print "DS:" + str(ds1)
fitpdf['ws'].Print("v")
fitpdf['pdf'].Print("v")
ds1.Print("v")
mistag = fitpdf['ws'].obj("mistag")
aveta = etaAvgList.At(i).getValV();
mistag.setVal(aveta);
mistag.setError(min([0.5 / NUMCAT / sqrt(12), abs(aveta) * 0.5, abs(aveta - 0.5) * 0.5]))
mistag.Print("v")
print 72 * "#"
print "\n-------PRINTING DS1--------\n"
ds1.Print('v')
for o in fitpdf['obs']:
if not o.InheritsFrom('RooAbsCategory'): continue
ds1.table(o).Print('v')
print "\n---------------------------\n"
"""
etaLow = ROOT.Double(0.0);
etaHigh = ROOT.Double(0.5);
ds1.getRange(ds1.get().find('eta'),etaLow,etaHigh);
ds1.get().find('eta').setRange(etaLow, etaHigh)
"""
#drawDsPlot(ds1,i);
#etaAvg = ds.meanVar(ds.get().find('eta'));
print "\n\n\nETA = ",ds.meanVar(ds.get().find('eta')).getValV(),"\n\n\n\n";
#raw_input('Press Enter to continue');
C, D, Dbar, S, Sbar,
resmodel, acc,
terrpdf, mistagpdf,
aprod, adet)
# generate "proto data" for mistag and time error
proto_data = WS(ws, mistagpdf_os.generate(RooArgSet(mistag_os), nevts))
mistag_ss_data = WS(ws, mistagpdf_ss.generate(RooArgSet(mistag_ss), nevts))
terr_data = WS(ws, terrpdf.generate(RooArgSet(timeerr), nevts))
proto_data.merge(mistag_ss_data, terr_data)
# generate events
obs = RooArgSet(qf, qt_os, qt_ss, time) #if use proto data, don't put mistag/time error observables here!
ds = WS(ws, genpdf.generate(obs, RooFit.ProtoData(proto_data)))
ds.Print('v')
ds.table(qf).Print('v')
ds.table(qt_os).Print('v')
ds.table(qt_ss).Print('v')
# HACK (2/2): restore correct eta range after generation
ds.get().find("mistag_os").setRange(0.0,0.5)
ds.get().find("mistag_ss").setRange(0.0,0.5)
# plot generated dataset + generating pdf
#plotAll(ds,genpdf,qf,qt_os,time,"GenPdfAndDataOS_decRateCoeff_Bd_tut_PEDTE.eps")
#plotAll(ds,genpdf,qf,qt_ss,time,"GenPdfAndDataSS_decRateCoeff_Bd_tut_PEDTE.eps")
# use workspace for fit pdf
config['CONTEXT'] = 'FIT'
acc, accnorm = buildSplineAcceptance(ws, time, 'acceptance_FIT',
