#Now we have the samples: Sort the masses and run the fits
N=0
for mass in sorted(samples.keys()):
print 'fitting',str(mass)
plotter=TreePlotter(args[0]+'/'+samples[mass]+'.root','tree')
plotter.setupFromFile(args[0]+'/'+samples[mass]+'.pck')
plotter.addCorrectionFactor('genWeight','tree')
plotter.addCorrectionFactor('xsec','tree')
plotter.addCorrectionFactor('puWeight','tree')
fitter.w.var("MH").setVal(mass)
histo = plotter.drawTH1(options.mvv,options.cut,"1",500,0,13000)
fitter.importBinnedData(histo,['MVV'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])
events=histo.Integral()*options.BR
graphs['yield'].SetPoint(N,mass,events)
fitter.projection("model","data","MVV","debugVV_"+str(mass)+".root")
for var,graph in graphs.iteritems():
if var=='yield':
continue
value,error=fitter.fetch(var)
graph.SetPoint(N,mass,value)
graph.SetPointError(N,0.0,error)
#Now we have the samples: Sort the masses and run the fits
N = 0
for mass in sorted(samples.keys()):
print 'fitting', str(mass)
plotter = TreePlotter(args[0] + '/' + samples[mass] + '.root', 'tree')
# plotter.setupFromFile(args[0]+'/'+samples[mass]+'.pck')
plotter.addCorrectionFactor('genWeight', 'tree')
# plotter.addCorrectionFactor('xsec','tree')
plotter.addCorrectionFactor('puWeight', 'tree')
fitter = Fitter(['x'])
fitter.jetResonance('model', 'x')
# fitter.w.var("MH").setVal(mass)
histo = plotter.drawTH1(options.mvv, options.cut, "1", 40, 40, 160)
fitter.importBinnedData(histo, ['x'], 'data')
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(0)])
fitter.fit('model', 'data',
[ROOT.RooFit.SumW2Error(0),
ROOT.RooFit.Minos(1)])
fitter.projection("model", "data", "x", "debugJJ_" + str(mass) + ".png")
for var, graph in graphs.iteritems():
value, error = fitter.fetch(var)
graph.SetPoint(N, mass, value)
graph.SetPointError(N, 0.0, error)
N = N + 1
samples[mass] = fname
print 'found',filename,'mass',str(mass)
#Now we have the samples: Sort the masses and run the fits
N=0
for mass in sorted(samples.keys()):
print 'fitting',str(mass)
plotter=TreePlotter(args[0]+'/'+samples[mass]+'.root','tree')
plotter.setupFromFile(args[0]+'/'+samples[mass]+'.pck')
plotter.addCorrectionFactor('genWeight','tree')
plotter.addCorrectionFactor('xsec','tree')
plotter.addCorrectionFactor('puWeight','tree')
histo = plotter.drawTH1(options.mvv,options.cut,"1",500,options.min,options.max)
yieldgraph.SetPoint(N,mass,histo.Integral()*options.BR)
N=N+1
func = ROOT.TF1("func",options.function,0,13000)
yieldgraph.Fit(func)
parameterization={'yield':returnString(func)}
f=open(options.output+".json","w")
json.dump(parameterization,f)
f.close()
samples[mass] = fname
print 'found', filename, 'mass', str(mass)
#Now we have the samples: Sort the masses and run the fits
N = 0
for mass in sorted(samples.keys()):
print 'fitting', str(mass)
plotter = TreePlotter(args[0] + '/' + samples[mass] + '.root', 'tree')
plotter.setupFromFile(args[0] + '/' + samples[mass] + '.pck')
plotter.addCorrectionFactor('genWeight', 'tree')
plotter.addCorrectionFactor('xsec', 'tree')
plotter.addCorrectionFactor('puWeight', 'tree')
histo = plotter.drawTH1(
options.mvv, options.cut + '*(jj_LV_mass>%f&&jj_LV_mass<%f)' %
(mass * 0.8, mass * 1.2), "1", 500, options.min, options.max)
err = ROOT.Double(0)
integral = histo.IntegralAndError(1, histo.GetNbinsX(), err)
yieldgraph.SetPoint(N, mass, integral * options.BR)
yieldgraph.SetPointError(N, 0.0, err * options.BR)
N = N + 1
func = ROOT.TF1("func", options.function, 0, 13000)
yieldgraph.Fit(func)
parameterization = {'yield': returnString(func)}
f = open(options.output + ".json", "w")
json.dump(parameterization, f)
f.close()
#Now we have the samples: Sort the masses and run the fits
N = 0
for mass in sorted(samples.keys()):
print 'fitting', str(mass)
plotter = TreePlotter(args[0] + '/' + samples[mass] + '.root', 'tree')
# plotter.setupFromFile(args[0]+'/'+samples[mass]+'.pck')
plotter.addCorrectionFactor('genWeight', 'tree')
# plotter.addCorrectionFactor('xsec','tree')
plotter.addCorrectionFactor('puWeight', 'tree')
fitter = Fitter(['MVV'])
fitter.signalResonance('model', 'MVV')
fitter.w.var("MH").setVal(mass)
histo = plotter.drawTH1(options.mvv, options.cut, "1", 1000, 0, 8000)
fitter.importBinnedData(histo, ['MVV'], 'data')
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(0)])
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(0)])
fitter.projection("model", "data", "MVV", "debugVV_" + str(mass) + ".root")
for var, graph in graphs.iteritems():
value, error = fitter.fetch(var)
graph.SetPoint(N, mass, value)
graph.SetPointError(N, 0.0, error)
N = N + 1
F = ROOT.TFile(options.output, "RECREATE")
N=0
for mass in sorted(samples.keys()):
if mass>6000: continue
print 'fitting',str(mass)
plotter=TreePlotter(args[0]+'/'+samples[mass]+'.root','tree')
plotter.addCorrectionFactor('genWeight','tree')
plotter.addCorrectionFactor('puWeight','tree')
if options.scaleFactors!='':
for s in scaleFactors:
plotter.addCorrectionFactor(s,'tree')
fitter=Fitter(['MVV'])
fitter.signalResonanceCBGaus('model','MVV',mass)
fitter.w.var("MH").setVal(mass)
histo = plotter.drawTH1(options.mvv,options.cut+"*(jj_LV_mass>%f&&jj_LV_mass<%f)"%(0.8*mass,1.2*mass),"1",140,700,8000)
fitter.importBinnedData(histo,['MVV'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])#,ROOT.RooFit.Range(1000,8000)])
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])#,ROOT.RooFit.Range(1000,8000)])
# fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])#,ROOT.RooFit.Range(1000,8000)])
#fitter.projection("model","data","MVV","debugVV_"+options.output+"_"+str(mass)+".root")
fitter.projection("model","data","MVV","debugVV_"+options.output+"_"+str(mass)+".png","M_{jj} (GeV)",mass)
fitter.w.Print()
for var,graph in graphs.iteritems():
value,error=fitter.fetch(var)
graph.SetPoint(N,mass,value)
graph.SetPointError(N,0.0,error)
fitter.jetResonance('model','x')
else:
fitter.jetResonanceNOEXP('model','x')
if options.fixPars!="1":
fixedPars =options.fixPars.split(',')
for par in fixedPars:
parVal = par.split(':')
if len(parVal) > 1:
fitter.w.var(parVal[0]).setVal(float(parVal[1]))
fitter.w.var(parVal[0]).setConstant(1)
# fitter.w.var("MH").setVal(mass)
histo = plotter.drawTH1(options.mvv,options.cut,"1",int((options.maxi-options.mini)/4),options.mini,options.maxi)
fitter.importBinnedData(histo,['x'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0),ROOT.RooFit.Minos(1)])
fitter.projection("model","data","x","debugJ"+leg+"_"+options.output+"_"+str(mass)+".png")
for var,graph in graphs.iteritems():
value,error=fitter.fetch(var)
graph.SetPoint(N,mass,value)
graph.SetPointError(N,0.0,error)
N=N+1
F=ROOT.TFile(options.output,"RECREATE")
F.cd()
