata:
uvdata = model_spigots
else:
uvdata = None
predict = meqmaker.make_predict_tree(ns, uvdata=uvdata)
else:
predict = None
output_title = "Uncorrected residuals"
# make nodes to compute residuals
if do_output in [CORRECTED_RESIDUALS, RESIDUALS]:
residuals = ns.residuals
for p, q in array.ifrs():
if corrupt_uvdata:
residuals(p, q) << Meq.Subtract(spigots(
p, q), corrupt_uvdata(p, q), predict(p, q))
else:
residuals(p, q) << spigots(p, q) - predict(p, q)
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(residuals, "Uncorrected residuals")
outputs = residuals
# and now we may need to correct the outputs
if do_output in [CORRECTED_DATA, CORRECTED_RESIDUALS]:
if do_correct_sky:
srcs = meqmaker.get_source_list(ns)
if do_correct_sky is FIRST_SOURCE:
sky_correct = srcs and srcs[0]
else:
srcs = [
src for src in srcs
if fnmatch.fnmatchcase(src.name, do_correct_sky)
]
sky_correct = srcs and srcs[0]
else:
sky_correct = None
outputs = meqmaker.correct_uv_data(ns,
outputs,
sky_correct=sky_correct,
flag_jones=flag_jones)
output_title = "Corrected data" if do_output is CORRECTED_DATA else "Corrected residuals"
elif do_output == CORRUPTED_MODEL:
outputs = predict
output_title = "Predict"
elif do_output == CORRUPTED_MODEL_ADD:
outputs = ns.output
for p, q in array.ifrs():
outputs(p, q) << spigots(p, q) + predict(p, q)
output_title = "Data+predict"
# make flaggers
if flag_enable and do_output in [
CORRECTED_DATA, RESIDUALS, CORRECTED_RESIDUALS
]:
flaggers = []
if flag_res is not None or flag_mean_res is not None:
for p, q in array.ifrs():
ns.absres(p, q) << Meq.Abs(outputs(p, q))
# make flagger for residuals
if flag_res is not None:
for p, q in array.ifrs():
ns.flagres(p, q) << Meq.ZeroFlagger(
ns.absres(p, q) - flag_res,
oper='gt',
flag_bit=Meow.MSUtils.FLAGMASK_OUTPUT)
flaggers.append(ns.flagres)
# ...and an inspector for them
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(ns.flagres,
"Residual amplitude flags")
# make flagger for mean residuals
if flag_mean_res is not None:
ns.meanabsres << Meq.Mean(
*[ns.absres(p, q) for p, q in array.ifrs()])
ns.flagmeanres << Meq.ZeroFlagger(
ns.meanabsres - flag_mean_res,
oper='gt',
flag_bit=Meow.MSUtils.FLAGMASK_OUTPUT)
Meow.Bookmarks.Page("Mean residual amplitude flags").add(
ns.flagmeanres, viewer="Result Plotter")
flaggers.append(lambda p, q: ns.flagmeanres)
# merge flags into output
if flaggers:
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(outputs,
output_title + " (unflagged)")
for p, q in array.ifrs():
ns.flagged(p, q) << Meq.MergeFlags(
outputs(p, q), *[f(p, q) for f in flaggers])
outputs = ns.flagged
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(outputs, output_title)
abs_outputs = outputs('abs')
for p, q in array.ifrs():
abs_outputs(p, q) << Meq.Abs(outputs(p, q))
meqmaker.make_per_ifr_bookmarks(abs_outputs,
output_title + " (mean amplitudes)")
# make solve trees
if do_solve:
# parse ifr specification
solve_ifrs = array.subset(calibrate_ifrs, strict=False).ifrs()
if not solve_ifrs:
raise RuntimeError(
"No interferometers selected for calibration. Check your ifr specification (under calibration options)."
)
# inputs to the solver are based on calibration type
if corrupt_uvdata:
[
ns.diff(p, q) << spigots(p, q) - corrupt_uvdata(p, q)
for p, q in solve_ifrs
]
rhs = ns.diff
else:
rhs = spigots
lhs = predict
weights = modulo = None
# if calibrating visibilities, feed them to condeq directly, else take ampl/phase
if cal_what == CAL.VIS:
pass
elif cal_what == CAL.AMPL:
[
x('ampl', p, q) << Meq.Abs(x(p, q)) for p, q in ifrs
for x in [rhs, lhs]
]
lhs = lhs('ampl')
rhs = rhs('ampl')
elif cal_what == CAL.LOGAMPL:
[
x('logampl', p, q) << Meq.Log(Meq.Abs(x(p, q)))
for p, q in ifrs for x in [rhs, lhs]
]
lhs = lhs('logampl')
rhs = rhs('logampl')
elif cal_what == CAL.PHASE:
[
x('phase', p, q) << Meq.Arg(x(p, q)) for p, q in ifrs
for x in [rhs, lhs]
]
[rhs('ampl', p, q) << Meq.Abs(rhs(p, q)) for p, q in ifrs]
lhs = lhs('phase')
rhs = rhs('phase')
weights = rhs('ampl')
modulo = 2 * math.pi
else:
raise ValueError("unknown cal_what setting: " + str(cal_what))
# make a solve tree
solve_tree = StdTrees.SolveTree(ns,
lhs,
solve_ifrs=solve_ifrs,
weights=weights,
modulo=modulo)
# the output of the sequencer is either the residuals or the spigots,
# according to what has been set above
outputs = solve_tree.sequencers(inputs=rhs, outputs=outputs)
post = ((enable_inspectors and meqmaker.get_inspectors()) or [])
StdTrees.make_sinks(ns,
outputs,
spigots=spigots0,
post=post,
corr_index=mssel.get_corr_index())
if not do_solve:
name = "Generate " + output_title.lower()
comment = "Generated " + output_title.lower()
if name:
# make a TDL job to run the tree
def run_tree(mqs, parent, wait=False, **kw):
global tile_size
purrpipe.title("Calibrating").comment(comment)
return mqs.execute(Meow.Context.vdm.name,
mssel.create_io_request(tile_size),
wait=wait)
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."""
), TDLJob(run_tree, name, job_id='generate_visibilities'))
# 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 not mssel.msname:
raise RuntimeError,"MS not set";
if run_purr:
Timba.TDL.GUI.purr(mssel.msname+".purrlog",[mssel.msname,'.']);
# create Purr pipe
global purrpipe;
purrpipe = Purr.Pipe.Pipe(mssel.msname);
# setup contexts from MS
mssel.setup_observation_context(ns,prefer_baseline_uvw=True);
array = Meow.Context.array;
# make spigot nodes for data
if do_solve or do_output not in [CORRUPTED_MODEL]:
mssel.enable_input_column(True);
spigots = spigots0 = outputs = array.spigots(corr=mssel.get_corr_index());
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(spigots,"Input visibilities");
# add IFR-based errors, if any
spigots = meqmaker.apply_visibility_processing(ns,spigots);
else:
mssel.enable_input_column(False);
spigots = spigots0 = None;
# make spigot nodes for model
corrupt_uvdata = model_spigots = None;
if read_ms_model:
mssel.enable_model_column(True);
model_spigots = array.spigots(column="PREDICT",corr=mssel.get_corr_index());
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(model_spigots,"UV-model visibilities");
# if calibrating on (input-corrupt model), make corrupt model
if do_solve and cal_type == CAL.DIFF:
corrupt_uvdata = meqmaker.corrupt_uv_data(ns,model_spigots);
# if needed, then make a predict tree using the MeqMaker
if do_solve or do_output != CORRECTED_DATA:
if model_spigots and not corrupt_uvdata:
uvdata = model_spigots;
else:
uvdata = None;
predict = meqmaker.make_predict_tree(ns,uvdata=uvdata);
else:
predict = None;
output_title = "Uncorrected residuals";
# make nodes to compute residuals
if do_output in [CORRECTED_RESIDUALS,RESIDUALS]:
residuals = ns.residuals;
for p,q in array.ifrs():
if corrupt_uvdata:
residuals(p,q) << Meq.Subtract(spigots(p,q),corrupt_uvdata(p,q),predict(p,q));
else:
residuals(p,q) << spigots(p,q) - predict(p,q);
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(residuals,"Uncorrected residuals");
outputs = residuals;
# and now we may need to correct the outputs
if do_output in [CORRECTED_DATA,CORRECTED_RESIDUALS]:
if do_correct_sky:
srcs = meqmaker.get_source_list(ns);
if do_correct_sky is FIRST_SOURCE:
sky_correct = srcs and srcs[0];
else:
srcs = [ src for src in srcs if fnmatch.fnmatchcase(src.name,do_correct_sky) ];
sky_correct = srcs and srcs[0];
else:
sky_correct = None;
outputs = meqmaker.correct_uv_data(ns,outputs,sky_correct=sky_correct,
flag_jones=flag_jones);
output_title = "Corrected data" if do_output is CORRECTED_DATA else "Corrected residuals";
elif do_output == CORRUPTED_MODEL:
outputs = predict;
output_title = "Predict";
elif do_output == CORRUPTED_MODEL_ADD:
outputs = ns.output;
for p,q in array.ifrs():
outputs(p,q) << spigots(p,q) + predict(p,q);
output_title = "Data+predict";
# make flaggers
if flag_enable and do_output in [ CORRECTED_DATA,RESIDUALS,CORRECTED_RESIDUALS ]:
flaggers = [];
if flag_res is not None or flag_mean_res is not None:
for p,q in array.ifrs():
ns.absres(p,q) << Meq.Abs(outputs(p,q));
# make flagger for residuals
if flag_res is not None:
for p,q in array.ifrs():
ns.flagres(p,q) << Meq.ZeroFlagger(ns.absres(p,q)-flag_res,oper='gt',flag_bit=Meow.MSUtils.FLAGMASK_OUTPUT);
flaggers.append(ns.flagres);
# ...and an inspector for them
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(ns.flagres,"Residual amplitude flags");
# make flagger for mean residuals
if flag_mean_res is not None:
ns.meanabsres << Meq.Mean(*[ns.absres(p,q) for p,q in array.ifrs()]);
ns.flagmeanres << Meq.ZeroFlagger(ns.meanabsres-flag_mean_res,oper='gt',flag_bit=Meow.MSUtils.FLAGMASK_OUTPUT);
Meow.Bookmarks.Page("Mean residual amplitude flags").add(ns.flagmeanres,viewer="Result Plotter");
flaggers.append(lambda p,q:ns.flagmeanres);
# merge flags into output
if flaggers:
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(outputs,output_title+" (unflagged)");
for p,q in array.ifrs():
ns.flagged(p,q) << Meq.MergeFlags(outputs(p,q),*[f(p,q) for f in flaggers]);
outputs = ns.flagged;
if enable_inspectors:
meqmaker.make_per_ifr_bookmarks(outputs,output_title);
abs_outputs = outputs('abs');
for p,q in array.ifrs():
abs_outputs(p,q) << Meq.Abs(outputs(p,q));
meqmaker.make_per_ifr_bookmarks(abs_outputs,output_title+" (mean amplitudes)");
# make solve trees
if do_solve:
# parse ifr specification
solve_ifrs = array.subset(calibrate_ifrs,strict=False).ifrs();
if not solve_ifrs:
raise RuntimeError,"No interferometers selected for calibration. Check your ifr specification (under calibration options).";
# inputs to the solver are based on calibration type
if corrupt_uvdata:
[ ns.diff(p,q) << spigots(p,q) - corrupt_uvdata(p,q) for p,q in solve_ifrs ];
rhs = ns.diff;
else:
rhs = spigots;
lhs = predict;
weights = modulo = None;
# if calibrating visibilities, feed them to condeq directly, else take ampl/phase
if cal_what == CAL.VIS:
pass;
elif cal_what == CAL.AMPL:
[ x('ampl',p,q) << Meq.Abs(x(p,q)) for p,q in ifrs for x in rhs,lhs ];
lhs = lhs('ampl');
rhs = rhs('ampl');
elif cal_what == CAL.LOGAMPL:
[ x('logampl',p,q) << Meq.Log(Meq.Abs(x(p,q))) for p,q in ifrs for x in rhs,lhs ];
lhs = lhs('logampl');
rhs = rhs('logampl');
elif cal_what == CAL.PHASE:
[ x('phase',p,q) << Meq.Arg(x(p,q)) for p,q in ifrs for x in rhs,lhs ];
[ rhs('ampl',p,q) << Meq.Abs(rhs(p,q)) for p,q in ifrs ];
lhs = lhs('phase');
rhs = rhs('phase');
weights = rhs('ampl');
modulo = 2*math.pi;
else:
raise ValueError,"unknown cal_what setting: "+str(cal_what);
# make a solve tree
solve_tree = StdTrees.SolveTree(ns,lhs,solve_ifrs=solve_ifrs,weights=weights,modulo=modulo);
# the output of the sequencer is either the residuals or the spigots,
# according to what has been set above
outputs = solve_tree.sequencers(inputs=rhs,outputs=outputs);
post = ( ( enable_inspectors and meqmaker.get_inspectors() ) or [] );
StdTrees.make_sinks(ns,outputs,spigots=spigots0,post=post,corr_index=mssel.get_corr_index());
if not do_solve:
name = "Generate "+output_title.lower();
comment = "Generated "+output_title.lower();
if name:
# make a TDL job to run the tree
def run_tree (mqs,parent,wait=False,**kw):
global tile_size;
purrpipe.title("Calibrating").comment(comment);
return mqs.execute(Meow.Context.vdm.name,mssel.create_io_request(tile_size),wait=wait);
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."""),
TDLJob(run_tree,name,job_id='generate_visibilities')
);
# 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 not mssel.msname:
raise RuntimeError('MS name not set')
mssel.setup_observation_context(ns)
array = Context.array
# Data and model input
if do_solve or output_type.need_data:
mssel.enable_input_column(True)
spigots = array.spigots(corr=mssel.get_corr_index())
meqmaker.make_per_ifr_bookmarks(spigots, 'Input visibilities')
else:
mssel.enable_input_column(False)
spigots = None
if do_solve or output_type.need_model:
predict = meqmaker.make_predict_tree(ns, uvdata=None)
else:
predict = None
# Data output
outputs = output_type.apply(ns, meqmaker, array.ifrs(), spigots, predict)
# Flagging
if flag_enable and output_type.flag_data:
flaggers = []
if flag_res is not None or flag_mean_res is not None:
for p, q in array.ifrs():
ns.absres(p, q) << Meq.Abs(outputs(p, q))
if flag_res is not None:
for p, q in array.ifrs():
ns.flagres(p, q) << Meq.ZeroFlagger(
ns.absres(p, q) - flag_res,
oper='gt',
flag_bit=MSUtils.FLAGMASK_OUTPUT)
flaggers.append(ns.flagres)
meqmaker.make_per_ifr_bookmarks(ns.flagres,
'Residual amplitude flags')
if flag_mean_res is not None:
ns.meanabsres << Meq.Mean(
*[ns.absres(p, q) for p, q in array.ifrs()])
ns.flagmeanres << Meq.ZeroFlagger(ns.meanabsres - flag_mean_res,
oper='gt',
flag_bit=MSUtils.FLAGMASK_OUTPUT)
Bookmarks.Page('Mean residual amplitude flags').add(
ns.flagmeanres, viewer='Result Plotter')
flaggers.append(lambda p, q: ns.flagmeanres)
if flaggers:
meqmaker.make_per_ifr_bookmarks(outputs,
output_type.desc + ' (unflagged)')
for p, q in array.ifrs():
ns.flagged(p, q) << Meq.MergeFlags(
outputs(p, q), *[f(p, q) for f in flaggers])
outputs = ns.flagged
meqmaker.make_per_ifr_bookmarks(outputs, output_type.desc)
# Solve trees
if do_solve:
# parse ifr specification
solve_ifrs = array.subset(calibrate_ifrs, strict=False).ifrs()
if not solve_ifrs:
raise RuntimeError(
'No interferometers selected for calibration. '
'Check your ifr specification (under calibration options).')
lhs, rhs, weights, modulo = cal_quant.apply(solve_ifrs, predict,
spigots)
solve_tree = StdTrees.SolveTree(ns,
lhs,
solve_ifrs=solve_ifrs,
weights=weights,
modulo=modulo)
outputs = solve_tree.sequencers(inputs=rhs, outputs=outputs)
StdTrees.make_sinks(ns,
outputs,
spigots=spigots,
post=meqmaker.get_inspectors() or [],
corr_index=mssel.get_corr_index())
if not do_solve:
name = 'Generate ' + output_type.desc.lower()
comment = 'Generated ' + output_type.desc.lower()
def run_tree(mqs, parent, wait=False, **kw):
return mqs.execute(Context.vdm.name,
mssel.create_io_request(tile_size),
wait=wait)
doc = """Input data are sliced by time, and processed in chunks (tiles) of
the indicated size. Larger tiles are faster, but use more memory."""
TDLRuntimeMenu(
name,
TDLOption('tile_size',
'Tile size, in timeslots', [10, 60, 120, 240],
more=int,
doc=doc),
TDLJob(run_tree, name, job_id='generate_visibilities'))
# 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))
if do_solve:
TDLRuntimeOptions(*ParmGroup.get_solvejob_options())
imsel = mssel.imaging_selector(npix=512,
arcmin=meqmaker.estimate_image_size())
TDLRuntimeMenu('Make an image', *imsel.option_list())
meqmaker.close()
def _define_forest (ns, parent=None, **kw):
if not mssel.msname:
raise RuntimeError ('MS name not set')
mssel.setup_observation_context (ns)
array = Context.array
# Data and model input
if do_solve or output_type.need_data:
mssel.enable_input_column (True)
spigots = array.spigots (corr=mssel.get_corr_index ())
meqmaker.make_per_ifr_bookmarks (spigots, 'Input visibilities')
else:
mssel.enable_input_column (False)
spigots = None
if do_solve or output_type.need_model:
predict = meqmaker.make_predict_tree (ns, uvdata=None)
else:
predict = None
# Data output
outputs = output_type.apply (ns, meqmaker, array.ifrs (), spigots, predict)
# Flagging
if flag_enable and output_type.flag_data:
flaggers = []
if flag_res is not None or flag_mean_res is not None:
for p, q in array.ifrs ():
ns.absres(p,q) << Meq.Abs (outputs(p,q))
if flag_res is not None:
for p, q in array.ifrs ():
ns.flagres(p,q) << Meq.ZeroFlagger (ns.absres(p,q) - flag_res,
oper='gt',
flag_bit=MSUtils.FLAGMASK_OUTPUT)
flaggers.append (ns.flagres)
meqmaker.make_per_ifr_bookmarks (ns.flagres, 'Residual amplitude flags')
if flag_mean_res is not None:
ns.meanabsres << Meq.Mean (*[ns.absres(p,q) for p, q in array.ifrs()])
ns.flagmeanres << Meq.ZeroFlagger (ns.meanabsres - flag_mean_res,
oper='gt', flag_bit=MSUtils.FLAGMASK_OUTPUT)
Bookmarks.Page ('Mean residual amplitude flags').add (ns.flagmeanres,
viewer='Result Plotter')
flaggers.append (lambda p, q: ns.flagmeanres)
if flaggers:
meqmaker.make_per_ifr_bookmarks (outputs, output_type.desc + ' (unflagged)')
for p, q in array.ifrs ():
ns.flagged(p,q) << Meq.MergeFlags (outputs(p,q), *[f(p,q) for f in flaggers])
outputs = ns.flagged
meqmaker.make_per_ifr_bookmarks (outputs, output_type.desc)
# Solve trees
if do_solve:
# parse ifr specification
solve_ifrs = array.subset (calibrate_ifrs, strict=False).ifrs()
if not solve_ifrs:
raise RuntimeError ('No interferometers selected for calibration. '
'Check your ifr specification (under calibration options).')
lhs, rhs, weights, modulo = cal_quant.apply (solve_ifrs, predict, spigots)
solve_tree = StdTrees.SolveTree (ns, lhs, solve_ifrs=solve_ifrs,
weights=weights, modulo=modulo)
outputs = solve_tree.sequencers (inputs=rhs, outputs=outputs)
StdTrees.make_sinks (ns, outputs, spigots=spigots,
post=meqmaker.get_inspectors () or [],
corr_index=mssel.get_corr_index ())
if not do_solve:
name = 'Generate ' + output_type.desc.lower ()
comment = 'Generated ' + output_type.desc.lower ()
def run_tree (mqs, parent, wait=False, **kw):
return mqs.execute (Context.vdm.name, mssel.create_io_request (tile_size),
wait=wait)
doc = """Input data are sliced by time, and processed in chunks (tiles) of
the indicated size. Larger tiles are faster, but use more memory."""
TDLRuntimeMenu(name, TDLOption ('tile_size', 'Tile size, in timeslots',
[10, 60, 120, 240], more=int, doc=doc),
TDLJob (run_tree, name, job_id='generate_visibilities'))
# 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))
if do_solve:
TDLRuntimeOptions (*ParmGroup.get_solvejob_options ())
imsel = mssel.imaging_selector (npix=512, arcmin=meqmaker.estimate_image_size ())
TDLRuntimeMenu ('Make an image', *imsel.option_list ())
meqmaker.close()
