def buildLegend(self, legendFile, objects ):
'''For each object, use the corresponding legend entry'''
legendDesc = open( legendFile )
FLOAT = '-*\d+[.\d*]*'
pattmp = '\s*(FLOAT)\s+(FLOAT)\s+(FLOAT)\s+(FLOAT)\s*'
pattern = re.compile( pattmp.replace('FLOAT', FLOAT) )
legendEntries = []
for line in legendDesc.readlines():
line = line.rstrip()
match = pattern.match( line )
if match!=None:
x1 = float(match.group(1))
x2 = float(match.group(2))
y1 = float(match.group(3))
y2 = float(match.group(4))
else:
legendEntries.append( line )
# print x1, x2, y1, y2, legendEntries
if len(legendEntries)!= len(objects):
raise ValueError('if you specify a legend file, it should have the correct number of lines.')
TLegend.__init__( self, x1, x2, y1, y2)
for entry,hist in zip(legendEntries,objects):
self.AddEntry( hist, entry)
def main():
"""
Simply plot semi-visible jets histograms stored in nanoAOD files for a quick look at distributions.
User specifies a list of the files they wish to be read in, and one file corresponds to one "model",
(i.e., a combination of semi-visible jet-specific parameters). The current collection of histograms
gives a quick look at the kinematics. If the user wishes to add more, the histogram must be
initialised in the SVJModel class with the others, added to hist_list, then filled in the big for()
loop in this function.
"""
if not os.path.exists(os.path.join(os.getcwd(), 'Plots')):
os.mkdir('Plots')
# Build list of files from input argument
with open(args.file_list) as input_files:
input_file_list = input_files.readlines()
# Create list of models, each element in list contains object with all information about each model
models = []
for file in input_file_list:
if file.startswith('#') or file.startswith('\n'):
continue
file = file.replace('\n', '')
model_name = os.path.basename(file.replace('.root', ''))
models.append(SVJModel(name=model_name, file_path=file))
# For each model, initialise histograms, set line colours and write legend entry
for i, model in enumerate(models):
model.initialise_histos()
model.get_hist_colour(i)
model.write_legend_entry(model.name)
# Initialise the canvas and set aesthetics
canv = TCanvas("canv", "canv", 800, 600)
canv.SetLogy()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
# Initialise legend and set colours
leg_height = len(
models) * 0.06 # make y-length of legend dependent on n_models
legend_frame = TLegend(0.6, 0.9 - leg_height, 0.9, 0.9)
#leg_height = len(models) * 0.03
#legend_frame = TLegend(0.2, 0.9 - leg_height, 0.8, 0.9)
#legend_frame.SetNColumns(2)
legend_frame.SetTextSize(0.022)
# Open root files, then fill and draw individual histograms
for i, model in enumerate(models):
print(Fore.MAGENTA +
"Running over model {0}/{1}".format(i + 1, len(models)))
openFile = TFile(model.file_path)
tree = openFile.Get("Events")
nEvents = tree.GetEntries()
# Initialise progress bar
widgets = [Percentage(), Bar('>'), ETA()]
pbar = ProgressBar(widgets=widgets, maxval=nEvents).start()
# Fill all histograms
for entry in xrange(nEvents):
treeEntry = tree.GetEntry(entry)
model.nJetHist.Fill(tree.nJet)
for jet in xrange(len(tree.Jet_pt)):
model.allJetPtHist.Fill(tree.Jet_pt[jet])
if len(tree.Jet_pt) > 0:
model.leadJetPtHist.Fill(tree.Jet_pt[0])
dPhiMetJ = tree.MET_phi - tree.Jet_phi[0]
model.dPhiMetJHist.Fill(dPhiMetJ)
model.metPtHist.Fill(tree.MET_pt)
if len(tree.Jet_phi) >= 2:
deltaPhi = tree.Jet_phi[0] - tree.Jet_phi[1]
model.dPhiJJHist.Fill(deltaPhi)
pbar.update(entry + 1)
pbar.finish()
# Normalise histograms, then draw and save
for hist in model.hist_list:
hist.Scale(
1. / hist.GetEntries()
) # fine for most hists, but means allJetPt gets normalised by total n_jets
drawIndivHistos(model, hist, canv)
# Draw histograms for each model overlaid and save
n_hist_types = len(models[0].hist_list)
for hist in xrange(n_hist_types):
drawMultipleHistos([model.hist_list[hist] for model in models], canv,
legend_frame)
hist.Scale(sf)
hist.Draw("GOFF")
max_hist = hist.GetMaximum()
title = hist.GetTitle()
hist.SetTitle("")
hist.GetYaxis().SetRangeUser(0, 1.2 * max_hist)
# set title and axis labels
hist.GetXaxis().SetLabelSize(0.03)
hist.GetXaxis().SetTitle("%s [GeV]" % (title))
hist.GetYaxis().SetLabelSize(0.03)
hist.GetYaxis().SetTitleOffset(1.5)
hist.GetYaxis().SetTitle("Events / %s [GeV]" % (hist.GetBinWidth(1)))
# fill legend
leg = TLegend(0.7, 0.75, 0.88, 0.88)
leg.AddEntry(hist, "TTbar", "f")
leg.SetFillColor(0)
leg.SetBorderSize(0)
# line at W mass
l = TLine(80.4, 0, 80.4, 1.05 * max_hist)
l.SetLineStyle(3)
# and draw
c.cd()
hist.Draw('hist')
leg.Draw('sames')
if (variable == "goodJ3J4_mass"): l.Draw()
cpr.prelim_alt(19700, 0.05)
"D1=0 kV D2= 3 kV E1= 4 kV E2 =4 kV",
"D1=0 kV D2= 3 kV E1= 4 kV E2 =3 kV",
"D1=0 kV D2= 3 kV E1= 4 kV E2 =2 kV",
"D1=0 kV D2= 3 kV E1= 4 kV E2 =1 kV",
"D1=0 kV D2= 3 kV E1= 4 kV E2 =0 kV"
]
for config in reversed(listOfFiles):
histogram = addClusters(config)
histogram[0].SetFillColorAlpha(colorCounter, 0.6)
histogram[0].SetLineColor(colorCounter)
histogram[0].SetLineWidth(5)
colorCounter = colorCounter + 1
histogramList.append(histogram[0])
nameList.append(config)
legend = TLegend(0.55, 0.55, 0.90, 0.90)
counter = 0
for histogram in histogramList:
if histogram == histogramList[0]:
histogram.GetYaxis().SetRangeUser(0, 5)
histogram.Draw("histsame")
else:
histogram.Draw("histsame")
legend.AddEntry(histogram, nameList[counter])
counter = counter + 1
canvas.Update()
legend.Draw("same")
canvas.Print("/home/helga/gitThesis/thesis/Grace/fig/scan2RealData.pdf")
linex = TLine(145, 0, 205, 0)
linex.SetLineWidth(1)
linex.Draw()
liney = TLine(173, -0.00825, 173, 0.00725)
if mass == "167":
liney = TLine(173, -0.0096, 173, 0.0059)
if nlo:
liney = TLine(173, -0.01025, 173, 0.0055)
liney.SetLineWidth(1)
liney.SetLineStyle(2)
liney.Draw()
l = TLegend(0.6, 0.6, 0.8, 0.8)
if mass == "167":
l = TLegend(0.6, 0.62, 0.8, 0.82)
if nlo:
l = TLegend(0.6, 0.65, 0.80, 0.85)
l.AddEntry(final2, "n = 2", "L")
l.AddEntry(final3, "n = 3", "L")
l.AddEntry(final5, "n = 5", "L")
l.AddEntry(final15, "n = 15", "L")
l.SetTextSize(0.04)
l.SetLineColor(0)
l.SetFillColor(0)
l.Draw()
if nlo:
def plotFit():
axPeak = 2.464
# load workspace
f = TFile("./data/splitWorkspace.root")
fitWorkspace = f.Get("fitWorkspace")
fData = fitWorkspace.allData().front()
fitResult = fitWorkspace.allGenericObjects().front()
nPars = fitResult.floatParsFinal().getSize()
fEnergy = fitWorkspace.var("energy_keV")
modelPDF = fitWorkspace.pdf("model")
# fitWorkspace.Print()
pdfNames = ["ext-axion"]
# -- create spectrum rooplot --
nCol = float(gStyle.GetNumberOfColors())
binSize = 0.04
nBins = int((eHi - eLo) / binSize + 0.5)
fSpec = fEnergy.frame(RF.Range(eLo, eHi), RF.Bins(nBins))
fData.plotOn(fSpec)
modelPDF.plotOn(fSpec, RF.LineColor(ROOT.kRed), RF.Name("FullModel"))
chiSquare = fSpec.chiSquare(nPars)
modelPDF.plotOn(fSpec, RF.Components(pdfNames[0]),
RF.LineColor(ROOT.kBlue), RF.Name(pdfNames[0]))
# -- make a fake Gaussian --
# gaus = ROOT.TF1("g","gaus",-3, eHi)
# gaus.SetParameters(10., axPeak, getSigma(axPeak))
# gaus.SetParameter(0,10.)
# gaus.SetParameter(1, axPeak)
# gaus.SetParameter(2, getSigma(axPeak))
# erf
# rrvGaus = ROOT.RooRealVar("ax","ax",-3.,3.)
# rarGaus = RF.bindFunction("gaus", ROOT.TMath.Erf, rrvGaus)
# rarGaus.Print()
# frame2 = rrvGaus.frame(RF.Title("mygaus"))
# rarGaus.plotOn(frame2, RF.LineColor(ROOT.kGreen), RF.LineStyle(ROOT.kDashed), RF.Name("axGaus"))
# c0 = TCanvas("c0","",800,600)
# frame2.Draw()
# c0.Print("./plots/testGaus.pdf")
# -- print fit results --
print "-- SHIFTFIT RESULTS -- "
print "%-10s = %.3f" % ("chiSq", chiSquare)
fitValsFinal = getRooArgDict(fitResult.floatParsFinal())
bkgVal = 0.
for name in sorted(fitValsFinal):
fitVal = fitValsFinal[name]
if "amp-" in name:
error = fitWorkspace.var(name).getError()
print "%-10s = best %-7.3f error %.3f (w/o profile)" % (
name, fitVal, error)
elif "mu-" in name:
# compare the energy offset
pkName = name[3:]
pct = 100 * (1 - fitVal / axPeak)
print "%-10s : fit %-6.3f lit %-6.3f (%.3f%%)" % (name, fitVal,
axPeak, pct)
elif "sig-" in name:
# compare the sigma difference
pkName = name[4:]
pct = 100 * (1 - fitVal / getSigma(axPeak))
print "%-10s : fit %-6.3f func %-6.3f (%.3f%%)" % (
name, fitVal, getSigma(axPeak), pct)
else:
print "%s = %.4f (%.4f)" % (name, fitVal, fitVal / nBins)
bkgVal = fitVal / nBins
continue
# -- make spectrum plot --
c = TCanvas("c", "Bob Ross's Canvas", 1400, 1000)
c.SetRightMargin(0.2)
fSpec.SetTitle(" ")
fSpec.Draw()
ymax = fSpec.GetMaximum()
l1 = ROOT.TLine(axPeak, 0., axPeak, ymax)
l1.SetLineColor(ROOT.kBlue)
l1.SetLineWidth(2)
l1.Draw("same")
leg = TLegend(0.83, 0.6, 0.97, 0.9)
leg.AddEntry(fSpec.findObject("FullModel"),
"model #chi^{2}=%.3f" % chiSquare, "l")
leg.AddEntry(fSpec.findObject("FullModel"), "cts/bin=%.2f" % bkgVal, "")
leg.AddEntry(fSpec.findObject(pdfNames[0]), "axion gaussian", "l")
leg.AddEntry(l1, "axion-%.2f" % axPeak, "l")
leg.Draw("same")
c.Print("./plots/shiftFit.pdf")
# -- get FC Limit --
# Calculate the confidence interval for the axion peak, which is too low to use profile likelihood.
# TFeldmanCousins version:
# https://root.cern.ch/root/html/tutorials/math/FeldmanCousins.C.html
# RooStats version:
# https://root.cern.ch/root/html/tutorials/roostats/StandardFeldmanCousinsDemo.C.html
# FC Paper: https://arxiv.org/pdf/physics/9711021.pdf
f = TFeldmanCousins(0.95)
N_obs = 1.
N_bkg = bkgVal / 3.
ul = f.CalculateUpperLimit(N_obs, N_bkg)
ll = f.GetLowerLimit()
print "For %.2f events observed, and %.2f background events," % (N_obs,
N_bkg)
print "F-C method gives UL: %.2f and LL %.2f (90%% CL)" % (ul, ll)
plot = TGraph(6, x, std)
plot.SetTitle('#sigma zones')
plot.SetMarkerStyle(21)
c = TCanvas("c", "c", 1200, 400)
c.Divide(3, 1)
c.cd(1)
hist_double.Draw("COL")
c.cd(2)
hist1.Draw()
hist1.SetStats(0)
hist2.Draw("SAME")
hist3.Draw("SAME")
hist4.Draw("SAME")
hist5.Draw("SAME")
hist6.Draw("SAME")
legend1 = TLegend(0.65, 0.55, 0.95, 0.85)
legend1.AddEntry(hist1, zonas[0], "l")
legend1.AddEntry(hist2, zonas[1], "l")
legend1.AddEntry(hist3, zonas[2], "l")
legend1.AddEntry(hist4, zonas[3], "l")
legend1.AddEntry(hist5, zonas[4], "l")
legend1.AddEntry(hist6, zonas[5], "l")
legend1.SetTextSize(0.023)
legend1.Draw()
c.cd(3)
plot.Draw("")
c.Draw()
if tn == L:
c.SaveAs(
"/home/camsdiaz/Documents/Tandem/work2-findingvariables/plots/"
+ name + " L.png")
SipMC[i].GetXaxis().SetTitle("SIP")
SipMC[i].GetXaxis().SetLabelFont(43)
SipMC[i].GetXaxis().SetLabelSize(15)
SipMC[i].GetYaxis().SetTitleSize(20)
SipMC[i].GetYaxis().SetTitleFont(43)
SipMC[i].GetYaxis().SetTitleOffset(1.8)
SipMC[i].GetYaxis().SetLabelFont(43)
SipMC[i].GetYaxis().SetLabelSize(15)
SipMC[i].GetYaxis().SetTitle("Events")
SipMC[i].SetTitle("")
gStyle.SetOptStat(0)
# legend
legend = TLegend(0.74, 0.68, 0.94, 0.87)
legend.AddEntry(SipDATA[i], "Data", "p")
legend.AddEntry(SipMC[i], "Drell-Yan MC", "f")
legend.SetFillColor(kWhite)
legend.SetLineColor(kBlack)
legend.SetTextFont(43)
legend.SetTextSize(20)
legend.Draw()
canvas.Update()
#lower plot pad
canvas.cd()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetGridy()
pad2.Draw()
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
latex2.SetNDC()
latex2.SetTextSize(0.04)
latex2.SetTextAlign(31) # align right
latex2.DrawLatex(0.87, 0.95, "19.6 fb^{-1} at #sqrt{s} = 8 TeV")
latex4 = TLatex()
latex4.SetNDC()
latex4.SetTextSize(0.04)
latex4.SetTextAlign(31) # align right
latex4.DrawLatex(0.80, 0.87, "")
legend = TLegend(.566, .684, .84, .84)
legend.AddEntry(observed_p, '95% C.L. Observed', "lp")
legend.AddEntry(expected_p, '95% C.L. Expected', "l")
legend.AddEntry(expected68, '#pm 1#sigma Expected', "f")
legend.AddEntry(expected95, '#pm 2#sigma Expected', "f")
legend.AddEntry(theory, "G_{KK} c = 0.01", "l")
legend.SetShadowColor(0)
legend.SetFillColor(0)
legend.SetLineColor(0)
legend.Draw()
c4.SaveAs("limit_c01.root")
raw_input("Press Enter to continue...")
def plotDataMC(args,plot):
hCanvas = TCanvas("hCanvas", "Distribution", 800,800)
if args.ratio:
plotPad = ROOT.TPad("plotPad","plotPad",0,0.3,1,1)
ratioPad = ROOT.TPad("ratioPad","ratioPad",0,0.,1,0.3)
setTDRStyle()
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
else:
plotPad = ROOT.TPad("plotPad","plotPad",0,0,1,1)
setTDRStyle()
plotPad.UseCurrentStyle()
plotPad.Draw()
plotPad.cd()
# Data load processes
colors = createMyColors()
if args.use2016:
data = Process(Data2016, normalized=True)
elif args.use2018:
data = Process(Data2018, normalized=True)
else:
data = Process(Data, normalized=True)
eventCounts = totalNumberOfGeneratedEvents(path,plot.muon)
negWeights = negWeightFractions(path,plot.muon)
#print negWeights
# Background load processes
backgrounds = copy(args.backgrounds)
if plot.useJets:
if "Wjets" in backgrounds:
backgrounds.remove("Wjets")
backgrounds.insert(0,"Jets")
processes = []
for background in backgrounds:
if args.use2016:
if background == "Jets":
processes.append(Process(getattr(Backgrounds2016,background),eventCounts,negWeights,normalized=True))
else:
processes.append(Process(getattr(Backgrounds2016,background),eventCounts,negWeights))
else:
if background == "Jets":
processes.append(Process(getattr(Backgrounds,background),eventCounts,negWeights,normalized=True))
else:
processes.append(Process(getattr(Backgrounds,background),eventCounts,negWeights))
# Signal load processes
signals = []
for signal in args.signals:
if args.use2016:
if args.ADD: signals.append(Process(getattr(Signals2016ADD, signal), eventCounts, negWeights))
else: signals.append(Process(getattr(Signals2016,signal),eventCounts,negWeights))
else:
if args.ADD: signals.append(Process(getattr(SignalsADD, signal), eventCounts, negWeights))
else: signals.append(Process(getattr(Signals,signal),eventCounts,negWeights))
legend = TLegend(0.55, 0.6, 0.925, 0.925)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legendEta = TLegend(0.35, 0.55, 0.9, 0.9)
legendEta.SetFillStyle(0)
legendEta.SetBorderSize(0)
legendEta.SetTextFont(42)
legendEta.SetNColumns(2)
latex = ROOT.TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = ROOT.TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.06)
latexCMS.SetNDC(True)
latexCMSExtra = ROOT.TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.045)
latexCMSExtra.SetNDC(True)
legendHists = []
# Modify legend information
legendHistData = ROOT.TH1F()
if args.data:
legend.AddEntry(legendHistData,"Data","pe")
legendEta.AddEntry(legendHistData,"Data","pe")
for process in reversed(processes):
if not plot.muon and "#mu^{+}#mu^{-}" in process.label:
process.label = process.label.replace("#mu^{+}#mu^{-}","e^{+}e^{-}")
temphist = ROOT.TH1F()
temphist.SetFillColor(process.theColor)
legendHists.append(temphist.Clone)
legend.AddEntry(temphist,process.label,"f")
legendEta.AddEntry(temphist,process.label,"f")
if args.signals !=0:
processesWithSignal = []
for process in processes:
processesWithSignal.append(process)
for Signal in signals:
processesWithSignal.append(Signal)
temphist = ROOT.TH1F()
temphist.SetFillColor(Signal.theColor)
temphist.SetLineColor(Signal.theLineColor)
legendHists.append(temphist.Clone)
legend.AddEntry(temphist,Signal.label,"l")
legendEta.AddEntry(temphist,Signal.label,"l")
# Modify plot pad information
nEvents=-1
ROOT.gStyle.SetOptStat(0)
intlumi = ROOT.TLatex()
intlumi.SetTextAlign(12)
intlumi.SetTextSize(0.045)
intlumi.SetNDC(True)
intlumi2 = ROOT.TLatex()
intlumi2.SetTextAlign(12)
intlumi2.SetTextSize(0.07)
intlumi2.SetNDC(True)
scalelabel = ROOT.TLatex()
scalelabel.SetTextAlign(12)
scalelabel.SetTextSize(0.03)
scalelabel.SetNDC(True)
metDiffLabel = ROOT.TLatex()
metDiffLabel.SetTextAlign(12)
metDiffLabel.SetTextSize(0.03)
metDiffLabel.SetNDC(True)
chi2Label = ROOT.TLatex()
chi2Label.SetTextAlign(12)
chi2Label.SetTextSize(0.03)
chi2Label.SetNDC(True)
hCanvas.SetLogy()
# Luminosity information
plotPad.cd()
plotPad.SetLogy(0)
logScale = plot.log
if logScale == True:
plotPad.SetLogy()
if args.use2016:
lumi = 35.9*1000
if plot.muon:
lumi = 36.3*1000
elif args.use2018:
lumi = 59.97*1000
if plot.muon:
lumi = 61.608*1000
else:
lumi = 41.529*1000
if plot.muon:
lumi = 42.135*1000
if args.use2016:
zScaleFac = zScale2016["muons"]
if not plot.muon:
zScaleFac = zScale2016["electrons"]
elif args.use2018:
zScaleFac = zScale2018["muons"]
if not plot.muon:
zScaleFac = zScale2018["electrons"]
else:
zScaleFac = zScale["muons"]
if not plot.muon:
zScaleFac = zScale["electrons"]
# Data and background loading
if plot.plot2D:
datahist = data.loadHistogramProjected(plot,lumi,zScaleFac)
stack = TheStack2D(processes,lumi,plot,zScaleFac)
else:
datahist = data.loadHistogram(plot,lumi,zScaleFac)
stack = TheStack(processes,lumi,plot,zScaleFac)
if args.data:
yMax = datahist.GetBinContent(datahist.GetMaximumBin())
if "Mass" in plot.fileName:
yMin = 0.00001
else:
yMin = 0.01
xMax = datahist.GetXaxis().GetXmax()
xMin = datahist.GetXaxis().GetXmin()
else:
yMax = stack.theHistogram.GetBinContent(datahist.GetMaximumBin())
yMin = 0.01
xMax = stack.theHistogram.GetXaxis().GetXmax()
xMin = stack.theHistogram.GetXaxis().GetXmin()
if plot.yMax == None:
if logScale:
yMax = yMax*10000
else:
yMax = yMax*1.5
else: yMax = plot.yMax
if "Mass" in plot.fileName:
yMax = 20000000
if not plot.yMin == None:
yMin = plot.yMin
if not plot.xMin == None:
xMin = plot.xMin
if not plot.xMax == None:
xMax = plot.xMax
#if args.ADD and args.use2016:
# xMin = 1700
# xMax = 4000
# yMax = 1.0
if "CosThetaStarBBM1800" in plot.fileName:
yMax = 3
plotPad.DrawFrame(xMin,yMin,xMax,yMax,"; %s ; %s" %(plot.xaxis,plot.yaxis))
drawStack = stack
#~ print datahist.Integral(datahist.FindBin(60),datahist.FindBin(120))/drawStack.theHistogram.Integral(drawStack.theHistogram.FindBin(60),drawStack.theHistogram.FindBin(120))
#~ low = 900
#~ high = 1300
#~ print datahist.Integral(datahist.FindBin(low),datahist.FindBin(high))
#~ print drawStack.theHistogram.Integral(datahist.FindBin(low),datahist.FindBin(high))
# Draw background from stack
drawStack.theStack.Draw("samehist")
# Draw signal information
if len(args.signals) != 0:
signalhists = []
for Signal in signals:
if plot.plot2D: # plot collins-soper angle
signalhist = Signal.loadHistogramProjected(plot,lumi, zScaleFac)
signalhist.SetLineWidth(2)
signalBackgrounds = deepcopy(backgrounds)
signalBackgrounds.remove("DrellYan")
signalProcesses = []
for background in signalBackgrounds:
if background == "Jets":
signalProcesses.append(Process(getattr(Backgrounds,background),eventCounts,negWeights,normalized=True))
else:
signalProcesses.append(Process(getattr(Backgrounds,background),eventCounts,negWeights))
signalStack = TheStack2D(signalProcesses,lumi,plot, zScaleFac)
signalhist.Add(signalStack.theHistogram)
signalhist.SetMinimum(0.1)
signalhist.Draw("samehist")
signalhists.append(signalhist)
else:
signalhist = Signal.loadHistogram(plot,lumi,zScaleFac)
signalhist.SetLineWidth(2)
signalBackgrounds = deepcopy(backgrounds)
signalBackgrounds.remove("DrellYan") # signalBackgrounds = ["Jets", "Other"]
signalProcesses = []
for background in signalBackgrounds:
if background == "Jets":
signalProcesses.append(Process(getattr(Backgrounds,background),eventCounts,negWeights,normalized=True))
else:
signalProcesses.append(Process(getattr(Backgrounds,background),eventCounts,negWeights))
signalStack = TheStack(signalProcesses,lumi,plot,zScaleFac)
signalhist.Add(signalStack.theHistogram)
signalhist.SetMinimum(0.0001)
signalhist.Draw("samehist")
signalhists.append(signalhist)
# Draw data
datahist.SetMinimum(0.0001)
if args.data:
datahist.Draw("samep")
# Draw legend
if "Eta" in plot.fileName or "CosTheta" in plot.fileName:
legendEta.Draw()
else:
legend.Draw()
plotPad.SetLogx(plot.logX)
latex.DrawLatex(0.95, 0.96, "%.1f fb^{-1} (13 TeV)"%(float(lumi)/1000,))
yLabelPos = 0.85
cmsExtra = "Private Work"
if not args.data:
cmsExtra = "#splitline{Private Work}{Simulation}"
yLabelPos = 0.82
latexCMS.DrawLatex(0.19,0.89,"CMS")
latexCMSExtra.DrawLatex(0.19,yLabelPos,"%s"%(cmsExtra))
#~ print datahist.Integral()
if args.ratio:
try:
ratioPad.cd()
ratioPad.SetLogx(plot.logX)
except AttributeError:
print ("Plot fails. Look up in errs/failedPlots.txt")
outFile =open("errs/failedPlots.txt","a")
outFile.write('%s\n'%plot.filename%("_"+run.label+"_"+dilepton))
outFile.close()
plot.cuts=baseCut
return 1
ratioGraphs = ratios.RatioGraph(datahist,drawStack.theHistogram, xMin=xMin, xMax=xMax,title="Data / MC",yMin=0.0,yMax=2,ndivisions=10,color=ROOT.kBlack,adaptiveBinning=10000)
ratioGraphs.draw(ROOT.gPad,True,False,True,chi2Pos=0.8)
ROOT.gPad.RedrawAxis()
plotPad.RedrawAxis()
if args.ratio:
ratioPad.RedrawAxis()
if not os.path.exists("plots"):
os.makedirs("plots")
if args.use2016:
hCanvas.Print("plots/"+plot.fileName+"_2016.pdf")
elif args.use2018:
hCanvas.Print("plots/"+plot.fileName+"_2018.pdf")
else:
hCanvas.Print("plots/"+plot.fileName+"_2017.pdf")
sys.exit(2)
if vsbin:
if tree.GetSelectedRows() >= 15:
print("Maximum number of bins is 15")
if line or noline:
if many:
first = True
color = 1
grall = []
vmin = 1.e+38
vmax = -1.e+38
cut0 = cut_cond
data1 = data
leg = TLegend(0.7, 0.7, 0.9, 0.9)
for ros in range(rosmin, rosmax):
for mod in range(modmin - 1, modmax):
for chn in range(chanmin, chanmax + 1):
for adc in range(gainmin, gainmax + 1):
if "%d" in data:
data1 = data.replace(
"%d", str((adc - gainmin) * nval + val_n))
if "%d" in cut_cond:
cut0 = cut_cond.replace(
"%d", str((adc - gainmin) * nval + val_n))
cut1 = "(ros == %d && module == %d && channel == %d) && %s" % (
ros, mod, chn, cut0)
tree.Project("htemp", data1, cut1)
if tree.GetSelectedRows() > 0:
color = color + 1
"D1=0 kV D2= 3 kV E1= 1 kV E2 =3 kV",
"D1=0 kV D2= 3 kV E1=2 kV E2 =3 kV",
"D1=0 kV D2= 3 kV E1= 3 kV E2 =3 kV",
"D1=0 kV D2= 3 kV E1= 4 kV E2 =3 kV",
"D1=0 kV D2= 3 kV E1= 5 kV E2 =3 kV"
]
for j in [0, 1000, 2000, 3000, 4000, 5000]:
timeDelay = analyseFile("../../ibsimuData/onDetector/D1_0D2_3000E1_" +
str(j) + "E2_3000_scanning33um.txt")
timeDelay.SetLineWidth(5)
listOfHistograms.append(timeDelay)
colorCounter = 1
canvas = TCanvas()
legend = TLegend(0.55, 0.55, 0.90, 0.90)
for i in range(5, -1, -1):
print "mean", listOfHistograms[i].GetMean()
listOfHistograms[i].GetXaxis().SetTitle("Estimated time delay [ns]")
listOfHistograms[i].GetYaxis().SetTitle("Antiprotons per shoot")
if i == 5:
listOfHistograms[i].SetFillColorAlpha(colorCounter, 0.6)
listOfHistograms[i].SetLineColor(colorCounter)
listOfHistograms[i].Draw("hist")
listOfHistograms[i].GetYaxis().SetRangeUser(0, 5)
#listOfHistograms[i].Draw("hist")
else:
listOfHistograms[i].SetFillColorAlpha(colorCounter, 0.6)
listOfHistograms[i].SetLineColor(colorCounter)
listOfHistograms[i].Draw("hist same")
def main(): #pylint: disable=too-many-statements
"""
main function of script
"""
SetGlobalStyle(padleftmargin=0.18,
padbottommargin=0.14,
titleoffsety=1.6,
maxdigits=2,
optstat=0)
n_bins = 1001
bin_width = 0.05 # GeV
bin_lims = [i * bin_width for i in range(0, n_bins + 1)]
hFONLLBtoDCentral = TH1D(
'hDfromBpred_central',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBtoDMin = TH1D(
'hDfromBpred_min',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBtoDMax = TH1D(
'hDfromBpred_max',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBCentral = TH1D(
'hBpred_central',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBMin = TH1D(
'hBpred_min',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBMax = TH1D(
'hBpred_max',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
fonllBtoD_df = pd.read_csv("fonll/FONLL_DfromB_pp5_y05_toCook.txt",
sep=" ",
header=14).astype('float64')
fonllB_df = pd.read_csv("fonll/FONLL_B_pp5_y05.txt", sep=" ",
header=13).astype('float64')
for pt, cen, low, up in zip(fonllBtoD_df['pt'].to_numpy(),
fonllBtoD_df['central'].to_numpy(),
fonllBtoD_df['min'].to_numpy(),
fonllBtoD_df['max'].to_numpy()):
hFONLLBtoDCentral.Fill(pt, cen)
hFONLLBtoDMin.Fill(pt, low)
hFONLLBtoDMax.Fill(pt, up)
for pt, cen, low, up in zip(fonllB_df['pt'].to_numpy(),
fonllB_df['central'].to_numpy(),
fonllB_df['min'].to_numpy(),
fonllB_df['max'].to_numpy()):
hFONLLBCentral.Fill(pt, cen)
hFONLLBMin.Fill(pt, low)
hFONLLBMax.Fill(pt, up)
for iBin in range(hFONLLBtoDCentral.GetNbinsX()):
hFONLLBtoDCentral.SetBinError(iBin + 1, 1.e-3)
hFONLLBtoDMin.SetBinError(iBin + 1, 1.e-3)
hFONLLBtoDMax.SetBinError(iBin + 1, 1.e-3)
hFONLLBCentral.SetBinError(iBin + 1, 1.e-3)
hFONLLBMin.SetBinError(iBin + 1, 1.e-3)
hFONLLBMax.SetBinError(iBin + 1, 1.e-3)
hFONLL = [hFONLLBtoDCentral, hFONLLBtoDMin, hFONLLBtoDMax]
hBFONLL = [hFONLLBCentral, hFONLLBMin, hFONLLBMax]
labels = ['Central', 'Min', 'Max']
canvas = []
for label in labels:
canvas.append(TCanvas(f'cComp{label}', '', 2000, 1000))
for (histo, histo_std, label, canv) in zip(hFONLL, hBFONLL, labels,
canvas):
pt_lims = np.array(histo.GetXaxis().GetXbins(), 'd')
pt_min = list(pt_lims)[0]
pt_max = list(pt_lims)[-1]
histo_std = histo_std.Rebin(histo.GetNbinsX(), f'hBFONLL{label}',
pt_lims)
histo.Rebin(5)
histo_std.Rebin(5)
histo.Scale(1.e-6)
histo_std.Scale(1.e-6)
histo_ratio = histo.Clone(f'hRatioDfromBOverB{label}')
histo_ratio.Divide(histo, histo_std)
histo_ratio.GetYaxis().SetTitle('FONLL D from B / FONLL B')
print(
f'\nFONLL B {label} integral (ub): {histo_std.Integral("width"):.4e}'
)
print(
f'FONLL B from D {label} integral (ub): {histo.Integral("width"):.4e}\n'
)
canv.Divide(2, 1)
canv.cd(1).DrawFrame(
pt_min, 1e-4, pt_max, 100.,
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (#mub GeV^{-1} #it{c})'
)
leg = TLegend(0.4, 0.7, 0.8, 0.9)
leg.SetTextSize(0.045)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
SetObjectStyle(histo, color=kAzure + 4, markerstyle=kFullSquare)
SetObjectStyle(histo_std, color=kRed, markerstyle=kFullCircle)
histo.Draw('same')
histo_std.Draw('same')
leg.AddEntry(histo, f'FONLL D from B {label}', 'p')
leg.AddEntry(histo_std, f'FONLL B {label}', 'p')
gPad.SetLogy()
leg.Draw()
hframe_ratio = canv.cd(2).DrawFrame(
pt_min, 0., pt_max, 3.5,
';#it{p}_{T} (GeV/#it{c}); FONLL D from B / FONLL B')
hframe_ratio.GetYaxis().SetDecimals()
SetObjectStyle(histo_ratio, color=kAzure + 4, markerstyle=kFullSquare)
histo_ratio.Draw('same')
canv.Update()
canv.SaveAs(f'CompFONLL_DfromBvsB_{label}.pdf')
input('Press enter to exit')
if i == 0:
graphs[i].GetYaxis().SetTitleOffset(1.25)
graphs[i].GetXaxis().SetLabelSize(fontsize)
graphs[i].GetXaxis().SetTitleSize(fontsize)
graphs[i].GetYaxis().SetLabelSize(fontsize)
graphs[i].GetYaxis().SetTitleSize(fontsize)
graphs[i].SetMarkerStyle(i+33)
graphs[i].GetXaxis().SetTitle("Anode voltage [kV]")
graphs[i].GetYaxis().SetTitle("Average peak height [mV]")
graphs[i].SetTitle("Gas amplification")
graphs[i].Draw("AP")
else:
graphs[i].SetMarkerStyle(i+19)
graphs[i].Draw("P")
leg = TLegend(0.67,0.13,0.87,0.45)
leg.SetBorderSize(0)
leg.SetTextSize(fontsize)
leg.AddEntry("gr3","{} bar".format(preassures[2]/1000),"p")
leg.AddEntry("gr2","{} bar".format(preassures[1]/1000),"p")
leg.AddEntry("gr4","{} bar".format(preassures[3]/1000),"p")
leg.AddEntry("gr5","{} bar".format(preassures[4]/1000),"p")
leg.AddEntry("gr1","{} bar".format(preassures[0]/1000),"p")
leg.Draw()
c1.Draw()
c1.Print("../plots/gasamp.pdf")
f.Close()
erry.append(i[2])
for i in range(len(erry) ): erry[i] = (yVals[i] * (1 - yVals[i]) / erry[i]) **(1/2.0)
g750N = TGraphErrors(9,xVals,yVals,errx,erry)
g750N.SetLineColor(ROOT.kRed+1)
g750N.SetLineWidth(3)
g750N.SetMarkerColor(ROOT.kRed+1)
g750N.SetLineStyle(2)
g750N.SetMarkerSize(1)
g750N.SetMarkerStyle(8)
g750N.SetTitle("")
g750N.GetXaxis().SetTitle("m_{a} GeV")
#g750N.GetXaxis().SetTitleSize(1.1)
g750N.GetYaxis().SetTitle("Reconstruction Efficiency")
#g750N.GetYaxis().SetTitleSize(1.1)
leg = TLegend(.3,.18,.55,.4)
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.035)
leg.SetHeader("Muon Cleaned HPS")
leg.AddEntry(g125C,"m_{h} = 125 GeV","L")
leg.AddEntry(g300C,"m_{h} = 300 GeV","L")
leg.AddEntry(g750C,"m_{h} = 750 GeV","L")
leg2 = TLegend(.6,.18,.85,.4)
leg2.SetBorderSize(0)
leg2.SetFillColor(0)
leg2.SetFillStyle(0)
leg2.SetTextFont(42)
def createLegends(sample, h1, h2, h3, h4):
if sample == "data":
leg = TLegend(0.65, 0.75, 0.92, 0.90)
if sample == "zjets":
leg = TLegend(0.60, 0.75, 0.90, 0.90)
leg.SetHeader("Likelihood")
if sample == "truth":
leg = TLegend(0.60, 0.55, 0.90, 0.72)
leg.SetHeader("Truth-matching")
leg.SetFillStyle(0)
#leg.SetBorderSize(0)
leg.SetTextFont(62)
leg.SetTextSize(0.03)
leg.AddEntry(h1, "p_{T} #in [10, 60] GeV ", "l")
leg.AddEntry(h2, "p_{T} #in [60, 90] GeV ", "l")
leg.AddEntry(h3, "p_{T} #in [90, 130] GeV ", "l")
leg.AddEntry(h4, "p_{T} #in [130, 1000] GeV ", "l")
return leg
hist_rnk = create_and_fill_rank_hist(ranks, pattern)
plot_modifier(hist_rnk, "rank", "N", ROOT.kBlack)
hist_rnk.Draw()
canvas.Print("{f}/figures/hw_rank.pdf".format(f=fnames['base']))
hists_input = create_and_fill_muon_hists(hist_parameters, in_muons,
pattern + "in")
hists_output = create_and_fill_muon_hists(hist_parameters, out_muons,
pattern + "out")
hists_imd = create_and_fill_muon_hists(hist_parameters,
intermediate_muons,
pattern + "imd")
for var in hist_parameters:
hw_leg = TLegend(0.4, 0.7, 0.7, 0.85)
set_legend_style(hw_leg)
a = hists_input[var].GetBinContent(
hists_input[var].GetMaximumBin())
b = hists_output[var].GetBinContent(
hists_output[var].GetMaximumBin())
c = max(a, b)
plot_modifier(hists_input[var], hist_parameters[var][0], "N",
ROOT.kAzure - 4)
plot_modifier(hists_output[var], hist_parameters[var][0], "N",
ROOT.kBlack, 20)
plot_modifier(hists_imd[var], hist_parameters[var][0], "N",
ROOT.kBlack)
hists_imd[var].SetFillStyle(0)
hists_imd[var].SetLineStyle(2)
line6 = TLine(31, 1.55, 42, 1.55)
line7 = TLine(42, 1.55, 42, 2)
line8 = TLine(42, 2, 60, 2)
line9 = TLine(60, 2, 90, 2)
line10 = TLine(90, 2, 90, 0)
line11 = TLine(90, 0, 20, 0)
lineVector = [
line1, line2, line3, line4, line5, line6, line7, line8, line9, line10,
line11
]
for line in lineVector:
line.SetLineColor(ROOT.kBlack)
line.SetLineWidth(3)
line.Draw()
leg = TLegend(.61, .55, .65, .59)
leg.SetFillStyle(0)
leg.SetLineWidth(4)
leg.Draw()
text = TLatex(.67, .55, "MIPP Coverage")
text.SetTextColor(ROOT.kBlack)
text.SetNDC()
text.SetTextSize(1.4 / 30.)
text.SetTextAlign(11)
#text.DrawLatex(.48, .55, "#Square");
text.Draw()
raw_input("Please press enter to exit.")
'/discriminantVsMu').ProjectionX().Clone()
objHolder.append(canvas.cd(etaBinIdx + 1))
h1.SetTitle(
('; Neural Network (Discriminant); Signal Counts (%s)') %
(binname.replace('_', ',')))
h1.SetFillColor(kAzure + 6)
h1.SetLineColor(kAzure + 6)
h1.Rebin(10)
h2.SetLineColor(kBlack)
h2.Rebin(10)
h1.Scale(1. / h1.GetMaximum())
h2.Scale(1. / h2.GetMaximum())
h1.Draw()
h2.Draw('same')
leg1 = TLegend(0.2, 0.75, 0.5, 0.95)
setLegend1(leg1)
leg1.AddEntry(h1, 'MC')
leg1.AddEntry(h2, 'Data')
leg1.Draw()
objHolder[-1].Update()
path = mcBasepath + '/fakes/' + pid + '/' + ringerName + '/' + binname
h3 = storeMC.histogram(path +
'/discriminantVsMu').ProjectionX().Clone()
path = ppBasepath + '/fakes/' + pid + '/' + ringerName + '/' + binname
h4 = storePP.histogram(path +
'/discriminantVsMu').ProjectionX().Clone()
objHolder.append(canvas.cd(etaBinIdx + 1 + nEtaBins))
h3.SetTitle(
evhistonames = ['hEvForNorm', 'hEvForNorm']
colors = [kRed + 1, kAzure + 4]
markers = [kFullSquare, kFullCircle]
legendnames = ['title1', 'title2']
SetGlobalStyle(padleftmargin=0.18,
padtopmargin=0.05,
padbottommargin=0.14,
titleoffsety=1.8,
titlesize=0.045,
labelsize=0.04,
maxdigits=2)
hSignal, hRatioSignal, hBackground, hRatioBkg, hSoverB, hSignif, hRatioSignif, hEv = [], [], [], [], [], [], [], []
leg = TLegend(0.5, 0.73, 0.8, 0.93)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextSize(0.04)
for iFile, filename in enumerate(inputfilenames):
inputfile = TFile(f'{inputdir}/{filename}')
hSignal.append(inputfile.Get(signalhistonames[iFile]))
hBackground.append(inputfile.Get(bkghistonames[iFile]))
hSignif.append(inputfile.Get(signifhistonames[iFile]))
hSoverB.append(inputfile.Get(SoverBhistonames[iFile]))
hEv.append(inputfile.Get(evhistonames[iFile]))
hSignal[iFile].SetDirectory(0)
hBackground[iFile].SetDirectory(0)
hSoverB[iFile].SetDirectory(0)
hSignif[iFile].SetDirectory(0)
def plot(path, ecms, xmin, xmax, num_charm):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :'+str(entries_data))
except:
logging.error(path[0] + 'is invalid!')
sys.exit()
try:
f_side1 = TFile(path[1])
t_side1 = f_side1.Get('save')
entries_side1 = t_side1.GetEntries()
logging.info('data(side1) entries :'+str(entries_side1))
except:
logging.error(path[1] + ' is invalid!')
sys.exit()
try:
f_side2 = TFile(path[2])
t_side2 = f_side2.Get('save')
entries_side2 = t_side2.GetEntries()
logging.info('data(side2) entries :'+str(entries_side2))
except:
logging.error(path[2] + ' is invalid!')
sys.exit()
try:
f_side3 = TFile(path[3])
t_side3 = f_side3.Get('save')
entries_side3 = t_side3.GetEntries()
logging.info('data(side3) entries :'+str(entries_side3))
except:
logging.error(path[3] + ' is invalid!')
sys.exit()
try:
f_side4 = TFile(path[4])
t_side4 = f_side4.Get('save')
entries_side4 = t_side4.GetEntries()
logging.info('data(side4) entries :'+str(entries_side4))
except:
logging.error(path[4] + ' is invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
xbins = 120
ytitle = 'Eentries'
xtitle = 'P(OtherTrks) (GeV)'
h_data = TH1F('data', 'data', xbins, xmin, xmax)
set_histo_style(h_data, xtitle, ytitle, 1, -1)
p_othertrks_fill(t_data, h_data, num_charm)
h_side1 = TH1F('side1', 'side1', xbins, xmin, xmax)
set_histo_style(h_side1, xtitle, ytitle, 3, 3004)
p_othertrks_fill(t_side1, h_side1, num_charm)
h_side2 = TH1F('side2', 'side2', xbins, xmin, xmax)
set_histo_style(h_side2, xtitle, ytitle, 3, 3004)
p_othertrks_fill(t_side2, h_side2, num_charm)
h_side3 = TH1F('side3', 'side3', xbins, xmin, xmax)
set_histo_style(h_side3, xtitle, ytitle, 3, 3004)
p_othertrks_fill(t_side3, h_side3, num_charm)
h_side4 = TH1F('side4', 'side4', xbins, xmin, xmax)
set_histo_style(h_side4, xtitle, ytitle, 3, 3004)
p_othertrks_fill(t_side4, h_side4, num_charm)
h_side1.Add(h_side2)
h_side1.Scale(0.5)
h_side3.Add(h_side4)
h_side3.Scale(0.25)
h_side1.Add(h_side3, -1)
h_data.Draw('E1')
hs = THStack('hs', 'Stacked')
hs.Add(h_side1)
hs.Draw('same')
h_data.Draw('sameE1')
legend = TLegend(0.5, 0.6, 0.8, 0.85)
leg_title = str(ecms) + ' MeV'
set_legend(legend, h_data, h_side1, leg_title)
legend.Draw()
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/p_othertrks_'+str(ecms)+'_'+str(num_charm)+'.pdf')
raw_input('Enter anything to end...')
def compareData():
""" Compare Frank's table with Graham's data.
NOTES:
fs idx 10 = 1.74 kev
fs idx 12 = 1.84 kev
fs idx 24 = 2.31 kev
fs idx 28 = 2.46 kev
linear fit gives 0.0396 ~ 0.04 bins/kev.
that means that fLo = 1.84 - n*0.04, n = 12, *** fLo = 1.36 ***
and fHi = 1.36 + len(fs)*0.04, len(fs)=39, *** fHi = 2.92 ***
"""
malbekExposure = 89.5 # kg-d
# -- frank's table --
col1 = [
2.40, 3.07, 2.23, 4.34, 1.83, 2.48, 2.79, 2.23, 2.23, 1.96, 2.51, 1.95,
1.96, 2.51, 2.79, 1.68, 2.79, 2.51, 4.75, 1.40, 2.23
]
col2 = [
5.86, 1.92, 2.40, 3.07, 2.23, 4.34, 1.84, 2.48, 2.79, 2.23, 2.23, 1.95,
2.51, 1.96, 1.96, 2.51, 2.79, 1.68, 2.79, 2.51, 4.75
]
col3 = [
1.96, 2.51, 2.79, 1.68, 2.79, 2.51, 4.75, 1.40, 2.23, 3.07, 4.47, 3.63,
1.96, 3.07, 2.23, 2.79, 1.12, 3.91, 1.40, 1.68, 3.07
]
col4 = [
2.79, 2.51, 4.75, 1.40, 2.23, 3.07, 4.47, 3.63, 1.95, 3.07, 2.23, 2.79,
1.12, 3.91, 1.40, 1.68, 3.07, 1.12, 2.23, 2.23, 2.79
]
col5 = [
13.01, 10.01, 12.17, 10.49, 9.08, 12.40, 13.85, 9.74, 9.20, 10.33,
11.44, 10.32, 7.55, 11.45, 8.38, 8.66, 9.77, 9.22, 11.17, 7.82, 12.84
] # verified the sum is correct.
axionCol = 10 # 0-indexed
# frank's spectrum (from comparing when #'s in columns 1-4 repeat)
fs = [
5.86, 1.92, 2.40, 3.07, 2.23, 4.34, 1.84, 2.48, 2.79, 2.23, 2.23, 1.95,
2.51, 1.96, 1.96, 2.51, 2.79, 1.68, 2.79, 2.51, 4.75, 1.40, 2.23, 3.07,
4.47, 3.63, 1.96, 3.07, 2.23, 2.79, 1.12, 3.91, 1.40, 1.68, 3.07, 1.12,
2.23, 2.23, 2.79
]
# frank didn't adjust for kev/bin of 0.04 keV bins times exposure (0.04 * 89.5 = 3.58)
fs = [val * 3.58 for val in fs]
# set binning and endpoints, and fill a histogram
fBins = len(fs) # 39
binSize = 0.04
fLo = 1.84 - 12 * binSize # 1.3648
fHi = fLo + fBins * binSize # 2.9092
hF = TH1D("hF", "", fBins, fLo, fHi)
for i in range(fBins):
hF.SetBinContent(i, fs[i])
hF.SetLineColor(ROOT.kRed)
hF.Scale(1. / malbekExposure)
print "Frank's data: bins %d eLo %.3f eHi %.3f" % (fBins, fLo, fHi)
# -- graham's data --
f1 = TFile("./data/malbek_data.root")
t1 = f1.Get("malbek_wrt")
# duplicate the frank region only
hG1 = TH1D("hG1", "", fBins, fLo, fHi)
t1.Project("hG1", "energy_keV", "weight")
hG1.Scale(1. / malbekExposure)
hG1.SetLineColor(ROOT.kGreen)
# get histo for a larger energy region
eLo, eHi = 1.0, 3.5
nBins = int((eHi - eLo) / binSize + 0.5)
hG2 = TH1D("hG2", "", nBins, eLo, eHi)
t1.Project("hG2", "energy_keV", "weight")
hG2.SetLineColor(ROOT.kBlue)
hG2.GetXaxis().SetTitle("Energy (keV)")
hG2.Scale(1. / malbekExposure)
# -- plot all three --
c = TCanvas("c", "c", 800, 600)
gStyle.SetOptStat(0)
hG2.Draw("hist")
hG1.Draw("hist same")
hF.Draw("hist same")
leg = TLegend(0.5, 0.7, 0.85, 0.85)
leg.AddEntry(hF, "frank, %.2f-%.2f, %.3f b/kev" % (fLo, fHi, binSize), "l")
leg.AddEntry(hG1, "graham-1, %.2f-%.2f" % (fLo, fHi), "l")
leg.AddEntry(hG2, "graham-2, %.2f-%.2f" % (eLo, eHi), "l")
leg.Draw("same")
c.Print("./plots/testSpec.pdf")
gr_pre.SetMarkerStyle(21)
gr_pre.SetLineColor(4)
gr_post = TGraphErrors(6, X, Ypost, Xerr, Ypost_err)
gr_post.SetMarkerColor(2)
gr_post.SetMarkerStyle(21)
gr_post.SetLineColor(2)
mg = TMultiGraph()
mg.Add(gr_pre, 'p')
mg.Add(gr_post, 'p')
mg.SetTitle(channel)
mg.SetTitle(channel + ';bin')
leg = TLegend(0.7, 0.7, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(gr_pre, 'prefit', 'p')
leg.AddEntry(gr_post, 'postfit', 'p')
mg.Draw('a')
leg.Draw()
c1.Draw()
# In[151]:
mlfit.cd('shapes_prefit')
total_pre = gDirectory.Get('total_background')
total_pre.SetLineColor(4)
neutralLeptonPtHisto.Write()
canvas.Update()
canvas.Print(argv[1] + "_NeutralLeptonsPtHisto.png", "png")
canvas.Print(argv[1] + "_NeutralLeptonsPtHisto.svg", "svg")
# EW Boson 1 + EW Boson 2 + total distribution
ewBoson1PtHisto.SetLineColor(2)
ewBoson2PtHisto.SetLineColor(3)
ewBosonPtHisto.Draw()
ewBosonPtHisto.Write()
ewBoson1PtHisto.Draw("SAME")
ewBoson1PtHisto.Write()
ewBoson2PtHisto.Draw("SAME")
ewBoson2PtHisto.Write()
leg1 = TLegend(0.7,0.7,0.48,0.9)
leg1.SetHeader("Momentum distribution")
leg1.AddEntry(ewBosonPtHisto,"EW boson 1 + EW Boson 2","l")
leg1.AddEntry(ewBoson1PtHisto,"EW Boson 1","l")
leg1.AddEntry(ewBoson2PtHisto,"EW Boson 2","l")
leg1.Draw()
canvas.Update()
canvas.Print(argv[1] + "_EWBosonPtHistogramComparison_1.png", "png")
canvas.Print(argv[1] + "_EWBosonPtHistogramComparison_1.svg", "svg")
# Leading + sub-leading + total distribution
ewBosonLeadingPtHisto.SetLineColor(2)
ewBosonSubLeadingPtHisto.SetLineColor(3)
ewBosonPtHisto.Draw()
ewBosonPtHisto.Write()
ewBosonLeadingPtHisto.Draw("SAME")
h2.Sumw2()
h2.Scale(1 / h2.Integral())
h3.Sumw2()
h3.Scale(1 / h3.Integral())
h4.Sumw2()
h4.Scale(1 / h4.Integral())
h5.Sumw2()
h5.Scale(1 / h5.Integral())
a = TH1F("a", "a", 10, 0, 100)
a.Fill(1)
c = TCanvas("c1", "c1", 0, 0, 500, 500)
gStyle.SetOptStat(0)
leg = TLegend(0.15, 0.7, 0.45, 0.9)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextSize(0.04)
leg.SetBorderSize(0)
leg.SetTextFont(22)
leg.Draw()
h1.SetLineColor(1)
h1.SetLineWidth(2)
h1.SetLineStyle(1)
h2.SetLineColor(2)
h2.SetLineWidth(2)
h2.SetLineStyle(1)
def legend4Plot(plot, left = False):
if left:
theLeg = TLegend(0.2, 0.62, 0.55, 0.92, "", "NDC")
else:
theLeg = TLegend(0.60, 0.62, 0.92, 0.92, "", "NDC")
theLeg.SetName('theLegend')
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.045)
entryCnt = 0
for obj in range(0, int(plot.numItems())):
objName = plot.nameOf(obj)
if (not plot.getInvisible(objName)):
theObj = plot.getObject(obj)
objTitle = theObj.GetTitle()
if len(objTitle) < 1:
objTitle = objName
dopts = plot.getDrawOptions(objName).Data()
# print 'obj:',theObj,'title:',objTitle,'opts:',dopts,'type:',type(dopts)
if theObj.IsA().InheritsFrom('TNamed'):
theLeg.AddEntry(theObj, objTitle, dopts)
entryCnt += 1
theLeg.SetY1NDC(0.9 - 0.05*entryCnt - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
return theLeg
plotTitle = "Cluster Rates"
inFile1 = TFile(filename1)
inFile2 = TFile(filename2)
c = TCanvas("c", "c", 800, 600)
Histo1 = inFile1.Get(histoTitle)
Histo2 = inFile2.Get(histoTitle)
Histo3 = inFile2.Get("Energy")
Histo4 = inFile1.Get("Energy")
Histo5 = inFile2.Get("Energy Fiducial")
Histo6 = inFile1.Get("Energy Fiducial")
legend = TLegend(.68, .66, .92, .87)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.035)
legend.AddEntry(Histo3, "L0", "LP")
legend.AddEntry(Histo4, "Nominal", "LP")
Histo1.Scale(0.1)
Histo2.Scale(0.1)
c.Print("TriggerPlots.pdf[", "Title:histoTitle")
Histo1.Draw("COLZ")
Histo1.SetTitle(plotTitle + " Nominal")
def DrawOverlap(fileVec,
histVec,
titleVec,
legendtext,
pngname,
logstatus=[0, 0],
xRange=[-99999, 99999, 1]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
gStyle.SetTitleOffset(1.1, "Y")
gStyle.SetTitleOffset(0.9, "X")
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3)
i = 0
histList_ = []
histList = []
histList1 = []
maximum = []
## Legend
leg = TLegend(0.4, 0.70, 0.939, 0.89) #,NULL,"brNDC");
leg.SetBorderSize(0)
leg.SetNColumns(2)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(22)
leg.SetTextSize(0.045)
c = TCanvas("c1", "c1", 0, 0, 500, 500)
#c.SetBottomMargin(0.15)
#c.SetLeftMargin(0.15)
#c.SetLogy(0)
#c.SetLogx(0)
c1_2 = TPad("c1_2", "newpad", 0.04, 0.13, 1, 0.994)
c1_2.Draw()
print("you have provided " + str(len(fileVec)) + " files and " +
str(len(histVec)) + " histograms to make a overlapping plot")
print "opening rootfiles"
c.cd()
c1_2.SetBottomMargin(0.13)
c1_2.SetLogy(logstatus[1])
c1_2.SetLogx(logstatus[0])
c1_2.cd()
ii = 0
inputfile = {}
print str(fileVec[(len(fileVec) - 1)])
for ifile_ in range(len(fileVec)):
print("opening file " + fileVec[ifile_])
inputfile[ifile_] = TFile(fileVec[ifile_])
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print("printing histo " + str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
type_obj = type(histList[ii])
if (type_obj is TH1D) or (type_obj is TH1F) or (
type_obj is TH1) or (type_obj is TH1I):
histList[ii].Rebin(5)
histList[ii].Scale(1.0 / histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
maximum.sort()
ii = ii + 1
print histList
for ih in range(len(histList)):
tt = type(histList[ih])
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0:
if (tt is TGraphAsymmErrors) | (tt is TGraph):
histList[ih].Draw("AL3P")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("CHIST")
if ih > 0:
#histList[ih].SetLineWidth(2)
if (tt is TGraphAsymmErrors) | (tt is TGraph):
histList[ih].Draw("L3P same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("CHISTsame")
if (tt is TGraphAsymmErrors) | (tt is TGraph):
# histList[ih].SetMaximum(0.06)
# histList[ih].SetMinimum(0.02)
histList[ih].SetMaximum(10.2)
histList[ih].SetMinimum(10.1)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih], legendtext[ih], "PL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
leg.AddEntry(histList[ih], legendtext[ih], "L")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.045)
histList[ih].GetYaxis().SetTitleOffset(1.1000998)
histList[ih].GetYaxis().SetTitleFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelSize(.045)
histList[ih].GetXaxis().SetRangeUser(xRange[0], xRange[1])
histList[ih].GetXaxis().SetLabelSize(0.0000)
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
histList[ih].GetXaxis().SetTitleOffset(1.04)
histList[ih].GetXaxis().SetTitleFont(22)
histList[ih].GetXaxis().SetTickLength(0.07)
histList[ih].GetXaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
# histList[ih].GetXaxis().SetNdivisions(508)
#
i = i + 1
pt = TPaveText(0.0877181, 0.9, 0.9580537, 0.96, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(22)
pt.SetTextSize(0.046)
text = pt.AddText(0.05, 0.5, "CMS Preliminary")
#text = pt.AddText(0.5,0.5,"12.9 fb^{-1} (13 TeV)")
# text = pt.AddText(0.8,0.5," (13 TeV)")
# text = pt.AddText(0.65,0.5," AK8")
pt.Draw()
# t2a = TPaveText(0.0877181,0.81,0.9580537,0.89,"brNDC")
# t2a.SetBorderSize(0)
# t2a.SetFillStyle(0)
# t2a.SetTextSize(0.040)
# t2a.SetTextAlign(12)
# t2a.SetTextFont(62)
# histolabel1= str(fileVec[(len(fileVec)-1)])
# text1 = t2a.AddText(0.06,0.5,"CMS Internal")
# t2a.Draw()
leg.Draw()
#
# c.cd()
outputdirname = 'HGCAL/'
histname = outputdirname + pngname
c.SaveAs(histname + '.png')
c.SaveAs(histname + '.pdf')
def drawMassRes(data, mc, output, rapidity, ptda, ptmc, trackType, funct,
mcIsData, dataIsMC):
style = setTDRStyle()
pt_e = [0 for x in range(len(data))]
pt_x = [0 for x in range(len(data))]
for i, pt in enumerate(pt_x):
pt_x[i] = ptbins[i] + (ptbins[i + 1] - ptbins[i]) / 2.
pt_e[i] = (ptbins[i + 1] - ptbins[i]) / 2.
if dataIsMC:
(da_mean, da_meane, da_sig, da_sige,
da_nChi2) = doFit(data, output, rapidity, "MC2", trackType, funct)
else:
(da_mean, da_meane, da_sig, da_sige,
da_nChi2) = doFit(data, output, rapidity, "DATA", trackType, funct)
if mcIsData:
(mc_mean, mc_meane, mc_sig, mc_sige,
mc_nChi2) = doFit(mc, output, rapidity, "DATA2", trackType, funct)
else:
(mc_mean, mc_meane, mc_sig, mc_sige,
mc_nChi2) = doFit(mc, output, rapidity, "MC", trackType, funct)
result = {}
result["data_mean"] = da_mean
result["data_meane"] = da_meane
result["data_sig"] = da_sig
result["data_sige"] = da_sige
result["mc_mean"] = mc_mean
result["mc_meane"] = mc_meane
result["mc_sig"] = mc_sig
result["mc_sige"] = mc_sige
result["ptda"] = ptda
result["ptmc"] = ptmc
result["da_nChi2"] = da_nChi2
result["mc_nChi2"] = mc_nChi2
pklFile = open(
output + "/MassResolutionVsPt_%s_%s.pkl" % (trackType, rapidity), "wb")
pickle.dump(result, pklFile)
pklFile.close()
c2 = TCanvas("c2", "c2", 700, 700)
c2.cd()
# Upper plot will be in pad1
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetGrid() # Vertical grid
pad1.SetBottomMargin(0.1)
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
pad1.SetTicks()
res_data = TGraphAsymmErrors()
res_data.SetName("res_data")
res_mc = TGraphAsymmErrors()
res_mc.SetName("res_mc")
ratio = TGraphErrors()
ratio.SetName("ratio")
#~ print len(pt_x)
for i, pt in enumerate(pt_x):
res_data.SetPoint(i, ptda[i], da_sig[i])
res_data.SetPointError(i, ptda[i] - ptbins[i], ptbins[i + 1] - ptda[i],
da_sige[i], da_sige[i])
res_mc.SetPoint(i, ptmc[i], mc_sig[i])
res_mc.SetPointError(i, ptmc[i] - ptbins[i], ptbins[i + 1] - ptmc[i],
mc_sige[i], mc_sige[i])
if mc_sig[i] > 0:
ratio.SetPoint(i, pt, da_sig[i] / mc_sig[i])
ratio.SetPointError(i, pt_e[i], (da_sig[i] / mc_sig[i]) *
math.sqrt((da_sige[i] / da_sig[i])**2 +
(mc_sige[i] / mc_sig[i])**2))
res_data.SetMarkerStyle(22)
res_data.SetMarkerColor(kBlack)
res_data.SetLineColor(kBlack)
res_data.SetFillColor(0)
res_data.SetTitle("Dimuon mass resolution vs pT for %s tracks" % trackType)
res_data.GetYaxis().SetTitle("Mass resolution at Z peak [GeV]")
res_data.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
res_data.GetYaxis().SetTitleOffset(1.2)
res_data.GetYaxis().SetRangeUser(0., 6.)
if trackType == "Outer":
res_data.GetYaxis().SetRangeUser(1., 20.)
res_data.GetXaxis().SetRangeUser(ptbins[0], ptbins[len(ptda)])
res_data.Draw("AP E0")
res_mc.SetMarkerStyle(22)
res_mc.SetMarkerColor(kRed)
res_mc.SetLineColor(kRed)
res_mc.SetFillColor(0)
res_mc.SetTitle("Dimuon mass resolution vs pT for %s tracks" % trackType)
res_mc.GetYaxis().SetTitle("Mass resolution at Z peak [GeV]")
res_mc.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
res_mc.GetYaxis().SetTitleOffset(1.5)
res_mc.Draw("P E0 SAME")
if rapidity == "BB":
leg = TLegend(0.25, 0.6, 0.50, 0.80, "both muons |#eta| < 1.2",
"brNDC")
else:
leg = TLegend(0.25, 0.6, 0.5, 0.8, "at least one muon |#eta| > 1.2",
"brNDC")
if mcIsData:
leg.AddEntry(res_data, "DATA 2017")
leg.AddEntry(res_mc, "DATA 2016")
elif dataIsMC:
leg.AddEntry(res_data, "MC 2017")
leg.AddEntry(res_mc, "MC 2016")
else:
leg.AddEntry(res_data, "DATA", "p")
leg.AddEntry(res_mc, "Simulation")
leg.SetTextFont(42)
leg.SetBorderSize(0)
leg.SetTextSize(.04)
leg.Draw("SAME")
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055 / 0.7)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03 / 0.7)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.95, 0.96, "(13 TeV)")
cmsExtra = "Preliminary"
latexCMS.DrawLatex(0.78, 0.88, "CMS")
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.78, yLabelPos, "%s" % (cmsExtra))
c2.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.1, 1, 0.30)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.3)
pad2.SetGrid()
pad2.Draw()
pad2.cd()
pad2.SetTicks()
ratio.SetMarkerColor(kBlue - 4)
ratio.SetFillColor(kBlue - 4)
ratio.SetTitle("")
ratio.GetYaxis().SetTitle("Data/MC")
if mcIsData:
ratio.GetYaxis().SetTitle("Data 2017 / Data 2016")
elif dataIsMC:
ratio.GetYaxis().SetTitle("MC 2017 / MC 2016")
ratio.GetXaxis().SetTitle("p_{T} (#mu^{#pm}) [GeV]")
ratio.GetYaxis().SetRangeUser(0.5, 1.5)
ratio.GetXaxis().SetRangeUser(ptbins[0], ptbins[len(ptda)])
ratio.GetYaxis().SetTitleOffset(0.50)
ratio.GetYaxis().SetTitleSize(0.14)
ratio.GetYaxis().SetLabelSize(0.14)
ratio.GetYaxis().SetNdivisions(506)
ratio.GetXaxis().SetTitleSize(0.12)
ratio.GetXaxis().SetTitleOffset(1.2)
ratio.GetXaxis().SetLabelSize(0.20)
ratio.Draw("A P E2")
pad2.Update()
line = TLine(ptbins[0], 1, ptbins[len(ptda)], 1)
line.SetLineColor(kBlue + 1)
line.SetLineWidth(2)
line.Draw()
saveas = "/MassResolutionVsPt_%s_%s" % (trackType, rapidity)
c2.SaveAs(output + saveas + ".png")
c2.SaveAs(output + saveas + ".pdf")
c2.SaveAs(output + saveas + ".root")
c2.SaveAs(output + saveas + ".C")
def makePlot(sfHist,ofHist,selection,plot,runRange,region,cmsExtra,combination,dyHist=None,edgeShape=False,edgeShapeMC=False,differentEdgePositions=False):
colors = createMyColors()
hCanvas = TCanvas("hCanvas", "Distribution", 800,800)
plotPad = ROOT.TPad("plotPad","plotPad",0,0.3,1,1)
ratioPad = ROOT.TPad("ratioPad","ratioPad",0,0.,1,0.3)
style = setTDRStyle()
style.SetPadTopMargin(0.07)
ROOT.gStyle.SetOptStat(0)
plotPad.UseCurrentStyle()
ratioPad.UseCurrentStyle()
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
yMax = sfHist.GetBinContent(sfHist.GetMaximumBin())
if plot.yMax == 0:
#~ yMax = yMax*2.15
if edgeShape or edgeShapeMC or differentEdgePositions:
yMax = yMax*1.75
else:
yMax = yMax*1.5
else:
yMax = plot.yMax
plotPad.DrawFrame(plot.firstBin,0,plot.lastBin, yMax,"; %s ; %s" %(plot.xaxis,plot.yaxis))
#set overflow bin
#~ print sfHist.GetBinContent(sfHist.GetNbinsX()), sfHist.GetBinContent(sfHist.GetNbinsX()+1)
#~ sfHist.SetBinContent(sfHist.GetNbinsX(),sfHist.GetBinContent(sfHist.GetNbinsX())+sfHist.GetBinContent(sfHist.GetNbinsX()+1))
#~ sfHist.SetBinError(sfHist.GetNbinsX(),(sfHist.GetBinContent(sfHist.GetNbinsX())+sfHist.GetBinContent(sfHist.GetNbinsX()+1))**0.5)
#~ ofHist.SetBinContent(ofHist.GetNbinsX(),ofHist.GetBinContent(ofHist.GetNbinsX())+ofHist.GetBinContent(ofHist.GetNbinsX()+1))
#~ ofHist.SetBinError(ofHist.GetNbinsX(),(ofHist.GetBinContent(ofHist.GetNbinsX())+ofHist.GetBinContent(ofHist.GetNbinsX()+1))**0.5)
bkgHist = ofHist.Clone("bkgHist")
if dyHist is not None:
bkgHist.Add(dyHist)
sfHist.SetMarkerStyle(20)
sfHist.SetLineColor(ROOT.kBlack)
bkgHist.SetLineColor(ROOT.kBlue+3)
bkgHist.SetLineWidth(2)
dyHist.SetLineColor(ROOT.kGreen+2)
dyHist.SetFillColor(ROOT.kGreen+2)
#~ dyHist.SetFillStyle(3002)
#~ dyOnlyHist = dyHist.Clone("dyOnlyHist")
#~ dyOnlyHist.Scale(getattr(getattr(OnlyZPredictions,bSelection).SF,region).val / getattr(getattr(zPredictions,bSelection).SF,region).val)
#~
#~ rareBGHist = dyHist.Clone("rareBGHist")
#~ rareBGHist.Scale(getattr(getattr(OtherPredictions,bSelection).SF,region).val / getattr(getattr(zPredictions,bSelection).SF,region).val)
#~
#~ rareBGHist.SetLineColor(ROOT.kViolet+2)
#~ rareBGHist.SetFillColor(ROOT.kViolet+2)
from ROOT import THStack
#~ stack = THStack()
#~ stack.Add(rareBGHist)
#~ stack.Add(dyOnlyHist)
#~
bkgHistForLegend = bkgHist.Clone("bkgHistForLegend")
bkgHistForLegend.SetLineColor(ROOT.kBlue+3)
bkgHistForLegend.SetFillColor(ROOT.kWhite)
bkgHistForLegend.SetLineWidth(2)
latex = ROOT.TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.055)
latex.SetLineWidth(2)
latex.SetNDC(True)
latexCMS = ROOT.TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = ROOT.TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.93, 0.942, "%s fb^{-1} (13 TeV)"%runRange.printval)
latexCMS.DrawLatex(0.19,0.86,"CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19,yLabelPos,"%s"%(cmsExtra))
if combination == "SF":
rSFOFErr = getattr(rSFOF,region).err
elif combination == "EE":
rSFOFErr = getattr(rEEOF,region).err
elif combination == "MM":
rSFOFErr = getattr(rMMOF,region).err
errGraph, histUp, histDown = getErrHist(plot,combination,region,ofHist,dyHist,rSFOFErr)
errGraph.SetFillColor(myColors["MyBlueOverview"])
errGraph.SetFillStyle(3001)
#~ errGraph.SetLineColor(myColors["MyDarkBlue"])
#~ errGraph.SetMarkerColor(myColors["MyDarkBlue"])
if edgeShape:
edgeFile = ROOT.TFile("edgeShape.root","READ")
edgeHist = ROOT.TH1F()
edgeHist = edgeFile.Get("edgeHist300__inv")
edgeHist.Scale(1./edgeHist.Integral())
edgeHist500 = edgeFile.Get("edgeHist500__inv")
edgeHist500.Scale(1./edgeHist500.Integral())
edgeHist700 = edgeFile.Get("edgeHist700__inv")
edgeHist700.Scale(1./edgeHist700.Integral())
edgeHist.Scale(342)
edgeHist.Add(bkgHist.Clone())
edgeHist.SetLineColor(ROOT.kRed)
edgeHist.SetLineWidth(2)
edgeHist500.Scale(484)
edgeHist500.Add(bkgHist.Clone())
edgeHist500.SetLineColor(ROOT.kRed)
edgeHist500.SetLineWidth(2)
edgeHist500.SetLineStyle(ROOT.kDashed)
edgeHist700.Scale(654)
edgeHist700.Add(bkgHist.Clone())
edgeHist700.SetLineColor(ROOT.kRed)
edgeHist700.SetLineStyle(ROOT.kDotted)
edgeHist700.SetLineWidth(2)
for i in range(1,edgeHist.GetNbinsX()+1):
if i >= edgeHist.GetXaxis().FindBin(70.01):
edgeHist.SetBinContent(i,0)
edgeHist500.SetBinContent(i,0)
edgeHist700.SetBinContent(i,0)
if edgeShapeMC:
signalPath = locations.signalDataSetPath
denominatorFile = TFile("../SignalScan/T6bbllsleptonDenominatorHisto.root")
denominatorHisto = TH2F(denominatorFile.Get("massScan"))
cutsWithoutSignalScaleFactors = plot.cuts
plot.cuts = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*leptonFastSimScaleFactor1*leptonFastSimScaleFactor2*(%s)"%plot.cuts
if "Central" in selection.name:
EETriggerEff = 0.945
EMuTriggerEff = 0.937
MuMuTriggerEff = 0.929
RSFOF = 1.032
elif "Forward" in selection.name:
EETriggerEff = 0.943
EMuTriggerEff = 0.938
MuMuTriggerEff = 0.916
RSFOF = 1.092
else:
EETriggerEff = 0.949
EMuTriggerEff = 0.921811
MuMuTriggerEff = 0.929178
RSFOF = 1.05
EEHistSignal450, MMHistSignal450, EMHistSignal450 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_450_mneutralino_175")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(450),denominatorHisto.GetYaxis().FindBin(175))
xsection = getattr(sbottom_masses, "m_b_450").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal450.Scale(EETriggerEff * scalingLumi)
MMHistSignal450.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal450.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist450 = EEHistSignal450.Clone()
edgeHist450.Add(MMHistSignal450.Clone())
edgeHist450.Add(EMHistSignal450.Clone(),-1)
if combination == "EE":
edgeHist450 = EEHistSignal450.Clone()
edgeHist450.Add(EMHistSignal450.Clone(),-0.5)
if combination == "MM":
edgeHist450 = MMHistSignal450.Clone()
edgeHist450.Add(EMHistSignal450.Clone(),-0.5)
for i in range(0,edgeHist450.GetNbinsX()):
if edgeHist450.GetBinContent(i) < 0:
edgeHist450.SetBinContent(i,0.)
edgeHist450.Add(bkgHist.Clone())
edgeHist450.SetLineColor(ROOT.kRed)
edgeHist450.SetLineWidth(2)
EEHistSignal550, MMHistSignal550, EMHistSignal550 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_550_mneutralino_175")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(550),denominatorHisto.GetYaxis().FindBin(175))
xsection = getattr(sbottom_masses, "m_b_550").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal550.Scale(EETriggerEff * scalingLumi)
MMHistSignal550.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal550.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist550 = EEHistSignal550.Clone()
edgeHist550.Add(MMHistSignal550.Clone())
edgeHist550.Add(EMHistSignal550.Clone(),-1)
if combination == "EE":
edgeHist550 = EEHistSignal550.Clone()
edgeHist550.Add(EMHistSignal550.Clone(),-0.5)
if combination == "MM":
edgeHist550 = MMHistSignal550.Clone()
edgeHist550.Add(EMHistSignal550.Clone(),-0.5)
for i in range(0,edgeHist550.GetNbinsX()):
if edgeHist550.GetBinContent(i) < 0:
edgeHist550.SetBinContent(i,0.)
edgeHist550.Add(bkgHist.Clone())
edgeHist550.SetLineColor(ROOT.kRed)
edgeHist550.SetLineWidth(2)
edgeHist550.SetLineStyle(ROOT.kDashed)
EEHistSignal650, MMHistSignal650, EMHistSignal650 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_650_mneutralino_175")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(650),denominatorHisto.GetYaxis().FindBin(175))
xsection = getattr(sbottom_masses, "m_b_650").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal650.Scale(EETriggerEff * scalingLumi)
MMHistSignal650.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal650.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist650 = EEHistSignal650.Clone()
edgeHist650.Add(MMHistSignal650.Clone())
edgeHist650.Add(EMHistSignal650.Clone(),-1)
if combination == "EE":
edgeHist650 = EEHistSignal650.Clone()
edgeHist650.Add(EMHistSignal650.Clone(),-0.5)
if combination == "MM":
edgeHist650 = MMHistSignal650.Clone()
edgeHist650.Add(EMHistSignal650.Clone(),-0.5)
for i in range(0,edgeHist650.GetNbinsX()):
if edgeHist650.GetBinContent(i) < 0:
edgeHist650.SetBinContent(i,0.)
edgeHist650.Add(bkgHist.Clone())
edgeHist650.SetLineColor(ROOT.kRed)
edgeHist650.SetLineWidth(2)
edgeHist650.SetLineStyle(ROOT.kDotted)
plot.cuts = cutsWithoutSignalScaleFactors
if differentEdgePositions:
signalPath = locations.signalDataSetPath
denominatorFile = TFile("../SignalScan/T6bbllsleptonDenominatorHisto.root")
denominatorHisto = TH2F(denominatorFile.Get("massScan"))
cutsWithoutSignalScaleFactors = plot.cuts
plot.cuts = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*leptonFastSimScaleFactor1*leptonFastSimScaleFactor2*(%s)"%plot.cuts
if "Central" in selection.name:
EETriggerEff = 0.945
EMuTriggerEff = 0.937
MuMuTriggerEff = 0.929
RSFOF = 1.032
elif "Forward" in selection.name:
EETriggerEff = 0.943
EMuTriggerEff = 0.938
MuMuTriggerEff = 0.916
RSFOF = 1.092
else:
EETriggerEff = 0.949
EMuTriggerEff = 0.921811
MuMuTriggerEff = 0.929178
RSFOF = 1.05
EEHistSignal75, MMHistSignal75, EMHistSignal75 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_500_mneutralino_175")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(500),denominatorHisto.GetYaxis().FindBin(175))
xsection = getattr(sbottom_masses, "m_b_500").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal75.Scale(EETriggerEff * scalingLumi)
MMHistSignal75.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal75.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist75 = EEHistSignal75.Clone()
edgeHist75.Add(MMHistSignal75.Clone())
edgeHist75.Add(EMHistSignal75.Clone(),-1)
if combination == "EE":
edgeHist75 = EEHistSignal75.Clone()
edgeHist75.Add(EMHistSignal75.Clone(),-0.5)
if combination == "MM":
edgeHist75 = MMHistSignal75.Clone()
edgeHist75.Add(EMHistSignal75.Clone(),-0.5)
for i in range(0,edgeHist75.GetNbinsX()):
if edgeHist75.GetBinContent(i) < 0:
edgeHist75.SetBinContent(i,0.)
edgeHist75.Add(bkgHist.Clone())
edgeHist75.SetLineColor(ROOT.kRed)
edgeHist75.SetLineWidth(2)
EEHistSignal125, MMHistSignal125, EMHistSignal125 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_500_mneutralino_225")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(500),denominatorHisto.GetYaxis().FindBin(225))
xsection = getattr(sbottom_masses, "m_b_500").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal125.Scale(EETriggerEff * scalingLumi)
MMHistSignal125.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal125.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist125 = EEHistSignal125.Clone()
edgeHist125.Add(MMHistSignal125.Clone())
edgeHist125.Add(EMHistSignal125.Clone(),-1)
if combination == "EE":
edgeHist125 = EEHistSignal125.Clone()
edgeHist125.Add(EMHistSignal125.Clone(),-0.5)
if combination == "MM":
edgeHist125 = MMHistSignal125.Clone()
edgeHist125.Add(EMHistSignal125.Clone(),-0.5)
for i in range(0,edgeHist125.GetNbinsX()):
if edgeHist125.GetBinContent(i) < 0:
edgeHist125.SetBinContent(i,0.)
edgeHist125.Add(bkgHist.Clone())
edgeHist125.SetLineColor(ROOT.kRed)
edgeHist125.SetLineWidth(2)
edgeHist125.SetLineStyle(ROOT.kDashed)
EEHistSignal200, MMHistSignal200, EMHistSignal200 = getSignalMCHistograms(signalPath,plot,runRange,"T6bbllslepton_msbottom_500_mneutralino_300")
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(500),denominatorHisto.GetYaxis().FindBin(300))
xsection = getattr(sbottom_masses, "m_b_500").cross_section13TeV
scalingLumi = runRange.lumi*xsection/denominator
EEHistSignal200.Scale(EETriggerEff * scalingLumi)
MMHistSignal200.Scale(MuMuTriggerEff * scalingLumi)
EMHistSignal200.Scale(EMuTriggerEff * scalingLumi * RSFOF)
if combination == "SF":
edgeHist200 = EEHistSignal200.Clone()
edgeHist200.Add(MMHistSignal200.Clone())
edgeHist200.Add(EMHistSignal200.Clone(),-1)
if combination == "EE":
edgeHist200 = EEHistSignal200.Clone()
edgeHist200.Add(EMHistSignal200.Clone(),-0.5)
if combination == "MM":
edgeHist200 = MMHistSignal200.Clone()
edgeHist200.Add(EMHistSignal200.Clone(),-0.5)
for i in range(0,edgeHist200.GetNbinsX()):
if edgeHist200.GetBinContent(i) < 0:
edgeHist200.SetBinContent(i,0.)
edgeHist200.Add(bkgHist.Clone())
edgeHist200.SetLineColor(ROOT.kRed)
edgeHist200.SetLineWidth(2)
edgeHist200.SetLineStyle(ROOT.kDotted)
plot.cuts = cutsWithoutSignalScaleFactors
#~ lines = getLines(0, sfHist.GetBinContent(sfHist.GetMaximumBin())+10,xPos=[mllBins.lowMass.high,mllBins.onZ.low,mllBins.onZ.high, mllBins.highMass.low ])
#~ for line in lines:
#~ line.Draw()
if edgeShape or edgeShapeMC:
leg = TLegend(0.55, 0.4, 0.92, 0.91,"","brNDC")
elif differentEdgePositions:
leg = TLegend(0.55, 0.4, 0.95, 0.92,"","brNDC")
else:
leg = TLegend(0.6, 0.5, 0.95, 0.92,"","brNDC")
leg.SetFillColor(10)
leg.SetLineColor(10)
leg.SetShadowColor(0)
leg.SetBorderSize(1)
from ROOT import TH1F,kWhite
legendHistDing = TH1F()
legendHistDing.SetFillColor(kWhite)
if region == "inclusive":
leg.AddEntry(legendHistDing,"Inclusive signal region","h")
elif region == "central":
leg.AddEntry(legendHistDing,"Legacy signal region","h")
elif region == "forward":
leg.AddEntry(legendHistDing,"Forward signal region","h")
leg.AddEntry(sfHist,"Data","pe1")
#~ leg.AddEntry(bkgHist, "Total background","l")
leg.AddEntry(bkgHistForLegend, "Flavor symmetric","f")
leg.AddEntry(dyHist,"Drell-Yan", "f")
#~ leg.AddEntry(dyOnlyHist,"Z+jets", "f")
#~ leg.AddEntry(rareBGHist,"Other SM", "f")
leg.AddEntry(errGraph,"Total uncertainty", "f")
if edgeShape:
leg.AddEntry(legendHistDing,"Scaled 8 TeV signal fit:", "h")
leg.AddEntry(edgeHist,"m_{#tilde{b}} = 300 GeV hypothesis", "l")
leg.AddEntry(edgeHist500,"m_{#tilde{b}} = 500 GeV hypothesis", "l")
leg.AddEntry(edgeHist700,"m_{#tilde{b}} = 700 GeV hypothesis", "l")
if edgeShapeMC:
leg.AddEntry(legendHistDing,"Slepton signal model", "h")
leg.AddEntry(edgeHist450,"m_{#tilde{b}} = 450 GeV, m_{#tilde{#chi}_{2}^{0}} = 175 GeV", "l")
leg.AddEntry(edgeHist550,"m_{#tilde{b}} = 550 GeV, m_{#tilde{#chi}_{2}^{0}} = 175 GeV", "l")
leg.AddEntry(edgeHist650,"m_{#tilde{b}} = 650 GeV, m_{#tilde{#chi}_{2}^{0}} = 175 GeV", "l")
if differentEdgePositions:
leg.AddEntry(legendHistDing,"Slepton signal model, m_{#tilde{b}} = 500 GeV", "h")
leg.AddEntry(edgeHist75,"75 GeV edge position", "l")
leg.AddEntry(edgeHist125,"125 GeV edge position", "l")
leg.AddEntry(edgeHist200,"200 GeV edge position", "l")
leg.Draw("same")
errGraph.Draw("SAME02")
if edgeShape:
edgeHist.Draw("samehist")
edgeHist500.Draw("samehist")
edgeHist700.Draw("samehist")
if edgeShapeMC:
edgeHist450.Draw("samehist")
edgeHist550.Draw("samehist")
edgeHist650.Draw("samehist")
if differentEdgePositions:
edgeHist75.Draw("samehist")
edgeHist125.Draw("samehist")
edgeHist200.Draw("samehist")
bkgHist.Draw("samehist")
dyHist.Draw("samehist")
#~ stack.Draw("samehist")
sfHist.Draw("samepe1")
plotPad.RedrawAxis()
ratioPad.cd()
ratioGraphs = ratios.RatioGraph(sfHist,bkgHist, xMin=plot.firstBin, xMax=plot.lastBin,title="#frac{Data}{Prediction} ",yMin=0.0,yMax=2,ndivisions=10,color=ROOT.kBlack,adaptiveBinning=1000)
ratioGraphs.addErrorByHistograms( "rSFOF", histUp, histDown,color= myColors["MyBlue"],fillStyle=3001)
ratioGraphs.draw(ROOT.gPad,True,False,True,chi2Pos=0.8)
#~ leg2 = TLegend(0.175, 0.78, 0.475, 0.9,"","brNDC")
#~ leg2.SetFillColor(10)
#~ leg2.SetLineColor(10)
#~ leg2.SetShadowColor(0)
#~ leg2.SetBorderSize(1)
#~ leg2.AddEntry(errGraph,"Systematic uncert.", "f")
#~ leg2.Draw("same")
ROOT.gPad.RedrawAxis()
plotPad.RedrawAxis()
ratioPad.RedrawAxis()
if edgeShape:
hCanvas.Print("fig/mllResult_%s_%s_%s_edgeShape.pdf"%(selection.name,runRange.label,combination))
elif edgeShapeMC:
hCanvas.Print("fig/mllResult_%s_%s_%s_edgeShapeMC.pdf"%(selection.name,runRange.label,combination))
elif differentEdgePositions:
hCanvas.Print("fig/mllResult_%s_%s_%s_edgeShapeMC_differentEdgePositions.pdf"%(selection.name,runRange.label,combination))
else:
hCanvas.Print("fig/mllResult_%s_%s_%s.pdf"%(selection.name,runRange.label,combination))
def DrawBrazil(diagonal=175, doData=False, pname='limits', year=2018):
lumi = GetLumi(year)
import numpy as np
# 'mstop','mlsp','sm2','sm1','central','sp1','sp2', 'data'
d = GetDic(pname + '.p', diagonal)
x = np.array(d['mstop'])
e = np.array(d['exp'])
y1max = np.array(d['s+1'])
y2max = np.array(d['s+2'])
y1min = np.array(d['s-1'])
y2min = np.array(d['s-2'])
# observed
if (doData): o = np.array(d['obs'])
else: o = np.array(d['exp'])
c1 = TCanvas("c1", "CL", 10, 10, 800, 600)
#c1.SetGrid();
ymax = 4.5
ymin = 0.0
if (diagonal == 'down' or diagonal == 'Down' or diagonal == 'DOWN'):
ymax = 3.1
ymin = 0.30
elif (diagonal == 'up' or diagonal == 'Up' or diagonal == 'UP'):
ymax = 2.5
ymin = 0.15
#c1.DrawFrame(min(x)-2,min(d['sp2']+d['sm2'])-0.2,max(x)+2,max(d['sp2']+d['sm2'])+0.2);
c1.DrawFrame(min(x), ymin, max(x), ymax)
n = len(e)
gr1min = TGraph(n, x, y1min)
gr1max = TGraph(n, x, y1max)
gr2min = TGraph(n, x, y2min)
gr2max = TGraph(n, x, y2max)
gro = TGraph(n, x, o)
gre = TGraph(n, x, e)
gh = TGraph(n, x, np.linspace(0.999, 1, n))
gr1shade = TGraph(2 * n)
gr2shade = TGraph(2 * n)
#color1shade = 3; color2shade = 5;
color1shade = kOrange
color2shade = kGreen + 1
for i in range(n):
gr1shade.SetPoint(i, x[i], y1max[i] * scalefact)
gr1shade.SetPoint(n + i, x[n - i - 1], y1min[n - i - 1] * scalefact)
gr2shade.SetPoint(i, x[i], y2max[i] * scalefact)
gr2shade.SetPoint(n + i, x[n - i - 1], y2min[n - i - 1] * scalefact)
gre.SetPoint(i, x[i], e[i] * scalefact)
gh.SetPoint(i, x[i], np.linspace(0.999, 1, n)[i] * scalefact)
gro.SetPoint(i, x[i], o[i] * scalefact if doData else e[i] * scalefact)
gr2shade.SetFillColor(color2shade)
gr2shade.Draw("f")
gr1shade.SetFillColor(color1shade)
gr1shade.Draw("f")
gh.SetLineWidth(2)
gh.SetMarkerStyle(0)
gh.SetLineColor(46)
gh.SetLineStyle(2)
gh.Draw("LP")
gro.SetLineWidth(2)
gro.SetMarkerStyle(20)
gro.SetMarkerSize(0.7)
gro.SetLineColor(1)
if doData: gro.Draw("LP")
gre.SetLineWidth(2)
gre.SetMarkerStyle(0)
gre.SetLineColor(1)
gre.SetLineStyle(6)
gre.Draw("LP")
gre.SetTitle("Expected")
gro.SetTitle("Observed")
gr1shade.SetTitle("Expected 1#sigma")
gr2shade.SetTitle("Expected 2#sigma")
leg = TLegend(.1, .65, .4, .9)
leg.AddEntry(gro)
leg.AddEntry(gre)
leg.AddEntry(gr1shade, '', 'f')
leg.AddEntry(gr2shade, '', 'f')
leg.SetFillColor(0)
leg.Draw("same")
gre.SetFillColor(0)
gro.SetFillColor(0)
gr1shade.SetLineColor(color1shade)
gr2shade.SetLineColor(color2shade)
tit = "m_{#tilde{t}_{1}} - m_{#tilde{#chi}_{1}^{0}} = "
dm = "0"
ymax = 4.1
ymin = 0.3
if (diagonal == 'down' or diagonal == 'Down' or diagonal == 'DOWN'):
tit += "182.5 GeV" #" + 7.5 GeV"
dm = "m7p5"
ymax = 2.2
ymin = 0.15
elif (diagonal == 'up' or diagonal == 'Up' or diagonal == 'UP'):
tit += "167.5 GeV" #" - 7.5 GeV"
dm = "7p5"
ymax = 3.3
ymin = 0.30
else:
tit += "175 GeV"
Title = TLatex()
Title.SetTextSize(0.060)
Title.DrawLatexNDC(.42, .84, tit)
Xaxis = TLatex()
Xaxis.SetTextFont(42)
Xaxis.DrawLatexNDC(0.8, 0.03, "m_{#tilde{t}_{1}} (GeV)")
Yaxis = TLatex()
Yaxis.SetTextFont(42)
Yaxis.SetTextAngle(90)
Yaxis.DrawLatexNDC(0.05, 0.15, "95% CL limit on signal strength")
textCMS = TLatex()
textCMS.SetTextSize(0.06)
textCMS.SetTextSizePixels(22)
textCMS.SetTextAlign(12)
textCMS.DrawLatexNDC(.12, .93, "CMS")
textLumi = TLatex()
textLumi.SetTextFont(42)
textLumi.SetTextSize(0.06)
textLumi.SetTextSizePixels(22)
textLumi.DrawLatexNDC(.58, .91, "%1.1f fb^{-1} (13 TeV)" % (lumi)) #35.9
#name = "brazil_%i"%diagonal
name = "brazil_%s" % (pname)
for form in ['pdf', 'png']:
c1.Print(outputdir + name + '.%s' % form)
