def runFits(data,options):
jsonFile=open(options.json)
info=json.load(jsonFile)
meanF = ROOT.TFormula("meanF",info['mean'].replace('MH','x'))
sigmaF = ROOT.TFormula("sigmaF",info['sigma'].replace('MH','x'))
alphaF = ROOT.TFormula("alphaF",info['alpha'].replace('MH','x'))
nF = ROOT.TFormula("nF",info['n'].replace('MH','x'))
alpha2F = ROOT.TFormula("alpha2F",info['alpha2'].replace('MH','x'))
n2F = ROOT.TFormula("n2F",info['n2'].replace('MH','x'))
slopeF = ROOT.TFormula("slopeF",info['slope'].replace('MH','x'))
fF = ROOT.TFormula("fF",info['f'].replace('MH','x'))
# axis=ROOT.TAxis(10,array('d',[600,800,900,1000,1250,1500,2000,2500,3000,3500,4000]))
axis=ROOT.TAxis(10,array('d',[600,650,700,750,800,900,1000,1250,1500,2000,2500]))
graphs={'mean':ROOT.TGraphErrors(),'sigma':ROOT.TGraphErrors(),'alpha':ROOT.TGraphErrors(),'n':ROOT.TGraphErrors(),'alpha2':ROOT.TGraphErrors(),'n2':ROOT.TGraphErrors(),'f':ROOT.TGraphErrors(),'slope':ROOT.TGraphErrors()}
for i in range(1,axis.GetNbins()+1):
center=axis.GetBinCenter(i)
h = data.drawTH1(options.varx,options.cut+"*({vary}>{mini}&&{vary}<{maxi})".format(vary=options.vary,mini=axis.GetBinLowEdge(i),maxi=axis.GetBinUpEdge(i)),str(options.lumi),options.binsx,options.minx,options.maxx)
histo=copy.deepcopy(h)
fitter=Fitter(['M'])
fitter.w.var("M").setVal((options.maxx-options.minx)/2.0)
fitter.w.var("M").setMax(options.maxx)
fitter.w.var("M").setMin(options.minx)
if options.doExp>=0: ##MICHALIS CHANGED IT
fitter.jetResonance('model','M')
fitter.w.var("slope").setVal(slopeF.Eval(center))
fitter.w.var("slope").setMin(-2)
fitter.w.var("slope").setMax(2)
fitter.w.var("slope").setConstant(0)
fitter.w.var("f").setVal(fF.Eval(center))
fitter.w.var("f").setMin(0)
fitter.w.var("f").setMax(1.)
fitter.w.var("f").setConstant(0)
else:
fitter.jetResonanceNOEXP('model','M')
fitter.w.var("mean").setVal(meanF.Eval(center))
fitter.w.var("mean").setMin(meanF.Eval(center)*0.9)
fitter.w.var("mean").setMax(meanF.Eval(center)*1.1)
fitter.w.var("sigma").setVal(sigmaF.Eval(center))
fitter.w.var("sigma").setMin(sigmaF.Eval(center)*0.5)
fitter.w.var("sigma").setMax(sigmaF.Eval(center)*2)
# fitter.w.var("alpha").setVal(alphaF.Eval(center))
fitter.w.var("alpha").setVal(1.59049)
fitter.w.var("alpha").setConstant(1)
fitter.w.var("alpha2").setVal(1.3)
fitter.w.var("alpha2").setConstant(1)
# fitter.w.var("alpha2").setConstant(1)
fitter.importBinnedData(histo,['M'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Minos(0)])
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Minos(1)])
chi=fitter.projection("model","data","M","debugfitMJJTop_"+options.output+"_"+str(i)+".png")
for var,graph in graphs.iteritems():
value,error=fitter.fetch(var)
graph.SetPoint(i-1,center,value)
graph.SetPointError(i-1,0.0,error)
F=ROOT.TFile(options.output+".root","RECREATE")
F.cd()
for name,graph in graphs.iteritems():
graph.Write(name)
F.Close()
## Load the samples for all signal mass values
plotter = loadSignalNtuples(options.samples,args[0],options.minMX,options.maxMX)
## Sort the masses and run the fits
N=0
for mass in sorted(plotter.keys()):
print 'fitting',str(mass)
## Get the histo from MC
histo = plotter[mass].drawTH1(options.mvv,options.cut,"1",int((options.maxi-options.mini)/4),options.mini,options.maxi)
## Set up the fitter
fitter=Fitter(['x'])
if options.doExp==1:
fitter.jetResonance('model','x')
else:
fitter.jetResonanceNOEXP('model','x')
if options.fixPars!="":
fixedPars =options.fixPars.split(',')
for par in fixedPars:
parVal = par.split(':')
fitter.w.var(parVal[0]).setVal(float(parVal[1]))
fitter.w.var(parVal[0]).setConstant(1)
#fitter.w.var("MH").setVal(mass)
fitter.importBinnedData(histo,['x'],'data')
## fit
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0),ROOT.RooFit.Minos(1)])
