def runFits(data,options):
axis=ROOT.TAxis(options.binsy,options.miny,options.maxy)
#first pass
graphs=[]
for i in range(0,1):
graphs.append(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)
fitter.pow('model','M')
fitter.importBinnedData(histo,['M'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1)])
chi=fitter.projection("model","data","M","debugfit_"+str(i)+".pdf")
for j,g in enumerate(graphs):
c,cerr=fitter.fetch("c_"+str(j))
g.SetPoint(i-1,center,c)
g.SetPointError(i-1,0.0,cerr)
pol0=ROOT.TF1("pol3","pol3",options.minx,options.maxx)
graphs[0].Fit(pol0)
#create json
data={}
data['p0']=returnString(pol0,options)
f=open(options.output+".json","w")
json.dump(data,f)
f.close()
return graphs
fitter.importBinnedData(histo, ['x'], 'data')
if options.function == 'expo':
fitter.expo('model', 'x')
parameterization['type'] = 'expo'
if options.function == 'twoExp':
fitter.twoExp('model', 'x')
parameterization['type'] = 'erfexp'
if options.function == 'expoTail':
fitter.expoTail('model', 'x')
parameterization['type'] = 'expoTail'
if options.function == 'pow':
fitter.pow('model', 'x')
parameterization['type'] = 'expo'
if options.function == 'upperCB':
fitter.upperCB('model', 'x')
parameterization['type'] = 'upperCB'
parameterization['c_0'] = 0.0
parameterization['c_2'] = 1.0
parameterization['c_3'] = 5.0
parameterization['c_5'] = 5.0
if options.function == 'erfpow':
fitter.erfpow('model', 'x')
parameterization['type'] = 'erfpow'
if options.function == 'erfpowinv':