def runFits(data,options):
axis=ROOT.TAxis(options.binsy,options.miny,options.maxy)
#first pass
graphs=[]
for i in range(0,3):
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.erfpow('model','M')
fitter.importBinnedData(histo,['M'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Minos(1)])
# chi=fitter.projection("model","data","M","debugfitMVV_"+options.output+"_pass1_"+str(i)+".png")
for j,g in enumerate(graphs):
c,cerr=fitter.fetch("c_"+str(j))
if abs(c-fitter.w.var("c_"+str(j)).getMin())<0.1:
cerr=abs(c)*10000
g.SetPoint(i-1,center,c)
g.SetPointError(i-1,0.0,cerr)
parameter0=ROOT.TF1("pol0","pol0",options.minx,options.maxx)
graphs[0].Fit(parameter0)
#Second pass after fixing par0
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.erfpow('model','M')
fitter.w.var("c_0").setVal(parameter0.Eval(center))
fitter.w.var("c_0").setConstant(1)
fitter.importBinnedData(histo,['M'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Minos(1)])
# chi=fitter.projection("model","data","M","debugfitMVV_"+options.output+"_pass2_"+str(i)+".png")
for j,g in enumerate(graphs):
if j>0:
c,cerr=fitter.fetch("c_"+str(j))
if abs(c-fitter.w.var("c_"+str(j)).getMin())<0.1:
cerr=abs(c)*10000
g.SetPoint(i-1,center,c)
g.SetPointError(i-1,0.0,cerr)
parameter2=ROOT.TF1("pol3","pol3",options.minx,options.maxx)
# log0.SetParameters(1,1)
graphs[2].Fit(parameter2)
#Third pass after fixing par0
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)
#protect for negative weights
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.erfpow('model','M')
fitter.w.var("c_0").setVal(parameter0.Eval(center))
fitter.w.var("c_0").setConstant(1)
fitter.w.var("c_2").setVal(parameter2.Eval(center))
fitter.w.var("c_2").setConstant(1)
fitter.importBinnedData(histo,['M'],'data')
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1),ROOT.RooFit.Minos(1)])
# chi=fitter.projection("model","data","M","debugfitMVV_"+options.output+"_pass3_"+str(i)+".png")
for j,g in enumerate(graphs):
if j==1:
c,cerr=fitter.fetch("c_"+str(j))
if abs(c-fitter.w.var("c_"+str(j)).getMin())<0.1:
cerr=abs(c)*10000
g.SetPoint(i-1,center,c)
g.SetPointError(i-1,0.0,cerr)
parameter1=ROOT.TF1("pol3","pol3",options.minx,options.maxx)
graphs[1].Fit(parameter1)
#Fourth pass - plotting
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)
#protect for negative weights
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.erfpow('model','M')
fitter.w.var("c_0").setVal(parameter0.Eval(center))
fitter.w.var("c_0").setConstant(1)
fitter.w.var("c_1").setVal(parameter1.Eval(center))
fitter.w.var("c_1").setConstant(1)
fitter.w.var("c_2").setVal(parameter2.Eval(center))
fitter.w.var("c_2").setConstant(1)
fitter.importBinnedData(histo,['M'],'data')
chi=fitter.projection("model","data","M","debugfitMVV_"+options.output+"_pass3_"+str(i)+".png")
#create json
data={}
data['p0']=returnString(parameter0,options)
data['p1']=returnString(parameter1,options)
data['p2']=returnString(parameter2,options)
f=open(options.output+".json","w")
json.dump(data,f)
f.close()
return graphs
(options, args) = parser.parse_args()
parameterization = {}
f = ROOT.TFile(args[0])
histo = f.Get(options.histo)
fitter = Fitter(['x'])
fitter.importBinnedData(histo, ['x'], 'data')
if options.function == 'expo':
fitter.expo('model', 'x')
parameterization['type'] = 'expo'
if options.function == 'erfpow':
fitter.erfpow('model', 'x')
parameterization['type'] = 'erfpow'
if options.function == 'erfexp':
fitter.erfexp('model', 'x')
parameterization['type'] = 'erfexp'
if options.function == 'erfexpCB':
fitter.erfexpCB('model', 'x')
parameterization['type'] = 'erfexpCB'
if options.function == 'erfexpTimesCB':
fitter.erfexpTimesCB('model', 'x')
parameterization['type'] = 'erfexpTimesCB'
if options.function.find('bernstein') != -1:
