def compareMCGentoMCUnfolded(action, infileName):
if action == "herwigOnPythia" or action == "pythiaOnPythia":
unfoldingWasDoneOn = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
elif action == "pythiaOnHerwig" or action == "herwigOnHerwig":
unfoldingWasDoneOn = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
else:
print "compareMCGentoMCUnfolded: wrong action", action, "skipping (usually you can ignore this message)"
return
# detaGen_central_jet15
fileWithUnfoldedPlotsName = optionsReg["odir"]+"/mnxsHistos_unfolded_"+action +".root"
fileWithUnfoldedPlots = ROOT.TFile(fileWithUnfoldedPlotsName)
#mnxsHistos_unfolded_pythiaOnHerwig.root
histos = getHistos(infileName)
#print histos[unfoldingWasDoneOn].keys()
todo = ["_jet15", "_dj15fb"]
#todo = ["_jet15"]
c = ROOT.TCanvas()
for t in todo:
genHisto = histos[unfoldingWasDoneOn]["detaGen_central"+t]
unfoldedHistoName = t+"/xsunfolded_central"+t
unfoldedHisto = fileWithUnfoldedPlots.Get(unfoldedHistoName)
#print unfoldedHistoName, type(unfoldedHisto), unfoldedHisto.ClassName()
#genHisto.Scale(0.5)
genHisto.Draw()
genHisto.GetXaxis().SetTitle(DrawMNPlots.xLabels()["xs"])
genHisto.GetYaxis().SetTitleOffset(1.8)
genHisto.GetYaxis().SetTitle(DrawMNPlots.yLabels()["xsAsPB"])
genHisto.SetMarkerColor(2)
genHisto.SetLineColor(2)
unfoldedHisto.Draw("SAME")
trueMax = max(genHisto.GetMaximum(), unfoldedHisto.GetMaximum())
genHisto.SetMaximum(trueMax*1.07)
c.Print(optionsReg["odir"]+"/MConMCunfoldingTest_"+action+t+".png")
c.Print(optionsReg["odir"]+"/MConMCunfoldingTest_"+action+t+".pdf")
def main():
CommonFSQFramework.Core.Style.setTDRStyle()
parser = OptionParser(usage="usage: %prog [options] filename",
version="%prog 1.0")
parser.add_option("-l", "--label", action="store", dest="label", type="string", \
help="extra label when naming files")
parser.add_option("-a", "--histo1", action="store", dest="histo1", type="string", \
help="histo1")
parser.add_option("-b", "--histo2", action="store", dest="histo2", type="string", \
help="histo2")
parser.add_option("-v", "--variant", action="store", dest="variant", type="string", \
help="choose analysis variant")
parser.add_option("-d",
"--justDivide",
action="store_true",
dest="justDivide")
(options, args) = parser.parse_args()
if not options.variant:
print "Provide analysis variant"
sys.exit()
if not options.histo1:
# -a ptHat,QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp
print "Tell me what to do..."
sys.exit()
odir = "~/tmp/simpleMC_{}/".format(options.variant)
os.system("mkdir -p " + odir)
todo = []
todo.append(options.histo1.split(","))
labelRaw = options.histo1.split(",")[0]
if options.histo2:
todo.append(options.histo2.split(","))
label = ""
if options.label:
label = options.label + "_"
histofile = "plotsMNxs_{}.root".format(options.variant)
histos = getHistos(histofile)
toPlot = {}
cnt = 0
colors = [2, 1]
for t in todo:
toPlot[t[1] + "|" + t[0]] = histos[t[1]][t[0]]
toPlot[t[1] + "|" + t[0]].SetMarkerColor(colors[cnt])
toPlot[t[1] + "|" + t[0]].SetLineColor(colors[cnt])
cnt += 1
if options.justDivide:
toPlotOld = toPlot
toPlot = {}
toPlot["ratio"] = toPlotOld[todo[0][1] + "|" + todo[0][0]]
toPlot["ratio"].Divide(toPlotOld[todo[1][1] + "|" + todo[1][0]])
maxima = []
for t in toPlot:
maxima.append(toPlot[t].GetMaximum())
hmax = max(maxima) * 1.05
for t in toPlot:
toPlot[t].SetMaximum(hmax)
if len(toPlot) > 1:
c = ROOT.TCanvas()
c.Divide(1, 2)
c.cd(1)
split = 0.2
margin = 0.005
ROOT.gPad.SetPad(.005, split + margin, .995, .995)
c.cd(2)
ROOT.gPad.SetPad(.005, .005, .995, split)
c.cd(1)
ROOT.gPad.SetTopMargin(0.1)
else:
c = ROOT.TCanvas()
DrawMNPlots.banner()
firstPass = True
if not options.justDivide:
legend = ROOT.TLegend(0.6, 0.7, 0.9, 0.85)
legend.SetFillColor(0)
for h in toPlot.keys(
): # not sure if h in toPlot will always give the same order as h in toPlot.keys()
if firstPass:
toPlot[h].Draw()
nameH = toPlot[h].GetName()
xLab = yLab = ""
if "ptHat" == nameH:
xLab = "#hat{p}_{T}"
yLab = "events [a.u.]"
elif "miss" in nameH:
xLab = "#Delta #eta"
yLab = "p_{miss}"
toPlot[h].SetMinimum(0)
toPlot[h].SetMaximum(1)
elif "fake" in nameH:
xLab = "#Delta #eta"
yLab = "p_{fake}"
toPlot[h].SetMinimum(0)
toPlot[h].SetMaximum(1)
toPlot[h].GetXaxis().SetTitle(xLab)
toPlot[h].GetYaxis().SetTitle(yLab)
toPlot[h].GetYaxis().SetTitleOffset(1.8)
toPlot[h].GetXaxis().SetTitleOffset(1.5)
toPlot[h].SetMinimum(0)
firstPass = False
else:
toPlot[h].Draw("SAME")
if not options.justDivide:
label = "pythia"
if "herwig" in h: label = "herwig"
legend.AddEntry(toPlot[h], label, "pel")
if not options.justDivide:
legend.Draw("SAME")
if len(toPlot) > 1:
c.cd(2)
h1 = toPlot[toPlot.keys()[0]].Clone()
h2 = toPlot[toPlot.keys()[1]].Clone()
frame = ROOT.gPad.DrawFrame(h1.GetXaxis().GetXmin(), 0.5,
h1.GetXaxis().GetXmax(), 1.2)
frame.GetYaxis().SetTitle("ratio")
frame.GetYaxis().SetTitleSize(0.13)
frame.GetYaxis().SetTitleOffset(0.5)
h1.Divide(h2)
h1.SetLineColor(1)
h1.SetMarkerColor(1)
#f = ROOT.TF1("f1", "[0]*x+[1]", h1.GetXaxis().GetXmin(), h1.GetXaxis().GetXmax())
h1.Draw("SAME")
#h1.Fit(f, "", "", 20, h1.GetXaxis().GetXmax())
#f.SetLineColor(1)
#f.Draw("SAME")
#frame.GetXaxis().SetRangeUser(5,8)
c.Print(odir + "/" + label +
"simpleMCplot_{}_{}.png".format(labelRaw, options.variant))
c.Print(odir + "/" + label +
"simpleMCplot_{}_{}.pdf".format(labelRaw, options.variant))
def main():
CommonFSQFramework.Core.Style.setTDRStyle()
parser = OptionParser(usage="usage: %prog [options] filename",
version="%prog 1.0")
parser.add_option("-v", "--variant", action="store", dest="variant", type="string", \
help="choose analysis variant")
parser.add_option("-n", "--normalization", action="store", dest="normalization", type="string", \
help="how should I normalize the plots?")
parser.add_option("-b",
"--normalizeToBinWidth",
action="store_true",
dest="normalizeToBinWidth")
parser.add_option("-s", type="float", dest="scaleExtra")
(options, args) = parser.parse_args()
scaleExtra = 1.
if options.scaleExtra:
scaleExtra = 1. / options.scaleExtra
normalizeToBinWidth = False
if options.normalizeToBinWidth:
normalizeToBinWidth = True
if not options.variant:
print "Provide analysis variant"
sys.exit()
if not options.normalization:
print "Provide normalization variant"
sys.exit()
norms = ["xs", "area"]
if options.normalization not in norms:
print "Normalization not known. Possible choices: " + " ".join(norms)
sys.exit()
indir = "~/tmp/unfolded_{}/".format(options.variant)
(options, args) = parser.parse_args()
if not options.variant:
print "Provide analysis variant"
sys.exit()
indir = "~/tmp/unfolded_{}/".format(options.variant)
histofile = "plotsMNxs_{}.root".format(options.variant)
lumiUncertainty = 0.04
herwigIn = indir + "/mnxsHistos_unfolded_herwigOnData.root"
pythiaIn = indir + "/mnxsHistos_unfolded_pythiaOnData.root"
ofileName = indir + "/mnxsHistos_unfolded_onData_merged.root"
histos = {}
histos["herwig"] = getHistos(herwigIn)
histos["pythia"] = getHistos(pythiaIn)
#print histos["herwig"]["_jet15"].keys()
#sys.exit()
# TODO: test that dirs have the same contents
# ['xsunfolded_central_jet15', 'xsunfolded_jecDown_jet15', 'xs_central_jet15', 'xsunfolded_jerDown_jet15', 'xsunfolded_jecUp_jet15', 'xsunfolded_jerUp_jet15']
finalSet = {}
todo = ["_jet15", "_dj15fb"]
#todo = ["_jet15"]
for t in todo:
finalSet[t] = {}
for hName in histos["herwig"][t]:
if hName.startswith("xs_"):
continue # skip detector level histogram
hAvg = histos["herwig"][t][hName].Clone()
hAvg.Add(histos["pythia"][t][hName])
hAvg.Scale(0.5)
finalSet[t][hName] = hAvg
# add herwig/pythia central histo as variations
# in case we would have more than two MC - for every MC
# add a central value as "up" variation, as a "down"
# variation use the averaged histogram
# this way we have consistent list of up/down variations,
# where the down variation doesnt enlarge uncertainty band
if "_central_" in hName:
newNameHerwig = hName.replace("_central_", "_modelUp_")
newNamePythia = hName.replace("_central_", "_modelDown_")
finalSet[t][newNameHerwig] = histos["herwig"][t][hName].Clone(
newNameHerwig)
finalSet[t][newNamePythia] = histos["pythia"][t][hName].Clone(
newNamePythia)
# at the same point - use the averaged histogram to add lumi uncertainy
# BTW: should we do it here??
newNameAvgUp = hName.replace("_central_", "_lumiUp_")
newNameAvgDown = hName.replace("_central_", "_lumiDown_")
finalSet[t][newNameAvgUp] = hAvg.Clone(newNameAvgUp)
finalSet[t][newNameAvgDown] = hAvg.Clone(newNameAvgDown)
finalSet[t][newNameAvgUp].Scale(1. + lumiUncertainty)
finalSet[t][newNameAvgDown].Scale(1. - lumiUncertainty)
# add jet15 and dj15 histos
# note: histo binning should be the same from beginning!
finalSet["merged"] = {}
for t in finalSet["_jet15"]:
newName = t.replace("_jet15", "_jet15andDJ15FB")
finalHisto = finalSet["_jet15"][t].Clone(newName)
finalHisto.Add(finalSet["_dj15fb"][t.replace("_jet15",
"_dj15fb")].Clone())
if options.normalization == "area":
finalHisto.Scale(1. / finalHisto.Integral())
if normalizeToBinWidth:
finalHisto.Scale(1., "width")
finalHisto.Scale(scaleExtra)
finalSet["merged"][newName] = finalHisto
# save all to file
ofile = ROOT.TFile(ofileName, "RECREATE")
for dirName in finalSet:
odir = ofile.mkdir(dirName)
for h in finalSet[dirName]:
odir.WriteTObject(finalSet[dirName][h])
# make final plot, including uncertainty band
central = [
finalSet["merged"][hName] for hName in finalSet["merged"].keys()
if "_central_" in hName
]
if len(central) != 1:
raise Exception("Error: more than one central histo found")
central = central[0]
uncert = [
finalSet["merged"][hName] for hName in finalSet["merged"].keys()
if "_central_" not in hName
]
#uncert = [finalSet["merged"][hName] for hName in finalSet["merged"].keys() if "_model" in hName ]
uncResult = DrawPlots.getUncertaintyBand(uncert, central)
unc = uncResult["band"]
# get GEN level distributions
histosFromPyAnalyzer = getHistos(histofile)
herwigDir = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
pythiaDir = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
genHistoHerwig = histosFromPyAnalyzer[herwigDir][
"detaGen_central_jet15"].Clone()
genHistoHerwig.Add(
histosFromPyAnalyzer[herwigDir]["detaGen_central_dj15fb"])
genHistoPythia = histosFromPyAnalyzer[pythiaDir][
"detaGen_central_jet15"].Clone()
genHistoPythia.Add(
histosFromPyAnalyzer[pythiaDir]["detaGen_central_dj15fb"])
if options.normalization == "area":
map(lambda h: h.Scale(1. / h.Integral()),
[genHistoPythia, genHistoHerwig])
if normalizeToBinWidth:
map(lambda h: h.Scale(1, "width"), [genHistoPythia, genHistoHerwig])
map(lambda h: h.Scale(scaleExtra), [genHistoPythia, genHistoHerwig])
maxima = []
maxima.append(uncResult["max"])
for t in [unc, central, genHistoHerwig, genHistoPythia]:
maxima.append(t.GetMaximum())
c = ROOT.TCanvas()
c.Divide(1, 2)
c.cd(1)
split = 0.2
margin = 0.005
ROOT.gPad.SetPad(.005, split + margin, .995, .995)
c.cd(2)
ROOT.gPad.SetPad(.005, .005, .995, split)
c.cd(1)
ROOT.gPad.SetTopMargin(0.1)
#c.SetRightMargin(0.07)
central.SetMaximum(max(maxima) * 1.05)
unc.SetFillColor(17)
central.Draw()
#central.GetXaxis().SetRangeUser(5,8)
#central.GetYaxis().SetRangeUser(0,250000)
central.GetXaxis().SetTitle("#Delta#eta")
central.GetYaxis().SetTitle("#sigma [pb]")
central.GetYaxis().SetTitleOffset(1.8)
unc.Draw("2SAME")
central.Draw("SAME")
genHistoHerwig.Draw("SAME HIST")
genHistoHerwig.SetLineColor(2)
genHistoPythia.Draw("SAME HIST")
genHistoPythia.SetLineColor(4)
DrawMNPlots.banner()
legend = ROOT.TLegend(0.6, 0.7, 0.9, 0.85)
legend.SetFillColor(0)
legend.AddEntry(central, "data", "pel")
legend.AddEntry(unc, "syst. unc.", "f")
legend.AddEntry(genHistoHerwig, "herwig", "l")
legend.AddEntry(genHistoPythia, "pythia", "l")
legend.Draw("SAME")
c.cd(2)
frame = ROOT.gPad.DrawFrame(central.GetXaxis().GetXmin(), 0,
central.GetXaxis().GetXmax(), 3)
#frame.GetXaxis().SetRangeUser(5,8)
yUp = array('d')
yDown = array('d')
x = array('d')
y = array('d')
xDown = array('d')
xUp = array('d')
for iBin in xrange(1, central.GetNbinsX() + 1):
val = central.GetBinContent(iBin)
if val != 0:
valDown = unc.GetErrorYlow(iBin - 1) / central.GetBinContent(iBin)
valUp = unc.GetErrorYhigh(iBin - 1) / central.GetBinContent(iBin)
yDown.append(valDown)
yUp.append(valUp)
#print valDown, valUp
else:
yUp.append(0)
yDown.append(0)
#print
x.append(unc.GetX()[iBin - 1])
y.append(1)
xDown.append(unc.GetErrorXlow(iBin - 1))
xUp.append(unc.GetErrorXhigh(iBin - 1))
#print type(x)
uncRatio = ROOT.TGraphAsymmErrors(len(x), x, y, xDown, xUp, yDown, yUp)
uncRatio.SetFillStyle(3001)
uncRatio.SetFillColor(17)
uncRatio.Draw("2SAME")
centralRatio = central.Clone()
centralRatio.Divide(central)
centralRatio.Draw("SAME")
herwigRatio = genHistoHerwig.Clone()
herwigRatio.Divide(central)
pythiaRatio = genHistoPythia.Clone()
pythiaRatio.Divide(central)
herwigRatio.Draw("SAME L")
pythiaRatio.Draw("SAME L")
c.Print(indir + "/mergedUnfolded_{}.png".format(options.normalization))
c.Print(indir + "/mergedUnfolded_{}.pdf".format(options.normalization))
c.Print(indir + "/mergedUnfolded_{}.root".format(options.normalization))
c.cd(1)
ROOT.gPad.SetLogy()
c.Print(indir + "/mergedUnfolded_{}_log.png".format(options.normalization))
c.Print(indir + "/mergedUnfolded_{}_log.pdf".format(options.normalization))
def unfold(action, infileName):
possibleActions = getPossibleActions()
if action not in possibleActions:
print "Action", action, "not known. Possible actions "+ " ".join(possibleActions)
return
categories = {}
if action == "herwigOnData":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
categories["_dj15fb"] = ["METFwd-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
elif action == "pythiaOnData":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
categories["_dj15fb"] = ["METFwd-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
elif action == "pythiaOnHerwig":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "herwigOnPythia":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
categories["_dj15fb"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
elif action == "herwigOnHerwig":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "pythiaOnPythia":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
categories["_dj15fb"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
histos = getHistos(infileName)
#print histos.keys()
#print histos["JetMET-Run2010A-Apr21ReReco-v1"].keys()
knownResponses = set(filter(lambda x: x.startswith("response_"), histos[baseMC].keys()))
#print histos[baseMC].keys()
#responsesCentral = set(filter(lambda x: "_central_" in x, knownResponses))
#responsesVariations = knownResponses-responsesCentral
# _dj15fb',
#'response_jecDown_jet15
of = ROOT.TFile(optionsReg["odir"]+"/mnxsHistos_unfolded_"+action+".root","RECREATE")
# Warning: duplicated code for lumi calculation! See mnDraw.py
triggerToKey = {}
triggerToKey["_jet15"] = "lumiJet15"
triggerToKey["_dj15fb"] = "lumiDiJet15FB"
for c in categories:
odirROOTfile = of.mkdir(c)
centralHistoName = "xs_central"+c # in fact we should not find any other histogram in data than "central"
histo = None
sampleList=CommonFSQFramework.Core.Util.getAnaDefinition("sam")
lumi = 0.
for ds in categories[c]:
h = histos[ds][centralHistoName]
if not histo:
histo = h.Clone()
histo.SetDirectory(0)
else:
histo.Add(h)
if "Data" in action: #
lumiKeyName = triggerToKey[c]
lumi += sampleList[ds][lumiKeyName]
if "Data" in action:
histo.Scale(1./lumi)
print "Lumi", c, action, lumi
rawName = "xs_central"+c
odirROOTfile.WriteTObject(histo,rawName)
for r in knownResponses:
if c not in r: continue # do not apply dj15fb to jet15 and viceversa
variation = r.split("_")[1]
# Doing: _dj15fb response_central_dj15fb central
print "Doing: ", c, r, variation
rawName = "xsunfolded_" + variation+ c
sys.stdout.flush()
'''
histoWithChangedErrors = histo.Clone()
for i in xrange(histoWithChangedErrors.GetNbinsX()+2):
err = histoWithChangedErrors.GetBinError(i)
histoWithChangedErrors.SetBinError(i, err/2)
hReco = doUnfold(histoWithChangedErrors, histos[baseMC][r].Clone())[0] # chi2 is on second part of ntuple
'''
hReco = doUnfold(histo.Clone(), histos[baseMC][r].Clone())[0] # chi2 is on second part of ntuple
# '''
# unfolding sets the errors of the unfolded histo to the ones from
# covariance matrix (ie this do not correspond to stat errors from input distribution)
# so we treat those as just another variation
# note: disabled
if "central" in variation and False:
hAndErr=extractErrors(hReco)
centralValueWithoutErrors = hAndErr[0]
up = hAndErr[1]
down = hAndErr[2]
centralValueWithoutErrors.SetName(rawName)
centralValueWithoutErrorsRawName = rawName
# if toyMC was disabled write without errors
# if toyMC is enabled stat errors will be fetched from toyMC variations named "measured"
if optionsReg["disableToys"]:
odirROOTfile.WriteTObject(centralValueWithoutErrors, centralValueWithoutErrorsRawName)
upName = rawName.replace("central", "unfcovUp")
downName = rawName.replace("central", "unfcovDown")
up.SetName(upName)
odirROOTfile.WriteTObject(up,upName)
down.SetName(downName)
odirROOTfile.WriteTObject(down,downName)
else:
hReco.SetName(rawName)
odirROOTfile.WriteTObject(hReco, rawName)
# perform chi2 vs nIter scan (doesnt affect the final result
if "central" in variation:
scanName = "chi2scan_"+rawName
hScan = ROOT.TH1F(scanName, scanName+";iterations;chi^{2}", 8, 0.5, 8.5)
hScan.GetYaxis().SetTitleOffset(1.8)
for i in xrange(1,9):
chi2 = doUnfold(histo.Clone(), histos[baseMC][r].Clone(), i)[1]
iBin = hScan.FindBin(i)
hScan.SetBinContent(iBin, chi2)
canv = ROOT.TCanvas()
canv.SetLeftMargin(0.2)
hScan.Draw()
canv.Print(optionsReg["odir"]+"/chi2/{}_{}.png".format(action,scanName))
canv.Print(optionsReg["odir"]+"/chi2/{}_{}.pdf".format(action,scanName))
# now - toyMC approac to limited MC statistics
#todo = ["response", "fakes", "truth", "measured"]
todo = ["response", "fakes", "truth"]
#todo = ["truth"]
if optionsReg["disableToys"]:
todo = []
if variation == "central":
badToys = 0
for t in todo:
# TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Option_t* option = "")
bins = hReco.GetXaxis().GetXbins()
profile = TProfile("prof_{}_{}".format(rawName, t), "", bins.GetSize()-1, bins.GetArray())
for i in xrange(0, optionsReg["ntoys"]):
clonedResponse = histos[baseMC][r].Clone()
htruth = clonedResponse.Htruth()
hfakes = clonedResponse.Hfakes()
hresponse = clonedResponse.Hresponse()
hmeas = clonedResponse.Hmeasured()
histoToUnfold = histo.Clone()
if t == "truth":
vary(htruth)
elif t == "fakes":
fakesOrg = hfakes.Clone()
vary(hfakes)
fakesDiff = hfakes.Clone()
fakesDiff.Add(fakesOrg, -1)
hmeas.Add(fakesDiff)
elif t == "response":
vary(hresponse)
elif t == "measured":
vary(histoToUnfold)
else:
raise Exception("dont know what to do")
newResponse = ROOT.RooUnfoldResponse(hmeas, htruth, hresponse, \
"resp_{}_{}_{}".format(rawName, t,i))
hRecoVaried = doUnfold(histoToUnfold, newResponse)[0]
#print "TTT", hReco.Integral(), hRecoVaried.Integral()
binv1 = hRecoVaried.GetBinContent(1)
if math.isnan(binv1) or math.isinf(binv1):
badToys += 1
continue
for ix in xrange(0, hRecoVaried.GetNbinsX()+2):
binCenter = hRecoVaried.GetBinCenter(ix)
val = hRecoVaried.GetBinContent(ix)
if math.isnan(val) or math.isinf(val):
print "TOYmc Error: nan/inf value found"
continue
# what to do??
profile.Fill(binCenter, val)
#print "Var: ", variation
#rawName = "xsunfolded_" + variation+ c
rawNameUp = rawName.replace("central", "toyMC{}Up".format(t) )
rawNameDown = rawName.replace("central", "toyMC{}Down".format(t) )
hUp = hReco.Clone(rawNameUp)
hDown = hReco.Clone(rawNameDown)
for i in xrange(1, hReco.GetNbinsX()+1):
binc1 = hReco.GetBinCenter(i)
binc2 = profile.GetBinCenter(i)
val1 = hReco.GetBinContent(i)
val2 = profile.GetBinContent(i)
if val1 <= 0: continue
errProf = profile.GetBinError(i)
print "binc: {} {}, vals ratio {}, error: {}".format(binc1, binc2, val1/val2, errProf/val1)
hUp.SetBinContent(i, val1+errProf)
hDown.SetBinContent(i, val1-errProf)
if t != "measured":
odirROOTfile.WriteTObject(hUp, rawNameUp)
odirROOTfile.WriteTObject(hDown, rawNameDown)
else:
for i in xrange(0, centralValueWithoutErrors.GetNbinsX()+1):
valCen = centralValueWithoutErrors.GetBinContent(i)
valUp = hUp.GetBinContent(i)
valDown = hDown.GetBinContent(i)
err1 = abs(valCen-valUp)
err2 = abs(valCen-valDown)
err = (err1+err2)/2.
if valCen > 0:
print "Stat errors from toy:", err, valCen, err/valCen
centralValueWithoutErrors.SetBinError(i,err)
odirROOTfile.WriteTObject(centralValueWithoutErrors, centralValueWithoutErrorsRawName)
print "TOYmc done for", r, " bad toys:", badToys
def main():
CommonFSQFramework.Core.Style.setTDRStyle()
parser = OptionParser(usage="usage: %prog [options] filename", version="%prog 1.0")
parser.add_option(
"-l", "--label", action="store", dest="label", type="string", help="extra label when naming files"
)
parser.add_option("-a", "--histo1", action="store", dest="histo1", type="string", help="histo1")
parser.add_option("-b", "--histo2", action="store", dest="histo2", type="string", help="histo2")
parser.add_option("-v", "--variant", action="store", dest="variant", type="string", help="choose analysis variant")
parser.add_option("-d", "--justDivide", action="store_true", dest="justDivide")
(options, args) = parser.parse_args()
if not options.variant:
print "Provide analysis variant"
sys.exit()
if not options.histo1:
# -a ptHat,QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp
print "Tell me what to do..."
sys.exit()
odir = "~/tmp/simpleMC_{}/".format(options.variant)
os.system("mkdir -p " + odir)
todo = []
todo.append(options.histo1.split(","))
labelRaw = options.histo1.split(",")[0]
if options.histo2:
todo.append(options.histo2.split(","))
label = ""
if options.label:
label = options.label + "_"
histofile = "plotsMNxs_{}.root".format(options.variant)
histos = getHistos(histofile)
toPlot = {}
cnt = 0
colors = [2, 1]
for t in todo:
toPlot[t[1] + "|" + t[0]] = histos[t[1]][t[0]]
toPlot[t[1] + "|" + t[0]].SetMarkerColor(colors[cnt])
toPlot[t[1] + "|" + t[0]].SetLineColor(colors[cnt])
cnt += 1
if options.justDivide:
toPlotOld = toPlot
toPlot = {}
toPlot["ratio"] = toPlotOld[todo[0][1] + "|" + todo[0][0]]
toPlot["ratio"].Divide(toPlotOld[todo[1][1] + "|" + todo[1][0]])
maxima = []
for t in toPlot:
maxima.append(toPlot[t].GetMaximum())
hmax = max(maxima) * 1.05
for t in toPlot:
toPlot[t].SetMaximum(hmax)
if len(toPlot) > 1:
c = ROOT.TCanvas()
c.Divide(1, 2)
c.cd(1)
split = 0.2
margin = 0.005
ROOT.gPad.SetPad(0.005, split + margin, 0.995, 0.995)
c.cd(2)
ROOT.gPad.SetPad(0.005, 0.005, 0.995, split)
c.cd(1)
ROOT.gPad.SetTopMargin(0.1)
else:
c = ROOT.TCanvas()
DrawMNPlots.banner()
firstPass = True
if not options.justDivide:
legend = ROOT.TLegend(0.6, 0.7, 0.9, 0.85)
legend.SetFillColor(0)
for h in toPlot.keys(): # not sure if h in toPlot will always give the same order as h in toPlot.keys()
if firstPass:
toPlot[h].Draw()
nameH = toPlot[h].GetName()
xLab = yLab = ""
if "ptHat" == nameH:
xLab = "#hat{p}_{T}"
yLab = "events [a.u.]"
elif "miss" in nameH:
xLab = "#Delta #eta"
yLab = "p_{miss}"
toPlot[h].SetMinimum(0)
toPlot[h].SetMaximum(1)
elif "fake" in nameH:
xLab = "#Delta #eta"
yLab = "p_{fake}"
toPlot[h].SetMinimum(0)
toPlot[h].SetMaximum(1)
toPlot[h].GetXaxis().SetTitle(xLab)
toPlot[h].GetYaxis().SetTitle(yLab)
toPlot[h].GetYaxis().SetTitleOffset(1.8)
toPlot[h].GetXaxis().SetTitleOffset(1.5)
toPlot[h].SetMinimum(0)
firstPass = False
else:
toPlot[h].Draw("SAME")
if not options.justDivide:
label = "pythia"
if "herwig" in h:
label = "herwig"
legend.AddEntry(toPlot[h], label, "pel")
if not options.justDivide:
legend.Draw("SAME")
if len(toPlot) > 1:
c.cd(2)
h1 = toPlot[toPlot.keys()[0]].Clone()
h2 = toPlot[toPlot.keys()[1]].Clone()
frame = ROOT.gPad.DrawFrame(h1.GetXaxis().GetXmin(), 0.5, h1.GetXaxis().GetXmax(), 1.2)
frame.GetYaxis().SetTitle("ratio")
frame.GetYaxis().SetTitleSize(0.13)
frame.GetYaxis().SetTitleOffset(0.5)
h1.Divide(h2)
h1.SetLineColor(1)
h1.SetMarkerColor(1)
# f = ROOT.TF1("f1", "[0]*x+[1]", h1.GetXaxis().GetXmin(), h1.GetXaxis().GetXmax())
h1.Draw("SAME")
# h1.Fit(f, "", "", 20, h1.GetXaxis().GetXmax())
# f.SetLineColor(1)
# f.Draw("SAME")
# frame.GetXaxis().SetRangeUser(5,8)
c.Print(odir + "/" + label + "simpleMCplot_{}_{}.png".format(labelRaw, options.variant))
c.Print(odir + "/" + label + "simpleMCplot_{}_{}.pdf".format(labelRaw, options.variant))
def main():
CommonFSQFramework.Core.Style.setTDRStyle()
parser = OptionParser(usage="usage: %prog [options] filename",
version="%prog 1.0")
parser.add_option("-v", "--variant", action="store", dest="variant", type="string", \
help="choose analysis variant")
parser.add_option("-n", "--normalization", action="store", dest="normalization", type="string", \
help="how should I normalize the plots?")
parser.add_option("-b", "--normalizeToBinWidth", action="store_true", dest="normalizeToBinWidth")
parser.add_option("-s", type="float", dest="scaleExtra")
(options, args) = parser.parse_args()
scaleExtra = 1.
if options.scaleExtra:
scaleExtra = 1./options.scaleExtra
normalizeToBinWidth = False
if options.normalizeToBinWidth:
normalizeToBinWidth = True
if not options.variant:
print "Provide analysis variant"
sys.exit()
if not options.normalization:
print "Provide normalization variant"
sys.exit()
norms = ["xs", "area"]
if options.normalization not in norms:
print "Normalization not known. Possible choices: " + " ".join(norms)
sys.exit()
indir = "~/tmp/unfolded_{}/".format(options.variant)
(options, args) = parser.parse_args()
if not options.variant:
print "Provide analysis variant"
sys.exit()
indir = "~/tmp/unfolded_{}/".format(options.variant)
histofile = "plotsMNxs_{}.root".format(options.variant)
lumiUncertainty = 0.04
herwigIn=indir+"/mnxsHistos_unfolded_herwigOnData.root"
pythiaIn=indir+"/mnxsHistos_unfolded_pythiaOnData.root"
ofileName = indir+"/mnxsHistos_unfolded_onData_merged.root"
histos = {}
histos["herwig"]=getHistos(herwigIn)
histos["pythia"]=getHistos(pythiaIn)
#print histos["herwig"]["_jet15"].keys()
#sys.exit()
# TODO: test that dirs have the same contents
# ['xsunfolded_central_jet15', 'xsunfolded_jecDown_jet15', 'xs_central_jet15', 'xsunfolded_jerDown_jet15', 'xsunfolded_jecUp_jet15', 'xsunfolded_jerUp_jet15']
finalSet = {}
todo = ["_jet15", "_dj15fb"]
#todo = ["_jet15"]
for t in todo:
finalSet[t] = {}
for hName in histos["herwig"][t]:
if hName.startswith("xs_"): continue # skip detector level histogram
hAvg = histos["herwig"][t][hName].Clone()
hAvg.Add(histos["pythia"][t][hName])
hAvg.Scale(0.5)
finalSet[t][hName]=hAvg
# add herwig/pythia central histo as variations
# in case we would have more than two MC - for every MC
# add a central value as "up" variation, as a "down"
# variation use the averaged histogram
# this way we have consistent list of up/down variations,
# where the down variation doesnt enlarge uncertainty band
if "_central_" in hName:
newNameHerwig = hName.replace("_central_", "_modelUp_")
newNamePythia = hName.replace("_central_", "_modelDown_")
finalSet[t][newNameHerwig] = histos["herwig"][t][hName].Clone(newNameHerwig)
finalSet[t][newNamePythia] = histos["pythia"][t][hName].Clone(newNamePythia)
# at the same point - use the averaged histogram to add lumi uncertainy
# BTW: should we do it here??
newNameAvgUp = hName.replace("_central_", "_lumiUp_")
newNameAvgDown = hName.replace("_central_", "_lumiDown_")
finalSet[t][newNameAvgUp] = hAvg.Clone(newNameAvgUp)
finalSet[t][newNameAvgDown] = hAvg.Clone(newNameAvgDown)
finalSet[t][newNameAvgUp].Scale(1.+lumiUncertainty)
finalSet[t][newNameAvgDown].Scale(1.-lumiUncertainty)
# add jet15 and dj15 histos
# note: histo binning should be the same from beginning!
finalSet["merged"] = {}
for t in finalSet["_jet15"]:
newName = t.replace("_jet15", "_jet15andDJ15FB")
finalHisto = finalSet["_jet15"][t].Clone(newName)
finalHisto.Add(finalSet["_dj15fb"][t.replace("_jet15", "_dj15fb")].Clone())
if options.normalization == "area":
finalHisto.Scale(1./finalHisto.Integral())
if normalizeToBinWidth:
finalHisto.Scale(1., "width")
finalHisto.Scale(scaleExtra)
finalSet["merged"][newName] = finalHisto
# save all to file
ofile = ROOT.TFile(ofileName, "RECREATE")
for dirName in finalSet:
odir = ofile.mkdir(dirName)
for h in finalSet[dirName]:
odir.WriteTObject(finalSet[dirName][h])
# make final plot, including uncertainty band
central = [ finalSet["merged"][hName] for hName in finalSet["merged"].keys() if "_central_" in hName ]
if len(central) != 1:
raise Exception("Error: more than one central histo found")
central = central[0]
uncert = [finalSet["merged"][hName] for hName in finalSet["merged"].keys() if "_central_" not in hName ]
#uncert = [finalSet["merged"][hName] for hName in finalSet["merged"].keys() if "_model" in hName ]
uncResult= DrawPlots.getUncertaintyBand(uncert, central)
unc = uncResult["band"]
# get GEN level distributions
histosFromPyAnalyzer = getHistos(histofile)
herwigDir = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
pythiaDir = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
genHistoHerwig = histosFromPyAnalyzer[herwigDir]["detaGen_central_jet15"].Clone()
genHistoHerwig.Add(histosFromPyAnalyzer[herwigDir]["detaGen_central_dj15fb"])
genHistoPythia = histosFromPyAnalyzer[pythiaDir]["detaGen_central_jet15"].Clone()
genHistoPythia.Add(histosFromPyAnalyzer[pythiaDir]["detaGen_central_dj15fb"])
if options.normalization == "area":
map(lambda h: h.Scale(1./h.Integral()), [genHistoPythia, genHistoHerwig] )
if normalizeToBinWidth:
map(lambda h: h.Scale(1, "width"), [genHistoPythia, genHistoHerwig] )
map(lambda h: h.Scale(scaleExtra), [genHistoPythia, genHistoHerwig] )
maxima = []
maxima.append(uncResult["max"])
for t in [unc, central, genHistoHerwig, genHistoPythia]:
maxima.append(t.GetMaximum())
c = ROOT.TCanvas()
c.Divide(1,2)
c.cd(1)
split = 0.2
margin = 0.005
ROOT.gPad.SetPad(.005, split+margin, .995, .995)
c.cd(2)
ROOT.gPad.SetPad(.005, .005, .995, split)
c.cd(1)
ROOT.gPad.SetTopMargin(0.1)
#c.SetRightMargin(0.07)
central.SetMaximum(max(maxima)*1.05)
unc.SetFillColor(17);
central.Draw()
#central.GetXaxis().SetRangeUser(5,8)
#central.GetYaxis().SetRangeUser(0,250000)
central.GetXaxis().SetTitle("#Delta#eta")
central.GetYaxis().SetTitle("#sigma [pb]")
central.GetYaxis().SetTitleOffset(1.8)
unc.Draw("2SAME")
central.Draw("SAME")
genHistoHerwig.Draw("SAME HIST")
genHistoHerwig.SetLineColor(2)
genHistoPythia.Draw("SAME HIST")
genHistoPythia.SetLineColor(4)
DrawMNPlots.banner()
legend = ROOT.TLegend(0.6, 0.7, 0.9, 0.85)
legend.SetFillColor(0)
legend.AddEntry(central, "data", "pel")
legend.AddEntry(unc, "syst. unc.", "f")
legend.AddEntry(genHistoHerwig, "herwig", "l")
legend.AddEntry(genHistoPythia, "pythia", "l")
legend.Draw("SAME")
c.cd(2)
frame = ROOT.gPad.DrawFrame(central.GetXaxis().GetXmin(), 0, central.GetXaxis().GetXmax(), 3)
#frame.GetXaxis().SetRangeUser(5,8)
yUp = array('d')
yDown = array('d')
x = array('d')
y = array('d')
xDown = array('d')
xUp = array('d')
y4Rivet = array('d')
yUp4Rivet = array('d')
yDown4Rivet = array('d')
for iBin in xrange(1, central.GetNbinsX()+1):
val = central.GetBinContent(iBin)
if val == 0: continue
if val != 0:
binErr = central.GetBinError(iBin)
errUp = unc.GetErrorYhigh(iBin-1)
errDown = unc.GetErrorYlow(iBin-1)
valDown = errDown/val
valUp = errUp/val
yDown.append(valDown)
yUp.append(valUp)
valDown4Rivet = math.sqrt(errDown*errDown + binErr*binErr )
valUp4Rivet = math.sqrt(errUp*errUp + binErr*binErr )
yUp4Rivet.append(valUp4Rivet)
yDown4Rivet.append(valDown4Rivet)
#print valDown, valUp
else:
yUp.append(0)
yDown.append(0)
#print
x.append(unc.GetX()[iBin-1])
y.append(1)
ratio = unc.GetY()[iBin-1]/val
if max(ratio-1., 1.-ratio)>0.001:
raise Exception("Expected equal values")
y4Rivet.append(val)
xDown.append(unc.GetErrorXlow(iBin-1))
xUp.append(unc.GetErrorXhigh(iBin-1))
#print type(x)
uncRatio = ROOT.TGraphAsymmErrors(len(x), x, y, xDown, xUp, yDown, yUp)
result4Rivet = ROOT.TGraphAsymmErrors(len(x), x, y4Rivet, xDown, xUp, yDown4Rivet, yUp4Rivet)
#uncRatio = ROOT.TGraphAsymmErrors(len(x), x, y, xDown, xUp, yDown, yUp)
uncRatio.SetFillStyle(3001)
uncRatio.SetFillColor(17)
uncRatio.Draw("2SAME")
centralRatio = central.Clone()
centralRatio.Divide(central)
centralRatio.Draw("SAME")
herwigRatio = genHistoHerwig.Clone()
herwigRatio.Divide(central)
pythiaRatio = genHistoPythia.Clone()
pythiaRatio.Divide(central)
herwigRatio.Draw("SAME L")
pythiaRatio.Draw("SAME L")
c.Print(indir+"/mergedUnfolded_{}.png".format(options.normalization))
c.Print(indir+"/mergedUnfolded_{}.pdf".format(options.normalization))
c.Print(indir+"/mergedUnfolded_{}.root".format(options.normalization))
c.cd(1)
ROOT.gPad.SetLogy()
c.Print(indir+"/mergedUnfolded_{}_log.png".format(options.normalization))
c.Print(indir+"/mergedUnfolded_{}_log.pdf".format(options.normalization))
# rivet export
from do import todoCatAll
if len(todoCatAll) != 6:
raise Exception("Error: inconsistent number of categories in todoCatAll")
rivet = ROOT.TFile("toRivet.root", "RECREATE")
rivetNum = todoCatAll.index(options.variant)+1
if "area" == options.normalization:
rivetNum += 10
numAsStr = str(rivetNum)
if len (numAsStr) == 1:
numAsStr = "0"+numAsStr
rivetName = "d"+numAsStr+"-x01-y01"
print options.normalization, rivetNum, rivetName
rivet.WriteTObject(result4Rivet, rivetName)
rivet.Close()
del rivet
import os
r2f = "/cvmfs/cms.cern.ch/slc6_amd64_gcc481/external/rivet/1.8.2-cms8/bin/root2flat"
if not os.path.isfile(r2f):
raise Exception("Cannot find root2flat. Rivet export failed")
os.system(r2f + " toRivet.root")
import yoda
analysisobjects = yoda.readFLAT(rivetName+".dat")
#print type(analysisobjects)
#print analysisobjects.keys()
for k in analysisobjects:
pth = "/CMS_2015_FWD071/"+rivetName
#print dir(analysisobjects[k])
#analysisobjects[k].setTitle(pth)
#analysisobjects[k].setPath(pth)
analysisobjects[k].setAnnotation("Title", pth)
analysisobjects[k].setAnnotation("Path", pth)
yoda.writeYODA(analysisobjects, rivetName+".yoda")
def unfold(action, infileName):
possibleActions = getPossibleActions()
if action not in possibleActions:
print "Action", action, "not known. Possible actions "+ " ".join(possibleActions)
return
categories = {}
if action == "herwigOnData":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
categories["_dj15fb"] = ["METFwd-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
elif action == "pythiaOnData":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
categories["_dj15fb"] = ["METFwd-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1", "JetMET-Run2010A-Apr21ReReco-v1"]
elif action == "pythiaOnHerwig":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "herwigOnPythia":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
categories["_dj15fb"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
elif action == "herwigOnHerwig":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "pythiaOnPythia":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
categories["_dj15fb"] = ["QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"]
histos = getHistos(infileName)
#print histos.keys()
#print histos["JetMET-Run2010A-Apr21ReReco-v1"].keys()
knownResponses = set(filter(lambda x: x.startswith("response_"), histos[baseMC].keys()))
#print histos[baseMC].keys()
#responsesCentral = set(filter(lambda x: "_central_" in x, knownResponses))
#responsesVariations = knownResponses-responsesCentral
# _dj15fb',
#'response_jecDown_jet15
of = ROOT.TFile(odir+"/mnxsHistos_unfolded_"+action+".root","RECREATE")
# Warning: duplicated code for lumi calculation! See mnDraw.py
triggerToKey = {}
triggerToKey["_jet15"] = "lumiJet15"
triggerToKey["_dj15fb"] = "lumiDiJet15FB"
for c in categories:
odirROOTfile = of.mkdir(c)
centralHistoName = "xs_central"+c # in fact we should not find any other histogram in data than "central"
histo = None
sampleList=CommonFSQFramework.Core.Util.getAnaDefinition("sam")
lumi = 0.
for ds in categories[c]:
h = histos[ds][centralHistoName]
if not histo:
histo = h.Clone()
histo.SetDirectory(0)
else:
histo.Add(h)
if "Data" in action: #
lumiKeyName = triggerToKey[c]
lumi += sampleList[ds][lumiKeyName]
if "Data" in action:
histo.Scale(1./lumi)
print "Lumi", c, action, lumi
rawName = "xs_central"+c
odirROOTfile.WriteTObject(histo,rawName)
for r in knownResponses:
if c not in r: continue # do not apply dj15fb to jet15 and viceversa
variation = r.split("_")[1]
# Doing: _dj15fb response_central_dj15fb central
print "Doing: ", c, r, variation
rawName = "xsunfolded_" + variation+ c
sys.stdout.flush()
hReco = doUnfold(histo.Clone(), histos[baseMC][r].Clone())
hReco.SetName(rawName)
odirROOTfile.WriteTObject(hReco, rawName)
# now - toyMC approac to limited MC statistics
todo = ["response", "fakes", "truth"]
#todo = ["truth"]
global ntoys
if variation == "central":
badToys = 0
for t in todo:
# TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Option_t* option = "")
bins = hReco.GetXaxis().GetXbins()
profile = TProfile("prof_{}_{}".format(rawName, t), "", bins.GetSize()-1, bins.GetArray())
for i in xrange(0, ntoys):
clonedResponse = histos[baseMC][r].Clone()
if t == "truth":
vary(clonedResponse.Htruth())
elif t == "fakes":
vary(clonedResponse.Hfakes())
elif t == "response":
vary(clonedResponse.Hresponse())
# TODO: wiezy!!
else:
raise Exception("dont know what to do")
hRecoVaried = doUnfold(histo.Clone(), clonedResponse)
#print "TTT", hReco.Integral(), hRecoVaried.Integral()
binv1 = hRecoVaried.GetBinContent(1)
if math.isnan(binv1) or math.isinf(binv1):
badToys += 1
continue
for ix in xrange(0, hRecoVaried.GetNbinsX()+2):
binCenter = hRecoVaried.GetBinCenter(ix)
val = hRecoVaried.GetBinContent(ix)
if math.isnan(val) or math.isinf(val):
print "TOYmc Error: nan/inf value found"
continue
# what to do??
profile.Fill(binCenter, val)
#print "Var: ", variation
#rawName = "xsunfolded_" + variation+ c
rawNameUp = rawName.replace("central", "toyMC{}Up".format(t) )
rawNameDown = rawName.replace("central", "toyMC{}Down".format(t) )
hUp = hReco.Clone(rawNameUp)
hDown = hReco.Clone(rawNameDown)
for i in xrange(1, hReco.GetNbinsX()+1):
binc1 = hReco.GetBinCenter(i)
binc2 = profile.GetBinCenter(i)
val1 = hReco.GetBinContent(i)
val2 = profile.GetBinContent(i)
if val1 <= 0: continue
errProf = profile.GetBinError(i)
print "binc: {} {}, vals ratio {}, error: {}".format(binc1, binc2, val1/val2, errProf/val1)
hUp.SetBinContent(i, val1+errProf)
hDown.SetBinContent(i, val1-errProf)
odirROOTfile.WriteTObject(hUp, rawNameUp)
odirROOTfile.WriteTObject(hDown, rawNameDown)
print "TOYmc done for", r, " bad toys:", badToys
def unfold(action, infileName):
possibleActions = getPossibleActions()
if action not in possibleActions:
print "Action", action, "not known. Possible actions " + " ".join(
possibleActions)
return
categories = {}
if action == "herwigOnData":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = [
"Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1",
"JetMET-Run2010A-Apr21ReReco-v1"
]
categories["_dj15fb"] = [
"METFwd-Run2010B-Apr21ReReco-v1",
"JetMETTau-Run2010A-Apr21ReReco-v1",
"JetMET-Run2010A-Apr21ReReco-v1"
]
elif action == "pythiaOnData":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = [
"Jet-Run2010B-Apr21ReReco-v1", "JetMETTau-Run2010A-Apr21ReReco-v1",
"JetMET-Run2010A-Apr21ReReco-v1"
]
categories["_dj15fb"] = [
"METFwd-Run2010B-Apr21ReReco-v1",
"JetMETTau-Run2010A-Apr21ReReco-v1",
"JetMET-Run2010A-Apr21ReReco-v1"
]
elif action == "pythiaOnHerwig":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "herwigOnPythia":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = [
"QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
]
categories["_dj15fb"] = [
"QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
]
elif action == "herwigOnHerwig":
baseMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
otherMC = "QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"
categories["_jet15"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
categories["_dj15fb"] = ["QCD_Pt-15to1000_TuneEE3C_Flat_7TeV_herwigpp"]
elif action == "pythiaOnPythia":
baseMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
otherMC = "QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
categories["_jet15"] = [
"QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
]
categories["_dj15fb"] = [
"QCD_Pt-15to3000_TuneZ2star_Flat_HFshowerLibrary_7TeV_pythia6"
]
histos = getHistos(infileName)
#print histos.keys()
#print histos["JetMET-Run2010A-Apr21ReReco-v1"].keys()
knownResponses = set(
filter(lambda x: x.startswith("response_"), histos[baseMC].keys()))
#print histos[baseMC].keys()
#responsesCentral = set(filter(lambda x: "_central_" in x, knownResponses))
#responsesVariations = knownResponses-responsesCentral
# _dj15fb',
#'response_jecDown_jet15
of = ROOT.TFile(odir + "/mnxsHistos_unfolded_" + action + ".root",
"RECREATE")
# Warning: duplicated code for lumi calculation! See mnDraw.py
triggerToKey = {}
triggerToKey["_jet15"] = "lumiJet15"
triggerToKey["_dj15fb"] = "lumiDiJet15FB"
for c in categories:
odirROOTfile = of.mkdir(c)
centralHistoName = "xs_central" + c # in fact we should not find any other histogram in data than "central"
histo = None
sampleList = CommonFSQFramework.Core.Util.getAnaDefinition("sam")
lumi = 0.
for ds in categories[c]:
h = histos[ds][centralHistoName]
if not histo:
histo = h.Clone()
histo.SetDirectory(0)
else:
histo.Add(h)
if "Data" in action: #
lumiKeyName = triggerToKey[c]
lumi += sampleList[ds][lumiKeyName]
if "Data" in action:
histo.Scale(1. / lumi)
print "Lumi", c, action, lumi
rawName = "xs_central" + c
odirROOTfile.WriteTObject(histo, rawName)
for r in knownResponses:
if c not in r:
continue # do not apply dj15fb to jet15 and viceversa
variation = r.split("_")[1]
# Doing: _dj15fb response_central_dj15fb central
print "Doing: ", c, r, variation
rawName = "xsunfolded_" + variation + c
sys.stdout.flush()
hReco = doUnfold(histo.Clone(), histos[baseMC][r].Clone())
hReco.SetName(rawName)
odirROOTfile.WriteTObject(hReco, rawName)
# now - toyMC approac to limited MC statistics
todo = ["response", "fakes", "truth"]
#todo = ["truth"]
global ntoys
if variation == "central":
badToys = 0
for t in todo:
# TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Option_t* option = "")
bins = hReco.GetXaxis().GetXbins()
profile = TProfile("prof_{}_{}".format(rawName, t), "",
bins.GetSize() - 1, bins.GetArray())
for i in xrange(0, ntoys):
clonedResponse = histos[baseMC][r].Clone()
if t == "truth":
vary(clonedResponse.Htruth())
elif t == "fakes":
vary(clonedResponse.Hfakes())
elif t == "response":
vary(clonedResponse.Hresponse())
# TODO: wiezy!!
else:
raise Exception("dont know what to do")
hRecoVaried = doUnfold(histo.Clone(), clonedResponse)
#print "TTT", hReco.Integral(), hRecoVaried.Integral()
binv1 = hRecoVaried.GetBinContent(1)
if math.isnan(binv1) or math.isinf(binv1):
badToys += 1
continue
for ix in xrange(0, hRecoVaried.GetNbinsX() + 2):
binCenter = hRecoVaried.GetBinCenter(ix)
val = hRecoVaried.GetBinContent(ix)
if math.isnan(val) or math.isinf(val):
print "TOYmc Error: nan/inf value found"
continue
# what to do??
profile.Fill(binCenter, val)
#print "Var: ", variation
#rawName = "xsunfolded_" + variation+ c
rawNameUp = rawName.replace("central",
"toyMC{}Up".format(t))
rawNameDown = rawName.replace("central",
"toyMC{}Down".format(t))
hUp = hReco.Clone(rawNameUp)
hDown = hReco.Clone(rawNameDown)
for i in xrange(1, hReco.GetNbinsX() + 1):
binc1 = hReco.GetBinCenter(i)
binc2 = profile.GetBinCenter(i)
val1 = hReco.GetBinContent(i)
val2 = profile.GetBinContent(i)
if val1 <= 0: continue
errProf = profile.GetBinError(i)
print "binc: {} {}, vals ratio {}, error: {}".format(
binc1, binc2, val1 / val2, errProf / val1)
hUp.SetBinContent(i, val1 + errProf)
hDown.SetBinContent(i, val1 - errProf)
odirROOTfile.WriteTObject(hUp, rawNameUp)
odirROOTfile.WriteTObject(hDown, rawNameDown)
print "TOYmc done for", r, " bad toys:", badToys
