TreePlotter(args[0] + '/' + fname + '.root', 'tree'))
if options.data == 0:
dataPlotters[-1].setupFromFile(args[0] + '/' + fname + '.pck')
dataPlotters[-1].addCorrectionFactor('xsec', 'tree')
dataPlotters[-1].addCorrectionFactor('genWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('puWeight', 'tree')
data = MergedPlotter(dataPlotters)
pvars = options.vars.split(',')
pmins = options.mins.split(',')
pmaxes = options.maxes.split(',')
pbins = options.bins.split(',')
if len(pvars) == 1:
histo = data.drawTH1(pvars[0], options.cut, "1", int(pbins[0]),
float(pmins[0]), float(pmaxes[0]))
if len(pvars) == 2:
histo = data.drawTH2(pvars[1] + ":" + pvars[0], options.cut, "1",
int(pbins[0]), float(pmins[0]), float(pmaxes[0]),
int(pbins[1]), float(pmins[1]), float(pmaxes[1]))
if len(pvars) == 3:
histo = data.drawTH3(pvars[2] + ":" + pvars[1] + ":" + pvars[0],
options.cut, "1", int(pbins[0]), float(pmins[0]),
float(pmaxes[0]), int(pbins[1]), float(pmins[1]),
float(pmaxes[1]), int(pbins[2]), float(pmins[2]),
float(pmaxes[2]))
histo.Scale(options.factor)
fnameParts=filename.split('.')
fname=fnameParts[0]
ext=fnameParts[1]
if ext.find("root") ==-1:
continue
dataPlotters.append(TreePlotter(args[0]+'/'+fname+'.root','tree'))
dataPlotters[-1].setupFromFile(args[0]+'/'+fname+'.pck')
dataPlotters[-1].addCorrectionFactor('xsec','tree')
dataPlotters[-1].addCorrectionFactor('genWeight','tree')
dataPlotters[-1].addCorrectionFactor('puWeight','tree')
data=MergedPlotter(dataPlotters)
histoQ=data.drawTH1(options.mvv+"/13000.0",options.cut+"&&"+options.quarkCut,"1",options.bins,options.min/13000.0,options.max/13000.0)
histoA=data.drawTH1(options.mvv+"/13000.0",options.cut,"1",options.bins,options.min/13000.0,options.max/13000.0)
histoQ.Divide(histoA)
func=ROOT.TF1("func",options.function,options.min/13000.0,options.max/13000.0)
histoQ.Fit(func)
parameterization={'quarkFraction':returnString(func)}
f=open(options.output+".json","w")
json.dump(parameterization,f)
f.close()
F=ROOT.TFile(options.output+".root",'RECREATE')
F.cd()
histoQ.Write("fraction")
F.Close()
if options.data==0:
dataPlotters[-1].setupFromFile(args[0]+'/'+fname+'.pck')
dataPlotters[-1].addCorrectionFactor('xsec','tree')
dataPlotters[-1].addCorrectionFactor('genWeight','tree')
dataPlotters[-1].addCorrectionFactor('puWeight','tree')
data=MergedPlotter(dataPlotters)
pvars=options.vars.split(',')
pmins=options.mins.split(',')
pmaxes=options.maxes.split(',')
pbins=options.bins.split(',')
if len(pvars)==1:
histo=data.drawTH1(pvars[0],options.cut,"1",int(pbins[0]),float(pmins[0]),float(pmaxes[0]))
if len(pvars)==2:
histo=data.drawTH2(pvars[1]+":"+pvars[0],options.cut,"1",int(pbins[0]),float(pmins[0]),float(pmaxes[0]),int(pbins[1]),float(pmins[1]),float(pmaxes[1]))
if len(pvars)==3:
histo=data.drawTH3(pvars[2]+":"+pvars[1]+":"+pvars[0],options.cut,"1",int(pbins[0]),float(pmins[0]),float(pmaxes[0]),int(pbins[1]),float(pmins[1]),float(pmaxes[1]),int(pbins[2]),float(pmins[2]),float(pmaxes[2]))
histo.Scale(options.factor)
F=ROOT.TFile(options.output,"RECREATE")
F.cd()
histo.Write("histo")
F.Close()
hGenPtUp.Fill(i + 0.5, fGenPtUp.Eval(i + 0.5))
hGenPtDn.Fill(i + 0.5, fGenPtDn.Eval(i + 0.5))
histograms = []
#ok lets populate!
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.factory("GM[15,215]")
fitter.factory("weight[0,1e+32]")
#datasetPDF=data.makeDataSet('lnujj_l2_gen_softDrop_mass,'+options.var,options.cut,-1)
histoPDF = data.drawTH1('lnujj_l2_gen_softDrop_mass', options.cut, "1",
options.binsx * 4, options.minx, options.maxx)
fitter.w.var("GM").setMin(histoPDF.GetXaxis().GetXmin())
fitter.w.var("GM").setMax(histoPDF.GetXaxis().GetXmax())
fitter.w.var("GM").setBins(histoPDF.GetXaxis().GetNbins())
cList = ROOT.RooArgSet(fitter.w.var("GM"), fitter.w.var("weight"))
fcache = ROOT.TFile("cache.root", "RECREATE")
dataset = ROOT.RooDataSet("datasetPDF", "", cList, "weight")
for i in range(1, histoPDF.GetNbinsX() + 1):
x = histoPDF.GetXaxis().GetBinCenter(i)
w = histoPDF.GetBinContent(i)
cList.setRealValue("GM", x)
cList.setRealValue("weight", w)
dataset.add(cList, w)
if filename.find(sampleType) != -1:
fnameParts = filename.split('.')
fname = fnameParts[0]
ext = fnameParts[1]
if ext.find("root") == -1:
continue
dataPlotters.append(
TreePlotter(args[0] + '/' + fname + '.root', 'tree'))
dataPlotters[-1].setupFromFile(args[0] + '/' + fname + '.pck')
dataPlotters[-1].addCorrectionFactor('xsec', 'tree')
dataPlotters[-1].addCorrectionFactor('genWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('puWeight', 'tree')
data = MergedPlotter(dataPlotters)
denominator = data.drawTH1("0.5", options.cutDenominator, "1", 1, 0, 1)
cut = options.cutNumerator
nominal = data.drawTH1("0.5", '*'.join([options.cutDenominator, cut]), "1", 1,
0, 1)
cut = options.cutNumerator.replace(
options.var, "(" + str(options.factor) + "*" + options.var + ")")
up = data.drawTH1("0.5", '*'.join([options.cutDenominator, cut]), "1", 1, 0, 1)
cut = options.cutNumerator.replace(
options.var, "(" + str(1.0 / options.factor) + "*" + options.var + ")")
down = data.drawTH1("0.5", '*'.join([options.cutDenominator, cut]), "1", 1, 0,
1)
effUp = abs(up.Integral() / nominal.Integral())
axis = ROOT.TAxis(len(cBins) - 1, array('d', cBins))
graph = {'scale': ROOT.TGraphErrors(), 'sigma': ROOT.TGraphErrors()}
#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()
if filename.find(sampleType) != -1:
fnameParts = filename.split('.')
fname = fnameParts[0]
ext = fnameParts[1]
if ext.find("root") == -1:
continue
dataPlotters.append(
TreePlotter(args[0] + '/' + fname + '.root', 'tree'))
dataPlotters[-1].setupFromFile(args[0] + '/' + fname + '.pck')
dataPlotters[-1].addCorrectionFactor('xsec', 'tree')
dataPlotters[-1].addCorrectionFactor('genWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('puWeight', 'tree')
data = MergedPlotter(dataPlotters)
histo = data.drawTH1(options.var, options.cut, "1", options.bins, options.min,
options.max)
histoUp = data.drawTH1(options.var + "Up", options.cut, "1", options.bins,
options.min, options.max)
histoDown = data.drawTH1(options.var + "Down", options.cut, "1", options.bins,
options.min, options.max)
histoRUp = data.drawTH1(options.var + "Smear", options.cut, "1", options.bins,
options.min, options.max)
histoRDown = mirror(histo, histoRUp)
F = ROOT.TFile(options.output, "UPDATE")
F.cd()
histo.Write(options.name)
histoUp.Write(options.name + "_" + options.systName + "ScaleUp")
histoDown.Write(options.name + "_" + options.systName + "ScaleDown")
histoRUp.Write(options.name + "_" + options.systName + "ResUp")
fitter.gaus('model', 'x')
if options.fixPars != "1":
fixedPars = options.fixPars.split(',')
if len(fixedPars) > 1:
print " - Fix parameters: ", fixedPars
for par in fixedPars:
if par == "c_0" or par == "c_1" or par == "c_2": continue
parVal = par.split(':')
fitter.w.var(parVal[0]).setVal(float(parVal[1]))
fitter.w.var(parVal[0]).setConstant(1)
#histo = plotter.drawTH1("jj_"+leg+"_softDrop_mass",options.cut+"*(jj_"+leg+"_mergedVTruth==1)","1",80,options.mini,options.maxi)
histo = plotter.drawTH1(
"jj_" + leg + "_softDrop_mass",
options.cut + "*(jj_" + leg + "_mergedVTruth==1)*(jj_" + leg +
"_softDrop_mass>60&&jj_" + leg + "_softDrop_mass<110)", "1", 25, 60,
110)
if leg.find("l1") != -1:
NRes[0] += histo.Integral()
else:
NRes[1] += histo.Integral()
fitter.importBinnedData(histo, ['x'], 'data')
fitter.fit('model', 'data',
[ROOT.RooFit.SumW2Error(1),
ROOT.RooFit.Save(1)])
#fitter.fit('model','data',[ROOT.RooFit.SumW2Error(0),ROOT.RooFit.Minos(1)])
fitter.projection("model", "data", "x",
"debugJ" + leg + "_" + options.output + "_Res.png")
#params[label+"_Res_"+leg]={"mean": {"val": fitter.w.var("mean").getVal(), "err": fitter.w.var("mean").getError()}, "sigma": {"val": fitter.w.var("sigma").getVal(), "err": fitter.w.var("sigma").getError()}, "alpha":{ "val": fitter.w.var("alpha").getVal(), "err": fitter.w.var("alpha")},"alpha2":{"val": fitter.w.var("alpha2").getVal(),"err": fitter.w.var("alpha2").getError()},"n":{ "val": fitter.w.var("n").getVal(), "err": fitter.w.var("n").getError()},"n2": {"val": fitter.w.var("n2").getVal(), "err": fitter.w.var("n2").getError()}}
histosSUP = []
histosSDWN = []
histosRUP = []
histosRDWN = []
bins = [
500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1500, 1750, 2000, 2500,
3000, 3500, 4000, 4500, 5000
]
axis = ROOT.TAxis(len(bins) - 1, array('d', bins))
for i in range(1, axis.GetNbins() + 1):
center = axis.GetBinCenter(i)
histo = data.drawTH1(
options.vary, options.cut +
"&&{MVV}>{low}&&{MVV}<={high}".format(MVV=options.varx,
low=axis.GetBinLowEdge(i),
high=axis.GetBinUpEdge(i)), "1",
options.binsy, options.miny, options.maxy)
histos.append(histo)
graphs, histo2D = interpolate(histos, axis, options)
renormalizeHisto(histo2D)
#renormalizeHisto(histoSUP)
#renormalizeHisto(histoSDWN)
#renormalizeHisto(histoRUP)
#renormalizeHisto(histoRDWN)
F = ROOT.TFile(options.output, "UPDATE")
F.cd()
histo2D.Write(options.name)
for bin, g in enumerate(graphs):
dataPlotters[-1].setupFromFile(args[0] + '/' + fname + '.pck')
dataPlotters[-1].addCorrectionFactor('xsec', 'tree')
dataPlotters[-1].addCorrectionFactor('genWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('puWeight', 'tree')
if options.data == 2:
sigmas = []
for d in dataPlotters:
sigmas.append(d.tree.GetMaximum("xsec") / d.weightinv)
sigmaW = max(sigmas)
for p in dataPlotters:
p.addCorrectionFactor(1.0 / sigmaW, 'flat')
parameterization = {}
data = MergedPlotter(dataPlotters)
dataset = data.makeDataSet(options.genvar, options.genCut, -1)
histo = data.drawTH1(options.var, options.cut, "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_" + options.output + ".root", 'x')
print 'Chi2', chi
print 'make systematics'
meanDefault = fitter.w.var("scale").getVal()
sigmaDefault = fitter.w.var("sigma").getVal()
f2 = ROOT.TFile(options.output, "RECREATE")
f2.cd()
histo = returnHisto("histo", fitter.w, options)
if ext.find("root") ==-1:
continue
dataPlotters.append(TreePlotter(args[0]+'/'+fname+'.root','tree'))
dataPlotters[-1].setupFromFile(args[0]+'/'+fname+'.pck')
dataPlotters[-1].addCorrectionFactor('xsec','tree')
dataPlotters[-1].addCorrectionFactor('genWeight','tree')
dataPlotters[-1].addCorrectionFactor('puWeight','tree')
dataPlotters[-1].addCorrectionFactor('lnujj_sf','branch')
dataPlotters[-1].addCorrectionFactor('lnujj_btagWeight','branch')
dataPlotters[-1].addCorrectionFactor('truth_genTop_weight','branch')
dataPlotters[-1].filename=fname
data=MergedPlotter(dataPlotters)
histI=data.drawTH1(options.var,options.cut,"1",1,0,1000000000)
norm=histI.Integral()
histograms={}
histograms['nominal'] = {'scale':1.0,'weight':'1'}
histograms['scale_Up'] = {'scale':1.2,'weight':'1'}
histograms['scale_Down'] = {'scale':0.8,'weight':'1'}
histograms['ptSpectrum_Up'] = {'scale':1.0,'weight':'(1.2*lnujj_l2_gen_pt)'}
histograms['ptSpectrum_Down'] = {'scale':1.0,'weight':'(0.8*lnujj_l2_gen_pt)'}
f=ROOT.TFile(options.output,"RECREATE")
f.cd()
for histo,info in histograms.iteritems():
histSource =data.drawTH1(str(info['scale'])+'*'+options.var,'('+options.cut+'*'+info['weight']+')',"1",int(options.binsx*options.kdeFactor),options.minx*0.8,options.maxx*1.5)
continue
dataPlotters.append(
TreePlotter(args[0] + '/' + fname + '.root', 'tree'))
dataPlotters[-1].setupFromFile(args[0] + '/' + fname + '.pck')
dataPlotters[-1].addCorrectionFactor('xsec', 'tree')
dataPlotters[-1].addCorrectionFactor('genWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('puWeight', 'tree')
dataPlotters[-1].addCorrectionFactor('lnujj_sf', 'branch')
dataPlotters[-1].addCorrectionFactor('lnujj_btagWeight', 'branch')
dataPlotters[-1].addCorrectionFactor('truth_genTop_weight',
'branch')
dataPlotters[-1].filename = fname
data = MergedPlotter(dataPlotters)
variables = options.vars.split(',')
histI = data.drawTH1(variables[0], options.cut, "1", 1, 0, 1000000000)
norm = histI.Integral()
histograms = {}
histograms['nominal'] = {'scalex': 1.0, 'scaley': 1.0, 'weight': '1'}
histograms['scalex_Up'] = {'scalex': 1.2, 'scaley': 1.0, 'weight': '1'}
histograms['scalex_Down'] = {'scalex': 0.8, 'scaley': 1.0, 'weight': '1'}
histograms['ptSpectrum_Up'] = {
'scalex': 1.0,
'scaley': 1.0,
'weight': '(1.1*lnujj_l2_gen_pt)'
}
histograms['ptSpectrum_Down'] = {
'scalex': 1.0,
'scaley': 1.0,
'weight': '(0.8*lnujj_l2_gen_pt)'
