def study_cal_eff():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/data_nn50.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_noise=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal.root")
t_mc.AddFriend("lumical_eff=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal_eff.root")
c = TCanvas()
hs = THStack("hs", "title")
t_data.Draw("energy*(layer > 1)>>h_data50(200, 0, 50)",
"energy*(layer > 1) > 0")
h_data50 = gROOT.FindObject("h_data50")
h_data50.SetTitle("Data")
h_data50.SetLineWidth(2)
h_data50.SetLineColor(1)
hs.Add(h_data50, "histo")
t_mc.Draw("cal_energy_new/0.0885>>h_mc(200, 0, 50)",
"cal_energy_new/0.0885 > 0", "", 100000)
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC smearing and cal eff")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(3)
h_mc.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc, "histo")
t_mc.Draw("cal_energy_smeared7/0.0885>>h_mc7(200, 0, 50)",
"cal_energy_smeared7/0.0885 > 0", "", 100000)
h_mc7 = gROOT.FindObject("h_mc7")
h_mc7.SetTitle("MC smearing=0.7*#sigma")
h_mc7.SetLineWidth(2)
h_mc7.SetLineColor(2)
h_mc7.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc7, "histo")
print(h_mc7.GetEntries())
hs.Draw("nostack")
hs.SetTitle(
"Deposited energy in the pads of calorimeter;E, [MIP]; N_{hits}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
def study_trigger():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_tr_clusters=lumical", "../trees_5gev_e/.root")
c = TCanvas()
t_data.Draw("tr1_n_clusters>>h_data(6, 0, 6)")
h_data = gROOT.FindObject("h_data")
h_data.SetTitle("Data")
h_data.SetLineWidth(2)
h_data.SetLineColor(1)
t_mc.Draw("tr1_n_hits>>h_mc(6, 0, 6)")
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(2)
h_mc.Scale(t_data.GetEntries() / t_mc.GetEntries())
t_mc.Draw("tr1_n_clusters>>h_mc2(6, 0, 6)", "n_triggers == 3")
h_mc2 = gROOT.FindObject("h_mc2")
h_mc2.SetTitle("MC w/ trigger")
h_mc2.SetLineWidth(2)
h_mc2.SetLineColor(4)
h_mc2.Scale(t_data.GetEntries() / t_mc.GetEntries())
hs = THStack("hs", "title")
hs.Add(h_data, "histo")
hs.Add(h_mc, "histo")
hs.Add(h_mc2, "histo")
hs.Draw("nostack")
hs.SetTitle(
"Number of clusters in the 1st tracker;N_{particles}; N_{events}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
legend.AddEntry(errorband, "Uncertainty", "f")
pad1.cd()
stack.Draw('HIST')
rebinnedData.Draw('E,X0,SAME')
legend.Draw("same")
stack.GetXaxis().SetTitle('')
stack.GetXaxis().SetLabelSize(0)
stack.GetYaxis().SetLabelSize(gStyle.GetLabelSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleSize(gStyle.GetTitleSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleOffset(gStyle.GetTitleYOffset() *
(1. - padRatio + padOverlap))
stack.SetTitle(';;Events/%i GeV ' % rebin)
CMS_lumi.channelText = channelText
CMS_lumi.channelText = regionText
CMS_lumi.writeChannelText = True
CMS_lumi.writeExtraText = True
CMS_lumi.CMS_lumi(pad1, 4, 11)
if not noData:
ratio = rebinnedData.Clone("temp")
temp = stack.GetStack().Last().Clone("temp")
for i_bin in range(1, temp.GetNbinsX() + 1):
temp.SetBinError(i_bin, 0.)
ratio.Divide(temp)
else:
ratio = rebinnedData.Clone("temp")
def plotStack():
inputfile = TFile(filename, "read")
if inputfile.IsZombie():
print("inputfile is Zombie")
sys.exit()
# loop over features
for feature, values in features.items():
file = open(
"%s%s_%s_isNorm%s_wtStat%s_isBlind%s.txt" %
(outputdir, feature, plotname, normalization, showStats, blind),
"w")
file.write(
"\\begin{table}[]\n\\resizebox{!}{.33\\paperheight}{\n \\begin{tabular}{|l|l|l|}\n\\hline\nProcess & Yield & Entries \\\\ \\hline \n"
)
# set up legend
legend = TLegend(0.2, 0.6, 0.7, 0.88)
legend.SetHeader("%i %s" % (year, region))
legend.SetNColumns(4)
legend.SetBorderSize(0)
c, pad1, pad2 = createCanvasPads()
hstack = THStack("hstack", "hstack")
hstack.SetTitle(
"#scale[0.9]{#font[22]{CMS} #font[12]{Preliminary} %i at %.2f fb^{-1}(13TeV)}"
% (year, luminosity[year]))
histName = "TTH_" + feature # assuming TTH is always there
if not inputfile.GetListOfKeys().Contains(histName):
print("%s doesn't have histogram %s, please use another hist " %
(inputfile, histName))
sys.exit()
h0 = inputfile.Get(histName)
h_totalsig = h0.Clone("h_totalsig")
h_totalsig.SetDirectory(0)
h_totalsig.Reset()
h_totalsig.SetMarkerStyle(20)
h_totalsig.Sumw2()
h_totalbkg = h0.Clone("h_totalbkg")
h_totalbkg.SetDirectory(0)
h_totalbkg.Reset()
h_totalbkg.SetMarkerStyle(20)
h_totalbkg.Sumw2()
h_totalmc = h0.Clone("h_totalmc")
h_totalmc.SetDirectory(0)
h_totalmc.Reset()
h_totalmc.SetLineColor(kBlack)
h_totalmc.SetFillColor(kGray + 3)
h_totalmc.SetFillStyle(3001)
h_totalmc.SetTitle("")
#h_totalmc.SetMinimum(0.8)
#h_totalmc.SetMaximum(1.35)
h_totalmc.Sumw2()
h_totalmc.SetStats(0)
h_dataobs = h0.Clone("h_dataobs")
h_dataobs.SetDirectory(0)
h_dataobs.Reset()
h_dataobs.SetMarkerStyle(20)
# loop over samples
for sample in Samples:
hist = h_totalmc.Clone(sample)
hist.SetDirectory(0)
hist.Reset()
if sample not in Process:
print("sample %s is not in Process " % sample)
continue
# loop over data:
if sample == "Data" or sample == "data":
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % s)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(inputfile, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
h_dataobs.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if not blind:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
# loop over mc
# loop over signal
elif sample in Signals:
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % s)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(filename, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
hist.Add(h1, -1)
h_totalsig.Add(h1, -1)
h_totalmc.Add(h1, -1)
else:
hist.Add(h1)
h_totalsig.Add(h1)
h_totalmc.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if h1.Integral() < 0.05 or h1.GetEntries() < 100:
file.write(
"\\textcolor{red}{%s} & %.2f +/- %.2f & %i \\\\ \\hline \n"
% (p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
else:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if sample == "TH" and tH != 1:
hist.Scale(tH)
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, "%s * %i" % (sample, tH), "f")
else:
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
# create required parts
# loop over bkg
else:
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % p)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(filename, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
hist.Add(h1, -1)
h_totalmc.Add(h1, -1)
h_totalbkg.Add(h1, -1)
else:
hist.Add(h1)
h_totalmc.Add(h1)
h_totalbkg.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if h1.Integral() < 0.05 or h1.GetEntries() < 100:
file.write(
"\\textcolor{red}{%s} & %.2f +/- %.2f & %i \\\\ \\hline \n"
% (p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
else:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if sample == "TH" and tH != 1:
hist.Scale(tH)
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, "%s * %i" % (sample, tH), "f")
else:
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
error = Double(0)
h_totalsig.IntegralAndError(0, h_totalsig.GetNbinsX(), error)
file.write("signal & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(h_totalsig.Integral(), error, h_totalsig.GetEntries()))
error = Double(0)
h_totalbkg.IntegralAndError(0, h_totalbkg.GetNbinsX(), error)
file.write("bkg & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(h_totalbkg.Integral(), error, h_totalbkg.GetEntries()))
# create required parts
if blind:
h_sqrtB = createSqrt(h_totalbkg)
h_MCerr = createTotalMCErr(h_sqrtB, values["xlabel"])
h_ratio = createRatio(h_totalsig, h_sqrtB, values["xlabel"],
normalization)
else:
h_MCerr = createTotalMCErr(h_totalmc, feature)
h_ratio = createRatio(h_dataobs, h_totalmc, values["xlabel"],
normalization)
legend.AddEntry(h_dataobs, "observed", "lep")
legend.AddEntry(h_totalmc, "Uncertainty", "f")
# draw everything
pad1.cd()
if values["logy"] == 1:
pad1.SetLogy()
maximum = h_dataobs.GetMaximum()
upperbound = 2. * maximum
lowerbound = -maximum / 40.
if values["logy"] == 1:
upperbound = 1000 * maximum
lowerbound = 0.1
hstack.SetMinimum(lowerbound)
hstack.SetMaximum(upperbound)
hstack.Draw("HISTY")
# Adjust y-axis settings
y = hstack.GetYaxis()
y.SetTitle("Events ")
y.SetTitleSize(25)
y.SetTitleFont(43)
y.SetTitleOffset(1.55)
y.SetLabelFont(43)
y.SetLabelSize(20)
nbins = h_ratio.GetNbinsX()
#hstack.GetXaxis().SetRange(0, nbins+1)
hstack.GetXaxis().SetRangeUser(values["min"], values["max"])
h_totalmc.Draw("e2same")
if not blind:
h_dataobs.Draw("same")
legend.Draw("same")
pad2.cd()
if blind:
h_ratio.SetMinimum(0.)
#maximum = h_ratio.GetMaximum()
#upperbound = 1.5*maximum
#h_ratio.SetMaximum(upperbound)
h_ratio.SetMaximum(3.)
h_ratio.GetXaxis().SetRangeUser(values["min"], values["max"])
h_ratio.Draw("")
else:
h_MCerr.SetMinimum(0.5)
h_MCerr.SetMaximum(1.8)
h_MCerr.GetXaxis().SetRangeUser(values["min"], values["max"])
h_MCerr.Draw("e2")
h_ratio.Draw("same")
c.SaveAs(
"%s%s_%s_isNorm%s_wtStat%s_isBlind%s_stack.png" %
(outputdir, feature, plotname, normalization, showStats, blind))
file.write("\\end{tabular}\n}\n\\end{table}\n")
file.close()
inputfile.Close()
legend.AddEntry(rebinnedHist[ih], template_categoryName[ih], 'f')
pad1.cd()
stack.Draw('HIST')
rebinnedData.Draw('E,X0,SAME')
legend.Draw("same")
stack.GetXaxis().SetTitle('')
stack.GetXaxis().SetLabelSize(0)
stack.GetYaxis().SetLabelSize(gStyle.GetLabelSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleSize(gStyle.GetTitleSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleOffset(gStyle.GetTitleYOffset() *
(1. - padRatio + padOverlap))
stack.SetTitle(';;<Events/GeV>') # '%rebin)
#CMS_lumi.channelText = (channelText+"\\n"+regionText)
#if postfitPlots: CMS_lumi.channelText =channelText+"\\n "+regionText+"\\n "+chi2Text
CMS_lumi.channelText = "#splitline{%s}{%s}" % (channelText, regionText)
if postfitPlots:
CMS_lumi.channelText = "#splitline{%s}{%s}" % (channelText + ";" +
regionText, chi2Text)
CMS_lumi.writeChannelText = True
CMS_lumi.writeExtraText = True
CMS_lumi.CMS_lumi(pad1, 4, 11)
if not noData:
ratio = rebinnedData.Clone("temp")
def readHists():
hstack = THStack("hstack", "hstack")
hstack.SetTitle(
"#scale[0.9]{#font[22]{CMS} #font[12]{Preliminary} 41.53 fb^{-1}(13TeV)}"
)
inputfile = read_rootfile() # assuming TTH_hww is always there
gROOT.cd()
histName = POI + "_TTH_hww"
h0 = inputfile.Get(histName)
h_totalbkg = h0.Clone("h_totalbkg")
h_totalbkg.SetDirectory(0)
h_totalbkg.Reset()
h_totalbkg.SetLineColor(kBlack)
h_totalbkg.SetFillColor(kGray + 3)
h_totalbkg.SetFillStyle(3001)
h_totalbkg.SetTitle("")
#h_totalbkg.SetMinimum(0.8)
#h_totalbkg.SetMaximum(1.35)
h_totalbkg.Sumw2()
h_totalbkg.SetStats(0)
h_totalsig = h0.Clone("h_totalsig")
h_totalsig.SetDirectory(0)
h_totalsig.Reset()
h_totalsig.SetMarkerStyle(20)
h_totalmc = h0.Clone("h_totalmc")
h_totalmc.SetDirectory(0)
h_totalmc.Reset()
h_totalmc.SetLineColor(kBlack)
h_totalmc.SetFillColor(kGray + 3)
h_totalmc.SetFillStyle(3001)
h_totalmc.SetTitle("")
#h_totalmc.SetMinimum(0.8)
#h_totalmc.SetMaximum(1.35)
h_totalmc.Sumw2()
h_totalmc.SetStats(0)
h_dataobs = h0.Clone("h_dataobs")
h_dataobs.SetDirectory(0)
h_dataobs.Reset()
h_dataobs.SetMarkerStyle(20)
latexString = ""
expString = " SM Exp"
dataString = " Data Obs"
latexString += ("\\begin{frame}\n\\frametitle{" + POI.replace("_", "\_") +
"}\n\\begin{table}[]\n\scalebox{0.8}{\n\\begin{tabular}{" +
"l" * (1 + len(Channels)) + "}\n")
latexString += ("& " + SPLIT.replace("_", "\_") + " \\\\ \n")
legend = TLegend(0.2, 0.6, 0.8, 0.88)
#legend.SetHeader("2017 "+fit_type+", 2lss l^{#pm}l^{#pm} #mu(ttH)=#hat#mu")
legend.SetHeader("2017 prefit, " + header_postfix)
legend.SetNColumns(3)
legend.SetBorderSize(0)
Samples.reverse()
for sample in Samples:
isSignal = False
if sample in signals: isSignal = True
hist = h_totalsig.Clone(sample)
hist.Reset()
if not sample == "Data": latexString += sample + " &"
# loop over data
if sample == "Data" or sample == "data":
for p in Process[sample]:
rootfile = read_rootfile(p)
if rootfile.IsZombie(): continue
gROOT.cd()
h1 = rootfile.Get(POI + "_" + p)
if SMOOTH == 1:
h1.Smooth()
#h1.SetDirectory(0)
h_dataobs.Add(h1)
rootfile.Close()
# loop over mc
# loop over signal
elif isSignal:
for p in Process[sample]:
rootfile = read_rootfile(p)
if rootfile.IsZombie(): continue
gROOT.cd()
h1 = rootfile.Get(POI + "_" + p)
if SMOOTH == 1:
h1.Smooth()
#h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
h_totalsig.Add(h1, -1)
h_totalmc.Add(h1, -1)
hist.Add(h1, -1)
else:
h_totalsig.Add(h1)
h_totalmc.Add(h1)
hist.Add(h1)
rootfile.Close()
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if hist.Integral() > 0.05:
error = ROOT.Double(0)
hist.IntegralAndError(0, hist.GetNbinsX(), error)
latexString += "$ " + str(
round(hist.Integral(), 2)) + " \\pm " + str(round(
error, 2)) + "$ \\\\ \n"
elif hist.Integral() > 0:
latexString += " \\textless{}0.05 \\\\ \n"
else:
latexString += " - \\\\ \n "
if WIDTH == 1:
hist = YDivideWidth(hist)
#hist.SetFillColor(Color[sample])
#hist.SetLineColor(kBlack)
#hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
# loop over bkg
else:
for p in Process[sample]:
rootfile = read_rootfile(p)
if rootfile.IsZombie(): continue
gROOT.cd()
#print " try to get histogram : " + POI+"_"+p
h1 = rootfile.Get(POI + "_" + p)
if SMOOTH == 1:
h1.Smooth()
h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
h_totalbkg.Add(h1, -1)
h_totalmc.Add(h1, -1)
hist.Add(h1, -1)
else:
h_totalbkg.Add(h1)
h_totalmc.Add(h1)
hist.Add(h1)
rootfile.Close()
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if hist.Integral() > 0.05:
error = ROOT.Double(0)
hist.IntegralAndError(0, hist.GetNbinsX(), error)
latexString += "$ " + str(
round(hist.Integral(), 2)) + " \\pm " + str(round(
error, 2)) + "$ \\\\ \n"
elif hist.Integral() > 0:
latexString += " \\textless{}0.05 \\\\ \n"
else:
latexString += " - "
if WIDTH == 1:
hist = YDivideWidth(hist)
#hist.SetFillColor(Color[sample])
#hist.SetLineColor(kBlack)
#hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
totMC_error = ROOT.Double(0)
h_totalmc.IntegralAndError(0, h_totalmc.GetNbinsX(), totMC_error)
expString += " & $ " + str(round(h_totalmc.Integral(),
2)) + " \\pm " + str(
round(totMC_error, 2)) + "$ \\\\ \n"
totbkg_error = ROOT.Double(0)
h_totalbkg.IntegralAndError(0, h_totalbkg.GetNbinsX(), totbkg_error)
expString += "Tot Bkg & $ " + str(
round(h_totalbkg.Integral(), 2)) + " \\pm " + str(
round(totbkg_error, 2)) + "$ \\\\ \n"
totdata_error = ROOT.Double(0)
h_dataobs.IntegralAndError(0, h_dataobs.GetNbinsX(), totdata_error)
dataString += " & $ " + str(round(h_dataobs.Integral(),
2)) + " \\pm " + str(
round(totdata_error, 2)) + "$ \n"
if not blind == 1: latexString += (expString + dataString)
else: latexString += expString
latexString += (
"\\end{tabular}\n}\n\\end{table}\n\\end{frame}\n \\end{document}\n")
if LATEX == 1:
nameFix = ""
if SMOOTH == 1:
nameFix = "_smooth"
latexfile = file(
outputDir + "/" + POI + "_" + region + "_" + SPLIT + "_" +
Expected + "_yield" + nameFix + ".tex", "w")
latexfile.write(
"\\documentclass{beamer}\n\\usetheme{Warsaw}\n\n\\usepackage{graphicx}\n\\useoutertheme{infolines}\n\\setbeamertemplate{headline}{}\n\n\\begin{document}\n\n"
)
latexfile.write(latexString)
inputfile.Close()
return h_totalsig, h_totalbkg, h_totalmc, h_dataobs, hstack, legend
canvas = TCanvas("canvas", "canvas")
#canvas.SetCanvasSize(600, 600)
#canvas.SetLeftMargin(0.125)
canvas.SetRightMargin(0.04)
#canvas.SetBottomMargin(0.105)
legend = ROOT.TLegend(0.6, 0.65, 0.925, 0.925)
#legend.SetFillStyle(0)
legend.SetBorderSize(0)
#legend.SetBorderSize(1)
legend.SetTextSize(0.0425)
stack = THStack("stack", "stack")
stack.SetTitle("#tau_{h} prompt rate (%s)" % (era))
# Loose
h2_tau_pT_L = inputFile.Get(d_histName[era]["L"]).Clone()
h2_tau_pT_L.Sumw2()
# Tight
h2_tau_pT_T = inputFile.Get(d_histName[era]["T"]).Clone()
h2_tau_pT_T.Sumw2()
l_h1 = []
for iDM in range(0, len(l_DM)):
DM = l_DM[iDM]
def main(argv):
P0DBANFFStyle = INTERFACE.GetThisStyle()
INTERFACE.SetStyle(P0DBANFFStyle.GetName())
OUTPUTFILENAME = "BANFFReactionCodeStacksOfficial_Joint2018_Numode_Prefit_P0DOnly_Momentum.pdf"
inputFile = TFile(sys.argv[0])
dataFile = TFile(sys.argv[1])
dummyPage = TCanvas()
dummyPage.Print(OUTPUTFILENAME + "[")
sampleNames = [
"P0D_Air_NuMu_CC_CC1Track", "P0D_Air_NuMu_CC_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
fileNameRoots = [
"P0D_Air_NuMu_CC1Track", "P0D_Air_NuMu_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
stackHistos = [
"#nu CCQE", "#nu CC 2p-2h", "#nu CC Res 1#pi", "#nu CC Coh 1#pi",
"#nu CC Other", "#nu NC modes", "#bar{#nu} modes"
]
P0DBANFFStyle.SetTitleBorderSize(0)
P0DBANFFStyle.SetTitleX(0.02)
P0DBANFFStyle.SetTitleY(0.95)
P0DBANFFStyle.SetTitleW(0.3)
P0DBANFFStyle.SetTitleH(0.07)
# Align left, centered in y)
P0DBANFFStyle.SetTitleAlign(11)
stackColours = [1300, 1302, 1303, 1304, 1305, 1308, 1310]
stackFillStyle = [1001, 1001, 1001, 1001, 1001, 1001, 1001]
"""
The graphs will be of the form <sampleName>_rxnPredMC_<rxnCode+100>
So for each sample, we'll loop from 0-200, and if the THnD is not NULL when we
try and get it, add it to a list for stacking.
for sampleName in sampleNames:
"""
for ii in xrange(0, len(sampleNames)):
sampleName = sampleNames[ii]
# Total the number of events for this sample going into the histogram.
# (Remember: Sand is excluded here.)
mcSampleTotal = 0.0
# Now make the set of histograms to show, which combine several interaction
# types.
# Load in the data histogram and use it to assemble the TH2Ds for the MC, as
# well as making it one of the histograms on the stack.
dataHist = dataFile.Get(sampleName + "_data").Projection(1, 0)
dataMomProj = dataFile.Get(sampleName + "_data").Projection(
1, 0).ProjectionX("dataratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
dataMomProj.SetDirectory(0)
dataMomProj.SetTitle("")
prefitMomProj = inputFile.Get(sampleName + "_prefit").Projection(
1, 0).ProjectionX("prefitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
prefitMomProj.SetDirectory(0)
postfitMomProj = inputFile.Get(sampleName + "_postfit_0_0").Projection(
1, 0).ProjectionX("postfitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
postfitMomProj.SetDirectory(0)
for ibx in xrange(1, dataMomProj.GetNbinsX() + 1):
dataMomProj.SetBinContent(
ibx,
dataMomProj.GetBinContent(ibx) / prefitMomProj.GetBinContent(ibx))
dataMomProj.SetBinError(
ibx,
ROOT.Math.sqrt(dataMomProj.GetBinContent(ibx)) /
prefitMomProj.GetBinContent(ibx))
print "Data = " + str(
dataMomProj.GetBinContent(ibx)) + " MC = " + str(
prefitMomProj.GetBinContent(ibx)) + " Ratio = " + str(
dataMomProj.GetBinContent(ibx) /
prefitMomProj.GetBinContent(ibx))
dataMomProj.GetYaxis().SetRangeUser(0.75, 1.25)
dataMomProj.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
dataMomProj.GetXaxis().SetTitle("Reconstructed muon momentum (MeV/c)")
dataMomProj.GetYaxis().SetTitle("Data / Sim.")
# There is only one data plot for each stack, so:
# Momentum projection
# Assemble them here.
dataOneDimPlots = []
dataOneDimPlots.append(
dataHist.ProjectionX("data" + sampleName + "_px", 1,
dataHist.GetNbinsY()))
dataNXBins = dataHist.GetXaxis().GetNbins()
dataxarray = dataHist.GetXaxis().GetXbins().GetArray()
# Now, loop through all the theta bins and add the slices in.
for iby in xrange(1, dataHist.GetNbinsY() + 1):
thetaLow = dataHist.GetYaxis().GetBinLowEdge(iby)
thetaHigh = dataHist.GetYaxis().GetBinUpEdge(iby)
thetaString = " < cos# theta < "
thetaString = str(thetaLow) + thetaString + str(thetaHigh)
dataOneDimPlots.append(
TH1D("dataslice_" + sampleName + str(iby), thetaString, dataNXBins,
dataxarray))
# Now, go through the x bins in this slice and fill the plot we
# just created.
for ibx in xrange(1, dataHist.GetNbinsX() + 1):
# Get the bin dimensions for dividing purposes to area
# normalize.
dataOneDimPlots[len(dataOneDimPlots) - 1].SetBinContent(
ibx, dataHist.GetBinContent(ibx, iby))
# Now that the dataOneDimPlots array is filled, loop through a area
# normalize the bins.
for dataOneDimHist in dataOneDimPlots:
for ibx in xrange(1, dataOneDimHist.GetNbinsX() + 1):
mpdim = (dataOneDimHist.GetXaxis().GetBinUpEdge(ibx) -
dataOneDimHist.GetXaxis().GetBinLowEdge(ibx)) / 100.0
dataOneDimHist.SetBinError(
ibx,
math.sqrt(dataOneDimHist.GetBinContent(ibx)) / mpdim)
dataOneDimHist.SetBinContent(
ibx,
dataOneDimHist.GetBinContent(ibx) / mpdim)
histosToStack = []
for histo in stackHistos:
histosToStack.append(
TH2D(sampleName + "_" + histo, sampleName + "_" + histo,
dataHist.GetXaxis().GetNbins(),
dataHist.GetXaxis().GetXbins().GetArray(),
dataHist.GetYaxis().GetNbins(),
dataHist.GetYaxis().GetXbins().GetArray()))
# Now pull the histograms from the file, and add their content to the
# relevant combined histogram.
for i in xrange(0, 200):
if inputFile.Get(sampleName + "_rxnPredMC_" + str(i)):
histosToStack[CorrespondingIndex(i)].Add(
inputFile.Get(sampleName + "_rxnPredMC_" + str(i)).Projection(
1, 0))
"""
# OK, so now we have an array of 2D histograms for this sample that we'd
# like to stack. Need to divide it up into individual ones. Do Momentum
# Projection, then momentum in theta slices. Normalize by bin area.
# For storing all the one dimensional plots for this sample.
"""
oneDimPlots = []
# Add nothing for the momentum projection, will append the thetaStrings as
# needed.
stackTitles = []
for i in xrange(0, len(stackHistos)):
# For storing all the one dimensional plots for this reaction code.
oneDimPlotsThisReac = []
nameBase = histosToStack[i].GetTitle()
oneDimPlotsThisReac.append(histosToStack[i].ProjectionX(
nameBase + "_px", 1, histosToStack[i].GetNbinsY()))
stackTitles.append("Momentum projection")
# Area normalize this now.
NXBins = histosToStack[i].GetXaxis().GetNbins()
for ibx in xrange(1, NXBins + 1):
mpdim = (histosToStack[i].GetXaxis().GetBinUpEdge(ibx) -
histosToStack[i].GetXaxis().GetBinLowEdge(ibx)) / 100.0
oneDimPlotsThisReac[0].SetBinContent(
ibx, oneDimPlotsThisReac[0].GetBinContent(ibx) / mpdim)
mcSampleTotal += oneDimPlotsThisReac[0].GetSumOfWeights()
# Now that the x bins are filled, loop on to the next histogram.
# With all the y-slices done, oneDimPlotsThisReac is now full. Append
# it to oneDimPlots.
oneDimPlots.append(oneDimPlotsThisReac)
""""
At this point in time, oneDimPlots has a momentum projection and theta
slices for each reaction code grouping in this sample. So now we just need
to make TStacks for them and print them out to PDF.
The stack contains the plot from the same index of each entry of
oneDimPlots.
They should all have the same length, so use the first one to loop
through (number of slices plus 1 full momenutm projection.)
"""
for odi in xrange(0, len(oneDimPlots[0])):
# For each one create a TStack
sampleStack = THStack(
"mpStack_" + sampleName + str(odi),
";Reconstructed muon momentum (MeV/c);Events/(100 MeV/c)")
leg = TLegend(0.7, 0.15, 0.94, 0.93)
leg.SetFillColor(0)
# Make data the first entry.
leg.AddEntry(dataOneDimPlots[odi], "Data", "LEP")
dataOneDimPlots[odi].SetMarkerStyle(20)
dataOneDimPlots[odi].SetMarkerColor(kBlack)
dataOneDimPlots[odi].SetLineColor(kBlack)
for odj in xrange(0, len(oneDimPlots)):
# Apply the appropriate plot options from the beginning.
oneDimPlots[odj][odi].SetFillColor(stackColours[odj])
oneDimPlots[odj][odi].SetFillStyle(stackFillStyle[odj])
oneDimPlots[odj][odi].SetMarkerStyle(1) # Don't want P0DBANFFStyle to interfere.
sampleStack.Add(oneDimPlots[odj][odi])
print oneDimPlots[odj][odi].GetTitle() + " " + str(
oneDimPlots[odj][odi].GetSumOfWeights())
leg.AddEntry(oneDimPlots[odj][odi], stackHistos[odj], "F")
c1 = TCanvas()
upperPad = TPad("upperPad", "upperPad", .05, .35, .96, .95)
lowerPad = TPad("lowerPad", "lowerPad", .05, .1, .96, .35)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
gPad.SetBottomMargin(1e-5)
c1.SetTicks(1, 1)
sampleStack.Draw("")
dataOneDimPlots[odi].Draw("PEsame")
sampleStack.SetMaximum(
max(sampleStack.GetMaximum(),
FindPlotMax(dataOneDimPlots[odi], dataOneDimPlots[odi])))
leg.Draw()
sampleStack.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
sampleStack.GetXaxis().SetTitle("")
sampleStack.GetXaxis().SetTickLength(0)
sampleStack.GetXaxis().SetLabelSize(0)
sampleStack.SetTitle("# nu-mode")
lowerPad.cd()
ROOT.gPad.SetTopMargin(1e-5)
ROOT.gPad.SetBottomMargin(0.35)
ROOT.gPad.SetTickx()
dataMomProj.SetLineColor(2)
dataMomProj.SetMarkerColor(2)
dataMomProj.GetYaxis().SetNdivisions(8, 1)
dataMomProj.GetYaxis().SetLabelSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.07)
dataMomProj.GetYaxis().SetTitleSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.05)
dataMomProj.GetYaxis().SetTitleOffset(0.335)
dataMomProj.GetXaxis().SetLabelSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.08)
dataMomProj.GetXaxis().SetTitleSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.04)
dataMomProj.GetXaxis().SetTitleOffset(0.9)
dataMomProj.GetXaxis().SetTickLength(0.04)
dataMomProj.Draw("PE")
line = TLine(0.0, 1.0, 5000.0, 1.0)
line.SetLineWidth(2)
line.SetLineColor(1)
line.Draw("SAME")
c1.Modified()
c1.Update()
c1.Print(OUTPUTFILENAME)
outFileName = fileNameRoots[sampleNames.index(
sampleName)] + "_mumom_rxn_postfit"
c1.Print(outFileName + ".pdf")
c1.Print(outFileName + ".png")
c1.Print(outFileName + ".eps")
dummyPage.Print(OUTPUTFILENAME + ']')
def calcAndDrawSignificance(SignalHist,
BkgrHist,
xtitle,
legendNames,
DataName,
destination,
year,
ylog=False,
customLabels=None,
extraText=None,
scaleSignalToBkgr=False,
DivideByLine=None):
GeneralSettings()
#Make sure the code works for single backgrounds
if not isinstance(BkgrHist, (list, )):
BkgrHist = [BkgrHist]
if not isinstance(SignalHist, (list, )):
SignalHist = [SignalHist]
BkgrHist, legendNames = orderHist(BkgrHist, legendNames, True)
#Add all backgrounds
totBkgr = BkgrHist[0].Clone("TotBkgr")
for h in BkgrHist[1:]:
totBkgr.Add(h)
#Normalize signal to background if needed
if scaleSignalToBkgr:
for h in SignalHist:
h.scale(totBkgr.GetSumOfWeights() / h.GetSumOfWeights)
#Define a canvas
Canv = TCanvas("Canv" + destination, "Canv" + destination, 1500, 1000)
#Set Histogram Styles
for h, n in zip(BkgrHist, legendNames):
h.SetFillColor(TColor.GetColor(GetStackColorTauPOGbyName(n)))
h.SetLineColor(TColor.GetColor(GetStackColorTauPOGbyName(n)))
for i, h in enumerate(SignalHist):
h.SetMarkerColor(TColor.GetColor(GetLineColor(i)))
h.SetLineColor(TColor.GetColor(GetLineColor(i)))
h.SetMarkerStyle(GetMarker(i))
list_of_significance_hists = []
for i, sh in enumerate(SignalHist):
#Calculate significance
total = totBkgr.Clone("Total")
total.Add(sh)
sqrt_total = total.Clone('Sqrt_Total')
for xbin in xrange(
1,
total.GetSize() - 1
): #GetSize returns nbins + 2 (for overflow and underflow bin)
sqrt_x = np.sqrt(total.GetBinContent(xbin))
sqrt_total.SetBinContent(xbin, sqrt_x)
sqrt_total.SetBinError(xbin,
0.5 * total.GetBinError(xbin) / sqrt_x)
significance = sh.Clone('Signal' + str(i))
significance.Divide(sqrt_total)
list_of_significance_hists.append(significance)
#First pad
plotpad = TPad("plotpad", "plotpad", 0, .3, 1, 0.98)
plotpad.SetBottomMargin(0.025)
plotpad.Draw()
plotpad.cd()
#Create Stack (Change with most logical ordering)
hs = THStack("hs", "hs")
for h in BkgrHist:
hs.Add(h)
hs.Draw(
"EHist"
) #Draw before using GetHistogram, see https://root-forum.cern.ch/t/thstack-gethistogram-null-pointer-error/12892/4
title = " ; ; Events"
hs.SetTitle(title)
# hs.GetHistogram().GetXaxis().SetTickLength(0)
hs.GetHistogram().GetXaxis().SetLabelOffset(9999999)
#hs.GetHistogram().SetMaximum(1)
#Set range
overallMin = GetOverallMinimum(BkgrHist + SignalHist, True)
overallMax = GetOverallMaximum([totBkgr] + SignalHist)
if ylog:
if overallMin == 0.:
overallMin = 0.1
ymin = 0.3 * overallMin
ymax = 30 * overallMax
plotpad.SetLogy()
else:
ymin = 0.7 * overallMin
ymax = 1.3 * overallMax
hs.SetMinimum(ymin)
hs.SetMaximum(ymax)
for h in SignalHist:
h.Draw("EPSame")
#Create Legend
legend = TLegend(0.7, .7, .9, .9)
for h, n in zip(BkgrHist, legendNames):
legend.AddEntry(h, n)
for h, n in zip(SignalHist, DataName):
legend.AddEntry(h, n)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
#Draw lines if needed
if DivideByLine is not None:
tdrStyle_Left_Margin = 0.16
tdrStyle_Right_Margin = 0.02
plot_size_hor = 1 - tdrStyle_Left_Margin - tdrStyle_Right_Margin
#Option one, user provides the number of divisions and we divide equally
if isinstance(DivideByLine[0], int):
x_pos = np.linspace(totBkgr.GetXaxis().GetXmin(),
totBkgr.GetXaxis().GetXmax(),
DivideByLine[0] + 1)
#Option two, user provides the specific boundaries
if isinstance(DivideByLine[0], (list, )):
x_pos = DivideByLine[0]
#Draw the lines
lines = []
for i, x in enumerate(x_pos[1:-1]):
lines.append(TLine(x, ymin, x, ymax))
lines[i].SetLineColor(ROOT.kRed)
lines[i].SetLineStyle(10)
for line in lines:
line.Draw('same')
#Add extra text
for i, name in enumerate(DivideByLine[1]):
x = ((x_pos[i + 1] + x_pos[i]) /
(2 * (x_pos[-1] - x_pos[0]) /
plot_size_hor)) + tdrStyle_Left_Margin
extraText.append(extraTextFormat(name, x, 0.1, None, 22))
#Draw extra text if needed
if extraText is not None:
DrawExtraText(plotpad, extraText)
#Return to canvas
Canv.cd()
#Second pad
ratiopad = TPad("ratiopad", "ratiopad", 0, 0.05, 1, .3)
ratiopad.SetTopMargin(0.05)
ratiopad.SetBottomMargin(0.25)
ratiopad.Draw()
ratiopad.cd()
#print list_of_significance_hists[0].GetMaximum(), list_of_significance_hists[1].GetMaximum(), list_of_significance_hists[2].GetMaximum()
overallMin = GetOverallMinimum(list_of_significance_hists)
overallMax = GetOverallMaximum(list_of_significance_hists)
significance = list_of_significance_hists[0]
#Prepare lines before changing maximum
lines_bottom = []
for i, sig in enumerate(list_of_significance_hists):
lines_bottom.append(
TLine(sig.GetXaxis().GetXmin(), sig.GetMaximum(),
sig.GetXaxis().GetXmax(), sig.GetMaximum()))
lines_bottom[i].SetLineColor(TColor.GetColor(GetLineColor(i)))
lines_bottom[i].SetLineStyle(3)
#Set Style for bottom plot
significance.SetTitle(";" + xtitle + "; S/#sqrt{S+B}")
significance.GetXaxis().SetTitleSize(.12)
significance.GetYaxis().SetTitleSize(.12)
significance.GetYaxis().SetTitleOffset(.6)
significance.GetXaxis().SetLabelSize(.12)
significance.GetYaxis().SetLabelSize(.12)
significance.SetMinimum(0.)
significance.SetMaximum(1.3 * overallMax)
significance.Draw("EP")
for sig in list_of_significance_hists[1:]:
sig.Draw('EPSame')
for line in lines_bottom:
line.Draw("same")
#Set custom x labels
xaxis = significance.GetXaxis()
if customLabels != None:
number_of_bins = significance.GetNbinsX()
if number_of_bins != len(customLabels):
if DivideByLine is not None:
for i in range(number_of_bins):
xaxis.SetBinLabel(
i + 1, customLabels[i % len(customLabels)]
) #Only works when DivideByLine[0] is an integer
else:
print 'Please provide ' + str(
number_of_bins) + ' labels instead of ' + str(
len(customLabels))
return
else:
for i, label in zip(range(number_of_bins), customLabels):
xaxis.SetBinLabel(i + 1, label)
#Throw CMs lumi at it
cl.CMS_lumi(Canv, 4, 11, year, 'Simulation Preliminary', True)
#Save everything
savePlots(Canv, destination)
ROOT.SetOwnership(
Canv, False
) #https://root-forum.cern.ch/t/tlatex-crashing-in-pyroot-after-many-uses/21638/4
return
def DCRComp(run1Start,run1End,run2Start,run2End,filePath):
# Setup variables
firstruns = ""
secondruns = ""# Used for each run in the first run range
calibrationSkim1 = TChain("skimTree","skimTree")
calibrationSkim2 = TChain("skimTree","skimTree")
# Destination folder, trying to prevent a common formatting error
if (filePath[-1] != '/'): filePath += '/'
fileName = filePath + "DCRcompforRuns%d-%d_and_%d-%d.pdf" % (run1Start,run1End,run2Start,run2End)
# Add functionality for multiple datasets?
# NOTE: Be wary of comparing across multiple datasets. This hasn't been tested yet
# So far, only datasets 5 and 6 are known by the program (see below)
skimPath1 = "/global/project/projectdirs/majorana/data/mjd/surfmjd/analysis/skim/"
skimPath2 = "/global/project/projectdirs/majorana/data/mjd/surfmjd/analysis/skim/"
runBoundariesDS5cM1 = [24305,24318,24539,24552,24761,24775,24971,24984,25248,25261,25479,25492]
runBoundariesDS5cM2 = [23960,23970,24322,24332,24556,24567,24779,24789,24988,24998,25265,25275,25496,25506]
# MAKE SURE THIS HAS THE CORRECT CUTS
cuts = "trapENFCal < 2635 && trapENFCal > 2605 && isGood && !wfDCBits && !muVeto && mH == 1"
# If there is only one module being calibrated in DS5
cutStr5cM1 = " && C == 1"
cutStr5cM2 = " && C == 2"
if (run1Start <= 25507 and run1Start > 18712): # DS5c
skimPath1 += "DS5ccal/GAT-v01-07-164-g8e0a877/skimDS5_"
if (run1Start in runBoundariesDS5cM1): cuts += cutStr5cM1
if (run1Start in runBoundariesDS5cM2): cuts += cutStr5cM2
else: raise ValueError('First run boundary is not in either DS5c calibration range')
if (run1Start > 25507): # DS6
skimPath1 += "DS6cal/GAT-v01-06/skimDS6_"
if (run2Start <= 25507 and run2Start > 18712): # DS5c
skimPath2 += "DS5ccal/GAT-v01-07-164-g8e0a877/skimDS5_"
if (run1Start in runBoundariesDS5cM1): cuts += cutStr5cM1
if (run1Start in runBoundariesDS5cM2): cuts += cutStr5cM2
else: raise ValueError('First run boundary is not in either DS5c calibration range')
if (run2Start > 25507): # DS6
skimPath2 += "DS6cal/GAT-v01-06/skimDS6_"
cutStr = cuts
# Build the first run range's TChain
for i in range(run1Start, run1End+1):
firstruns = skimPath1 + "run%d_small.root" % i
calibrationSkim1.Add(firstruns,0)
# Build the second run range's TChain
for i in range(run2Start, run2End+1):
secondruns = skimPath2 + "run%d_small.root" % i
calibrationSkim2.Add(secondruns,0)
# channel list from the built data in P3LTP
# I don't need to use this now, but can check against it
# IE check that every channel in this list has data
# and that every channel with data is in this list
chanList = [680,681,678,679,674,675,672,673,632,633,630,631,626,627,690,691,692,693,648,649,640,641,642,643,664,665,662,663,660,661,658,659,616,617,610,611,608,609,584,585,600,601,598,599,592,593,696,697,624,625,628,629,688,689,694,695,614,615,1122,1123,1108,1109,1106,1107,1128,1129,1204,1205,1110,1111,1126,1127,1124,1125,1202,1203,1170,1171,1172,1173,1174,1175,1176,1177,1168,1169,1120,1121,1206,1207,1208,1209,1232,1233,1236,1237,1238,1239,1234,1235,1328,1329,1298,1299,1296,1297,1302,1303,1332,1333,1268,1269,1304,1305,1330,1331]
# Test case channel list
#chanList = [1204,1174,1173]
# Draw the whole dcr distribution vs channel
# make sure there are enough bins that each channel is one bin
c2 = TCanvas('c2','c2',400,400)
drawStr = "dcr90:channel>>bighist1"
bighist1 = TH2F("bighist1", "All Channels DCR Comparison",800,550,1350,1000,-0.001,0.001)
calibrationSkim1.Draw(drawStr,cutStr,"COLZ")
bighist1.SetLineColor(30)
bighist1.SetStats(False)
bighist1.GetXaxis().SetTitle("Channel")
bighist1.GetYaxis().SetTitle("DCR99")
bighist1.GetYaxis().SetTitleOffset(2.1)
drawStr = "dcr90:channel>>bighist2"
bighist2 = TH2F("bighist2", "All Channels DCR Comparison",800,550,1350,1000,-0.001,0.001)
calibrationSkim2.Draw(drawStr,cutStr, "SAME COLZ")
bighist2.SetLineColor(45)
bighist2.SetStats(False)
bighist2.GetXaxis().SetTitle("Channel")
bighist2.GetYaxis().SetTitle("DCR99")
bighist2.GetYaxis().SetTitleOffset(2.1)
c2.SetLogz()
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.12)
# Fix the different z scales
maxz = max(bighist1.GetMaximum(),bighist2.GetMaximum())
bighist1.GetZaxis().SetRangeUser(0,maxz)
bighist2.GetZaxis().SetRangeUser(0,maxz)
c2.Update()
# Save the big TH2F as a root file
rootfileName = filePath + "DCRcomp%d-%d_and_%d-%d.root" % (run1Start, run1End, run2Start, run2End)
c2.SaveAs(rootfileName)
# Start the big pdf
c2.Print(fileName + "(","Title: All Channels")
c2.Close()
# TCanvas for the individual channels
c3 = TCanvas('c3','c3', 400, 400)
c3.cd()
# Make txt file for chanList problems
txtFileTitle = "channelProblemsforRuns%d-%d_and_%d-%d.txt" % (run1Start,run1End,run2Start,run2End)
chanFile = open(txtFileTitle,"w+")
# Want a hist of all DCR90 efficiencies for the run range
# Only do this for the second run range (so I'm not doubling the plot)
# Remove entries from this if they don't have any results
active_channels = [680,681,678,679,674,675,672,673,632,633,630,631,626,627,690,691,692,693,648,649,640,641,642,643,664,665,662,663,660,661,658,659,616,617,610,611,608,609,584,585,600,601,598,599,592,593,696,697,624,625,628,629,688,689,694,695,614,615,1122,1123,1108,1109,1106,1107,1128,1129,1204,1205,1110,1111,1126,1127,1124,1125,1202,1203,1170,1171,1172,1173,1174,1175,1176,1177,1168,1169,1120,1121,1206,1207,1208,1209,1232,1233,1236,1237,1238,1239,1234,1235,1328,1329,1298,1299,1296,1297,1302,1303,1332,1333,1268,1269,1304,1305,1330,1331]
active_channels = sorted(active_channels)
active_chan_effs = []
# Channel loop
for channel in range(550,1351):
# Conver channel to a bin number for slices
bigX = bighist1.GetXaxis()
currentbin = bigX.FindBin(channel)
# Hack to get all the titles properly in the pdf
canvastitle = "Channel %d" % channel
bighist1.SetTitle(canvastitle)
bighist2.SetTitle(canvastitle)
# Get slices / bin content for each channel (the x axis of bighist)
histname1 = "hist1_chan%d" % channel
histname2 = "hist2_chan%d" % channel
hist1 = bighist1.ProjectionY(histname1,currentbin,currentbin,"d")
hist2 = bighist2.ProjectionY(histname2,currentbin,currentbin,"d")
# Only proceed if the hists aren't empty
if ((hist1.GetEntries() == 0 and hist2.GetEntries() == 0)):
# Check that every empty channel isn't in chanList
if (channel in chanList):
chanFile.write("Channel %d has no output for runs %d - %d and %d - %d \n" % (channel, run1Start, run1End, run2Start, run2End))
active_channels.remove(channel)
else:
# Make sure every channel with output is in chanList
if (channel not in chanList):
chanFile.write("Channel %d is not in chanList \n" % channel)
c3.Clear()
c3.SetTitle(canvastitle)
hstack = THStack("hstack","")
hstack.SetTitle(canvastitle)
hist1.SetLineColor(40)
gPad.SetLogy()
hstack.Add(hist1)
# Get the dcr99 value by integrating the hist
xaxis1 = hist1.GetXaxis()
minbin = xaxis1.FindBin(-0.001)
maxbin = xaxis1.FindBin(0.001)
zerobin = xaxis1.FindBin(0)
goodintegral1 = hist1.Integral(minbin,zerobin)
totalintegral1 = hist1.Integral(minbin,maxbin)
# Don't divide by zero
if (goodintegral1 != 0):
dcr90_1 = goodintegral1/totalintegral1
else :
dcr90_1 = 0
hist2.SetLineColor(30)
hstack.Add(hist2)
# Get the second dcr99 value
xaxis2 = hist2.GetXaxis()
minbin = xaxis2.FindBin(-0.001)
maxbin = xaxis2.FindBin(0.001)
zerobin = xaxis2.FindBin(0)
goodintegral2 = hist2.Integral(minbin,zerobin)
totalintegral2 = hist2.Integral(minbin,maxbin)
# Don't divide by zero
if (goodintegral2 != 0):
dcr90_2 = goodintegral2/totalintegral2
else:
dcr90_2 = 0
active_chan_effs.append(dcr90_2)
# Formatting for the THStack
hstack.Draw()
xaxisStack = hstack.GetXaxis()
xaxisStack.SetTitle("DCR99")
hstack.GetYaxis().SetTitle("Count")
xaxisStack.SetLabelSize(0.02)
xaxisStack.SetTitleOffset(1.2)
# Draw vertical line at dcr90 = 0 for reference
c3.Update()
ymax = max(hist1.GetMaximum(), hist2.GetMaximum())
vert = TLine(0,0,0,ymax)
vert.SetLineColor(2)
vert.Draw()
# Draw the legend
chanlegend = TLegend(0.6,0.8,0.9,0.9)
entry1 = chanlegend.AddEntry(hist1, "#splitline{Run Range %d - %d}{%f Efficiency}" % (run1Start, run1End, dcr90_1), "l")
entry2 = chanlegend.AddEntry(hist2, "#splitline{Run Range %d - %d}{%f Efficiency}" % (run2Start, run2End, dcr90_2), "l")
# If the Efficiencies differ by more than 1%, make their text red
if (abs(dcr90_1 - dcr90_2) > 0.01):
entry1.SetTextColor(2)
entry2.SetTextColor(2)
entry1.SetTextSize(0.02)
entry2.SetTextSize(0.02)
chanlegend.Draw()
# Save as one big pdf file
pagetitle = "Title: " + canvastitle
c3.Update()
c3.Print(fileName,pagetitle)
# Close the pdf with a blank page
c3.Clear()
c3.Print(fileName + ")","I need to close the document somehow")
c3.Close()
# I want a spreadsheet of all the DCR90 efficiencies
# Will make a file for each calibration run set, and then concatenate them later
spreadsheetfilename = filePath + "eff_sheet_%d-%d.txt" % (run2Start,run2End)
spreadsheet = open(spreadsheetfilename,"w+")
# Make the dcr90 efficiency plot for the second run set only
c_eff = TCanvas('c_eff','c_eff',800,400)
c_eff.cd()
# active_channels should be the x-axis labels
# active_chan_effs should be the y-values
num_chan = len(active_channels)
eff_hist = TH1F('eff_hist',"DCR99 Efficiencies for Calibration Runs %d-%d" % (run2Start, run2End),num_chan,0,num_chan)
eff_xaxis = eff_hist.GetXaxis()
# Input data and write to the spreadsheet
for i in range(0,num_chan):
eff_xaxis.SetBinLabel(i+1, str(active_channels[i]))
eff_hist.SetBinContent(i+1,active_chan_effs[i])
spreadsheet.write("%d %d %d %f \n" % (run2Start,run2End,active_channels[i],active_chan_effs[i]))
c_eff.SetGridx()
eff_hist.SetStats(False)
eff_xaxis.SetLabelSize(0.03)
eff_xaxis.SetTitle("Channel")
eff_xaxis.SetTitleOffset(1.4)
eff_hist.GetYaxis().SetTitle("DCR99 Efficiency")
eff_hist.Draw()
c_eff.Update()
# Save
eff_fileName = filePath + "DCR99_Efficiencies_Runs_%d-%d.pdf" % (run2Start,run2End)
eff_title = "DCR99 Efficiencies for Calibration Runs %d-%d" % (run2Start, run2End)
c_eff.SaveAs(eff_fileName,eff_title)
c_eff.Close()
spreadsheet.close()
outDir = "plots/tauIdIsoEfficiency/DYJetsToLL_mc"
os.system("mkdir -p " + outDir)
inputFile = TFile.Open(
"DYJetsToLL_mc/output_tauIdIsoEfficiency/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8_tauIdIsoEfficiency/custom_all_tauIdIsoEfficiency.root"
)
process = "DYJetsToLL"
####################################################################################################
canvas = TCanvas("canvas", "canvas")
canvas.SetCanvasSize(800, 800)
legend = TLegend(0.55, 0.5, 0.895, 0.895)
stack = THStack("stack", "stack")
stack.SetTitle("#tau^{gen}_{h} p_{T} fraction [%s]" % (process))
tauTemplate_pTbin = [20, 30, 50, 100]
for iBin in range(0, len(tauTemplate_pTbin)):
histName = ""
histLabel = ""
lineColor = iBin + 1
if (iBin < len(tauTemplate_pTbin) - 1):
histName = "tauh_pTfraction_gen_pT-%dto%d" % (
tauTemplate_pTbin[iBin], tauTemplate_pTbin[iBin + 1])
histLabel = "%d < p^{#tau^{gen}}_{T} < %d GeV" % (
def plot(plotInfo):
tdrstyle.setTDRStyle()
#ROOT.gROOT.ForceStyle()
canvas = TCanvas("canvas", "canvas")
#canvas.SetCanvasSize(600, 600)
#canvas.SetLeftMargin(0.125)
canvas.SetRightMargin(0.04)
#canvas.SetBottomMargin(0.105)
legend = ROOT.TLegend(0.5, 0.65, 0.925, 0.925)
#legend.SetFillStyle(0)
legend.SetBorderSize(0)
#legend.SetBorderSize(1)
legend.SetTextSize(0.0425)
stack = THStack("stack", "stack")
stack.SetTitle("#tau_{h} fake rate (%s)" % (era))
# Loose-Loose
info_str = "looseLoose_dR03_SS"
#h2_LL = inputFile.Get("%s/tau_pT_vs_DM_reco_%s" %(info_str, info_str)).Clone()
h2_LL = getHist(l_inputFileName,
"%s/%s_%s" % (info_str, plotInfo.histName, info_str))
h2_LL.Sumw2()
# Tight-Loose
info_str = "tightLoose_dR03_SS"
#h2_TL = inputFile.Get("%s/tau_pT_vs_DM_reco_%s" %(info_str, info_str)).Clone()
h2_TL = getHist(l_inputFileName,
"%s/%s_%s" % (info_str, plotInfo.histName, info_str))
h2_TL.Sumw2()
# Tight-Tight
info_str = "tightTight_dR03_SS"
#h2_TT = inputFile.Get("%s/tau_pT_vs_DM_reco_%s" %(info_str, info_str)).Clone()
h2_TT = getHist(l_inputFileName,
"%s/%s_%s" % (info_str, plotInfo.histName, info_str))
h2_TT.Sumw2()
l_h1_LLtoTL = []
l_h1_TLtoTT = []
for iDM in range(0, len(l_DM)):
DM = l_DM[iDM]
iBin = h2_LL.GetXaxis().FindBin(DM)
h1_LL = h2_LL.ProjectionY("_py", iBin, iBin)
h1_LL.Sumw2()
h1_LL = h1_LL.Rebin(
len(plotInfo.l_rebin) - 1, "", array.array("d", plotInfo.l_rebin))
#print "h1_LL %f" %(h1_LL.GetBinContent(h1_LL.GetNbinsX()))
h1_TL = h2_TL.ProjectionY("_py", iBin, iBin)
h1_TL.Sumw2()
h1_TL = h1_TL.Rebin(
len(plotInfo.l_rebin) - 1, "", array.array("d", plotInfo.l_rebin))
#print "h1_TL %f" %(h1_TL.GetBinContent(h1_TL.GetNbinsX()))
h1_TT = h2_TT.ProjectionY("_py", iBin, iBin)
h1_TT.Sumw2()
h1_TT = h1_TT.Rebin(
len(plotInfo.l_rebin) - 1, "", array.array("d", plotInfo.l_rebin))
#print "h1_TT %f" %(h1_TT.GetBinContent(h1_TT.GetNbinsX()))
# LL to TL
h1_temp = h1_TL.Clone()
h1_temp.Divide(h1_LL)
h1_temp.SetLineColor(iDM + 1)
h1_temp.SetLineWidth(3)
h1_temp.SetLineStyle(2)
l_h1_LLtoTL.append(h1_temp.Clone())
stack.Add(l_h1_LLtoTL[-1], "hist E")
legend.AddEntry(l_h1_LLtoTL[-1], "LL #rightarrow TL (DM %d)" % (DM))
# TL to TT
h1_temp = h1_TT.Clone()
h1_temp.Divide(h1_TL)
h1_temp.SetLineColor(iDM + 1)
h1_temp.SetLineWidth(3)
l_h1_TLtoTT.append(h1_temp.Clone())
stack.Add(l_h1_TLtoTT[-1], "hist E")
legend.AddEntry(l_h1_TLtoTT[-1], "TL #rightarrow TT (DM %d)" % (DM))
stack.Draw("nostack")
legend.Draw()
stack.GetXaxis().SetTitle(plotInfo.xTitle)
stack.GetYaxis().SetTitle("#tau_{h} misid. probability")
stack.GetXaxis().SetLabelSize(0.05)
stack.GetYaxis().SetLabelSize(0.05)
stack.GetXaxis().SetTitleOffset(1.05)
stack.GetYaxis().SetTitleOffset(1.25)
stack.GetXaxis().SetTitleSize(0.055)
stack.GetYaxis().SetTitleSize(0.055)
stack.GetXaxis().CenterTitle(True)
stack.GetYaxis().CenterTitle(True)
if (plotInfo.setXmin):
stack.GetXaxis().SetRangeUser(plotInfo.xMin, plotInfo.l_rebin[-1])
if (plotInfo.centerLabelsX):
stack.GetXaxis().CenterLabels()
stack.SetMinimum(0)
stack.SetMaximum(1.5)
if (len(plotInfo.l_rebin)):
if (plotInfo.isXinteger):
stack.GetXaxis().SetNdivisions(len(plotInfo.l_rebin), 1, 1)
else:
stack.GetXaxis().SetNdivisions(len(plotInfo.l_rebin), 5, 1)
#canvas.SetGridx()
#canvas.SetGridy()
# CMS label
CMS_lumi.CMS_lumi(pad=canvas,
iPeriod=0,
iPosX=0,
CMSextraText=Common.getCMSextraText(era),
lumiText=Common.getLumitext(era))
canvas.SaveAs(outDir + "/%s%s.pdf" % (plotInfo.outFileName, suffix))
canvas.Clear()
legend.Clear()
stack.Clear()
def doCombination(file_list, output, debug=False):
xmin = 500
xmax = 3000
hsig16 = TH1F("hsig16", ";Gen_mjj; Events ", 10, 500, 3000)
hsig17 = TH1F("hsig17", ";Gen_mjj; Events ", 10, 500, 3000)
hsig18 = TH1F("hsig18", ";Gen_mjj; Events ", 10, 500, 3000)
hint16 = TH1F("hint16", ";Gen_mjj; Events ", 10, 500, 3000)
hint17 = TH1F("hint17", ";Gen_mjj; Events ", 10, 500, 3000)
hint18 = TH1F("hint18", ";Gen_mjj; Events ", 10, 500, 3000)
gStyle.SetOptFit(0)
gStyle.SetOptStat(0)
gROOT.SetBatch(1)
hs = THStack()
leg = TLegend(0.58, 0.75, 0.89, 0.89)
for ifile in file_list:
if not os.path.exists(ifile):
print '>>> This file does not exist :', ifile, " --> Check path..."
continue
print '>>> Opening File :', ifile
inFile = TFile.Open(ifile, " READ ")
c1 = inFile.Get("c1")
if '2018' in ifile:
hint18 = c1.GetListOfPrimitives().FindObject('hint')
hsig18 = c1.GetListOfPrimitives().FindObject('hsig')
print "Extract 2018"
if '2017' in ifile:
hint17 = c1.GetListOfPrimitives().FindObject('hint')
hsig17 = c1.GetListOfPrimitives().FindObject('hsig')
print "Extract 2017"
if '2016' in ifile:
hint16 = c1.GetListOfPrimitives().FindObject('hint')
hsig16 = c1.GetListOfPrimitives().FindObject('hsig')
print "Extract 2016"
hsigcomb = hsig16.Clone()
hsigcomb.SetName("hsigcomb")
hsigcomb.Add(hsig17)
hsigcomb.Add(hsig18)
hintcomb = hint16.Clone()
hintcomb.SetName("hintcomb")
hintcomb.Add(hint17)
hintcomb.Add(hint18)
c = TCanvas()
c.cd()
hintcomb.SetLineColor(1)
hsigcomb.SetLineColor(4)
hs.Add(hintcomb)
hs.Add(hsigcomb)
leg.AddEntry(hsigcomb, "WpWmJJ_EWK_noTop")
leg.AddEntry(hintcomb, "Interference")
hs.SetTitle("; Gen mjj [GeV]; Events")
hs.Draw("pe nostack")
line1 = TLine(xmin, 0.0, xmax, 0.0)
line1.SetLineWidth(2)
line1.SetLineStyle(2)
line1.SetLineColor(2)
line1.Draw("same")
#leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.Draw("same")
c.SaveAs(output + ".png")
c.SaveAs(output + ".pdf")
c.SaveAs(output + ".root")
def doIntPerYear(data, canvas, output, debug=False):
xmin = 500
xmax = 3000
#hqcd= TH1F("hsum",";Gen_mjj; Events ",10,500,3000)
#hewk= TH1F()
#htot= TH1F("hsig",";Gen_mjj; Events ",10,500,3000)
gStyle.SetOptFit(0)
gStyle.SetOptStat(0)
gROOT.SetBatch(1)
hs = THStack()
hqcd_hewk = THStack()
leg = TLegend(0.58, 0.75, 0.89, 0.89)
for isample in data:
if not os.path.exists(isample['ipath']):
print "Can't find this file :", isample['ipath'], " --> Skipping"
sys.stdout.flush()
return 0
print '>>> Opening File :', isample['ipath']
sys.stdout.flush()
inFile = TFile.Open(isample['ipath'], " READ ")
c1 = inFile.Get(canvas)
if 'hewk_qcd' in isample['obj']:
htot = c1.GetListOfPrimitives().FindObject(isample['obj'])
if 'ewk' not in isample['obj']:
hqcd = c1.GetListOfPrimitives().FindObject(isample['obj'])
if 'qcd' not in isample['obj']:
hewk = c1.GetListOfPrimitives().FindObject(isample['obj'])
hsig = hewk.Clone()
hsig.SetName("hsig")
hsum = hewk.Clone()
hsum.Add(hqcd)
hint = htot.Clone()
hint.SetName("hint")
hint.Add(hint, hsum, 1, -1)
c = TCanvas()
c.SetName("c1")
c.cd()
hint.SetLineColor(1)
hqcd.SetLineColor(2)
hsig.SetLineColor(3)
htot.SetLineColor(4)
hs.Add(hint)
hs.Add(htot)
hqcd_hewk.Add(hsig)
hqcd_hewk.Add(hqcd)
#hsig.Draw("pe")
#hint.Draw("pe, same")
leg.AddEntry(hsig, "WpWmJJ_EWK_noTop")
leg.AddEntry(hqcd, "WpWmJJ_QCD_noTop")
leg.AddEntry(htot, "WpWmJJ_EWK_QCD_noTop")
leg.AddEntry(hint, "Interference")
xtitle = hewk.GetXaxis().GetTitle()
ytitle = hewk.GetYaxis().GetTitle()
#print xtitle
#print ytitle
hs.SetTitle(";" + xtitle + ";" + ytitle)
hs.Draw("pe nostack")
hqcd_hewk.Draw("pe same")
line1 = TF1("line1", "pol0", -100, 4000)
line1.FixParameter(0, 0)
line1.SetLineWidth(2)
line1.SetLineStyle(2)
line1.SetLineColor(2)
line1.Draw("same")
#leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.Draw("same")
c.SaveAs(output + ".png")
c.SaveAs(output + ".pdf")
c.SaveAs(output + ".root")
def plotStack(canvas, pad, tree1, tree2, param, fileNum, outpath):
# the name
tprofname1 = tree1.GetName() + param + str(fileNum)
tprofname2 = tree2.GetName() + param + str(fileNum)
# they already exist in gDirectory, get them
tmpCanv = TCanvas(tprofname1 + 'stack')
tmpCanv.cd()
tprofile1 = gDirectory.Get(tprofname1)
tprofile2 = gDirectory.Get(tprofname2)
# get the overall upper and lower bin limits
lower1 = tprofile1.GetBinLowEdge(1)
lower2 = tprofile2.GetBinLowEdge(1)
# lower2 = tprofile2.GetBinCenter(1) - tprofile2.GetXAxis.GetBinWidth()/2.
# upper1 = tprofile1.GetBinCenter(200) + tprofile1.GetXAxis.GetBinWidth()/2.
upper1 = tprofile1.GetBinLowEdge(201)
upper2 = tprofile2.GetBinLowEdge(201)
newmin = min(lower1, lower2)
newmax = max(upper1, upper2)
# draw the plots again with new binning
tprofname1new = tprofname1 + 'rebin'
tprofname2new = tprofname2 + 'rebin'
tree1.Draw(
param + '>>' + tprofname1new + '(200,' + str(newmin) + ',' +
str(newmax) + ')', '', 'prof,goff')
tree2.Draw(
param + '>>' + tprofname2new + '(200,' + str(newmin) + ',' +
str(newmax) + ')', '', 'prof,goff')
tprofile1new = gDirectory.Get(tprofname1new)
tprofile2new = gDirectory.Get(tprofname2new)
print '[>] In plotStack() with option :', fileNum
if pad > 0:
canvas.cd(pad)
else:
canvas.cd()
# projection to TH1F for stacking
h1st = tprofile1new.ProjectionX(tprofname1new + 'h1')
h2st = tprofile2new.ProjectionX(tprofname2new + 'h2')
ts = THStack()
title = tree1.GetName().split('_')[1] + ' TOTAL'
ts.SetTitle(title)
print '[>] Stack:', tree1.GetName().split(
'_')[1], ' newmin =', newmin, ' newmax =', newmax
h1st.GetXaxis().SetTitle(param.split(':')[1])
h1st.GetYaxis().SetTitle(param.split(':')[0])
h2st.GetXaxis().SetTitle(param.split(':')[1])
h2st.GetYaxis().SetTitle(param.split(':')[0])
if fileNum:
h1st.SetLineColor(fileNum)
h1st.SetMarkerColor(fileNum)
h1st.SetMarkerStyle(8)
h1st.SetMarkerSize(0.35)
h1st.Draw("p,same")
h2st.SetLineColor(fileNum + 10)
h2st.SetMarkerColor(fileNum + 10)
h2st.SetMarkerStyle(8)
h2st.SetMarkerSize(0.35)
h2st.Draw("p,same")
ts.Add(h1st)
ts.Add(h2st)
ts.Draw()
else:
h1st.SetLineColor(1)
h1st.SetFillColor(5)
h1st.Draw('hist')
h2st.SetLineColor(1)
h2st.SetFillColor(17)
h2st.Draw('hist')
ts.Add(h1st, 'hist')
ts.Add(h2st, 'hist')
ts.Draw()
canvas.SaveAs(outpath)
tmp_histogram_sublist[-1].SetMarkerColor(flavour_color)
#tmp_histogram_sublist[-1].SetStats(kTRUE)
#tmp_histogram_sublist[-1].Draw()
#tmp_canvas_single.Write()
#tmp_canvas_single.SaveAs(histogram_name+"_"+flavour_name+'_Canvas.pdf')
tmp_stack.Add(tmp_histogram_sublist[-1])
tmp_legend.AddEntry(tmp_histogram_sublist[-1],flavour_name,'f')
tmp_process_sublist.append(tmp_histogram_sublist)
stack_process_sublist.append(tmp_stack)
tmp_canvas=TCanvas(histogram_name+'_'+process_name.split('.')[-1]+'_'+'CanvasStack','')
#tmp_canvas.cd()
#tmp_histogram_sublist[-1].Draw('histo')
tmp_stack.Draw('histo')
tmp_stack.GetXaxis().SetTitle(histogram_xaxis)
tmp_stack.GetYaxis().SetTitle(histogram_yaxis)
tmp_stack.SetTitle(histogram_description)
tmp_legend.Draw()
#tmp_canvas.BuildLegend(0.2,0.2,0.8,0.8)#tmp_legend.Draw()
#autolegend=tmp_canvas.BuildLegend()
stackcanvas_process_sublist.append(tmp_canvas)
# outfile.Cd()
if histogram_name in ["subTrackNPixelHits","subTrackMomentum","subTrackJetDistVal","subTrackDecayLenVal","subTrackSip3dVal","subTrackSip3dSig","subJetNSecondaryVertices","subCSV"]:
tmp_canvas.SetLogy()
tmp_canvas.Write()
if histogram_name in ["subTrackNPixelHits","subTrackMomentum","subTrackJetDistVal","subTrackDecayLenVal","subTrackSip3dVal","subTrackSip3dSig","subFlightDistance3dSig","subVertexMassJTC","subJetNSecondaryVertices","subVertexNTracks","subCSV"]:
tmp_canvas.SaveAs(histogram_name+'_'+process_name.split('.')[-1]+'_'+'CanvasStack.pdf')
#tmp_stack.Write()
input_histos.append(tmp_process_sublist)
output_histos.append(stack_process_sublist)
output_canvases.append(stackcanvas_process_sublist)
# ttbar_histos.append()
ROOT.gPad.SetBottomMargin(0.02)
#ROOT.gPad.SetTopMargin(0.01)
pad2.cd()
#ROOT.gPad.SetBottomMargin(0.001)
ROOT.gPad.SetTopMargin(0.02)
else:
pad1 = TPad("pad1", "This is pad1", 0., 0., 1., 1.)
pad1.Draw()
pad1.cd()
# copy these before the drawing settings applied
data_rel = data.Clone()
mc_sum_rel = mc_sum.Clone()
hs.SetTitle(args.title)
mc_sum.SetTitle(args.title)
#data.GetYaxis().SetTitleFont(63)
#data.GetYaxis().SetTitleSize(20)
mc_sum.GetYaxis().SetTitleFont(63)
mc_sum.GetYaxis().SetTitleSize(20)
mc_sum.GetXaxis().SetTitleFont(63)
mc_sum.GetXaxis().SetTitleSize(20)
# axis labels
#data.GetYaxis().SetLabelSize(0.02)
#data.GetXaxis().SetLabelSize(0.02)
mc_sum.GetYaxis().SetLabelSize(0.02)
mc_sum.GetXaxis().SetLabelSize(0.02)
def plotDataVSMC(DataHist,
MCHist,
xtitle,
legendNames,
DataName,
destination,
year,
ytitle_bottom='Data/MC',
ylog=False):
GeneralSettings()
Canv = TCanvas("Canv" + destination, "Canv" + destination, 1000, 1000)
#Set Histogram Styles
for i, h in enumerate(MCHist):
h.SetFillColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
h.SetLineColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
DataHist.SetMarkerColor(ROOT.kBlack)
DataHist.SetLineColor(ROOT.kBlack)
DataHist.SetMarkerStyle(20)
#Add all MC samples to use in ratios
totBkgr = MCHist[0].Clone("totBkgr")
for h in MCHist[1:]:
totBkgr.Add(h)
DataOverMC = DataHist.Clone("DataOverMC")
DataOverMC.Divide(totBkgr)
#Errors
predStatError = totBkgr.Clone("PredictedStatError")
predSystError = totBkgr.Clone("PredictedSystError")
predTotError = totBkgr.Clone("PredictedTotalError")
for b in xrange(predSystError.GetNbinsX() + 1):
predSystError.SetBinError(b, 0.3 * totBkgr.GetBinContent(b))
syst = predSystError.GetBinError(b)
stat = predStatError.GetBinError(b)
predTotError.SetBinError(b, np.sqrt(stat * stat + syst * syst))
#predTotError.SetFillStyle(3013)
#predTotError.SetFillColor(ROOT.kGray+2)
#predTotError.SetMarkerStyle(0)
#predTotError.Draw("E2 Same")
#First pad
plotpad = TPad("plotpad", "plotpad", 0, .3, 1, 0.98)
plotpad.SetBottomMargin(0.025)
plotpad.Draw()
plotpad.cd()
#Create Stack (Change with most logical ordering)
hs = THStack("hs", "hs")
for h in MCHist:
hs.Add(h)
hs.Draw(
"EHist"
) #Draw before using GetHistogram, see https://root-forum.cern.ch/t/thstack-gethistogram-null-pointer-error/12892/4
title = " ; ; Events / " + str(MCHist[0].GetBinWidth(1)) + " GeV"
hs.SetTitle(title)
# hs.GetHistogram().GetXaxis().SetTickLength(0)
hs.GetHistogram().GetXaxis().SetLabelOffset(9999999)
hs.GetHistogram().SetMaximum(1)
#Set range
overallMin = GetOverallMinimum(MCHist + [DataHist])
overallMax = GetOverallMaximum([totBkgr] + [DataHist])
if ylog:
plotpad.SetLogy()
hs.SetMinimum(0.3 * overallMin)
hs.SetMaximum(10 * overallMax)
else:
hs.SetMinimum(0.5 * overallMin)
hs.SetMaximum(1.7 * overallMax)
DataHist.Draw("EPSame")
predTotError.SetFillStyle(3013)
predTotError.SetFillColor(ROOT.kGray + 2)
predTotError.SetMarkerStyle(0)
predTotError.Draw("E2 Same")
#Create Legend
legend = TLegend(0.7, .7, .9, .9)
for h, n in zip(MCHist, legendNames):
legend.AddEntry(h, n)
legend.AddEntry(DataHist, DataName)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
#Return to canvas
Canv.cd()
#Second pad
ratiopad = TPad("ratiopad", "ratiopad", 0, 0.05, 1, .3)
ratiopad.SetTopMargin(0.05)
ratiopad.SetBottomMargin(0.25)
ratiopad.Draw()
ratiopad.cd()
#Errors
StatErrorRatio = predStatError.Clone("StatErrorRatio")
StatErrorRatio.SetFillStyle(1001)
StatErrorRatio.SetFillColor(TColor.GetColor('#6EF9F5'))
TotErrorRatio = predTotError.Clone("SystErrorRatio")
TotErrorRatio.SetFillColor(TColor.GetColor('#63E2C6'))
TotErrorRatio.SetFillStyle(1001)
for b in xrange(StatErrorRatio.GetNbinsX() + 1):
if (StatErrorRatio.GetBinContent(b) != 0):
StatErrorRatio.SetBinError(
b,
StatErrorRatio.GetBinError(b) /
StatErrorRatio.GetBinContent(b))
TotErrorRatio.SetBinError(
b,
TotErrorRatio.GetBinError(b) / TotErrorRatio.GetBinContent(b))
StatErrorRatio.SetBinContent(b, 1.)
TotErrorRatio.SetBinContent(b, 1.)
else:
StatErrorRatio.SetBinContent(b, 0)
TotErrorRatio.SetBinContent(b, 0)
#Set Style for bottom plot
DataOverMC.SetTitle(";" + xtitle + "; " + ytitle_bottom)
DataOverMC.GetXaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleOffset(.6)
DataOverMC.GetXaxis().SetLabelSize(.12)
DataOverMC.GetYaxis().SetLabelSize(.12)
DataOverMC.SetMinimum(0.3)
DataOverMC.SetMaximum(1.7)
DataOverMC.Draw("EP")
TotErrorRatio.Draw("E2 same")
StatErrorRatio.Draw("E2 same")
DataOverMC.Draw("EPsame")
#Draw a guide for the eye
line = TLine(DataOverMC.GetXaxis().GetXmin(), 1,
DataOverMC.GetXaxis().GetXmax(), 1)
line.SetLineColor(ROOT.kRed)
line.SetLineWidth(1)
line.SetLineStyle(1)
line.Draw("Same")
#Create Legend
legend_bottom = TLegend(0.2, .8, .9, .9)
legend_bottom.SetNColumns(3)
legend_bottom.AddEntry(DataOverMC, "Obs./Pred.")
legend_bottom.AddEntry(StatErrorRatio, "Stat. Unc.")
legend_bottom.AddEntry(TotErrorRatio, "Tot. Unc.")
legend_bottom.SetFillStyle(0)
legend_bottom.SetBorderSize(0)
legend_bottom.Draw()
#Throw CMs lumi at it
cl.CMS_lumi(Canv, 4, 11, year, 'Preliminary', True)
print 'save'
#Save everything
savePlots(Canv, destination)
print 'saved'
def readHists(postfix=""):
hstack = THStack("hstack","hstack")
hstack.SetTitle("#scale[0.9]{#font[22]{CMS} #font[12]{Preliminary} 41.53 fb^{-1}(13TeV)}")
inputfile = read_rootfile(postfix)
gROOT.cd()
dirName = "ttH_"+region+"_"+Channels[0]
h0 = inputfile.Get("shapes_"+fit_type+"/"+dirName+"/total")
h_totalbkg = h0.Clone("h_totalbkg")
h_totalbkg.SetDirectory(0)
h_totalbkg.Reset()
h_totalsig = h0.Clone("h_totalsig")
h_totalsig.SetDirectory(0)
h_totalsig.Reset()
h_totalmc = h0.Clone("h_totalmc")
h_totalmc.SetDirectory(0)
h_totalmc.Reset()
h_totalmc.SetLineColor(kBlack)
h_totalmc.SetFillColor(kGray+3)
h_totalmc.SetFillStyle(3001)
h_totalmc.SetTitle("")
h_totalmc.SetMinimum(0.8)
h_totalmc.SetMaximum(1.35)
h_totalmc.Sumw2()
h_totalmc.SetStats(0)
h_dataobs = h0.Clone("h_dataobs")
h_dataobs.SetDirectory(0)
h_dataobs.Reset()
h_dataobs.SetMarkerStyle(20)
latexString =""
expString =" SM Exp"
dataString =" Data Obs"
latexString += ("\\begin{frame}\n\\frametitle{"+POI.replace("_","\_")+" "+fit_type.replace("_","\_")+"}\n\\begin{table}[]\n\scalebox{0.38}{\n\\begin{tabular}{"+"l"*(2+len(Channels))+"}\n")
for channel in Channels:
FitDir = inputfile.Get("shapes_"+fit_type)
directory = "shapes_"+fit_type+"/ttH_"+region+"_"+channel+"/"
SkipChannel = False
if not FitDir.GetListOfKeys().Contains("ttH_"+region+"_"+channel):
print " skip " + directory
SkipChannel = True
else:
h_bkg = inputfile.Get(directory+"total_background").Clone(directory+"total_background")
h_totalbkg.Add(h_bkg)
h_sig = inputfile.Get(directory+"total_signal").Clone(directory+"total_signal")
h_totalsig.Add(h_sig)
h_total = inputfile.Get(directory+"total").Clone(directory+"total")
h_totalmc.Add(h_total)
g_data = inputfile.Get(directory+"data").Clone(directory+"data")
h_data = h_total.Clone("h_data")
h_data.Reset()
TGraphToTH1(h_data, g_data)
h_dataobs.Add(h_data)
if not SkipChannel:
SM_error = ROOT.Double(0)
h_total.IntegralAndError(0,h_total.GetNbinsX(),SM_error)
expString += " & $ "+str(round(h_total.Integral(),2))+" \\pm "+str(round(SM_error,2))+"$"
data_error = ROOT.Double(0)
h_data.IntegralAndError(0,h_data.GetNbinsX(),data_error)
dataString += " & $ "+str(round(h_data.Integral(),2))+" \\pm "+str(round(data_error,2))+"$"
else:
expString += " & - "
dataString += " & - "
latexString += ("& "+channel.replace("_","\_")+" ")
totMC_error = ROOT.Double(0)
h_totalmc.IntegralAndError(0,h_totalmc.GetNbinsX(),totMC_error)
expString += " & $ "+str(round(h_totalmc.Integral(),2))+" \\pm "+str(round(totMC_error,2))+"$ \\\\ \n"
totdata_error = ROOT.Double(0)
h_dataobs.IntegralAndError(0,h_dataobs.GetNbinsX(),totdata_error)
dataString += " & $ "+str(round(h_dataobs.Integral(),2))+" \\pm "+str(round(totdata_error,2))+"$ \n"
latexString += "& total "+ region +" \\\\ \n"
legend = TLegend(0.2,0.6,0.8,0.88)
#legend.SetHeader("2017 "+fit_type+", 2lss l^{#pm}l^{#pm} #mu(ttH)=#hat#mu")
legend.SetHeader("2017 "+fit_type+", "+header_postfix)
legend.SetNColumns(3)
legend.SetBorderSize(0)
Samples.reverse()
for sample in Samples:
hist=h_totalsig.Clone(sample)
hist.Reset()
latexString += sample
for channel in Channels:
h_sample =hist.Clone(sample+"_sample")
h_sample.Reset()
latexString += " & "
for p in Process[sample]:
histname = "shapes_"+fit_type+"/ttH_"+region+"_"+channel+"/"+p
h1 = inputfile.Get(histname)
if h1:
#print h1.Integral()
hist.Add(h1)
h_sample.Add(h1)
if h_sample.Integral()>0.05:
error = ROOT.Double(0)
h_sample.IntegralAndError(0,h_sample.GetNbinsX(),error)
latexString += "$ "+str(round(h_sample.Integral(),2))+" \\pm "+str(round(error,2))+"$"
elif h_sample.Integral()>0:
latexString += " \\textless{}0.05"
else: latexString += " - "
if hist.Integral()>0:
hist.SetDirectory(0)
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist,sample,"f")
if hist.Integral()>0.05:
IntegralError = ROOT.Double(0)
hist.IntegralAndError(0,hist.GetNbinsX(),IntegralError)
latexString += " & $ "+str(round(hist.Integral(),2))+" \\pm "+str(round(IntegralError,2))+" $ \\\\ \n"
else:
latexString += " & \\textless{}0.05 \\\\ \n"
else: latexString += " & - \\\\ \n"
latexString += (expString + dataString)
latexString += ("\\end{tabular}\n}\n\\end{table}\n\\end{frame}\n \\end{document}\n")
if LATEX==1 :
latexfile = file(DirOfRootplas+SubCat+"/"+POI+"/"+SubCat+"_"+POI+"_"+region+"_"+fit_type+"_"+SPLIT+"_"+Expected+"_yield.tex","w")
latexfile.write("\\documentclass{beamer}\n\\usetheme{Warsaw}\n\n\\usepackage{graphicx}\n\\useoutertheme{infolines}\n\\setbeamertemplate{headline}{}\n\n\\begin{document}\n\n")
latexfile.write(latexString)
inputfile.Close()
return h_totalsig, h_totalbkg, h_totalmc, h_dataobs, hstack, legend
def plotter(plotdir,
plot,
xTitle,
logY,
channels=['VV'],
includeData=False,
scaleSignal=0,
UserRange=[None, None, None, None],
initPath=''):
# channelTex={'WPWP':'W^{+}W^{+}', 'WPWM':'W^{+}W^{-}','WMWM':'W^{-}W^{-}','WPZ':'W^{+}Z','WMZ':'W^{-}Z','ZZ':'ZZ'}
channelTex = {'ZZ': 'ZZ'}
# plotstyle=[(1,1),(1,2),(2,1),(2,2),(4,1),(4,2)]
plotstyle = [(1, 1)]
# 0 1 2 3 4 5 6
# Backgrounds=['QCD', 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'TT', 'WW', 'WZ', 'ZZ']
# BGColors= [rt.kAzure+7, rt.kRed-4, rt.kOrange-2, rt.kGreen+2, rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
# BGTeX= ['QCD', 'W+JetsToQQ', 'Z+JetsToQQ', 'TTbar'], 'WW', 'WZ', 'ZZ']
#stackOrder= [4,5,6,2,1,3,0]
Backgrounds = [
'QCD'
] # 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'ZZ'] 'TT', 'WW', 'WZ', 'ZZ']
BGColors = [
rt.kAzure + 7
] #, rt.kOrange-2, rt.kGreen+2], rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
BGTeX = ['QCD'] #
stackOrder = [0]
PreSelection = [
'nocuts', 'common', 'corrections', 'cleaner', 'softdropmassCorr',
'AK4pfidfilter', 'AK8pfidfilter', 'invMAk8sel', 'detaAk8sel',
'AK8N2sel'
]
Selection = [
'preselection', 'softdropAK8sel', 'tau21sel', 'deltaR48', 'VVRegion',
'AK4N2sel', 'OpSignsel', 'detaAk4sel', 'invMAk4sel_1p0', 'Kin_AK8'
]
cutnames = [
'cleaner', 'AK8N2sel', 'invMAk8sel', 'detaAk8sel', 'softdropAK8sel',
'tau21sel', 'AK4cleaner', 'AK4N2sel', 'OpSignsel', 'detaAk4sel',
'invMAk4sel_1p0'
]
cuts = {
'cleaner':
'#splitline{p_{T-AK8} > 200 GeV, |#eta_{AK8}| < 2.5}{p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0}',
'AK8N2sel': 'N_{AK8} #geq 2',
'invMAk8sel': 'M_{jj-AK8} > 1050 GeV',
'detaAk8sel': '|#Delta#eta_{jj-AK8}|<1.3',
'softdropAK8sel': '65 GeV <M_{SD}< 105 GeV',
'tau21sel': '0 #leq #tau_{2}/#tau_{1}<0.45',
# 'AK4cleaner':'p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0',
'AK4cleaner': '',
'AK4N2sel': 'N_{AK4} #geq 2',
'OpSignsel': '#eta_{1-AK4} #eta_{2-AK4} < 0',
'detaAk4sel': '|#Delta#eta_{jj-AK4}| > 3.0',
'invMAk4sel_1p0': 'M_{jj-AK4} > 1.0 TeV'
}
VV = ('VV' in channels)
seperate = (not VV)
if VV:
#channels=["WPWP","WPWM","WMWM","WPZ","WMZ","ZZ"]
channels = ["ZZ"]
plottitle = plotdir + '_' + plot
lumi = 36.814
xLabelSize = 18.
yLabelSize = 18.
xTitleSize = 20.
yTitleSize = 22.
xTitleOffset = 4.
yTitleOffset = 1.3
printout = False
Portrait = True
cutname = False
ratio = includeData
if ('highbin' in plot):
binning = 'dijetbinning'
else:
binning = 'default'
if (Portrait):
canvX = 600
canvY = 600
else:
canvX = 900
canvY = 675
if (plotdir in PreSelection):
region = 'PreSelection'
initPath = ''
referenceHistPath = 'detaAk8sel/N_pv'
if (PreSelection.index(plotdir) < 4 and ('softdrop' in plot)):
return 'SofdropMass not filled yet!'
else:
region = 'SignalRegion'
referenceHistPath = 'tau21sel/N_pv'
referenceHistPath = plotdir + '/' + plot
if (initPath == ''):
path = '/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/%s' % region
else:
path = initPath
outputPath = path.replace(
'/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/SignalRegion',
'plots/')
if (plotdir in PreSelection):
CutNumber = PreSelection.index(plotdir)
else:
CutNumber = Selection.index(plotdir)
outputPath = outputPath + '/%02i_%s' % (CutNumber, plotdir) + '/'
if (printout):
print('InputPath:', path)
print('OutputPath:', outputPath)
#check if OutputPath exists - and if not create it!
if not os.path.exists(outputPath):
os.makedirs(outputPath)
# path='/home/albrec/Master/signal/'
scaleVV = (scaleSignal != 0)
VVScale = scaleSignal
if (UserRange[2] == None or UserRange[3] == None):
YRangeUser = False
Ymin = UserRange[2]
Ymax = UserRange[3]
else:
YRangeUser = True
Ymin = UserRange[2]
Ymax = UserRange[3]
if (UserRange[0] == None or UserRange[1] == None):
XRangeUser = False
Xmin = UserRange[0]
Xmax = UserRange[1]
else:
XRangeUser = True
Xmin = UserRange[0]
Xmax = UserRange[1]
# YRangeUser=False
# Ymin=0.11
# Ymax=9*10**3
# XRangeUser=False
# Xmin=0
# Xmax=6000.
gROOT.ProcessLine("gErrorIgnoreLevel = 2001;")
SFiles = []
for i in range(len(channels)):
SFiles.append(
TFile(
path +
"/uhh2.AnalysisModuleRunner.MC.MC_aQGC_%sjj_hadronic_2016v3.root"
% channels[i]))
#uhh2.AnalysisModuleRunner.MC.MC_aQGC_ZZjj_hadronic_2016v3.root
##Open Files to get BackgroundHist:
BFiles = []
for i in range(len(Backgrounds)):
BFiles.append(
TFile(path +
"/uhh2.AnalysisModuleRunner.MC.MC_%s.root" % Backgrounds[i]))
#Open File to get DataHist:
# DataFile = TFile(path+"/uhh2.AnalysisModuleRunner.Data.DATA.root")
#gROOT.ProcessLine( "gErrorIgnoreLevel = 0;")
if (includeData == True):
#calculate QCDscale with Integrals from the following Histogram:
# referenceHistPath = 'tau21sel/N_AK4'
# referenceHistPath = 'detaAk8sel/N_pv'
# referenceHistPath = 'tau21sel/met_pt_over_sumptAK8_2'
QCDscale = float(DataFile.Get(referenceHistPath).Integral())
QCDNorm = 1
for i in range(len(BFiles)):
if ('QCD' in BFiles[i].GetName()):
QCDNorm = float(BFiles[i].Get(referenceHistPath).Integral())
else:
QCDscale -= float(BFiles[i].Get(referenceHistPath).Integral())
QCDscale /= QCDNorm
else:
QCDscale = 1.0
if (printout):
print('using QCDscale:', QCDscale)
SHists = []
for i in range(len(channels)):
SHists.append(SFiles[i].Get(plotdir + '/' + plot))
BHists = []
for i in range(len(BFiles)):
BHists.append(BFiles[i].Get(plotdir + '/' + plot))
if ('QCD' in BFiles[i].GetName()):
BHists[-1].Scale(QCDscale)
#if(includeData):
# DataHist=DataFile.Get(plotdir+'/'+plot)
if (binning == 'dijetbinning'):
fitbinning = array('d')
binwidth = 200
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
NBins = int(NBins)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
for i in range(len(channels)):
SHists[i] = SHists[i].Rebin(NBins, "new binning", fitbinning)
for i in range(len(Backgrounds)):
BHists[i] = BHists[i].Rebin(NBins, "new binning", fitbinning)
if (includeData):
DataHist = DataHist.Rebin(NBins, "new binning", fitbinning)
canv = TCanvas(plottitle, plottitle, canvX, canvY)
yplot = 0.7
yratio = 0.3
ymax = 1.0
xmax = 1.0
xmin = 0.0
if (ratio):
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot, xmax, ymax)
ratiopad = TPad("ratiopad", "Ratio", xmin, ymax - yplot - yratio, xmax,
ymax - yplot)
else:
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot - yratio, xmax,
ymax)
plotpad.SetTopMargin(0.08)
plotpad.SetLeftMargin(0.1)
plotpad.SetRightMargin(0.05)
plotpad.SetTicks()
plotpad.Draw()
if (ratio):
plotpad.SetBottomMargin(0.016)
ratiopad.SetTopMargin(0.016)
ratiopad.SetBottomMargin(0.35)
ratiopad.SetLeftMargin(0.1)
ratiopad.SetRightMargin(0.05)
ratiopad.SetTicks()
ratiopad.Draw()
else:
plotpad.SetBottomMargin(0.1)
if (logY):
plotpad.SetLogy()
canv.SetLogy()
if ('-logX' in xTitle):
plotpad.SetLogx()
if (ratio):
ratiopad.SetLogx()
canv.SetLogx()
drawOptions = "HE"
stack = THStack(plottitle, plottitle)
BHist = THStack(plottitle, plottitle)
# for i in range(len(Backgrounds)):
for i in stackOrder:
# BHists[i].SetFillColor(BGColors[i])
BHists[i].SetLineColor(BGColors[i])
BHist.Add(BHists[i], 'Hist')
BHist.SetTitle(plottitle)
BHistErr = BHists[0].Clone()
for i in range(1, len(Backgrounds)):
BHistErr.Add(BHists[i])
BHistErr.SetFillStyle(3204)
BHistErr.SetFillColor(rt.kGray + 2)
BHistErr.SetLineColor(1)
BGMax = BHist.GetMaximum()
SIGMax = 0
#if(VV): #new
# SIGMax=VVsum.GetMaximum()
#else:
for i in range(len(channels)):
tmpmax = SHists[i].GetMaximum()
if (tmpmax > SIGMax):
SIGMax = tmpmax
if (scaleVV):
SIGMax = SIGMax * VVScale
if (logY):
MAX = 0.9 * float(10**(magnitude(max(BGMax, SIGMax)) + 1))
MIN = float(10**(magnitude(max(BGMax, SIGMax)) - 5))
MIN += float(10**(magnitude(MIN)))
legendMIN = math.log(max(BGMax, SIGMax)) / math.log(MAX)
else:
MAX = (1.0 / 0.8) * max(BGMax, SIGMax)
legendMIN = 0.7
MIN = 0.
legendMIN = (legendMIN * 0.7) + 0.3 - 0.016
legend = TLegend(0.5, 0.75, 0.85, 0.89)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
legend.SetNColumns(2)
legend.SetColumnSeparation(0.3)
if (includeData):
DataHist.SetMarkerStyle(8)
DataHist.SetLineColor(1)
DataHist.SetTitle(plottitle)
if VV:
for i in range(len(channels)):
if (i == 0):
VVsum = SHists[i].Clone()
else:
VVsum.Add(SHists[i])
legentry = 'VVjj'
if (scaleVV):
VVsum.Scale(VVScale)
legentry += ' *%0.f' % VVScale
VVsum.SetLineColor(1)
VVsum.SetLineStyle(plotstyle[0][1])
VVsum.SetLineWidth(2)
legend.AddEntry(VVsum, legentry)
else:
for i in range(len(channels)):
SHists[i].SetLineColor(plotstyle[i][0])
SHists[i].SetLineStyle(plotstyle[i][1])
SHists[i].SetLineWidth(2)
legentry = "%sjj" % channelTex[channels[i]]
if (scaleVV):
SHists[i].Scale(VVScale)
legentry += ' *%.2E' % VVScale
legend.AddEntry(SHists[i], legentry)
for i in stackOrder:
legend.AddEntry(BHists[i], BGTeX[i], "f")
legend.AddEntry(BHistErr, "stat. Uncertainty", "f")
if (includeData):
legend.AddEntry(DataHist, "Data", "lep")
canv.SetTitle(plottitle)
BHistErr.GetYaxis().SetTitle('Events')
BHistErr.GetYaxis().SetRangeUser(MIN, MAX)
BHistErr.GetYaxis().SetTitleFont(43)
BHistErr.GetYaxis().SetTitleSize(yTitleSize)
BHistErr.GetYaxis().SetTitleOffset(yTitleOffset)
BHistErr.GetYaxis().SetLabelFont(43)
BHistErr.GetYaxis().SetLabelSize(yLabelSize)
if (ratio):
BHistErr.GetXaxis().SetTitleSize(0.0)
BHistErr.GetXaxis().SetLabelSize(0.0)
else:
BHistErr.GetXaxis().SetTitle(xTitle)
BHistErr.GetXaxis().SetTitleFont(43)
BHistErr.GetXaxis().SetTitleSize(xTitleSize)
# BHistErr.GetXaxis().SetTitleOffset(xTitleOffset)
BHistErr.GetXaxis().SetTitleOffset(1.2)
BHistErr.GetXaxis().SetLabelFont(43)
BHistErr.GetXaxis().SetLabelSize(xLabelSize)
# BHistErr.GetXaxis().SetTickLength(0.08)
# BHistErr.GetXaxis().SetNdivisions(506)
if (YRangeUser):
BHistErr.GetYaxis().SetRangeUser(Ymin, Ymax)
if (XRangeUser):
BHistErr.GetXaxis().SetRangeUser(Xmin, Xmax)
plotpad.cd()
BHistErr.Draw("E2")
BHist.Draw("HistSAME")
BHistErr.Draw("E2SAME")
if (VV):
VVsum.Draw("SAME" + drawOptions)
elif ('-noSig' not in xTitle):
for i in range(len(channels)):
SHists[i].Draw("SAME" + drawOptions)
if (includeData):
DataHist.Draw("APE1SAME")
plotpad.RedrawAxis()
if (ratio):
ratiopad.cd()
if (includeData):
ratioHist = DataHist.Clone()
else:
ratioHist = BHistErr.Clone()
ratioHist.SetLineColor(rt.kBlack)
# ratioHist.Sumw2()
ratioHist.SetStats(0)
ratioHist.Divide(BHistErr)
ratioHist.SetMarkerStyle(21)
ratioHist.SetMarkerSize(0.7)
#Yaxis
ratioHist.GetYaxis().SetRangeUser(0.3, 1.7)
ratioHist.GetYaxis().SetTitle("Data/BG")
ratioHist.GetYaxis().CenterTitle()
ratioHist.GetYaxis().SetTitleFont(43)
ratioHist.GetYaxis().SetTitleSize(yTitleSize)
ratioHist.GetYaxis().SetTitleOffset(yTitleOffset)
ratioHist.GetYaxis().SetLabelFont(43)
ratioHist.GetYaxis().SetLabelSize(yLabelSize)
ratioHist.GetYaxis().SetNdivisions(506)
#Xaxis
ratioHist.GetXaxis().SetTitle(xTitle)
ratioHist.GetXaxis().SetTitleFont(43)
ratioHist.GetXaxis().SetTitleSize(xTitleSize)
ratioHist.GetXaxis().SetTitleOffset(xTitleOffset)
ratioHist.GetXaxis().SetLabelFont(43)
ratioHist.GetXaxis().SetLabelSize(xLabelSize)
ratioHist.GetXaxis().SetTickLength(0.08)
ratioHist.GetXaxis().SetNdivisions(506)
# if(YRangeUser):
# ratioHist.GetYaxis().SetRangeUser(Ymin,Ymax)
if (XRangeUser):
ratioHist.GetXaxis().SetRangeUser(Xmin, Xmax)
ratioXMin = Xmin
ratioXMax = Xmax
else:
ratioXMin = ratioHist.GetXaxis().GetXmin()
ratioXMax = ratioHist.GetXaxis().GetXmax()
ratioHist.Draw("ep")
zeropercent = TLine(ratioXMin, 1, ratioXMax, 1)
zeropercent.Draw()
plus10percent = TLine(ratioXMin, 1.1, ratioXMax, 1.1)
plus10percent.SetLineStyle(rt.kDashed)
plus10percent.Draw()
minus10percent = TLine(ratioXMin, 0.9, ratioXMax, 0.9)
minus10percent.SetLineStyle(rt.kDashed)
minus10percent.Draw()
canv.cd()
gPad.RedrawAxis()
legend.Draw()
latex = TLatex()
latex.SetNDC(kTRUE)
latex.SetTextSize(20)
latex.DrawLatex(0.69, 0.953, "%.2f fb^{-1} (13 TeV)" % lumi)
latex.DrawLatex(0.1, 0.953, "private work")
lastcut = 'nocuts'
for cut in cutnames:
if cut in plotdir:
lastcut = cut
if (not (lastcut == 'nocuts') and cutname):
# latex.SetTextSize(0.03)
latex.SetTextSize(15)
for l in range(cutnames.index(lastcut) + 1):
latex.DrawLatex(0.12, 0.8 - l * 0.04, cuts[cutnames[l]])
canv.Update()
canv.Print(outputPath + '/%s_%s.pdf' % (plotdir, plot))
#prevents memory leak in Canvas Creation/Deletion
#see: https://root.cern.ch/root/roottalk/roottalk04/2484.html
gSystem.ProcessEvents()
if (ratio):
del ratiopad
del plotpad, canv
# gc.collect()
return 'done!'
def study_smearing():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/data_nn50.root", 'read')
t_data = f_data.data
t_data.AddFriend("data10=data", "../trees_5gev_e/data_nn10.root")
t_data.AddFriend("data20=data", "../trees_5gev_e/data_nn20.root")
t_data.AddFriend("data90=data", "../trees_5gev_e/data_nn90.root")
t_data.AddFriend("data99=data", "../trees_5gev_e/data_nn99.root")
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_noise=lumical",
"../trees_5gev_e/lucas_geocuts_noise_test.root")
c = TCanvas()
hs = THStack("hs", "title")
t_data.Draw("Sum$(data10.energy*(data10.layer == 0))>>h_data10(200, 0, 3)",
"Sum$(data10.energy*(data10.layer == 0)) > 0")
h_data10 = gROOT.FindObject("h_data10")
h_data10.SetTitle("Data nn>0.1")
h_data10.SetLineWidth(2)
h_data10.SetLineColor(3)
hs.Add(h_data10, "histo")
t_data.Draw("Sum$(data20.energy*(data20.layer == 0))>>h_data20(200, 0, 3)",
"Sum$(data20.energy*(data20.layer == 0)) > 0")
h_data20 = gROOT.FindObject("h_data20")
h_data20.SetTitle("Data nn>0.2")
h_data20.SetLineWidth(2)
h_data20.SetLineColor(4)
hs.Add(h_data20, "histo")
t_data.Draw("Sum$(energy*(layer == 0))>>h_data50(200, 0, 3)",
"Sum$(energy*(layer == 0)) > 0")
h_data50 = gROOT.FindObject("h_data50")
h_data50.SetTitle("Data nn>0.5")
h_data50.SetLineWidth(2)
h_data50.SetLineColor(1)
hs.Add(h_data50, "histo")
t_data.Draw("Sum$(data90.energy*(data90.layer == 0))>>h_data90(200, 0, 3)",
"Sum$(data90.energy*(data90.layer == 0)) > 0")
h_data90 = gROOT.FindObject("h_data90")
h_data90.SetTitle("Data nn>0.90")
h_data90.SetLineWidth(2)
h_data90.SetLineColor(5)
hs.Add(h_data90, "histo")
t_data.Draw("Sum$(data99.energy*(data99.layer == 0))>>h_data99(200, 0, 3)",
"Sum$(data99.energy*(data99.layer == 0)) > 0")
h_data99 = gROOT.FindObject("h_data99")
h_data99.SetTitle("Data nn>0.99")
h_data99.SetLineWidth(2)
h_data99.SetLineColor(6)
hs.Add(h_data99, "histo")
# t_mc.Draw("Sum$(tr1_energy/0.0885)>>h_mc(200, 0, 3)", "Sum$(tr1_energy/0.0885) > 0")
# h_mc = gROOT.FindObject("h_mc")
# h_mc.SetTitle("MC no smearing")
# h_mc.SetLineWidth(2)
# h_mc.SetLineColor(2)
# h_mc.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc, "histo")
# t_mc.Draw("Sum$(tr1_energy_smeared5/0.0885)>>h_mc5(200, 0, 3)", "Sum$(tr1_energy_smeared5/0.0885) > 0")
# h_mc5 = gROOT.FindObject("h_mc5")
# h_mc5.SetLineWidth(2)
# h_mc5.SetLineColor(3)
# h_mc5.SetMarkerStyle(0)
# h_mc5.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc5, "histo")
# t_mc.Draw("Sum$(tr1_energy_smeared6/0.0885)>>h_mc6(200, 0, 3)", "Sum$(tr1_energy_smeared6/0.0885) > 0")
# h_mc6 = gROOT.FindObject("h_mc6")
# h_mc6.SetTitle("MC smearing #sigma=0.6*stdNoise")
# h_mc6.SetLineWidth(2)
# h_mc6.SetLineColor(2)
# h_mc6.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc6, "histo")
t_mc.Draw("Sum$(tr1_energy_smeared7/0.0885)>>h_mc7(200, 0, 3)",
"Sum$(tr1_energy_smeared7/0.0885) > 0")
h_mc7 = gROOT.FindObject("h_mc7")
h_mc7.SetTitle("MC smearing #sigma=0.7*stdNoise")
h_mc7.SetLineWidth(2)
h_mc7.SetLineColor(2)
h_mc7.Scale(t_data.GetEntries() / t_mc.GetEntries())
hs.Add(h_mc7, "histo")
# t_mc.Draw("Sum$((tr1_energy_smeared10)/0.0885)>>h_mc10(200, 0, 3)", "Sum$((tr1_energy_smeared10)/0.0885) > 0")
# h_mc10 = gROOT.FindObject("h_mc10")
# h_mc10.SetTitle("MC smearing #sigma=stdNoise")
# h_mc10.SetLineWidth(2)
# h_mc10.SetLineColor(18)
# h_mc10.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc10, "histo")
hs.Draw("nostack")
hs.SetTitle("Deposited energy in the 1st tracker;E, [MIP]; N_{events}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
hs.Add(LepBDTMCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
LepBDTDATA_list[i].SetMaximum(
1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
hs.SetMinimum(10) #EF for ele BDT
hs.Draw("histo")
LepBDTDATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle("BDT score")
hs.GetXaxis().SetRangeUser(0.75, 1) #EF for ele BDT
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
gStyle.SetOptStat(0)
pad1.SetLogy()
# legend
def plotDataVSMC(DataHist,
MCHist,
xtitle,
legendNames,
DataName,
destination,
ytitle_bottom='Data/MC'):
GeneralSettings()
Canv = TCanvas("Canv" + destination, "Canv" + destination, 1000, 1000)
#Set Histogram Styles
for i, h in enumerate(MCHist):
h.SetFillColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
h.SetLineColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
DataHist.SetMarkerColor(ROOT.kBlack)
DataHist.SetLineColor(ROOT.kBlack)
DataHist.SetMarkerStyle(20)
#Add all MC samples to use in ratios
totBkgr = MCHist[0].Clone("totBkgr")
for h in MCHist[1:]:
totBkgr.Add(h)
DataOverMC = DataHist.Clone("DataOverMC")
DataOverMC.Divide(totBkgr)
#First pad
plotpad = TPad("plotpad", "plotpad", 0, .3, 1, 0.98)
plotpad.SetBottomMargin(0.025)
plotpad.Draw()
plotpad.cd()
#Create Stack (Change with most logical ordering)
hs = THStack("hs", "hs")
for h in MCHist:
hs.Add(h)
hs.Draw(
"EHist"
) #Draw before using GetHistogram, see https://root-forum.cern.ch/t/thstack-gethistogram-null-pointer-error/12892/4
title = " ; ; Events / " + str(MCHist[0].GetBinWidth(1)) + " GeV"
hs.SetTitle(title)
# hs.GetHistogram().GetXaxis().SetTickLength(0)
hs.GetHistogram().GetXaxis().SetLabelOffset(9999999)
hs.GetHistogram().SetMaximum(1)
#Set range
overallMin = GetOverallMinimum(MCHist + [DataHist])
overallMax = GetOverallMaximum([totBkgr] + [DataHist])
hs.SetMinimum(0.5 * overallMin)
hs.SetMaximum(1.2 * overallMax)
DataHist.Draw("EPSame")
#Create Legend
legend = TLegend(0.7, .7, .9, .9)
for h, n in zip(MCHist, legendNames):
legend.AddEntry(h, n)
legend.AddEntry(DataHist, DataName)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
#Return to canvas
Canv.cd()
#Second pad
ratiopad = TPad("ratiopad", "ratiopad", 0, 0.05, 1, .3)
ratiopad.SetTopMargin(0.05)
ratiopad.SetBottomMargin(0.25)
ratiopad.Draw()
ratiopad.cd()
#Set Style for bottom plot
DataOverMC.SetTitle(";" + xtitle + "; " + ytitle_bottom)
DataOverMC.GetXaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleOffset(.6)
DataOverMC.GetXaxis().SetLabelSize(.12)
DataOverMC.GetYaxis().SetLabelSize(.12)
DataOverMC.SetMinimum(0.6)
DataOverMC.SetMaximum(1.4)
DataOverMC.Draw("EP")
#Draw a guide for the eye
line = TLine(DataOverMC.GetXaxis().GetXmin(), 1,
DataOverMC.GetXaxis().GetXmax(), 1)
line.SetLineColor(ROOT.kRed)
line.SetLineWidth(1)
line.SetLineStyle(1)
line.Draw("Same")
#Throw CMs lumi at it
cl.CMS_lumi(Canv, 4, 11, 'Preliminary', True)
print 'save'
#Save everything
savePlots(Canv, destination)
print 'saved'
