def runFits(data, options):
axis = ROOT.TAxis(5, array('d', [600, 700, 800, 900, 1000, 2000]))
#first pass
graphs = []
for i in range(0, 12):
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.signalMJJCBBoth('model', 'M')
fitter.importBinnedData(histo, ['M'], 'data')
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(0)])
chi = fitter.projection(
"model", "data", "M",
"debugfitMJJTop_" + options.output + "_" + str(i) + ".png")
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)
data = {}
pol4 = ROOT.TF1("pol4", "pol4", options.minx, options.maxx)
pol3 = ROOT.TF1("pol3", "pol3", options.minx, options.maxx)
pol2 = ROOT.TF1("pol2", "pol2", options.minx, options.maxx)
pol1 = ROOT.TF1("pol1", "pol1", options.minx, options.maxx)
pol0 = ROOT.TF1("pol0", "pol0", options.minx, options.maxx)
graphs[0].Fit(pol1)
data['mean'] = returnString(pol1, options)
graphs[1].Fit(pol1)
data['sigma'] = returnString(pol1, options)
graphs[2].Fit(pol0)
data['alpha1'] = returnString(pol0, options)
graphs[3].Fit(pol0)
data['n1'] = returnString(pol0, options)
graphs[4].Fit(pol1)
data['alpha2'] = returnString(pol1, options)
graphs[5].Fit(pol0)
data['n2'] = returnString(pol0, options)
#create json
f = open(options.output + "_W.json", "w")
json.dump(data, f)
f.close()
data = {}
graphs[6].Fit(pol1)
data['mean'] = returnString(pol1, options)
graphs[7].Fit(pol1)
data['sigma'] = returnString(pol1, options)
graphs[8].Fit(pol0)
data['alpha1'] = returnString(pol0, options)
graphs[9].Fit(pol0)
data['n1'] = returnString(pol0, options)
graphs[10].Fit(pol1)
data['alpha2'] = returnString(pol1, options)
graphs[11].Fit(pol0)
data['n2'] = returnString(pol0, options)
f = open(options.output + "_top.json", "w")
json.dump(data, f)
f.close()
return graphs
events=histoYield.Integral()*options.BR*analysisIntegral/fitRangeIntegral
graphs['yield'].SetPoint(N,mass,events)
fitter.projection("model","data","m","fitjj_"+str(mass)+".root")
fitter.projection("model","data","M","fitVV_"+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)
N=N+1
#now the fits
pol5 = ROOT.TF1("pol5","pol5",0,13000)
pol3 = ROOT.TF1("pol3","pol3",0,13000)
pol0 = ROOT.TF1("pol0","pol0",0,13000)
pol1 = ROOT.TF1("pol1","pol1",0,13000)
fitter=Fitter(['MVV'])
fitter.signalResonance('model','MVV')
#fitter.w.var("MH").setVal(mass)
fitter.importBinnedData(histo,['MVV'],'data')
## fit
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])
#fitter.fit('model','data',[ROOT.RooFit.SumW2Error(1)])
fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0)])
## control plot
fitter.projection("model","data","MVV",options.debugFile+"_"+str(int(mass)).zfill(4)+".root",options.varx,[],[ROOT.RooFit.LineColor(color)])
fitter.projection("model","data","MVV",options.debugFile+"_"+str(int(mass)).zfill(4)+".png",options.varx,[],[ROOT.RooFit.LineColor(color)])
## Save parameters vs MX
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
## Store all graphs
F=ROOT.TFile(options.output,"RECREATE")
F.cd()
for name,graph in graphs.iteritems():
graph.Write(name)
F.Close()
#dataset = data.makeDataSet(options.genvar,options.genCut,-1)
for i in range(1, axis.GetNbins() + 1):
cBinLo = axis.GetBinLowEdge(i)
cBinHi = axis.GetBinUpEdge(i)
cBinCenter = axis.GetBinCenter(i)
dataset = data.makeDataSet(
options.genvar, options.genCut + '*({cvar}>{lo}&&{cvar}<={hi})'.format(
cvar=options.condVar, lo=cBinLo - 100, hi=cBinHi + 100), -1)
histo = data.drawTH1(
options.var, options.cut + '*({cvar}>{lo}&&{cvar}<={hi})'.format(
cvar=options.condVar, lo=cBinLo, hi=cBinHi), "1", options.bins,
options.mini, options.maxi)
fitter = Fitter([options.var])
fitter.importBinnedData(histo, [options.var], 'data')
fitter.gaussianSum('model', options.var, dataset, options.genvar)
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(1)])
chi = fitter.projection("model", "data", options.var,
"debugPlot_" + str(i) + "_" + options.output, 'x')
print i, 'Chi2', chi
for nuis in ['scale', 'sigma']:
v, vErr = fitter.fetch(nuis)
graph[nuis].SetPoint(i, cBinCenter, v)
graph[nuis].SetPointError(i, 0, vErr)
f2 = ROOT.TFile(options.output, "RECREATE")
f2.cd()
for g in graph.keys():
graph[g].Write(g)
f2.Close()
def interpolate(histos, axis, options):
#create 2D histogram
histo2D = ROOT.TH2D("histo2D", "histo2D", options.binsx, options.minx,
options.maxx, options.binsy, options.miny,
options.maxy)
if options.fitFunc == "bernstein3":
order = 3
if options.fitFunc == "bernstein4":
order = 4
if options.fitFunc == "bernstein5":
order = 5
if options.fitFunc == "bifur":
order = 3
graphs = []
for i in range(0, order):
graphs.append(ROOT.TGraphErrors())
#run the fits
for N, h in enumerate(histos):
center = axis.GetBinCenter(N + 1)
fitter = Fitter(['mjj'])
fitter.w.var("mjj").setVal(40)
fitter.w.var("mjj").setMax(options.maxy)
fitter.w.var("mjj").setMin(options.miny)
if options.fitFunc == "bernstein3":
fitter.bernstein('model', 'mjj', 3)
if options.fitFunc == "bernstein4":
fitter.bernstein('model', 'mjj', 4)
if options.fitFunc == "bernstein5":
fitter.bernstein('model', 'mjj', 5)
if options.fitFunc == "bifur":
fitter.bifur('model', 'mjj')
fitter.importBinnedData(h, ['mjj'], 'data')
fitter.fit('model', 'data')
fitter.fit('model', 'data')
fitter.projection("model", "data", "mjj",
"debugProjectionFit_" + str(N) + ".png")
for j, g in enumerate(graphs):
c, cerr = fitter.fetch("c_" + str(j))
g.SetPoint(N, center, c)
g.SetPointError(N, 0.0, cerr)
##OK now interpolate and make histograms!
#first make the function
fitter = Fitter(['mjj'])
if options.fitFunc == "bernstein3":
fitter.bernstein('model', 'mjj', 3)
order = 3
if options.fitFunc == "bernstein4":
fitter.bernstein('model', 'mjj', 4)
order = 4
if options.fitFunc == "bernstein5":
fitter.bernstein('model', 'mjj', 5)
order = 5
if options.fitFunc == "bifur":
fitter.bifur('model', 'mjj')
order = 3
fitter.w.var("mjj").setVal(80)
fitter.w.var("mjj").setMax(options.maxy)
fitter.w.var("mjj").setMin(options.miny)
for i in range(1, histo2D.GetNbinsX() + 1):
x = histo2D.GetXaxis().GetBinCenter(i)
print 'Bin:', i, 'Evaluating function at x=', x
for j, g in enumerate(graphs):
print "c_" + str(j), "=", g.Eval(x)
fitter.w.var("c_" + str(j)).setVal(g.Eval(x, 0, "S"))
histogram = fitter.w.pdf("model").createHistogram("mjj", options.binsy)
for k in range(1, histo2D.GetNbinsY() + 1):
bin = histo2D.GetBin(i, k)
histo2D.SetBinContent(bin, histogram.GetBinContent(k))
return graphs, histo2D
fitter.bernstein('model', 'x', order)
parameterization['type'] = 'bernstein'
parameterization['order'] = order
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(1)])
fitter.fit('model', 'data', [ROOT.RooFit.SumW2Error(1)])
fitter.projection("model", "data", "x", "debug" + options.output + ".root",
options.title)
chi = fitter.projection("model", "data", "x",
"debug" + options.output + ".png", options.title)
print 'Chi2', chi
if parameterization['type'] == 'expo':
val, err = fitter.fetch('c_0')
parameterization['c_0'] = val
parameterization['c_0Err'] = err
if parameterization['type'] == 'expoTail':
for i in range(0, 2):
var = 'c_' + str(i)
val, err = fitter.fetch(var)
parameterization[var] = val
parameterization[var + 'Err'] = err
if parameterization['type'] == 'erfexp':
for i in range(0, 3):
var = 'c_' + str(i)
val, err = fitter.fetch(var)
parameterization[var] = val
f = ROOT.TFile(args[0])
histo = f.Get(options.histo)
fitter = Fitter(['x', 'y'])
fitter.importBinnedData(histo, ['x', 'y'], 'data')
if options.function == 'erfexpW2D':
fitter.mjjParamErfExp('model', options.json)
fitter.fit('model', 'data', [
ROOT.RooFit.SumW2Error(1),
ROOT.RooFit.ConditionalObservables(ROOT.RooArgSet(fitter.w.var("x")))
])
fitter.fit('model', 'data', [
ROOT.RooFit.SumW2Error(1),
ROOT.RooFit.ConditionalObservables(ROOT.RooArgSet(fitter.w.var("x")))
])
fitter.projectionCond("model", "data", "y", "x",
"debug" + options.output + ".png")
for i in range(0, 4):
var = 'c_' + str(i)
val, err = fitter.fetch(var)
parameterization[var] = val
parameterization[var + 'Err'] = err
f = open(options.output + ".json", "w")
json.dump(parameterization, f)
f.close()
