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
def addSys(var, cut, sys):
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1]
binHigh = cut[cut.find("HighVal") + 7:]
binName = "bin_" + binLow + "_" + binHigh
cut = cut[:cut.find("binned")]
channel = cut
weight = "eventWeightLumi" #+ ("*stitchWeight" if any([x for x in back if x.endswith('b')]) else "")
cut = selection[cut]
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
weightUp = weightDown = weight
varUp = varDown = var
cutUp = cutDown = cut
# Systematics
if sys == 'CMS_scale_j':
if var != "MET_sign": varUp = var.replace('pt', 'ptScaleUp')
else: varUp = var.replace('sign', 'signScaleUp')
if var != "MET_sign": varDown = var.replace('pt', 'ptScaleDown')
else: varDown = var.replace('sign', 'signScaleDown')
cutUp = cut.replace('MET_pt', 'MET_ptScaleUp')
cutUp = cutUp.replace('Jets', 'JetsScaleUp')
cutUp = cutUp.replace('12', '12ScaleUp')
cutUp = cutUp.replace('mT>', 'mTScaleUp>')
cutUp = cutUp.replace('mT2', 'mT2ScaleUp')
cutDown = cut.replace('MET_pt', 'MET_ptScaleDown')
cutDown = cutDown.replace('Jets', 'JetsScaleDown')
cutDown = cutDown.replace('12', '12ScaleDown')
cutDown = cutDown.replace('mT>', 'mTScaleDown>')
cutDown = cutDown.replace('mT2', 'mT2ScaleDown')
elif sys == 'CMS_res_j':
if var != "MET_sign": varUp = var.replace('pt', 'ptResUp')
else: varUp = var.replace('sign', 'signResUp')
if var != "MET_sign": varDown = var.replace('pt', 'ptResDown')
else: varDown = var.replace('sign', 'signResDown')
cutUp = cut.replace('MET_pt', 'MET_ptResUp')
cutUp = cutUp.replace('Jets', 'JetsResUp')
cutUp = cutUp.replace('12', '12ResUp')
cutUp = cutUp.replace('mT>', 'mTResUp>')
cutUp = cutUp.replace('mT2', 'mT2ResUp')
cutDown = cut.replace('MET_pt', 'MET_ptResDown')
cutDown = cutDown.replace('Jets', 'JetsResDown')
cutDown = cutDown.replace('12', '12ResDown')
cutDown = cutDown.replace('mT>', 'mTResDown>')
cutDown = cutDown.replace('mT2', 'mT2ResDown')
elif sys == 'CMS_WqcdWeightRen':
weightUp += "*WqcdWeightRenUp/WqcdWeight"
weightDown += "*WqcdWeightRenDown/WqcdWeight"
elif sys == 'CMS_WqcdWeightFac':
weightUp += "*WqcdWeightFacUp/WqcdWeight"
weightDown += "*WqcdWeightFacDown/WqcdWeight"
elif sys == 'CMS_ZqcdWeightRen':
weightUp += "*ZqcdWeightRenUp/ZqcdWeight"
weightDown += "*ZqcdWeightRenDown/ZqcdWeight"
elif sys == 'CMS_ZqcdWeightFac':
weightUp += "*ZqcdWeightFacUp/ZqcdWeight"
weightDown += "*ZqcdWeightFacDown/ZqcdWeight"
elif sys == 'CMS_WewkWeight':
weightUp += "/WewkWeight"
weightDown += ""
elif sys == 'CMS_ZewkWeight':
weightUp += "/ZewkWeight"
weightDown += ""
elif sys == 'CMS_pdf':
weightUp += "*PDFWeightUp/eventWeight"
weightDown += "*PDFWeightDown/eventWeight"
elif sys == 'CMS_HF':
weightUp += "*1.20"
weightDown += "*0.8"
elif sys == 'CMS_eff_b':
weightUp += "*bTagWeightUp/bTagWeight"
weightDown += "*bTagWeightDown/bTagWeight"
elif sys == 'CMS_scale_pu':
weightUp += "*puWeightUp/puWeight"
weightDown += "*puWeightDown/puWeight"
elif sys == 'CMS_scale_top':
weightUp += "/TopWeight"
weightDown += ""
elif sys == 'CMS_eff_trigger':
weightUp += "*triggerWeightUp/triggerWeight"
weightDown += "*triggerWeightDown/triggerWeight"
elif sys == 'CMS_eff_e' and '2e' in cut or '1e' in channel:
weightUp += "*leptonWeightUp/leptonWeight"
weightDown += "*leptonWeightDown/leptonW\
eight"
elif sys == 'CMS_eff_m' and '2m' in cut or '1m' in channel:
weightUp += "*leptonWeightUp/leptonWeight"
weightDown += "*leptonWeightDown/leptonW\
eight"
elif sys == 'QCDscale_ren':
weightUp += "*QCDRenWeightUp"
weightDown += "*QCDRenWeightDown"
elif sys == 'QCDscale_fac':
weightUp += "*QCDFacWeightUp"
weightDown += "*QCDFacWeightDown"
# elif sys=='EWKscale_Z': weightDown += "/ZewkWeight"
# elif sys=='EWKscale_W': weightDown += "/WewkWeight"
else:
print "Systematic", sys, "not applicable or not recognized."
### Create and fill MC histograms ###
file = {}
tree = {}
hist = {}
histUp = {}
histDown = {}
isBlind = BLIND and 'SR' in channel
for i, s in enumerate(back + sign):
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
tree[s].Add(NTUPLEDIR + ss + ".root")
if not binLow == "":
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''), 1, float(binLow),
float(binHigh))
elif binLow == "" and variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
histUp[s] = hist[s].Clone(s + 'Up')
histDown[s] = hist[s].Clone(s + 'Down')
redFactorString = ""
redFactorValue = ""
#if isBlind and 'data' not in s and options.limit:
if isBlind and 'data' not in s:
redFactorValue = " / 15"
cutstring = ("*(" + cut + ")" if len(cut) > 0 else "")
cutstringUp = ("*(" + cutUp + ")" if len(cut) > 0 else "")
cutstringDown = ("*(" + cutDown + ")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
tree[s].Project(s, var,
"(" + weight + redFactorValue + ")" + cutstring)
if 'HF' not in sys or 'QCDscale' not in sys:
tree[s].Project(s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstring)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstring)
if 'HF' in sys:
if s.startswith('WJ') or s.startswith('ZJ') or s.startswith(
'DYJets'):
tree[s].Project(
s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstringDown)
else:
tree[s].Project(
s + 'Up', varUp,
"(" + weight + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weight + redFactorValue + ")" + cutstringDown)
if 'QCDscale' in sys:
if s.startswith('WJ') or s.startswith('ZJ') or s.startswith(
'DYJets'):
tree[s].Project(
s + 'Up', varUp,
"(" + weight + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weight + redFactorValue + ")" + cutstringDown)
else:
tree[s].Project(
s + 'Up', varUp,
"(" + weightUp + redFactorValue + ")" + cutstringUp)
tree[s].Project(
s + 'Down', varDown,
"(" + weightDown + redFactorValue + ")" + cutstringDown)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetLineWidth(2)
histUp[s].SetLineWidth(2)
histDown[s].SetLineWidth(2)
hist[s].SetLineColor(1)
histUp[s].SetLineColor(629)
histDown[s].SetLineColor(602)
# Rescale normalization for QCD scales FIXME
if 'QCDscale' in sys:
for s in back + sign: #['TTbar', 'TTbarSL', 'ST']:
if s in hist and histUp[s].Integral(
) > 0. and histDown[s].Integral() > 0.:
histUp[s].Scale(hist[s].Integral() / histUp[s].Integral())
histDown[s].Scale(hist[s].Integral() / histDown[s].Integral())
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset()
histUp['BkgSum'] = hist['BkgSum'].Clone("BkgSumUp")
histUp['BkgSum'].SetLineColor(629)
histUp['BkgSum'].Reset()
histDown['BkgSum'] = hist['BkgSum'].Clone("BkgSumDown")
histDown['BkgSum'].SetLineColor(602)
histDown['BkgSum'].Reset()
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s], 1)
histUp['BkgSum'].Add(histUp[s], 1)
histDown['BkgSum'].Add(histDown[s], 1)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False)
addOverflow(histUp[s], False)
addOverflow(histDown[s], False)
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.06)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
c1.GetPad(bool(RATIO)).SetLogy()
histUp['BkgSum'].SetMaximum(histUp['BkgSum'].GetMaximum() * 5)
histUp['BkgSum'].Draw("HIST")
histDown['BkgSum'].Draw("SAME, HIST")
hist['BkgSum'].Draw("SAME, HIST")
drawCMS(-1, "Simulation", False)
setHistStyle(histUp['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
errUp = histUp['BkgSum'].Clone("BkgUp;")
errUp.Add(hist['BkgSum'], -1)
errUp.Divide(hist['BkgSum'])
errUp.SetTitle("")
errUp.GetYaxis().SetTitle("#frac{shifted-central}{central}")
errUp.GetYaxis().SetNdivisions(503)
setBotStyle(errUp)
errUp.GetYaxis().SetRangeUser(-0.3, 0.3)
errUp.Draw("HIST")
errDown = histDown['BkgSum'].Clone("BkgDown;")
errDown.Add(hist['BkgSum'], -1)
errDown.Divide(hist['BkgSum'])
errDown.Draw("SAME, HIST")
f1 = TF1("myfunc", "[0]", -100000, 10000)
f1.SetLineColor(1)
f1.SetLineStyle(7)
f1.SetLineWidth(1)
f1.SetParameter(0, 0)
f1.Draw("same")
leg = TLegend(0.65, 0.80, 0.95, 0.80)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetHeader(sys.replace('CMS', '').replace('_', ' '))
leg.AddEntry(histUp['BkgSum'], "Up", "l")
leg.AddEntry(hist['BkgSum'], "Central", "l")
leg.AddEntry(histDown['BkgSum'], "Down", "l")
leg.SetY1(0.75 - leg.GetNRows() * 0.045)
c1.cd(1)
leg.Draw()
if options.saveplots:
if not os.path.exists("plotsSys_" + options.name + "/" + channel +
binName):
os.makedirs("plotsSys_" + options.name + "/" + channel + binName)
c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" +
sys + ".png")
c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" +
sys + ".pdf")
for i, s in enumerate(back + sign):
c2 = TCanvas(s + "canvas", "Signals", 800, 600)
c2.cd()
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.06)
c2.GetPad(0).SetTicky(2)
c2.GetPad(0).SetLogy()
histUp[s].SetMaximum(histUp[s].GetMaximum() * 5)
histUp[s].Draw("HIST")
histDown[s].Draw("SAME, HIST")
hist[s].Draw("SAME, HIST")
drawCMS(-1, "Simulation", False)
if options.saveplots:
c2.Print("plotsSys_" + options.name + "/" + channel + binName +
"/" + sys + "_" + s + ".png")
c2.Print("plotsSys_" + options.name + "/" + channel + binName +
"/" + sys + "_" + s + ".pdf")
saveHist(histUp, channel + binName, sys + 'Up')
saveHist(histDown, channel + binName, sys + 'Down')
print "Added systematic", sys, "to channel", channel
def plot(var, cut, norm=False, nm1=False):
### Preliminary Operations ###
doBinned = False
if options.mode == "binned": doBinned = True
fileRead = os.path.exists("combinedCards_" + options.name +
"/fitDiagnostics_" + options.file + ".root")
treeRead = not any(
x == cut
for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger'
]) #(var in variable.keys()) # Read from tree
#signal definition
if fileRead:
sign = ['ttDM_MChi1_MPhi200_scalar',
'tDM_MChi1_MPhi200_scalar'] #for postfit plot
if not fileRead and not options.limit:
sign = ['ttDM_MChi1_MPhi100_scalar',
'tDM_MChi1_MPhi100_scalar'] #for normal plotting
#bkg definition
if fileRead or options.limit:
back = [
"QCD", "DYJetsToNuNu_HT", "DYJetsToLL_HT", "VV", "ST",
"WJetsToLNu_HT", "TTbarSL"
] #for postfit or limit
if (cut).find('>250') or (cut).startswith('AH'): #for hadronic selections
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarV",
"TTbar2L", "TTbar1L", "DYJetsToNuNu_HT"
]
if fileRead or options.limit:
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarSL",
"DYJetsToNuNu_HT"
] #for postfit or limit
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1]
binHigh = cut[cut.find("HighVal") + 7:]
binName = "bin_" + binLow + "_" + binHigh
cut = cut[:cut.find("binned")]
useformula = False
if 'formula' in variable[var]:
useformula = True
channel = cut
plotdir = cut
plotname = var
weight = "eventWeightLumi" #*(2.2/35.9)
isBlind = BLIND and ('SR' in channel or 'ps' in channel)
if fileRead:
isBlind = False
options.saveplots = True
RESIDUAL = True
elif isBlind:
RATIO = 0
SIGNAL = 20
else:
RATIO = 4
SIGNAL = 1
RESIDUAL = False
showSignal = True #('SR' in channel)
cutSplit = cut.split()
for s in cutSplit:
if s in selection.keys():
plotdir = s
cut = cut.replace(s, selection[s])
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
#if treeRead and cut in selection: cut = cut.replace(cut, selection[cut])
# Determine Primary Dataset
pd = []
if any(w in cut
for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']):
pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x]
if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']):
pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x]
if any(w in cut for w in ['0l', 'isZtoNN']):
pd += [x for x in sample['data_obs']['files'] if 'MET' in x]
if len(pd) == 0:
raw_input("Warning: Primary Dataset not recognized, continue?")
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
print " cut :", weight
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
if fileRead:
var = 'MET_pt'
if channel.startswith('SL'): var = 'MET_sign'
if channel.endswith('ZR'): var = 'FakeMET_pt'
plotname = var
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
if doBinned:
bins = np.array([])
if 'bins' in variable[var].keys():
bins = np.array(variable[var]['bins'])
else:
binsize = (variable[var]['max'] -
variable[var]['min']) / variable[var]['nbins']
bins = np.arange(variable[var]['min'],
variable[var]['max'] + binsize, binsize)
bins = np.append(bins, 10000)
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not any(
t in s for t in ['data', 'tDM']
) else "rootfiles_" + options.name + "/" + channel + "bin_" + rbin + ".root"
histName = "shapes_fit_b/" + channel + "bin_" + rbin + "/" + s if not any(
t in s for t in ['data', 'tDM']) else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if 'data' not in s: hist[s].SetMarkerSize(0)
if tmphist:
hist[s].SetBinContent(i + 1, tmphist.GetBinContent(1))
hist[s].SetBinError(i + 1, tmphist.GetBinError(1))
else:
hist[s].SetBinContent(i + 1, 0.)
hist[s].SetBinError(i + 1, 0.)
else:
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not s == 'data_obs' else "rootfiles_" + options.name + "/" + channel + binName + ".root"
histName = "shapes_fit_b/" + channel + "/" + s if not s == 'data_obs' else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if tmphist == None:
tmphist = hist[back[0]].Clone(s)
tmphist.Reset("MICES")
print "Histogram", histName, "not found in file", fileName
if s == 'data_obs': hist[s] = tmphist
else:
hist[s] = hist['data_obs'].Clone(s)
hist[s].SetMarkerSize(0)
for i in range(tmphist.GetNbinsX() + 1):
hist[s].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
elif treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
tree[s].Add(NTUPLEDIR + ss + ".root")
if not binLow == "":
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''), 1, float(binLow),
float(binHigh))
elif binLow == "" and variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
redFactorString = ""
redFactorValue = ""
#if isBlind and 'data' in s:
if isBlind and 'data' in s and options.limit:
redFactorString = " && Entry$ % 15 == 1"
#if isBlind and 'data' not in s:
if isBlind and 'data' not in s and options.limit:
redFactorValue = " / 15"
cutstring = "(" + weight + redFactorValue + ")" + (
"*(" + cut + redFactorString + ")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
if useformula == True:
tree[s].Project(s, variable[var]['formula'], cutstring)
else:
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
else: # Histogram written to file
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
if file[ss].IsZombie():
print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
continue
tmphist = file[ss].Get(cut + "/" + var)
if tmphist == None: continue
if not s in hist.keys(): hist[s] = tmphist
else: hist[s].Add(tmphist)
if hist[s].Integral() < 0: hist[s].Scale(0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
#if 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30)
#if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91)
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s], 1)
if fileRead:
hist['PreFit'] = hist['BkgSum'].Clone("PreFit")
if doBinned:
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
tmphist = file[back[0]].Get("shapes_prefit/" + channel +
"bin_" + rbin + "/" +
"total_background")
if tmphist:
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(1))
else:
hist['PreFit'].SetBinContent(i + 1, 0.)
else:
tmphist = file[back[0]].Get("shapes_prefit/" + channel + "/" +
"total_background")
for i in range(tmphist.GetNbinsX() + 1):
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
addOverflow(hist['PreFit'], False)
hist['PreFit'].SetLineStyle(2)
hist['PreFit'].SetLineColor(617) #923
hist['PreFit'].SetLineWidth(3)
hist['PreFit'].SetFillStyle(0)
hist['BkgSum'].SetFillStyle(3002)
hist['BkgSum'].SetFillColor(1)
# Create data and Bkg sum histograms
# if options.blind or 'SR' in channel:
# hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
# hist['data_obs'].Reset("MICES")
# Set histogram style
hist[data[0]].SetMarkerStyle(20)
hist[data[0]].SetMarkerSize(1.25)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
#for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2])
if norm:
for i, s in enumerate(sign):
hist[s].Scale(hist['BkgSum'].Integral() / hist[s].Integral())
# for i, s in enumerate(back):
# hist[s].SetFillStyle(3005)
# hist[s].SetLineWidth(2)
# #for i, s in enumerate(sign):
# # hist[s].SetFillStyle(0)
# if not var=="Events":
# sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral()
# print "Applying SF:", sfnorm
# for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm)
if SIGNAL > 1:
if not var == "Events":
for i, s in enumerate(sign):
hist[s].Scale(SIGNAL)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
#leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg = TLegend(0.45, 0.63, 0.93, 0.92)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(3)
leg.SetTextFont(42)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if 'PreFit' not in hist:
leg.AddEntry(hist['BkgSum'], sample['BkgSum']['label'], "f")
else:
leg.AddEntry(hist['BkgSum'], 'MC unc.', "l")
leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l")
if showSignal:
for i, s in enumerate(sign):
if SIGNAL > 1:
if sample[s]['plot']:
leg.AddEntry(hist[s],
'%s (x%d)' % (sample[s]['label'], SIGNAL),
"l")
else:
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "l")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
if RESIDUAL:
c1.Divide(1, 3)
setFitTopPad(c1.GetPad(1), RATIO)
setFitBotPad(c1.GetPad(2), RATIO)
setFitResPad(c1.GetPad(3), RATIO)
else:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = ("log" in hist['BkgSum'].GetZaxis().GetTitle())
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0:
graph = fixData(hist[data[0]], USEGARWOOD)
graph.Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin()) +
hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin())))
if len(sign) > 0 and bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(),
hist[sign[-1]].GetMaximum()):
bkg.SetMaximum(
max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()) *
1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist[data[0]].GetMinimum()), 5.e-1) if log else 0.)
if log:
#bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
bkg.GetYaxis().SetMoreLogLabels(True)
else:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
leg.Draw()
if fileRead and 'SR' in channel:
drawCMS(LUMI / 15., "Preliminary")
else:
drawCMS(LUMI, "Preliminary")
drawRegion(channel, True)
drawAnalysis("DM" + channel[:2])
drawOverflow()
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
if RESIDUAL: setFitBotStyle(err)
else: setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist[data[0]].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if RESIDUAL: setFitBotStyle(res)
else: setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
if 'PreFit' in hist:
respre = hist[data[0]].Clone("ResiduesPreFit")
respre.Divide(hist['PreFit'])
respre.SetLineStyle(2)
respre.SetLineColor(617) #923
respre.SetLineWidth(3)
respre.SetFillStyle(0)
respre.Draw("SAME, HIST")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if RATIO and RESIDUAL:
c1.cd(3)
c1.SetGrid(1, 0)
resFit = hist[data[0]].Clone("Residues")
resFit.Reset("MICES")
resFit.SetTitle("")
#resFit.GetYaxis().SetTitle("Residuals")
resFit.GetYaxis().SetTitle(
"#frac{Data - Bkg}{#sqrt{#sigma_{Data}^{2}+#sigma_{Bkg}^{2}}}")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
resFit.SetBinContent(
i, (hist[data[0]].GetBinContent(i) -
hist['BkgSum'].GetBinContent(i)) / (math.sqrt(
math.pow(hist['BkgSum'].GetBinError(i), 2) +
math.pow(hist[data[0]].GetBinError(i), 2))))
setFitResStyle(resFit)
resFit.SetLineColor(15)
resFit.SetFillColor(15)
resFit.SetFillStyle(1001)
resFit.Draw("HIST")
resFitLine = resFit.Clone("resFitLine")
resFitLine.SetLineWidth(1)
resFitLine.SetFillStyle(0)
resFitLine.Draw("SAME, HIST")
c1.Update()
if gROOT.IsBatch(
) and options.saveplots: # and (treeRead and channel in selection.keys()):
AddString = ""
if not os.path.exists("plots_" + options.name + "/" + plotdir):
os.makedirs("plots_" + options.name + "/" + plotdir)
if fileRead:
if RESIDUAL: AddString = "_PostFit_Residual"
else: AddString = "_PostFit"
#c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+AddString+".png")
c1.Print("plots_" + options.name + "/" + plotdir + "/" + plotname +
binName + AddString + ".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if gROOT.IsBatch() and not fileRead and (
var == 'MET_pt' or
(channel.startswith('SL') and var == 'MET_sign') or
(channel.endswith('ZR') and var == 'FakeMET_pt')):
saveHist(hist, channel + binName)
def plot(var, cut, nm1=False):
### Preliminary Operations ###
treeRead = True if not FILE else False # Read from tree
channel = cut
isBlind = BLIND
showSignal = False if 'SB' in cut or 'TR' in cut else True
# Determine explicit cut
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if k in cut: cut = cut.replace(k, alias[k])
# Determine Primary Dataset
pd = []
if "isSingleMuonPhotonTrigger" in cut:
pd = [x for x in sample['data_obs']['files'] if "MuonEG" in x]
elif "isJPsiTrigger" in cut:
pd = [x for x in sample['data_obs']['files'] if "Charmonium" in x]
else:
print "Cannot determine Primary Dataset."
exit()
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
if isBlind and "SR" in channel and var in ["H_mass"]:
cut += " && ( isMC ? 1 : !(H_mass > 86 && H_mass < 96) && !(H_mass > 120 && H_mass < 130) )"
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
cutstring = "(eventWeightLumi)" + ("*(" + cut +
")" if len(cut) > 0 else "")
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('logx' if variable[var]['logx'] else '') +
('logy' if variable[var]['logy'] else ''), variable[var]['nbins'],
variable[var]['min'], variable[var]['max'])
hist[s].Sumw2()
tree[s] = TChain("Events")
for j, ss in enumerate(sample[s]['files']):
if s in data and not ss in pd: continue
if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss):
continue
if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss):
continue
if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss):
continue
for f in os.listdir(NTUPLEDIR + '/' + ss):
tree[s].Add(NTUPLEDIR + '/' + ss + '/' + f)
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
# jobs = []
# queue = multiprocessing.Queue()
# for i, s in enumerate(data+back+sign):
# for j, ss in enumerate(sample[s]['files']):
# if s in data and not ss in pd: continue
# if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss): continue
# if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss): continue
# if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss): continue
# if treeRead: # Project from tree
## hist[s] = loopProject(s, ss, variable[var], cutstring, True)
# p = multiprocessing.Process(target=parallelProject, args=(queue, s, ss, variable[var], cutstring, ))
# jobs.append(p)
# p.start()
# else: # Histogram written to file
# hist[s] = readhist(FILE, s, var, cut)
#
# # Wait for all jobs to finish
# for job in jobs:
# h = queue.get()
# if not h.GetOption() in hist: hist[h.GetOption()] = h
# else: hist[h.GetOption()].Add(h)
# for job in jobs:
# job.join()
# Histogram style
for i, s in enumerate(data + back + sign):
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'] if not options.norm else 0)
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
hist[s].SetLineWidth(sample[s]['linewidth'])
### Create Bkg Sum histogram ###
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
if options.norm:
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(hist[data[0]].Integral() / hist['BkgSum'].Integral())
for i, s in enumerate(sign):
hist[s].Scale(hist[data[0]].Integral() / hist[s].Integral())
# Create data and Bkg sum histograms
# if BLIND: # or 'SR' in channel:
# hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
# hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
# for i, s in enumerate(data+back+sign+['BkgSum']): addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s]['plot'] = True
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.04)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
logX, logY = "logx" in hist['BkgSum'].GetZaxis().GetTitle(
), "logy" in hist['BkgSum'].GetZaxis().GetTitle()
if logY: c1.GetPad(bool(RATIO)).SetLogy()
if logX: c1.GetPad(bool(RATIO)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
#data_graph.Draw("SAME, PE")
# if showSignal:
# smagn = 1. #if treeRead else 1.e2 #if logY else 1.e2
for i, s in enumerate(sign):
if sample[s]['plot']: hist[s].Draw("SAME, HIST")
# hist[s].Scale(smagn)
# hist[s].Draw("SAME, HIST") # signals Normalized, hist[s].Integral()*sample[s]['weight']
# #textS = drawText(0.80, 0.9-leg.GetNRows()*0.05 - 0.02, stype+" (x%d)" % smagn, True)
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((5. if logY else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if logY else 0.)
if logY:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if logY: bkg.SetMinimum(1)
leg.Draw()
drawCMS(LUMI[YEAR], "Preliminary")
if channel in aliasNames: drawRegion(aliasNames[channel], True)
#drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
#if len(sign) > 0:
# if channel.startswith('X') and len(sign)>0: drawNorm(0.9-0.05*(leg.GetNRows()+1), "#sigma(X) = %.1f pb" % 1.)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
if logX: c1.GetPad(2).SetLogx()
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
if var in ["H_mass"]:
c1.cd(bool(RATIO))
boxZ = drawBox(XZMIN, hist['data_obs'].GetMinimum(), XZMAX,
hist['data_obs'].GetMaximum() / 1.30, "Z")
boxH = drawBox(XHMIN, hist['data_obs'].GetMinimum(), XHMAX,
hist['data_obs'].GetMaximum() / 1.30, "H")
c1.Update()
if True: #gROOT.IsBatch():
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
c1.Print("plots/" + channel + "/" + varname + ".png")
c1.Print("plots/" + channel + "/" + varname + ".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
normCMS.SetLineColor(kBlue + 2)
normCMS.Draw('apz')
normCMS.SetMaximum(0.3)
leg = TLegend(0.4, 1. - gPad.GetTopMargin() - 0.03 - 0.18,
1. - gPad.GetRightMargin() - 0.02,
1. - gPad.GetTopMargin() - 0.03, '', 'NDC')
leg.SetMargin(0.15)
#leg.Dump()
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.AddEntry(normCMS, 'CMS, |y|<2, #sqrt{s}=7 TeV', 'pe')
if (D0):
D0.Draw('pz')
leg.AddEntry(D0, 'D#oslash, |y|<1.8, #sqrt{s}=1.96 TeV', 'pe')
else:
leg.SetY1(1. - gPad.GetTopMargin() - 0.03 - 0.12)
if (CDF):
CDF.Draw('pz')
leg.AddEntry(CDF, 'CDF, |y|<0.4, #sqrt{s}=1.8 TeV', 'pe')
#leg.Dump()
leg.Draw('same')
l.DrawLatex(1.0 - gPad.GetRightMargin() - 0.04,
leg.GetY1() - 0.06, '#varUpsilon(%iS)' % resonance)
gPad.SetLogy()
gPad.Update()
normCMS.GetXaxis().SetTitle('p_{T} (GeV/c)')
normCMS.GetYaxis().SetTitle('(d#sigma/dp_{T})/#sigma_{TOT} (GeV/c)^{-1}')
normCMS.GetXaxis().SetLimits(0., 30.)
gPad.Modified()
gPad.Print('TevatronCompare%iS.eps' % resonance)
gPad.Print('TevatronCompare%iS.png' % resonance)
class Plot(object):
"""Structural class for representing, accessing, and maintaining references
to ROOT graphical elements forming a plot, potentially with a ratio
subplot.
"""
# Plotting 'constants' for the plot class. Ideally, one would allow these
# to be flexible, but unfortunately ROOT's coordinate system is extremely
# inconsistent and fragile, so it is best to fix these values here. You
# can change them dynamically with Plot.Whatever = value, but it is
# probably best to leave them alone.
# TODO: 600x600 and 800x600 are the ATLAS default for square and
# rectangular plots respectively. Fix this when everything is calm.
PLOT_WIDTH = 1280 # px
PLOT_HEIGHT = 1024 # px
#PLOT_MARGINS = (0.125, 0.05, 0.1, 0.1) # Left, Right, Bottom, Top
PLOT_MARGINS = (0.125, 0.05, 0.1, 0.07) # Left, Right, Bottom, Top
PLOT_MARGINS_WITH_RATIO = (0.125, 0.05, 0.025, 0.1)
PLOT_RATIO_MARGINS = (0.125, 0.05, 0.325, 0.05)
PLOT_TITLE_X = 0.5
PLOT_TITLE_Y = 0.95
PLOT_TITLE_TEXT_SIZE = 0.04
PLOT_TITLE_TEXT_COLOR = 1
PLOT_TITLE_TEXT_FONT = 42
PLOT_HEADER_HEIGHT = 400 # px
PLOT_LEGEND_LEFT = 0.45
PLOT_LEGEND_RIGHT = 0.95
PLOT_LEGEND_BOTTOM = 0.7
PLOT_LEGEND_BOTTOM_WITH_RATIO = 0.63
PLOT_LEGEND_TOP = 0.90
PLOT_LEGEND_TOP_WITH_RATIO = 0.86
PLOT_LEGEND_TEXT_SIZE = 0.025
PLOT_LEGEND_TEXT_SIZE_WITH_RATIO = 0.03
PLOT_LEGEND_ROW_SIZE = 0.04
PLOT_LEGEND_ROW_SIZE_WITH_RATIO = 0.045
PLOT_LEGEND_N_COLUMNS = 1
PLOT_LEGEND_PIVOT_COLUMNS = True
PLOT_STAT_LEFT = 0.55
PLOT_STAT_LEFT_WITH_RATIO = 0.60
PLOT_STAT_RIGHT = 0.85
PLOT_STAT_RIGHT_WITH_RATIO = 0.93
PLOT_STAT_BOTTOM = 0.15
PLOT_STAT_BOTTOM_WITH_RATIO = 0.07
PLOT_STAT_TOP = 0.4
PLOT_STAT_TOP_WITH_RATIO = 0.45
PLOT_STAT_TEXT_FONT = 42
PLOT_STAT_TEXT_SIZE = 0.03
PLOT_STAT_TEXT_SIZE_WITH_RATIO = 0.04
PLOT_RATIO_FRACTION = 0.3 # fraction of canvas height
PLOT_X_AXIS_TITLE_SIZE = 0.042
PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO = 0.14
PLOT_X_AXIS_TITLE_OFFSET = 0.95
PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO = 0.96
PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO = 0.12
PLOT_Y_AXIS_LABEL_OFFSET = 0.01
PLOT_Y_AXIS_TITLE_SIZE = 0.042
PLOT_Y_AXIS_TITLE_SIZE_WITH_RATIO = 0.06
PLOT_Y_AXIS_TITLE_OFFSET = 1.0
PLOT_Y_AXIS_TITLE_OFFSET_WITH_RATIO = 0.95
PLOT_Y_AXIS_LABEL_SIZE_WITH_RATIO = 0.05
PLOT_RATIO_Y_AXIS_TITLE_SIZE = 0.12
PLOT_RATIO_Y_AXIS_TITLE_OFFSET = 0.40
PLOT_RATIO_Y_AXIS_LABEL_SIZE = 0.12
PLOT_RATIO_Y_AXIS_LABEL_OFFSET = PLOT_Y_AXIS_LABEL_OFFSET
PLOT_RATIO_Y_AXIS_NDIVISIONS = 504
PLOT_RATIO_Y_AXIS_MINIMUM = 0.6
PLOT_RATIO_Y_AXIS_MAXIMUM = 1.4
PLOT_ERROR_BAND_FILL_STYLE = 3254 # Diagonal lines
PLOT_ERROR_BAND_FILL_COLOR = 13 # Gray
PLOT_ERROR_BAND_LINE_WIDTH = 0
PLOT_ERROR_BAND_LINE_COLOR = 0
PLOT_RATIO_ERROR_BAND_FILL_STYLE = 3254 # Diagonal lines
PLOT_RATIO_ERROR_BAND_FILL_COLOR = 807 # Orange
PLOT_RATIO_ERROR_BAND_LINE_WIDTH = 0
PLOT_RATIO_ERROR_BAND_LINE_COLOR = 0
# Stamp settings
PLOT_ATLAS_STAMP_TEXT_SIZE = 0.035
PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO = 0.05
PLOT_ATLAS_STAMP_TEXT_COLOR = 1
PLOT_ATLAS_STAMP_TEXT_FONT = 42
PLOT_ATLAS_STAMP_LEFT = 0.18
PLOT_ATLAS_STAMP_TOP = 0.875
PLOT_ATLAS_STAMP_TOP_WITH_RATIO = 0.82
# Stamp specializations
PLOT_ATLAS_STAMP_ATLAS_TEXT_FONT = 72
PLOT_ATLAS_STAMP_ATLAS_LABEL_LEFT = 0.28
PLOT_ATLAS_STAMP_LUMINOSITY_OFFSET = 0.036
PLOT_ATLAS_STAMP_LUMINOSITY_OFFSET_WITH_RATIO = 0.05
PLOT_ATLAS_STAMP_LUMINOSITY_SIZE = 0.062
PLOT_ATLAS_STAMP_LUMINOSITY_SIZE_WITH_RATIO = 0.085
def __init__(self,
title='',
x_title=None,
y_title=None,
plot_header=True,
ratio=False,
x_range=None,
y_max=None,
y_log_scale=False):
"""Initializes a new instance of the Plot class.
Args:
title: The title to set for the histogram
plot_header: Whether or not to include whitespace at the top of the
plot for the ATLAS label and legend
ratio: Whether or not to include a ratio plot
x_range: A tuple of (x_min, x_max)
y_max: The maximum Y axis value
y_log_scale: Use log scale for Y axis
"""
# Store the title
self._title = title
self._x_title, self._y_title = x_title, y_title
# Store whether or not the user wants to create a plot header
self._plot_header = plot_header
# Calculate a unique name for the plot components
name = _rand_uuid()
# Create a canvas
self._canvas = TCanvas(name + '_canvas', name, int(self.PLOT_WIDTH),
int(self.PLOT_HEIGHT))
SetOwnership(self._canvas, False)
# Create the main plot and draw it
self._plot = TPad(name + '_plot', name, 0.0,
(self.PLOT_RATIO_FRACTION if ratio else 0.0), 1.0,
1.0)
SetOwnership(self._plot, False)
self._plot.SetMargin(
*(self.PLOT_MARGINS_WITH_RATIO if ratio else self.PLOT_MARGINS))
self._plot.Draw()
# HACK: Draw the plot title.
# https://root.cern.ch/phpBB3/viewtopic.php?t=18282. Wonderful.
self._draw_title()
# Store ranges
self._x_range = x_range
if y_max is not None:
self._set_maximum_value(y_max)
# Store log scale
self._y_log_scale = y_log_scale
# Switch back to the context of the canvas
self._canvas.cd()
# Create a ratio plot and draw it if requested
if ratio:
self._ratio_plot = TPad(name + '_ratio', name, 0.0, 0.0, 1.0,
self.PLOT_RATIO_FRACTION)
SetOwnership(self._ratio_plot, False)
self._ratio_plot.SetMargin(*self.PLOT_RATIO_MARGINS)
self._ratio_plot.SetGridy(True)
self._ratio_plot.Draw()
else:
self._ratio_plot = None
# Track whether or not we've already drawn to the main pad
self._drawn = False
# Track whether or not we've already drawn to the ratio pad
self._ratio_drawn = False
# Track that object which sets up the axes in the main plot
self._axes_object = None
# Create a structure to track any histograms we generate internally
# which need to be added to any legends created
self._legend_extras = []
# Create lists of the cloned drawables, just to be certain
self._drawables = []
self._ratio_drawables = []
def save(self, path, extensions=['pdf']):
"""Saves this plot to file.
Args:
path: The path where the plot should be saved.
"""
# Force an update of the canvas
self._canvas.Update()
# Save to file
for e in extensions:
self._canvas.SaveAs(path + '.' + e)
def _get_maximum_value(self):
"""Returns the currently set maximum value (possibly None).
"""
if hasattr(self, '_maximum_value'):
return self._maximum_value
return None
def _set_maximum_value(self, value):
"""Sets the current maximum value, possibly including room for a plot
header.
Args:
value: The value to set
"""
# Check if the current value is not None, and if so, throw an error
# because this property should not be set twice
if self._get_maximum_value() is not None:
raise RuntimeError('maximum value should not be set twice')
# If the value is None, ignore it
if value is None:
return
# If the user wants a plot header, then add space for one
if self._plot_header:
# Grab the plot pad height (in pixels)
plot_height = (self.PLOT_HEIGHT *
(self._plot.GetY2() - self._plot.GetY1()))
# Adjust the height
value *= (plot_height + self.PLOT_HEADER_HEIGHT) / plot_height
# Set the value
self._maximum_value = value
def draw(self, *drawables_styles_options):
"""Plots a collection of plottables to the main plot pad. All TH1
objects are drawn with error bars. THStack elements are only drawn
with an error band if one is provided.
This method may only be called once
Args:
drawables_styles_options: Each argument of this function must be
of the form (object, style, options), where object is one of
the following:
- A TH1 object
- A TH2 object
- A THStack object
- A tuple of the form (THStack, TGraph) where the latter
represents error bars
- A TGraph object
- A TLine object
style is a tuple of the form (line_color, fill_color,
marker_style), and options is a string which will be used for
the options argument of the object's Draw method. Plottables
will be rendered in the order provided. Axes drawing options
(e.g. 'a' or 'same' should not be provided and will be set
automatically). A TLine may not be the first drawable
element.
"""
# Make sure there are drawables
if len(drawables_styles_options) == 0:
raise ValueError('must provide at least one plottable')
# Check if we've already drawn
if self._drawn:
raise RuntimeError('cannot draw twice to a plot')
self._drawn = True
# Remove None-valued drawables
drawables_styles_options = tuple(
((d, s, o) for d, s, o in drawables_styles_options
if valid_drawable(d)))
# Extract drawables
drawables, _, _ = zip(*drawables_styles_options)
# Check if there is a maximum value set, and if not, set it
if self._get_maximum_value() is None:
self._set_maximum_value(maximum_value(drawables))
# Move to the context of the plot pad
self._plot.cd()
# Iterate through and draw drawables based on type
first = True
for drawable, style, option in drawables_styles_options:
# Check if this a tuple of histogram, error_band
if isinstance(drawable, tuple):
drawable, error_band = drawable
else:
error_band = None
# Make a clone of the drawable so we don't modify it
o = clone(drawable)
SetOwnership(o, False)
# Add it to the list of drawables
self._drawables.append(o)
# Set the title appropriately
if not is_line(o):
o.SetTitle(drawable.GetTitle())
# Style the drawable before it is drawn
if style is not None:
if is_line(drawable) or is_function(drawable):
if isinstance(style, dict):
style_line(o, **style)
else:
style_line(o, *style)
else:
if isinstance(style, dict):
style_histogram(o, **style)
else:
style_histogram(o, *style)
# Set the maximum value of the drawable if supported
# HACK: I wish this could go into _handle_axes, but apparently it
# can't because ROOT sucks and this has to be set on EVERY
# drawable, not just the one with the axes.
if is_scatter(o):
o.SetMinimum(1 if self._y_log_scale else 0)
if is_histo(o) or is_graph(o) or is_stack(o) or is_function(o):
o.SetMaximum(self._get_maximum_value())
# With TGraph, this is sometimes necessary. Perhaps with TH1
# too. I'm not sure what happens if we set log scale, but
# we'll cross that bridge then.
o.SetMinimum(1 if self._y_log_scale else 0)
# Include axes if we need to. Store the x-axis range.
if first:
if is_line(o):
raise ValueError('TLine may not be first drawable')
if is_graph(o):
option += 'a'
else:
option += 'same'
first = False
# Draw the drawable
o.Draw(option)
# TODO: This method of plotting the stats box is a huge hack. We
# should plot fit functions separately, and move the stats box
# plotting to its own function. Home grow everything, the only
# way you can make ROOT work.
if is_graph(o) or is_histo(o):
if len(o.GetListOfFunctions()) > 0:
# HACK: Need to call Update() to paint the fit stats
self._plot.Update()
stats = o.FindObject("stats")
if stats:
stats.SetTextFont(Plot.PLOT_STAT_TEXT_FONT)
stats.SetTextSize(
(Plot.PLOT_STAT_TEXT_SIZE_WITH_RATIO if
self._ratio_plot else Plot.PLOT_STAT_TEXT_SIZE))
stats.SetX1NDC(
(Plot.PLOT_STAT_LEFT_WITH_RATIO
if self._ratio_plot else Plot.PLOT_STAT_LEFT))
stats.SetY1NDC(
(Plot.PLOT_STAT_BOTTOM_WITH_RATIO
if self._ratio_plot else Plot.PLOT_STAT_BOTTOM))
stats.SetX2NDC(
(Plot.PLOT_STAT_RIGHT_WITH_RATIO
if self._ratio_plot else Plot.PLOT_STAT_RIGHT))
stats.SetY2NDC(
(Plot.PLOT_STAT_TOP_WITH_RATIO
if self._ratio_plot else Plot.PLOT_STAT_TOP))
# Handle axes
if not is_line(o):
self._handle_axes(o, option)
# If there is an error band, draw it
if error_band is not None:
self._draw_error_band(error_band)
if self._y_log_scale:
self._plot.SetLogy(1)
# TODO: Verify this. It breaks 2D plotting.
# HACK: Need to force a redraw of plot axes due to issue with ROOT:
# http://root.cern.ch/phpBB3/viewtopic.php?f=3&t=14034
#self._plot.RedrawAxis()
def _handle_axes(self, drawable, option):
"""If there is no object currently registered as the owner of the axes
drawn on the main plot, then this will set it.
Args:
drawable: The graph, histogram or stack whose axes were ALREADY
drawn
option: The option with which to draw the axes
"""
# If we already have an axes object, ignore this one
if self._axes_object is not None:
return
# Grab the histogram used for axes style/range manipulation
if is_stack(drawable) or is_graph(drawable):
axes_histogram = drawable.GetHistogram()
else:
axes_histogram = drawable
self._axes_object = axes_histogram
# Grab the histogram used for title manipulation
if is_stack(drawable):
title_histogram = drawable.GetHists()[0]
else:
title_histogram = drawable
# Grab axes
x_axis, y_axis = axes_histogram.GetXaxis(), axes_histogram.GetYaxis()
# Grab titles from first histogram if not set explicitly
if self._x_title is None:
self._x_title = title_histogram.GetXaxis().GetTitle()
if self._y_title is None:
self._y_title = title_histogram.GetYaxis().GetTitle()
if self._x_range is not None:
#x_axis.SetRangeUser(*self._x_range)
x_axis.SetLimits(*self._x_range)
# Style x-axis, or hide it if this plot has a ratio plot
if self._ratio_plot:
x_axis.SetLabelOffset(999)
x_axis.SetTitleOffset(999)
else:
x_axis.SetTitle(self._x_title)
x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE)
x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET)
# Style y-axis
if self._ratio_plot:
y_axis.SetLabelSize(self.PLOT_Y_AXIS_LABEL_SIZE_WITH_RATIO)
y_axis.SetLabelOffset(self.PLOT_Y_AXIS_LABEL_OFFSET)
y_axis.SetTitle(self._y_title)
y_axis.SetTitleSize(
(self.PLOT_Y_AXIS_TITLE_SIZE_WITH_RATIO
if self._ratio_plot else self.PLOT_Y_AXIS_TITLE_SIZE))
y_axis.SetTitleOffset(
(self.PLOT_Y_AXIS_TITLE_OFFSET_WITH_RATIO
if self._ratio_plot else self.PLOT_Y_AXIS_TITLE_OFFSET))
# Redraw the drawable with the new style
drawable.Draw(option)
def _draw_error_band(self, error_band):
"""Draws an error band on top of histogram objects.
Args:
error_band: The error band to draw (a TGraphAsymmErrors)
"""
# Style it
# HACK: Setting the marker style to 0 specifies this should be filled
# in the legend
error_band.SetMarkerStyle(0)
error_band.SetMarkerSize(0)
error_band.SetFillStyle(self.PLOT_ERROR_BAND_FILL_STYLE)
error_band.SetFillColor(self.PLOT_ERROR_BAND_FILL_COLOR)
error_band.SetLineWidth(self.PLOT_ERROR_BAND_LINE_WIDTH)
error_band.SetLineColor(self.PLOT_ERROR_BAND_LINE_COLOR)
# Draw it
error_band.Draw('e2same')
# Add it to the list of things we need to add to the legend
self._legend_extras.append(error_band)
def draw_ratio_histogram(self,
histogram,
draw_unity=True,
error_band=None):
"""Draws a ratio histogram to the ratio pad.
Args:
histogram: The ratio histogram to draw (use ratio_histogram)
draw_unity: Whether or not to draw a line at 1
error_band: An error band to draw under the ratio histogram
(see owls_hep.uncertainty.ratio_uncertainty_band)
The histogram X axis title is set by draw_histogram if not set
explicitly. draw_ratio_histogram should therefore be called after
draw_histogram.
"""
# Check if we've already drawn
if self._ratio_drawn:
raise RuntimeError('cannot draw twice to a plot')
self._ratio_drawn = True
# Switch to the context of the ratio pad
self._ratio_plot.cd()
# Clone the histogram
histogram = histogram.Clone(_rand_uuid())
SetOwnership(histogram, False)
# Style it
x_axis, y_axis = histogram.GetXaxis(), histogram.GetYaxis()
x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO)
x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO)
x_axis.SetLabelSize(self.PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO)
x_axis.SetTitle(self._x_title)
if self._x_range:
x_axis.SetLimits(*self._x_range)
#x_axis.SetRangeUser(*self._x_range)
else:
x_axis.SetLimits(self._axes_object.GetXaxis().GetXmin(),
self._axes_object.GetXaxis().GetXmax())
#x_axis.SetRangeUser(self._axes_object.GetXaxis().GetXmin(),
#self._axes_object.GetXaxis().GetXmax())
y_axis.SetTitleSize(self.PLOT_RATIO_Y_AXIS_TITLE_SIZE)
y_axis.SetTitleOffset(self.PLOT_RATIO_Y_AXIS_TITLE_OFFSET)
y_axis.SetLabelSize(self.PLOT_RATIO_Y_AXIS_LABEL_SIZE)
y_axis.SetLabelOffset(self.PLOT_RATIO_Y_AXIS_LABEL_OFFSET)
y_axis.SetRangeUser(self.PLOT_RATIO_Y_AXIS_MINIMUM,
self.PLOT_RATIO_Y_AXIS_MAXIMUM)
y_axis.SetNdivisions(self.PLOT_RATIO_Y_AXIS_NDIVISIONS, False)
# Draw it
# NOTE: Have to specify E0 or points out of the vertical range won't
# have their error bars drawn:
# https://root.cern.ch/phpBB3/viewtopic.php?f=3&t=13329
# histogram.Draw('e0p')
# NOTE: Or live with it and get rid of points from zero value bins
histogram.Draw('ep')
# Draw a line at unity if requested
if draw_unity:
# Calculate the line coordinates
line_min = histogram.GetBinLowEdge(1)
max_bin = histogram.GetNbinsX()
line_max = (histogram.GetBinLowEdge(max_bin) +
histogram.GetBinWidth(max_bin))
# Create and draw the line
unit_line = TLine(line_min, 1.0, line_max, 1.0)
SetOwnership(unit_line, False)
unit_line.SetLineColor(2) # Red
unit_line.SetLineWidth(2)
unit_line.Draw('same')
# If an error band was provided, draw it and add it to our legend
# elements
if error_band:
# Keep ownership of the error band
SetOwnership(error_band, False)
# Style it
error_band.SetMarkerSize(0)
error_band.SetFillStyle(self.PLOT_RATIO_ERROR_BAND_FILL_STYLE)
error_band.SetFillColor(self.PLOT_RATIO_ERROR_BAND_FILL_COLOR)
error_band.SetLineWidth(self.PLOT_RATIO_ERROR_BAND_LINE_WIDTH)
error_band.SetLineColor(self.PLOT_RATIO_ERROR_BAND_LINE_COLOR)
# Draw it
error_band.Draw('e2same')
# Now, if we've drawn unity or an error band, redraw our ratio
# histogram so that its point lie on top of the unity line or error
# band, but use 'same' so that the axes/ticks don't cover the red line
if draw_unity or error_band:
# histogram.Draw('e0psame')
histogram.Draw('epsame')
def draw_ratios(self,
drawables_styles_options,
draw_unity=True,
y_range=None,
y_title=None):
"""Draws a drawable to the ratio pad.
Args:
drawable: The drawable to draw
draw_unity: Whether or not to draw a line at 1
"""
# Check if we've already drawn
if self._ratio_drawn:
raise RuntimeError('cannot draw twice to a plot')
self._ratio_drawn = True
# Switch to the context of the ratio pad
self._ratio_plot.cd()
# Iterate through and draw drawables based on type
first = True
for drawable, style, option in drawables_styles_options:
# Make a clone of the drawable so we don't modify it
o = clone(drawable)
SetOwnership(o, False)
# Add it to the list of drawables
self._ratio_drawables.append(o)
# Set the title appropriately
if not is_line(o):
o.SetTitle(drawable.GetTitle())
# Style the drawable before it is drawn
if style is not None:
if is_line(drawable) or is_function(drawable):
style_line(o, *style)
else:
style_histogram(o, *style)
if not is_line(o):
if y_range is not None:
o.SetMinimum(y_range[0])
o.SetMaximum(y_range[1])
# Include axes if we need
if first:
if is_line(o):
raise ValueError('TLine may not be first drawable')
x_axis, y_axis = o.GetXaxis(), o.GetYaxis()
if is_graph(o):
option += 'a'
else:
option += 'same'
first = False
# Draw the drawable
o.Draw(option)
x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO)
x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO)
x_axis.SetLabelSize(self.PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO)
x_axis.SetTitle(self._x_title)
if self._x_range:
x_axis.SetLimits(*self._x_range)
#x_axis.SetRangeUser(*self._x_range)
else:
x_axis.SetLimits(self._axes_object.GetXaxis().GetXmin(),
self._axes_object.GetXaxis().GetXmax())
#x_axis.SetRangeUser(self._axes_object.GetXaxis().GetXmin(),
#self._axes_object.GetXaxis().GetXmax())
y_axis.SetTitleSize(self.PLOT_RATIO_Y_AXIS_TITLE_SIZE)
y_axis.SetTitleOffset(self.PLOT_RATIO_Y_AXIS_TITLE_OFFSET)
y_axis.SetLabelSize(self.PLOT_RATIO_Y_AXIS_LABEL_SIZE)
y_axis.SetLabelOffset(self.PLOT_RATIO_Y_AXIS_LABEL_OFFSET)
y_axis.SetNdivisions(self.PLOT_RATIO_Y_AXIS_NDIVISIONS, False)
if y_title is not None:
y_axis.SetTitle(y_title)
self._ratio_plot.Update()
def _draw_title(self):
"""Draws a title on the plot.
"""
title = TLatex()
title.SetTextColor(self.PLOT_TITLE_TEXT_COLOR)
title.SetTextFont(self.PLOT_TITLE_TEXT_FONT)
title.SetNDC()
title.SetTextSize(self.PLOT_TITLE_TEXT_SIZE)
title.SetTextAlign(22)
title.DrawLatex(self.PLOT_TITLE_X, self.PLOT_TITLE_Y, self._title)
def draw_atlas_label(self,
luminosity=None,
sqrt_s=None,
custom_label=None,
atlas_label=None):
"""Draws an ATLAS stamp on the plot, with an optional categorization
label.
It is recommended that you construct the Plot with plot_header = True
in order to make space for the label.
Args:
luminosity: The integrated luminosity, in pb^-1
sqrt_s: The center of mass energy, in MeV
label: The label to put after 'ATLAS', None to exclude the 'ATLAS'
categorization entirely
"""
# Change context to the plot pad
self._plot.cd()
# Create the latex object
# TODO: Consider using TPaveText to overwrite drawn graphs and
# histograms. At least for scatter plots.
# TODO: Increase readability: Create two sets of constants -
# one for plots with ratio and one for plots without. Select the
# correct one in draw_ratio_histogram.
stamp = TLatex()
# Style it
stamp.SetTextColor(self.PLOT_ATLAS_STAMP_TEXT_COLOR)
stamp.SetTextSize(
(self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO
if self._ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE))
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT)
stamp.SetNDC()
top = (self.PLOT_ATLAS_STAMP_TOP_WITH_RATIO
if self._ratio_plot else self.PLOT_ATLAS_STAMP_TOP)
# Print an ATLAS label on top
if atlas_label is not None:
# Draw the label
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_ATLAS_TEXT_FONT)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, 'ATLAS')
stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_ATLAS_LABEL_LEFT, top,
atlas_label)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
# Draw the luminosity and sqrt(s)
if luminosity is not None or sqrt_s is not None:
text = ''
if sqrt_s is not None:
text += '#sqrt{{s}} = {0:.0f} TeV'.format(sqrt_s / 1.0e6)
if luminosity is not None:
text += ', '
if luminosity is not None:
if luminosity >= 1000.0:
text += '{0:.1f} fb^{{-1}}'.format(luminosity / 1000.0)
elif luminosity > 100.0:
text += '{0:.2f} fb^{{-1}}'.format(luminosity / 1000.0)
else:
text += '{0:.1f} pb^{{-1}}'.format(luminosity)
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
# If requested, draw the custom label or the 'ATLAS' label,
# preferring the former
if custom_label is not None:
# Draw each line of text, decreasing top for each step
for text in [t for t in custom_label if t is not None]:
stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text)
top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3
def draw_pave(self, texts, position):
"""Draw a text box at the position and fill it with text.
Args:
texts: String or N-tuple of strings.
positon: Absolute position of the form (x1, x2, y1, y2) as an
N-tuple of floats, or a string with one of the values
"{top,bottom}{left,right}"
"""
if isinstance(position, basestring):
if position == 'topleft':
position = (0.15, 0.35, 0.85, 0.75)
elif position == 'topright':
position = (0.95, 0.75, 0.95, 0.85)
elif position == 'bottomleft':
position = (0.05, 0.25, 0.05, 0.15)
elif position == 'topright':
position = (0.95, 0.75, 0.05, 0.15)
# Switch to the context of the main plot
self._plot.cd()
# Create the pave
self._pave = TPaveText(position[0], position[2], position[1],
position[3], 'NDC')
SetOwnership(self._pave, False)
# Add the text
if isinstance(texts, basestring):
self._pave.AddText(texts)
else:
for t in texts:
self._pave.AddText(t)
# Draw the pave
self._pave.Draw()
def draw_legend(self, use_functions=False, legend_entries=None):
"""Draws a legend onto the plot with the specified histograms.
It is recommended that you construct the Plot with plot_header = True
in order to make space for the legend.
Args:
drawables: The elements to include in the legend (via AddEntry)
use_functions: Add associated functions to the legend
"""
# Check if we already have a legend
if hasattr(self, '_legend'):
raise RuntimeError('legend already exists on this plot')
# Check if the plot has been drawn
if not self._drawn:
raise RuntimeError('plot must be drawn before the legend')
# Remove None-valued drawables
drawables = tuple((d for d in self._drawables if d is not None))
# Remove TLine objects
drawables = tuple((d for d in drawables if not is_line(d)))
# If we shouldn't add functions to the legend, remove them
if not use_functions:
drawables = tuple((d for d in drawables if not isinstance(d, TF1)))
# Use only certain entries
if legend_entries is not None:
def get_drawable_by_title(title):
for d in drawables:
if d.GetTitle() == title:
return d
return None
drawables = tuple((get_drawable_by_title(e.GetTitle())
for e in legend_entries if e is not None))
drawables = tuple((d for d in drawables if d is not None))
# Switch to the context of the main plot
self._plot.cd()
# Create the legend
self._legend = TLegend(self.PLOT_LEGEND_LEFT,
(self.PLOT_LEGEND_BOTTOM_WITH_RATIO if
self._ratio_plot else self.PLOT_LEGEND_BOTTOM),
self.PLOT_LEGEND_RIGHT,
(self.PLOT_LEGEND_TOP_WITH_RATIO
if self._ratio_plot else self.PLOT_LEGEND_TOP))
SetOwnership(self._legend, False)
# Style it
self._legend.SetTextSize(
(self.PLOT_LEGEND_TEXT_SIZE_WITH_RATIO
if self._ratio_plot else self.PLOT_LEGEND_TEXT_SIZE))
self._legend.SetBorderSize(0)
self._legend.SetFillStyle(0) # transparent
self._legend.SetNColumns(self.PLOT_LEGEND_N_COLUMNS)
# Create a chained list of all drawables. We decompose THStack
# objects in reverse order, i.e. top-to-bottom.
drawables = \
list(chain(*(drawable_iterable(h, True, True)
for h
in drawables)))
# Add anything to this list that we created internally
drawables.extend(self._legend_extras)
# Because ROOT draws legend entries from left-to-right across rows and
# not top-to-bottom along columns, we need to do a bit of a pivot on
# the list so that the histograms appear in the vertical order of the
# stack
if self.PLOT_LEGEND_PIVOT_COLUMNS:
n_entries = len(drawables)
n_col = self.PLOT_LEGEND_N_COLUMNS
n_row = int(ceil(float(n_entries) / n_col))
self._legend.SetY1(self._legend.GetY2() - n_row *
(self.PLOT_LEGEND_ROW_SIZE_WITH_RATIO if self.
_ratio_plot else self.PLOT_LEGEND_ROW_SIZE))
legend_order = []
for r in xrange(0, n_row):
for c in xrange(0, n_col):
if (r * n_col + c) == n_entries:
# Don't need an outer break, this would only happen on the
# last row if n_row * n_col != n_entries
break
legend_order.append(drawables[r + c * n_row])
else:
legend_order = drawables
# Add the drawables
for drawable in legend_order:
SetOwnership(drawable, False)
title = drawable.GetTitle()
# HACK: Convention: legend for drawables with a non-default
# marker style (data) to be drawn as line with point, and with
# empty fill (signal) to be drawn as line
if drawable.GetMarkerStyle() != 0:
self._legend.AddEntry(drawable, title, 'ep')
elif drawable.GetFillColor() == 0:
self._legend.AddEntry(drawable, title, 'l')
else:
self._legend.AddEntry(drawable, title, 'f')
# Draw the legend
self._legend.Draw()
def efficiencyAll():
#signals = {'XZHeebb':['eebb'],'XZHmmbb':['mmbb'],'XZHnnbb':['nnbb'],'XZHee0b':['ee0b'],'XZHmm0b':['mm0b'],'XZHnn0b':['nn0b'],'XZHVBFeebbVBF':['eebbVBF'],'XZHVBFmmbbVBF':['mmbbVBF'],'XZHVBFnnbbVBF':['nnbbVBF'],'XZHVBFee0bVBF':['ee0bVBF'],'XZHVBFmm0bVBF':['mm0bVBF'],'XZHVBFnn0bVBF':['nn0bVBF']}
labels = {'XZHeebb' : "eeb#bar{b}",'XZHmmbb' : "#mu#mub#bar{b}",'XZHnnbb' : "#nu#nub#bar{b}",'XZHee0b' : "ee0b",'XZHmm0b' : "#mu#mu0b",'XZHnn0b' : "#nu#nu0b",'XZHVBFeebbVBF' : "eeb#bar{b}VBF",'XZHVBFmmbbVBF' : "#mu#mub#bar{b}VBF",'XZHVBFnnbbVBF' : "#nu#nub#bar{b}VBF",'XZHVBFee0bVBF' : "ee0bVBF",'XZHVBFmm0bVBF' : "#mu#mu0bVBF",'XZHVBFnn0bVBF' : "#nu#nu0bVBF"}
colors = {'XZHeebb' : 2, 'XZHmmbb' : 4, 'XZHnnbb' : 2,'XZHee0b' : 3, 'XZHmm0b' : 6, 'XZHnn0b' : 4,'XZHVBFeebbVBF' : 2, 'XZHVBFmmbbVBF' : 4, 'XZHVBFnnbbVBF' : 2,'XZHVBFee0bVBF' : 3, 'XZHVBFmm0bVBF' : 6, 'XZHVBFnn0bVBF' : 4}
styles = {'XZHeebb' : 1, 'XZHmmbb' : 1, 'XZHnnbb' : 1,'XZHee0b' : 1, 'XZHmm0b' : 1, 'XZHnn0b' : 1,'XZHVBFeebbVBF' : 1, 'XZHVBFmmbbVBF' : 1, 'XZHVBFnnbbVBF' : 1,'XZHVBFee0bVBF' : 1, 'XZHVBFmm0bVBF' : 1, 'XZHVBFnn0bVBF' : 1}
marker = {'XZHeebb' : 22, 'XZHmmbb' : 20, 'XZHnnbb' : 22,'XZHee0b' : 22, 'XZHmm0b' : 20, 'XZHnn0b' : 20, 'XZHVBFeebbVBF' : 22, 'XZHVBFmmbbVBF' : 20, 'XZHVBFnnbbVBF' : 22,'XZHVBFee0bVBF' : 22, 'XZHVBFmm0bVBF' : 20, 'XZHVBFnn0bVBF' : 20}
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
eff = {}
for signal_samples in ['ZlepHinc','ZinvHinc','ZinvHincVBF','ZlepHincVBF']:
if signal_samples == 'ZinvHinc':
signals = {'XZHnnbb':['nnbb'],'XZHnn0b':['nn0b']}
sign_list = ['XZHnnbb','XZHnn0b']
elif signal_samples == 'ZlepHinc':
signals = {'XZHeebb':['eebb'],'XZHmmbb':['mmbb'],'XZHee0b':['ee0b'],'XZHmm0b':['mm0b']}
sign_list = ['XZHeebb','XZHmmbb','XZHee0b', 'XZHmm0b']
elif signal_samples == 'ZinvHincVBF':
signals = {'XZHVBFnnbbVBF':['nnbbVBF'],'XZHVBFnn0bVBF':['nn0bVBF']}
sign_list = ['XZHVBFnnbbVBF', 'XZHVBFnn0bVBF']
elif signal_samples == 'ZlepHincVBF':
signals = {'XZHVBFeebbVBF':['eebbVBF'],'XZHVBFmmbbVBF':['mmbbVBF'],'XZHVBFee0bVBF':['ee0bVBF'],'XZHVBFmm0bVBF':['mm0bVBF']}
sign_list = ['XZHVBFeebbVBF', 'XZHVBFmmbbVBF','XZHVBFee0bVBF', 'XZHVBFmm0bVBF']
for sign, channels in signals.iteritems():
treeSign = {}
ngenSign = {}
nevtSign = {}
eff[sign] = TGraphErrors()
eff[sign].SetTitle(sign)
eff[sign].SetMarkerColor(colors[sign])
eff[sign].SetMarkerSize(1.25)
eff[sign].SetLineColor(colors[sign])
eff[sign].SetLineWidth(2)
eff[sign].SetLineStyle(styles[sign])
eff[sign].SetMarkerStyle(marker[sign])
for i, m in enumerate(genPoints):
neff = 0.
for channel in channels:
if signal_samples == 'ZinvHinc':
file_list = ['Ntuples2016/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m,'Ntuples2017/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m,'Ntuples2018/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m]
elif signal_samples == 'ZlepHinc':
file_list = ['Ntuples2016/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m,'Ntuples2017/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m,'Ntuples2018/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m]
elif signal_samples == 'ZinvHincVBF':
file_list = ['Ntuples2016/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m,'Ntuples2017/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m,'Ntuples2018/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m]
elif signal_samples == 'ZlepHincVBF':
file_list = ['Ntuples2016/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m,'Ntuples2017/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m,'Ntuples2018/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m]
#if not 'VBF' in channel:
# signMass = "XZH_M%d" % m
#else:
# signMass = "XZHVBF_M%d" % m
ngenSign[m] = 0.
nevtSign[m] = 0.
#for j, ss in enumerate(sample[signMass]['files']):
for j, ss in enumerate(file_list):
sfile = TFile(NTUPLEDIR + ss + ".root", "READ")
if not sfile.Get("Events")==None:
ngenSign[m] += sfile.Get("Events").GetEntries()
# From trees
treeSign[m] = sfile.Get("tree")
nevtSign[m] += treeSign[m].GetEntries(selection[channel] + selection['SR'])
else:
ngenSign[m] = -1
print "Failed reading file", NTUPLEDIR + ss + ".root"
sfile.Close()
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
# Gen Br
#print "m:",m
#print "nevtSign:",nevtSign[m]
#print "ngenSign:",ngenSign[m]
neff += nevtSign[m]/ngenSign[m]
if 'ln' in sign or 'll' in sign: neff *= 1.5
n = eff[sign].GetN()
eff[sign].SetPoint(n, m, neff)
eff[sign].SetPointError(n, 0, 0)
n = 0. #max([eff[x].GetN() for x in channels])
maxEff = 0.
#sign_list = ['XZHeebb','XZHmmbb','XZHnnbb', 'XZHee0b', 'XZHmm0b', 'XZHnn0b','XZHVBFeebbVBF', 'XZHVBFmmbbVBF', 'XZHVBFnnbbVBF','XZHVBFee0bVBF', 'XZHVBFmm0bVBF', 'XZHVBFnn0bVBF']
leg = TLegend(0.15, 0.15, 0.95, 0.35)
#leg = TLegend(0.15, 0.7, 0.95, 0.8)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
for sign in sign_list:
#if eff[sign].GetN() > 0:
leg.AddEntry(eff[sign], labels[sign], "pl")
n += 1
leg.SetNColumns(int(n/3))
leg.SetY1(leg.GetY2()-n*0.045/leg.GetNColumns())
n_error = max([eff[x].GetN() for x in sign_list])
# Total efficiency
eff["sum"] = TGraphErrors(n_error)
eff["sum"].SetMarkerStyle(24)
eff["sum"].SetMarkerColor(1)
eff["sum"].SetLineWidth(2)
for i in range(n_error):
tot, mass = 0., 0.
for sign in sign_list:
if eff[sign].GetN() > i:
tot += eff[sign].GetY()[i]
mass = eff[sign].GetX()[i]
if tot > maxEff: maxEff = tot
eff["sum"].SetPoint(i, mass, tot)
#legS = TLegend(0.55, 0.85-0.045, 0.95, 0.85)
legS = TLegend(0.55, 0.35-0.045, 0.95, 0.35)
legS.SetBorderSize(0)
legS.SetFillStyle(0) #1001
legS.SetFillColor(0)
legS.AddEntry(eff['sum'], "%s Total efficiency"%signal_samples, "pl")
c1 = TCanvas("c1", "Signal Efficiency", 1200, 800)
c1.cd(1)
c1.GetPad(0).SetTicks(1, 1)
c1.SetLogy()
first = sign_list[0]
#if eff['XZHeebb'].GetN()!=0:
# first = 'XZHeebb'
#else:
# first = 'XZHnnbb'
eff[first].Draw("APL")
for sign, channels in signals.iteritems():
eff[sign].Draw("APL" if i==0 else "SAME, PL")
eff["sum"].Draw("SAME, PL")
leg.Draw()
legS.Draw()
setHistStyle(eff[first], 1.1)
eff[first].SetTitle(";m_{X} (GeV);Acceptance #times efficiency")
eff[first].SetMinimum(0.)
eff[first].SetMaximum(max(1., maxEff*1.5)) #0.65
eff[first].GetXaxis().SetTitleSize(0.045)
eff[first].GetYaxis().SetTitleSize(0.045)
eff[first].GetXaxis().SetLabelSize(0.045)
eff[first].GetYaxis().SetLabelSize(0.045)
eff[first].GetYaxis().SetTitleOffset(1.1)
eff[first].GetXaxis().SetTitleOffset(1.05)
eff[first].GetXaxis().SetRangeUser(750,5500)
eff[first].GetYaxis().SetRangeUser(0., 0.4)
drawCMS(-1,YEAR, "Simulation")
"""
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.05)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(13)
latex.DrawLatex(0.83, 0.99, "(13 TeV)")
latex.SetTextFont(62)
latex.SetTextSize(0.06)
latex.DrawLatex(0.15, 0.90, "CMS")
latex.SetTextSize(0.05)
latex.SetTextFont(52)
"""
#c1.Print("plotsSignal/Efficiency/Efficiency.pdf")
#c1.Print("plotsSignal/Efficiency/Efficiency.png")
c1.Print("plotsSignal/Efficiency/%s_Efficiency.pdf"%signal_samples)
c1.Print("plotsSignal/Efficiency/%s_Efficiency.png"%signal_samples)
def DeepCSV_pt_distribution(
year): ## everything below is jsut copy&past from above
from root_numpy import root2array, fill_hist, array2root
import numpy.lib.recfunctions as rfn
from aliases import alias_deepCSV, WP_deepCSV
### Preliminary Operations ###
treeRead = True
var = 'jpt_1'
channel = 'preselection'
cut = alias_deepCSV['preselection']
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False
showSignal = True
stype = "HVT model B"
if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias_deepCSV.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(
k, alias_deepCSV[k].format(WP=WP_deepCSV[BTAGGING][year]))
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
N_signal_tot = 0.
N_signal_tag = 0.
### Create and fill MC histograms ###
for i, s in enumerate(back + sign):
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";jet p_{T};Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " );" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s,
";jet p_{T};Events" + ('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s:
if year == "run2" or year in ss:
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_1", "eventWeightLumi"],
selection=cut + " && jdeepCSV_1>" +
str(WP_deepCSV[BTAGGING][year]))
if 'signal' in ss.lower():
N_signal_tag += len(arr['jpt_1'][arr['jpt_1'] > 3500])
print "imported " + NTUPLEDIR + ss + ".root"
fill_hist(hist[s],
arr["jpt_1"],
weights=arr["eventWeightLumi"])
arr = None
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_2", "eventWeightLumi"],
selection=cut + " && jdeepCSV_2>" +
str(WP_deepCSV[BTAGGING][year]))
print "imported " + NTUPLEDIR + ss + ".root"
if 'signal' in ss.lower():
N_signal_tag += len(arr['jpt_2'][arr['jpt_2'] > 3500])
fill_hist(hist[s],
arr["jpt_2"],
weights=arr["eventWeightLumi"])
arr = None
if 'signal' in ss.lower():
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_1", "eventWeightLumi"],
selection=cut)
N_signal_tot += len(arr['jpt_1'][arr['jpt_1'] > 3500])
arr = None
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_2", "eventWeightLumi"],
selection=cut)
N_signal_tot += len(arr['jpt_2'][arr['jpt_2'] > 3500])
arr = None
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var == "Events":
sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data + back + ['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
# Create stack
if variable[var]['nbins'] > 0:
bkg = THStack(
"Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " )")
else:
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02,
stype + " (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
drawCMS(LUMI[year], "", suppressCMS=True)
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(2)
res.SetMarkerStyle(31)
res.Reset()
for i in range(0, res.GetNbinsX() + 1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(
i, hist['data_obs'].GetBinContent(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(
i, hist['data_obs'].GetBinError(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if gROOT.IsBatch():
if channel == "": channel = "nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING
c1.Print("plots/MANtag_study/deepCSV_plots/pt_" + year + suffix +
".png")
c1.Print("plots/MANtag_study/deepCSV_plots/pt_" + year + suffix +
".pdf")
# Print table
printTable(hist, sign)
print 'deepCSV efficiency:', N_signal_tag / N_signal_tot
def plot(var, cut, year, norm=False, nm1=False):
### Preliminary Operations ###
treeRead = not cut in [
"nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em",
"emqq", "qqqq"
] # Read from tree
channel = cut
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel
showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
stype = "HVT model B"
if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if BTAGGING == 'semimedium':
if k in cut:
if ADDSELECTION:
cut = cut.replace(
k, aliasSM[k] + SELECTIONS[options.selection])
else:
cut = cut.replace(k, aliasSM[k])
else:
if k in cut:
if ADDSELECTION:
cut = cut.replace(
k, alias[k].format(WP=working_points[BTAGGING]) +
SELECTIONS[options.selection])
else:
cut = cut.replace(
k, alias[k].format(WP=working_points[BTAGGING]))
# Determine Primary Dataset
pd = sample['data_obs']['files']
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
if var == 'jj_deltaEta_widejet':
if "jj_deltaEta_widejet<1.1 && " in cut:
print
print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution"
print
cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "")
else:
print
print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly"
print
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
if treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
if year == "run2" or year in ss:
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " );" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "(eventWeightLumi)" + ("*(" + cut +
")" if len(cut) > 0 else "")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
else: # Histogram written to file
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
if file[ss].IsZombie():
print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
continue
tmphist = file[ss].Get(cut + "/" + var)
if tmphist == None: continue
if not s in hist.keys(): hist[s] = tmphist
else: hist[s].Add(tmphist)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
if options.norm:
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(hist[data[0]].Integral() / hist['BkgSum'].Integral())
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var == "Events":
sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data + back + ['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if SYNC and var == "jj_mass_widejet" and year in ["2016", "2017", "2018"]:
#iFile = TFile("sync/JetHT_run" + year + "_red_cert_scan.root", "READ")
#hist['sync'] = iFile.Get("Mjj")
if year == '2016':
iFile = TFile("sync/2016/2016_07Aug2017_1246_1p1.root", "READ")
hist['sync'] = iFile.Get("h_mjj_data")
elif year == '2017':
iFile = TFile(
"sync/2017/histos_Run2017BCDEF_17Nov2017_JEC2017_mjj1530_cemf_lt_0p8_deltaETA_lt_1p1.root",
"READ")
hist['sync'] = iFile.Get("h_mjj_data")
elif year == '2018':
iFile = TFile(
"sync/2018/Double_sideband_inputs_18v10_preliminary_v2.root",
"READ")
hist['sync'] = iFile.Get("h_mjj")
# hist['sync'] = tmp.Rebin(len(dijet_bins)-1, "sync", array('d', dijet_bins))
# hist['sync'] = tmp.Rebin(100, "sync")
hist['sync'].SetMarkerStyle(31)
hist['sync'].SetMarkerSize(1.25)
hist['sync'].SetMarkerColor(2)
print "Imported and drawing sync file"
# Create stack
if variable[var]['nbins'] > 0:
bkg = THStack(
"Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " )")
else:
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02,
stype + " (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
#drawCMS(LUMI[year], "Work in Progress", suppressCMS=True)
drawCMS(LUMI[year], "", suppressCMS=True)
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
if SYNC:
err.GetYaxis().SetTitle("Nano/Mini")
else:
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(1)
res.SetMarkerStyle(20)
res.Reset()
for i in range(0, res.GetNbinsX() + 1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(
i, hist['data_obs'].GetBinContent(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(
i, hist['data_obs'].GetBinError(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
if SYNC: err.GetYaxis().SetRangeUser(0.9, 1.1)
c1.Update()
if gROOT.IsBatch():
if channel == "": channel = "nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING
if ADDSELECTION: suffix += "_" + options.selection
c1.Print("plots/" + channel + "/" + varname + "_" + year + suffix +
".png")
c1.Print("plots/" + channel + "/" + varname + "_" + year + suffix +
".pdf")
# Print table
printTable(hist, sign)
# if True:
# sFile = TFile("sync/data_2016.root", "RECREATE")
# sFile.cd()
# hist['data_obs'].
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def plotPrePost(category, category2):
if len(category) == 0:
print "Please select a category with the -c option"
exit()
is2 = len(category2) > 0
if 'l' in category:
category, category2, is2 = category.replace('l',
'e'), category.replace(
'l', 'm'), True
isAH = len(category) >= 5
X_name = "JJ_mass" if isAH else "VH_mass"
signalName = signal + category + "_M%d" % mass
data = ["data_obs"]
back = ["Bkg_" + category] if isAH else [
"VV_" + category, "Top_" + category, "Vjets_" + category
]
sign = [signalName] if len(signal) > 0 else []
fitr = ["total", "total_signal", "total_background"]
labels = {
"data_obs": "Data",
"VV_" + category: "VV, Vh",
"Top_" + category: "t#bar{t}, t+X",
"Vjets_" + category: "V+jets",
"Bkg_" + category: "Bkg fit",
signalName: "Signal"
}
if 'ee' in category or 'mm' in category:
labels["Vjets_" + category] = "Z(ll)+jets"
elif 'en' in category or 'mn' in category:
labels["Vjets_" + category] = "W(l#nu)+jets"
elif 'nn' in category:
labels["Vjets_" + category] = "Z(#nu#nu),W(l#nu)+jets"
lastBin = 3500. if 'ee' in category or 'mm' in category or 'nn' in category else 4500.
# xs = 0.
# if 'XWH' in signal or 'XVH' in signal: xs += HVT['B3']['W']['XS'][mass]*HVT['B3']['W']['BR'][mass]
# if 'XZH' in signal or 'XVH' in signal: xs += HVT['B3']['Z']['XS'][mass]*HVT['B3']['Z']['BR'][mass]
xs = getCrossSection(signal, category, mass)
#if len(signal2)>0: xs[signal2] = getCrossSection(signal2, category, 0)
histData, histPre, histPost, graphPre, graphPost = None, {}, {}, {}, {}
if readData:
dataFile = TFile("workspace/" + category + ".root", "READ")
workspace = dataFile.Get("VH_2016")
variable = workspace.var(X_name)
dataset = workspace.data(data[0])
if is2:
dataFile2 = TFile("workspace/" + category2 + ".root", "READ")
workspace2 = dataFile2.Get("VH_2016")
dataset2 = workspace2.data(data[0])
dataset.append(dataset2)
data2 = dataset.createHistogram(variable, variable,
variable.getBinning().numBins() / 10,
1)
data1 = data2.ProjectionX()
data1.SetMarkerStyle(20)
data1.SetMarkerSize(1.25)
data1.SetLineColor(1)
histData = data1
graphData = convertHistToGraph(histData, True)
width = histData.GetXaxis().GetBinWidth(1)
inFile = TFile(options.fileName, "READ")
if inFile == None:
print "File", options.fileName, "not found"
return
if not inFile.GetDirectory("shapes_prefit/" + category):
print "Category", category, "not recognized"
return
for i, h in enumerate(back + sign + fitr):
histPre[h] = inFile.Get("shapes_prefit/" + category + "/" + h)
if is2:
histPre[h].Add(
inFile.Get("shapes_prefit/" + category2 + "/" +
h.replace(category, category2)))
histPre[h].SetName(h + '_pre')
histPre[h].SetLineColor(getColor(h, category))
histPre[h].SetFillColor(getColor(h, category))
histPre[h].SetLineWidth(3)
if h in back: histPre[h].SetFillStyle(1001)
elif h in fitr: histPre[h].SetFillStyle(3002)
elif h in sign:
histPre[h].SetTitle("m_{%s'} = %d GeV" % (signal[1], mass))
histPre[h].SetOption("HVT model B g_{V}=3")
histPre[h].SetFillStyle(1)
histPre[h].SetLineStyle(3)
histPre[h].SetLineWidth(6)
if xs > 0.: histPre[h].Scale(xs * 1000.)
# histPre[h].Rebin(10)
if readData:
histPre[h].Scale(width)
histPre[h].GetYaxis().SetTitle("Events / ( %d GeV )" % width)
if 'nn' in category:
histPre[h].GetXaxis().SetTitle(
histPre[h].GetXaxis().GetTitle().replace(
'm_{VH}', 'm^{T}_{VH}'))
for i, h in enumerate(back + sign + fitr):
histPost[h] = inFile.Get("shapes_fit_b/" + category + "/" + h)
if is2:
histPost[h].Add(
inFile.Get("shapes_fit_b/" + category2 + "/" +
h.replace(category, category2)))
histPost[h].SetName(h + '_post')
histPost[h].SetLineColor(getColor(h, category))
histPost[h].SetFillColor(getColor(h, category))
histPost[h].SetLineWidth(3)
if h in back: histPost[h].SetFillStyle(1001)
elif h in fitr: histPost[h].SetFillStyle(3002)
elif h in sign:
histPost[h].SetTitle("m_{%s'} = %d GeV" % (signal[1], mass))
histPost[h].SetOption("HVT model B g_{V}=3")
histPost[h].SetFillStyle(1)
histPost[h].SetLineStyle(3)
histPost[h].SetLineWidth(6)
if xs > 0.: histPost[h].Scale(xs * 1000.)
# histPost[h].Rebin(10)
if readData:
histPost[h].Scale(width)
histPost[h].GetYaxis().SetTitle("Events / ( %d GeV )" % width)
if 'nn' in category:
histPost[h].GetXaxis().SetTitle(
histPost[h].GetXaxis().GetTitle().replace(
'm_{VH}', 'm^{T}_{VH}'))
# Set errors ot zero to have smooth curves
for i, h in enumerate(back + sign):
for i in range(histPre[h].GetNbinsX()):
histPre[h].SetBinError(i + 1, 0.)
for i in range(histPost[h].GetNbinsX()):
histPost[h].SetBinError(i + 1, 0.)
stackPre = THStack(
"Pre", ";" + histPre['total'].GetXaxis().GetTitle() + ";" +
histPre['total'].GetYaxis().GetTitle())
for i, s in enumerate(back):
stackPre.Add(histPre[s])
stackPost = THStack(
"Post", ";" + histPost['total'].GetXaxis().GetTitle() + ";" +
histPost['total'].GetYaxis().GetTitle())
for i, s in enumerate(back):
stackPost.Add(histPost[s])
for i, h in enumerate(back):
tmpPre = histPre[back[i]].Clone(back[i] + "_stack_pre")
for j in range(i):
tmpPre.Add(histPre[back[j]])
graphPre[back[i]] = convertHistToGraph(tmpPre)
tmpPost = histPost[back[i]].Clone(back[i] + "_stack_post")
for j in range(i):
tmpPost.Add(histPost[back[j]])
graphPost[back[i]] = convertHistToGraph(tmpPost)
# Additional signal, if present
inFiles = {}
for i, f in enumerate(
signals): #combine/test/mlfit_monoHnn_MZ3000_MA300.root
signalName2 = f.replace('combine/test/mlfit_', '').replace('.root', '')
if 'AZh' in signalName2:
signalName2 = signalName2.replace('AZh', 'AZh' + category)
signalSmpl2 = signalName2.replace(category, '')
signal2 = signalName2.split('_M')[0].replace(category, '')
try:
mass2, mass2A = int(signalName2.split('_M')[1]), 0
except:
mass2, mass2A = int(
signalName2.split('_MZ')[1].split('_MA')[0]), int(
signalName2.split('_MA')[1])
#
inFiles[signalName2] = TFile(f, "READ")
histSign2 = inFiles[signalName2].Get("shapes_prefit/" + category +
"/" + signalName2)
if is2:
histSign2.Add(inFiles[signalName2].Get(
"shapes_prefit/" + category2 + "/" +
signalName2.replace(category, category2)))
histSign2.SetName(signalName2 + '_pre')
if signal2.startswith('X'):
histSign2.SetTitle("m_{V'} = %d GeV" % mass2)
elif signal2.startswith('A'):
histSign2.SetTitle("m_{A} = %d GeV" % mass2)
elif not mass2 == 0:
histSign2.SetTitle("m_{Z'} = %d GeV" % mass2)
if i == 0:
histSign2.SetOption(
"Z'-2HDM\nm_{A}=300 GeV" if 'monoH' in signalName2 else
"Type-II 2HDM\ncos(#beta-#alpha) = 0.25\ntan#beta = 1")
histSign2.SetLineColor(getColor(signalName2, category))
histSign2.SetFillColor(getColor(signalName2, category))
histSign2.SetFillStyle(1)
histSign2.SetLineStyle(5 + i)
histSign2.SetLineWidth(5)
if readData: histSign2.Scale(width)
xs2 = getCrossSection(signalName2, category, 0)
if xs2 > 0.: histSign2.Scale(xs2 * 1000.)
sign += [signalName2]
histPre[signalName2] = histSign2
histPost[signalName2] = histSign2
leg = TLegend(0.6 - 0.005, 0.6, 0.925, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if readData:
leg.AddEntry(graphData, labels[data[0]], "pe")
for i, s in reversed(list(enumerate(back))):
leg.AddEntry(histPre[s], labels[s], "f")
#for i, s in enumerate(sign):
# leg.AddEntry(histPre[s], labels[s], "fl")
###
c1 = TCanvas("c1", "Pre-Post Fit", 800, 800)
c1.Divide(1, 2)
c1.cd(1)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.GetPad(1).SetTopMargin(0.06)
c1.GetPad(1).SetRightMargin(0.05)
c1.GetPad(1).SetBottomMargin(0.01)
c1.GetPad(1).SetTicks(1, 1)
#histData.Draw("APE" if d==0 else "SAME, PE")
#for i, h in enumerate(back): graphPost[h].Draw("ACL" if i==0 else "C")
stackPost.Draw("C")
setHistStyle(stackPost, 1.2)
stackPost.SetMaximum(stackPost.GetMaximum() * 5.)
stackPost.SetMinimum(max(stackPost.GetMinimum(), 0.2))
stackPost.GetXaxis().SetRangeUser(stackPost.GetXaxis().GetXmin(), lastBin)
histPost['total'].SetLineWidth(1)
histPost['total'].Draw("SAME, E3")
histPre['total_background'].SetLineColor(921)
histPre['total_background'].SetLineStyle(2)
histPre['total_background'].SetLineWidth(3)
histPre['total_background'].SetFillColor(1)
histPre['total_background'].SetFillStyle(0)
histPre['total_background'].Draw("SAME, HIST")
for i, s in enumerate(sign):
histPre[s].Draw("SAME, L")
if readData:
graphData.Draw("SAME, PE0")
leg.AddEntry(histPost['total'], "Bkg. unc.", "f")
leg.AddEntry(histPre['total_background'], "Pre-fit", "l")
for i, s in enumerate(sign):
for o in histPre[s].GetOption().split('\n'):
if len(o) > 0.: leg.AddEntry(None, o, "")
leg.AddEntry(histPre[s], histPre[s].GetTitle(), "l")
leg.SetY1(0.9 - leg.GetNRows() * 0.060)
leg.Draw()
# if len(sign)>0:
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.045)
# latex.SetTextFont(42)
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "HVT model B g_{V}=3")
drawCMS(LUMI, "") #Preliminary
drawRegion('XVH' + options.category, True)
drawAnalysis(category)
c1.GetPad(1).SetLogy()
c1.cd(2)
err = histPost['total'].Clone("BkgErr;")
err.SetTitle("")
err.Reset("MICES")
err.GetYaxis().SetTitle("(N^{data}-N^{bkg})/#sigma")
setBotStyle(err, 4 + 1)
err.GetXaxis().SetTitleSize(0.16)
#err.GetXaxis().SetTitleOffset(1.25);
err.GetYaxis().SetTitleOffset(0.33)
err.GetXaxis().SetRangeUser(err.GetXaxis().GetXmin(), lastBin)
err.GetYaxis().SetRangeUser(-5., 5.)
err.SetLineWidth(2)
err.SetLineStyle(2)
err.SetFillStyle(0)
err.Draw("L") #"E2"
if readData:
pulls = makeResidHist(graphData, histPost['total'])
#setBotStyle(pulls, RATIO, False)
pulls.Draw("SAME, PE0")
#drawRatio(hist['data_obs'], hist['BkgSum'])
#drawStat(hist['data_obs'], hist['BkgSum'])
chi2, nbins, npar = 0., 0, 0
for i in range(0, pulls.GetN()):
if graphData.GetY()[i] > 1.e-3:
nbins = nbins + 1
chi2 += pulls.GetY()[i]**2
#drawChi2(chi2, nbins-npar, True)
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsPrePost/BkgSR_" + options.category + ".png")
c1.Print("plotsPrePost/BkgSR_" + options.category + ".pdf")
###
if VERBOSE:
c2 = TCanvas("c2", "Pre-Post Fit", 1200, 600)
c2.Divide(2, 1)
c2.cd(1)
c2.GetPad(1).SetTopMargin(0.06)
c2.GetPad(1).SetRightMargin(0.05)
c2.GetPad(1).SetBottomMargin(0.10)
c2.GetPad(1).SetTicks(1, 1)
#histData.Draw("APE" if d==0 else "SAME, PE")
#for i, h in enumerate(back): graphPre[h].Draw("FL" if i==0 else "SAME, FL")
stackPre.Draw("HIST")
histPre['total'].Draw("SAME, E2")
stackPre.SetMaximum(stackPre.GetMaximum() * 5.)
stackPre.SetMinimum(max(stackPre.GetMinimum(), 0.01))
histData.Draw("SAME, PE")
leg.Draw()
drawCMS(LUMI, "Preliminary")
drawRegion('XVH' + category, True)
drawAnalysis(category)
c2.cd(2)
c2.GetPad(2).SetTopMargin(0.06)
c2.GetPad(2).SetRightMargin(0.05)
c2.GetPad(2).SetBottomMargin(0.10)
c2.GetPad(2).SetTicks(1, 1)
#histData.Draw("APE" if d==0 else "SAME, PE")
#for i, h in enumerate(back): graphPost[h].Draw("ACL" if i==0 else "C")
stackPost.Draw("HIST")
histPost['total'].Draw("SAME, E2")
stackPost.SetMaximum(stackPost.GetMaximum() * 5.)
stackPost.SetMinimum(max(stackPost.GetMinimum(), 0.01))
histData.Draw("SAME, PE")
leg.Draw()
drawCMS(LUMI, "Preliminary")
drawRegion('XVH' + category, True)
drawAnalysis(category)
c2.GetPad(1).SetLogy()
c2.GetPad(2).SetLogy()
c2.Print("combine/test/" + signalName + "_prepost.png")
c2.Print("combine/test/" + signalName + "_prepost.pdf")
c2.Close()
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("out_all.root")["outA/Tevts"]
branch_mc = [
"MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta",
"MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi",
"MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta",
"MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi",
"MC_pis_pt", "MC_q2"
]
branch_rec = [
"B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt",
"Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P",
"mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt",
"q2"
]
nvariable = len(branch_mc)
x_train = tree.array(branch_mc[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_train = np.vstack(
(x_train, tree.array(branch_mc[i], entrystop=options.maxevents)))
x_test = tree.array(branch_rec[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_test = np.vstack(
(x_test, tree.array(branch_rec[i], entrystop=options.maxevents)))
x_train = x_train.T
x_test = x_test.T
x_test = array2D_float(x_test)
#Different type of reconstruction variables
#BN normalization
gamma = 0
beta = 0.2
ar = np.array(x_train)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_train = np.vstack((x_train, a))
x_train = x_train.T
ar = np.array(x_test)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_test = np.vstack((x_test, a))
x_test = x_test.T
#Add noise, remain to be improved
noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape)
x_test_noisy = x_test + noise
x_train = np.clip(x_train, -1., 1.)
x_test = np.clip(x_test, -1., 1.)
x_train_noisy = np.clip(x_train_noisy, -1., 1.)
x_test_noisy = np.clip(x_test_noisy, -1., 1.)
# Network parameters
input_shape = (x_train.shape[1], )
batch_size = 128
latent_dim = 2
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Shape info needed to build Decoder Model
shape = K.int_shape(x)
# Generate the latent vector
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(shape[1])(latent_inputs)
x = Reshape((shape[1], ))(x)
outputs = Activation('tanh', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded = autoencoder.predict(x_test_noisy)
# Draw Comparision Plots
c = TCanvas("c", "c", 700, 700)
fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00)
fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29)
fPads1.SetBottomMargin(0.007)
fPads1.SetLeftMargin(0.10)
fPads1.SetRightMargin(0.03)
fPads2.SetLeftMargin(0.10)
fPads2.SetRightMargin(0.03)
fPads2.SetBottomMargin(0.25)
fPads1.Draw()
fPads2.Draw()
fPads1.cd()
nbin = 50
min = -1.
max = 1.
variable = "P^{B}"
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[options.branch - 1]
ytitle = "Events/" + lbin + "GeV"
h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_rec.Sumw2()
h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_pre.Sumw2()
for i in range(x_test_noisy.shape[0]):
h_rec.Fill(x_test_noisy[i][options.branch - 1])
h_pre.Fill(x_decoded[i][options.branch - 1])
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
h_rec.SetLineColor(2)
h_rec.SetFillStyle(0)
h_rec.SetLineWidth(2)
h_rec.SetLineStyle(1)
h_pre.SetLineColor(3)
h_pre.SetFillStyle(0)
h_pre.SetLineWidth(2)
h_pre.SetLineStyle(1)
h_rec.SetStats(0)
h_pre.SetStats(0)
h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_rec.Draw("HIST")
h_pre.Draw("same HIST")
h_rec.GetYaxis().SetTitleSize(0.06)
h_rec.GetYaxis().SetTitleOffset(0.78)
theLeg = TLegend(0.5, 0.45, 0.95, 0.82, "", "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(.05)
theLeg.AddEntry(h_rec, "Reconstruction", "L")
theLeg.AddEntry(h_pre, "Prediction", "L")
theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
fPads1.cd()
theLeg.Draw()
title = TLatex(
0.91, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.05)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
fPads2.cd()
h_Ratio = h_pre.Clone("h_Ratio")
h_Ratio.Divide(h_rec)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, 2)
h_Ratio.GetYaxis().SetNdivisions(504, 0)
h_Ratio.GetYaxis().SetTitle("Pre/Rec")
h_Ratio.GetYaxis().SetTitleOffset(0.35)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetLabelSize(0.11)
h_Ratio.GetXaxis().SetLabelSize(0.1)
h_Ratio.GetXaxis().SetTitleOffset(0.8)
h_Ratio.GetXaxis().SetTitleSize(0.14)
h_Ratio.SetStats(0)
axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L")
axis1.SetLineColor(1)
axis1.SetLineWidth(1)
for i in range(1, h_Ratio.GetNbinsX() + 1, 1):
D = h_rec.GetBinContent(i)
eD = h_rec.GetBinError(i)
if D == 0: eD = 0.92
B = h_pre.GetBinContent(i)
eB = h_pre.GetBinError(i)
if B < 0.1 and eB >= B:
eB = 0.92
Err = 0.
if B != 0.:
Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) /
(B * B * B * B))
h_Ratio.SetBinContent(i, D / B)
h_Ratio.SetBinError(i, Err)
if B == 0.:
Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) /
(eB * eB * eB * eB))
h_Ratio.SetBinContent(i, D / 0.92)
h_Ratio.SetBinError(i, Err)
if D == 0 and B == 0:
h_Ratio.SetBinContent(i, -1)
h_Ratio.SetBinError(i, 0)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
nc = int((lg - 1) / 12 + 1)
leg.SetNColumns(nc)
extra = (2 + 0.4 * int(lg / nc + 1))
dv = (vmax - vmin) / 10.
if valmin is None:
grall[0].SetMinimum(vmin - dv)
else:
grall[0].SetMinimum(valmin)
if valmax is None:
grall[0].SetMaximum(vmax + dv * extra)
else:
grall[0].SetMaximum(valmax)
if nc > 8:
nc = 8
leg.SetX1(0.9 - 0.1 * nc)
leg.SetY1(0.9 - 0.8 * (extra - 1) / (11 + extra))
leg.Draw()
else:
gr = TGraph(tree.GetSelectedRows(), tree.GetV2(), tree.GetV1())
gr.SetTitle(gtitle)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1.3)
gr.SetMarkerColor(2)
gr.SetLineColor(2)
gr.GetXaxis().SetNoExponent(kTRUE)
if one_run:
gr.GetXaxis().SetTitle("Lumi")
else:
gr.GetXaxis().SetTitle("Runs")
if valmin is not None:
grall[0].SetMinimum(valmin)
legDistr.AddEntry(hRawYieldFDVsCut[iPt], 'Non-prompt', 'f')
legDistr.AddEntry(hRawYieldsVsCutReSum[iPt], 'Prompt + non-prompt',
'l')
legEff.AddEntry(hEffPromptVsCut[iPt], 'Prompt', 'lpe')
legEff.AddEntry(hEffFDVsCut[iPt], 'Non-prompt', 'lpe')
legFrac.AddEntry(hPromptFracVsCut[iPt], 'Prompt', 'lpe')
legFrac.AddEntry(hFDFracVsCut[iPt], 'Non-prompt', 'lpe')
deltaY = 0.
if compareToFc:
legFrac.AddEntry(gPromptFracFcVsCut[iPt], 'Prompt #it{f}_{c}',
'fp')
legFrac.AddEntry(gFDFracFcVsCut[iPt], 'Non-prompt #it{f}_{c}',
'fp')
deltaY += 0.1
legFrac.SetY1(0.83 - deltaY)
if compareToNb:
legFrac.AddEntry(gPromptFracNbVsCut[iPt], 'Prompt #it{N}_{b}',
'fp')
legFrac.AddEntry(gFDFracNbVsCut[iPt], 'Non-prompt #it{N}_{b}',
'fp')
deltaY += 0.1
legFrac.SetY1(0.83 - deltaY)
cEff.append(TCanvas(f'cEff_{ptString}', '', 800, 800))
cEff[iPt].DrawFrame(0.5, hEffPromptVsCut[iPt].GetMinimum() / 5,
nSets + 0.5, 1., f'{commonString};efficiency')
cEff[iPt].SetLogy()
hEffPromptVsCut[iPt].DrawCopy('same')
hEffFDVsCut[iPt].DrawCopy('same')
legEff.Draw()
def hvt(benchmark=['B3', 'A1']):
hxs = {}
hw = {}
gxs = {}
gw = {}
mg = TMultiGraph()
for m in massPoints:
hxs[m] = TH2F("hxs_M%d" % m, ";;", 50, -0.04, 3.96, 100, 0., 2.)
hw[m] = TH2F("hw_M%d" % m, ";;", 50, -0.04, 3.96, 50, 0., 2.)
for m in massPoints:
file = TFile.Open("HVT/scanHVT_M%s.root" % m, "READ")
tree = file.Get("tree")
for entry in range(
tree.GetEntries()): # Fill mass points only if NOT excluded
tree.GetEntry(entry)
gH, gF = tree.gv * tree.ch, tree.g * tree.g * tree.cq / tree.gv
XsBr = tree.CX0 * tree.BRbb * 1000. # in fb
if XsBr < observed[m]: hxs[m].Fill(gH, gF)
if tree.total_widthV0 / float(m) < width: hw[m].Fill(gH, gF)
for b in range(hxs[m].GetNbinsX() * hxs[m].GetNbinsY()):
hxs[m].SetBinContent(b, 1. if hxs[m].GetBinContent(b) > 0. else 0.)
hw[m].SetBinContent(b, 1. if hw[m].GetBinContent(b) > 0. else 0.)
#hxs[m].Smooth(20)
#hw[m].Smooth(20)
gxs[m] = getCurve(hxs[m])
for i, g in enumerate(gxs[m]):
g.SetLineColor(massColors[m])
g.SetFillColor(massColors[m])
g.SetFillStyle(massFill[m]) #(3345 if i>1 else 3354)
g.SetLineWidth(503 * (1 if i < 2 else -1))
mg.Add(g)
#if m==3000:
if m == massPoints[-1]:
gw[m] = getCurve(hw[m])
for i, g in enumerate(gw[m]):
g.SetPoint(0, 0., g.GetY()[0])
g.SetLineWidth(501 * (1 if i < 2 else -1))
g.SetLineColor(920 + 2)
g.SetFillColor(920 + 1)
g.SetFillStyle(3003)
mg.Add(g)
if options.root:
outFile = TFile("plotsLimit/Model.root", "RECREATE")
outFile.cd()
for m in massPoints:
mg[m].Write("X_M%d" % m)
mgW.Write("width")
outFile.Close()
print "Saved histogram in file plotsLimit/Model.root, exiting..."
exit()
### plot ###
c1 = TCanvas("c1", "HVT Exclusion Limits", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
mg.Draw("AC")
#mg.GetXaxis().SetTitle("g_{V} c_{H}")
mg.GetXaxis().SetTitle("Higgs and vector boson coupling g_{H}")
mg.GetXaxis().SetRangeUser(-3., 3.)
mg.GetXaxis().SetLabelSize(0.045)
mg.GetXaxis().SetTitleSize(0.045)
mg.GetXaxis().SetTitleOffset(1.)
#mg.GetYaxis().SetTitle("g^{2} c_{F} / g_{V}")
mg.GetYaxis().SetTitle("Fermion coupling g_{F}")
mg.GetYaxis().SetLabelSize(0.045)
mg.GetYaxis().SetTitleSize(0.045)
mg.GetYaxis().SetTitleOffset(1.)
mg.GetYaxis().SetRangeUser(-1.2, 1.2)
mg.GetYaxis().SetNdivisions(505)
# hxs[3500].Draw("CONTZ")
drawCMS(LUMI, "Preliminary", False)
# drawAnalysis("XVH"+category, False)
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextFont(62)
# latex.SetTextSize(0.06)
# latex.DrawLatex(0.10, 0.925, "CMS")
# model B
g_model = {}
for i, b in enumerate(benchmark):
g_model[i] = TGraph(1)
g_model[i].SetTitle(models_name[b])
g_model[i].SetPoint(0, models_point[b][0], models_point[b][1])
g_model[i].SetMarkerStyle(models_style[b])
g_model[i].SetMarkerColor(models_color[b])
g_model[i].SetMarkerSize(1.5)
g_model[i].Draw("PSAME")
# text
latex = TLatex()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
latex.SetTextColor(630)
# for b in benchmark: latex.DrawLatex(models_point[b][0]+0.02, models_point[b][1]+0.02, models_name[b])
latex.SetTextColor(920 + 2)
latex.DrawLatex(-2.8, -0.875,
"#frac{#Gamma_{Z'}}{m_{Z'}} > %.0f%%" % (width * 100, ))
#leg = TLegend(0.68, 0.60, 0.95, 0.94)
leg = TLegend(0.68, 0.34, 0.95, 0.66)
leg.SetBorderSize(1)
leg.SetFillStyle(1001)
leg.SetFillColor(0)
for m in massPoints:
leg.AddEntry(gxs[m][0], "m_{Z'} = %.1f TeV" % (m / 1000.), "fl")
for i, b in enumerate(benchmark):
leg.AddEntry(g_model[i], g_model[i].GetTitle(), "P")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.050)
leg.SetMargin(0.35)
leg.Draw()
gxs_ = gxs[massPoints[0]][0].Clone("gxs_")
gxs_.SetLineColor(1)
# gxs_.SetFillColor(1)
latex.SetNDC()
latex.SetTextColor(1)
latex.SetTextSize(0.04)
latex.SetTextFont(52)
latex.DrawLatex(0.15, 0.95, "q#bar{q} #rightarrow Z' #rightarrow b#bar{b}")
c1.Print("plots/model/HVT.png")
c1.Print("plots/model/HVT.pdf")
c1.Print("plots/model/HVT.root")
c1.Print("plots/model/HVT.C")
#g = 0.646879, cH = 0.976246, cF = 1.02433
print "model B = [", 3 * 0.976246, ",", 0.646879 * 0.646879 * 1.02433 / 3, "]"
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
drawCMS(LUMI, "") #Preliminary
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"95% CL upper limits"
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
def efficiency(year):
import numpy as np
from root_numpy import tree2array, fill_hist
from aliases import AK8veto, electronVeto, muonVeto
genPoints = [
1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000
]
eff = {}
vetoes = {"AK8": AK8veto, "electron": electronVeto, "muon": muonVeto}
VETO = "AK8" ##could change the veto to investigate here
if SEPARATE: eff_add = {}
#channels = ['none', 'qq', 'bq', 'bb', 'mumu']
channels = ['qq', 'bq', 'bb', 'mumu']
for channel in channels:
treeSign = {}
ngenSign = {}
nevtSign = {}
eff[channel] = TGraphErrors()
if SEPARATE:
nevtSign_add = {}
eff_add[channel] = TGraphErrors()
for i, m in enumerate(genPoints):
signName = "ZpBB_M" + str(m)
ngenSign[m] = 0.
nevtSign[m] = 0.
if SEPARATE: nevtSign_add[m] = 0.
for j, ss in enumerate(sample[signName]['files']):
if year == "run2" or year in ss:
sfile = TFile(NTUPLEDIR + ss + ".root", "READ")
ngenSign[m] += sfile.Get("Events").GetBinContent(1)
treeSign[m] = sfile.Get("tree")
if BTAGGING == 'semimedium':
#if SEPARATE:
# temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel].replace(vetoes[VETO], ""))
#else:
temp_array = tree2array(
treeSign[m],
branches='BTagAK4Weight_deepJet',
selection=aliasSM[channel])
temp_hist = TH1F('pass', 'pass', 1, 0, 1)
fill_hist(temp_hist,
np.zeros(len(temp_array)),
weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array = None
temp_hist.Reset()
if SEPARATE:
temp_array = tree2array(
treeSign[m],
branches='BTagAK4Weight_deepJet',
selection=aliasSM[channel].replace(
vetoes[VETO], ""))
temp_hist = TH1F('pass', 'pass', 1, 0, 1)
fill_hist(temp_hist,
np.zeros(len(temp_array)),
weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array = None
temp_hist.Reset()
else:
#if SEPARATE:
# temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format(WP=working_points[BTAGGING]).replace(vetoes[VETO], ""))
#else:
temp_array = tree2array(
treeSign[m],
branches='BTagAK4Weight_deepJet',
selection=alias[channel].format(
WP=working_points[BTAGGING]))
temp_hist = TH1F('pass', 'pass', 1, 0, 1)
fill_hist(temp_hist,
np.zeros(len(temp_array)),
weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array = None
temp_hist.Reset()
if SEPARATE:
temp_array = tree2array(
treeSign[m],
branches='BTagAK4Weight_deepJet',
selection=alias[channel].format(
WP=working_points[BTAGGING]).replace(
vetoes[VETO], ""))
temp_hist = TH1F('pass', 'pass', 1, 0, 1)
fill_hist(temp_hist,
np.zeros(len(temp_array)),
weights=temp_array)
nevtSign_add[m] += temp_hist.GetBinContent(1)
temp_array = None
temp_hist.Reset()
sfile.Close()
print channel, ss, ":", nevtSign[m], "/", ngenSign[
m], "=", nevtSign[m] / ngenSign[m]
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
n = eff[channel].GetN()
eff[channel].SetPoint(n, m, nevtSign[m] / ngenSign[m])
eff[channel].SetPointError(n, 0,
math.sqrt(nevtSign[m]) / ngenSign[m])
if SEPARATE:
eff_add[channel].SetPoint(n, m, nevtSign_add[m] / ngenSign[m])
eff_add[channel].SetPointError(
n, 0,
math.sqrt(nevtSign_add[m]) / ngenSign[m])
eff[channel].SetMarkerColor(color[channel])
eff[channel].SetMarkerStyle(20)
eff[channel].SetLineColor(color[channel])
eff[channel].SetLineWidth(2)
if SEPARATE:
eff_add[channel].SetMarkerColor(color[channel] +
color_shift[channel])
eff_add[channel].SetMarkerStyle(21)
eff_add[channel].SetLineColor(color[channel] +
color_shift[channel])
eff_add[channel].SetLineWidth(2)
eff_add[channel].SetLineStyle(7)
if channel == 'qq' or channel == 'none': eff[channel].SetLineStyle(3)
n = max([eff[x].GetN() for x in channels])
maxEff = 0.
# Total efficiency
eff["sum"] = TGraphErrors(n)
eff["sum"].SetMarkerStyle(24)
eff["sum"].SetMarkerColor(1)
eff["sum"].SetLineWidth(2)
if SEPARATE:
eff_add["sum"] = TGraphErrors(n)
eff_add["sum"].SetMarkerStyle(25)
eff_add["sum"].SetMarkerColor(1)
eff_add["sum"].SetLineWidth(2)
eff_add["sum"].SetLineStyle(7)
for i in range(n):
tot, mass = 0., 0.
if SEPARATE: tot_add = 0.
for channel in channels:
if channel == 'qq' or channel == 'none':
continue #not sure if I should include 2mu category in sum
if eff[channel].GetN() > i:
tot += eff[channel].GetY()[i]
if SEPARATE: tot_add += eff_add[channel].GetY()[i]
mass = eff[channel].GetX()[i]
if tot > maxEff: maxEff = tot
eff["sum"].SetPoint(i, mass, tot)
if SEPARATE: eff_add["sum"].SetPoint(i, mass, tot_add)
if SEPARATE:
leg = TLegend(0.15, 0.50, 0.95, 0.8)
else:
leg = TLegend(0.15, 0.60, 0.95, 0.8)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(len(channels) / 4)
for i, channel in enumerate(channels):
if eff[channel].GetN() > 0:
leg.AddEntry(eff[channel], getChannel(channel), "pl")
if SEPARATE:
leg.AddEntry(eff_add[channel],
getChannel(channel) + " no " + VETO + "-veto",
"pl")
if SEPARATE:
leg.SetY1(leg.GetY2() -
len([x for x in channels if eff[x].GetN() > 0]) * 0.045)
else:
leg.SetY1(leg.GetY2() -
len([x for x in channels if eff[x].GetN() > 0]) / 2. * 0.045)
if SEPARATE:
legS = TLegend(0.5, 0.8 - 0.045, 0.9, 0.85)
else:
legS = TLegend(0.5, 0.85 - 0.045, 0.9, 0.85)
legS.SetBorderSize(0)
legS.SetFillStyle(0) #1001
legS.SetFillColor(0)
legS.AddEntry(eff['sum'],
"Total b tag efficiency (1 b tag + 2 b tag + 2 #mu)", "pl")
if SEPARATE:
legS.AddEntry(eff_add['sum'],
"Total b tag efficiency, no " + VETO + "-veto", "pl")
c1 = TCanvas("c1", "Signal Efficiency", 1200, 800)
c1.cd(1)
eff['sum'].Draw("APL")
if SEPARATE: eff_add['sum'].Draw("SAME, PL")
for i, channel in enumerate(channels):
eff[channel].Draw("SAME, PL")
if SEPARATE: eff_add[channel].Draw("SAME, PL")
leg.Draw()
legS.Draw()
setHistStyle(eff["sum"], 1.1)
eff["sum"].SetTitle(";m_{Z'} (GeV);Acceptance #times efficiency")
eff["sum"].SetMinimum(0.)
eff["sum"].SetMaximum(max(1., maxEff * 1.5)) #0.65
if SEPARATE:
eff_add["sum"].SetTitle(";m_{Z'} (GeV);Acceptance #times efficiency")
eff_add["sum"].SetMinimum(0.)
eff_add["sum"].SetMaximum(1.)
eff["sum"].GetXaxis().SetTitleSize(0.045)
eff["sum"].GetYaxis().SetTitleSize(0.045)
eff["sum"].GetYaxis().SetTitleOffset(1.1)
eff["sum"].GetXaxis().SetTitleOffset(1.05)
eff["sum"].GetXaxis().SetRangeUser(1500, 8000)
c1.SetTopMargin(0.05)
#drawCMS(-1, "Simulation Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
if SEPARATE:
c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + "_no" + VETO +
"veto.pdf")
c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + "_no" + VETO +
"veto.png")
else:
c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + ".pdf")
c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + ".png")
# print
print "category",
for m in range(0, eff["sum"].GetN()):
print " & %d" % int(eff["sum"].GetX()[m]),
print "\\\\", "\n\\hline"
for i, channel in enumerate(channels + ["sum"]):
if channel == 'sum': print "\\hline"
print getChannel(channel).replace("high ", "H").replace(
"low ", "L").replace("purity", "P").replace("b-tag", ""),
for m in range(0, eff[channel].GetN()):
print "& %.1f" % (100. * eff[channel].GetY()[m]),
print "\\\\"
def Direct_Estimator(var, cut, year):
from root_numpy import root2array, fill_hist, array2root
import numpy.lib.recfunctions as rfn
### Preliminary Operations ###
treeRead = not cut in [
"nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em",
"emqq", "qqqq"
] # Read from tree
channel = cut
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel
showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
stype = "HVT model B"
if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if BTAGGING == 'semimedium':
if k in cut:
cut = cut.replace(k, aliasSM[k])
else:
if k in cut:
cut = cut.replace(
k, alias[k].format(WP=working_points[BTAGGING]))
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
if var == 'jj_deltaEta_widejet':
if "jj_deltaEta_widejet<1.1 && " in cut:
print
print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution"
print
cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "")
else:
print
print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly"
print
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back + sign):
if True: #FIXME
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " );" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s:
if year == "run2" or year in ss:
arr = root2array(
NTUPLEDIR + ss + ".root",
branches=[
var, "jpt_1", "jpt_2", "eventWeightLumi",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)!=5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)!=5"
],
selection=cut if len(cut) > 0 else "")
print "imported " + NTUPLEDIR + ss + ".root"
arr.dtype.names = [
var, "jpt_1", "jpt_2", "eventWeightLumi", "bb",
"bq", "qb", "qq"
]
MANtag_eff1 = np.array(map(MANtag_eff, arr["jpt_1"]))
MANtag_eff2 = np.array(map(MANtag_eff, arr["jpt_2"]))
MANtag_mis1 = np.array(map(MANtag_mis, arr["jpt_1"]))
MANtag_mis2 = np.array(map(MANtag_mis, arr["jpt_2"]))
MANtag_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(arr['bb'],
np.multiply(MANtag_eff1, MANtag_eff2))
+ np.multiply(
arr['bq'], np.multiply(MANtag_eff1,
MANtag_mis2)) +
np.multiply(arr['qb'],
np.multiply(MANtag_mis1,
MANtag_eff2)) +
np.multiply(arr['qq'],
np.multiply(MANtag_mis1, MANtag_mis2)))
fill_hist(hist[s], arr[var], weights=MANtag_weight)
deepCSV_eff1 = np.array(map(deepCSV_eff, arr["jpt_1"]))
deepCSV_eff2 = np.array(map(deepCSV_eff, arr["jpt_2"]))
deepCSV_mis1 = np.array(map(deepCSV_mis, arr["jpt_1"]))
deepCSV_mis2 = np.array(map(deepCSV_mis, arr["jpt_2"]))
deepCSV_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(
arr['bb'],
np.multiply(deepCSV_eff1, deepCSV_eff2)) +
np.multiply(
arr['bq'],
np.multiply(deepCSV_eff1, deepCSV_mis2)) +
np.multiply(
arr['qb'],
np.multiply(deepCSV_mis1, deepCSV_eff2)) +
np.multiply(
arr['qq'],
np.multiply(deepCSV_mis1, deepCSV_mis2)))
if var == "jj_mass_widejet" and options.save and not "data" in ss:
arr = rfn.append_fields(arr,
"MANtag_weight",
MANtag_weight,
usemask=False)
arr = rfn.append_fields(arr,
"deepCSV_weight",
deepCSV_weight,
usemask=False)
array2root(arr,
NTUPLEDIR + "MANtag/" + ss + "_" +
BTAGGING + ".root",
treename="tree",
mode='recreate')
print "saved as", NTUPLEDIR + "MANtag/" + ss + "_" + BTAGGING + ".root"
arr = None
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var == "Events":
sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data + back + ['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
# Create stack
if variable[var]['nbins'] > 0:
bkg = THStack(
"Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " )")
else:
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02,
stype + " (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
drawCMS(LUMI[year], "Work in Progress", suppressCMS=True)
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(2)
res.SetMarkerStyle(31)
res.Reset()
for i in range(0, res.GetNbinsX() + 1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(
i, hist['data_obs'].GetBinContent(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(
i, hist['data_obs'].GetBinError(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if gROOT.IsBatch():
if channel == "": channel = "nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".png")
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".pdf")
# Print table
printTable(hist, sign)
def plot(var, cut, norm=False, nm1=False):
### Preliminary Operations ###
fileRead = os.path.exists(options.file)
treeRead = not any(x==cut for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger'])#(var in variable.keys()) # Read from tree
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal")+6:cut.find("HighVal")-1]
binHigh = cut[cut.find("HighVal")+7:]
binName = "bin_"+binLow+"_"+binHigh
cut = cut[:cut.find("binned")]
useformula = True
if 'formula' in variable[var]:
print variable[var]['formula']
useformula = True
channel = cut
plotdir = cut
plotname = var
weight = "eventWeightLumi" #*(2.2/35.9)
isBlind = BLIND and 'SR' in channel
showSignal = True#('SR' in channel)
cutSplit = cut.split()
for s in cutSplit:
if s in selection.keys():
plotdir = s
cut = cut.replace(s, selection[s])
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
#if treeRead and cut in selection: cut = cut.replace(cut, selection[cut])
# Determine Primary Dataset
pd = []
print cut
if any(w in cut for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']): pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x]
if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']): pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x]
if any(w in cut for w in ['0l', 'isZtoNN']): pd += [x for x in sample['data_obs']['files'] if 'MET' in x]
if len(pd)==0: raw_input("Warning: Primary Dataset not recognized, continue?")
print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
print " cut :", weight
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data+back+sign):
if fileRead:
fileName = options.file if not s=='data_obs' else "rootfiles_"+options.name+"/"+channel+binName+".root"
histName = "shapes_fit_b/"+channel+"/"+s if not s=='data_obs' else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if tmphist==None:
tmphist = hist[back[0]].Clone(s)
tmphist.Reset("MICES")
print "Histogram", histName, "not found in file", fileName
if s=='data_obs': hist[s] = tmphist
else:
hist[s] = hist['data_obs'].Clone(s)
#hist[s].Reset("MICES")
hist[s].SetMarkerSize(0)
for i in range(tmphist.GetNbinsX()+1): hist[s].SetBinContent(i+1, tmphist.GetBinContent(i+1))
elif treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
tree[s].Add(NTUPLEDIR + ss + ".root")
if not binLow == "":
hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), 1, float(binLow), float(binHigh))
elif binLow == "" and variable[var]['nbins']>0:
hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max'])
else:
hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), len(variable[var]['bins'])-1, array('f', variable[var]['bins']))
hist[s].Sumw2()
redFactorString = ""
redFactorValue = ""
if isBlind and 'data' in s:
redFactorString = " && Entry$ % 15 == 0"
if isBlind and 'data' not in s:
redFactorValue = " / 15"
cutstring = "("+weight+redFactorValue+")" + ("*("+cut+redFactorString+")" if len(cut)>0 else "")
if '-' in s: cutstring = cutstring.replace(cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
if useformula == True:
tree[s].Project(s, variable[var]['formula'], cutstring)
else:
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree()==None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
else: # Histogram written to file
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
if file[ss].IsZombie():
print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
continue
tmphist = file[ss].Get(cut+"/"+var)
if tmphist==None: continue
if not s in hist.keys(): hist[s] = tmphist
else: hist[s].Add(tmphist)
if hist[s].Integral() < 0: hist[s].Scale(0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
#if 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30)
#if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91)
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back): hist['BkgSum'].Add(hist[s], 1)
if fileRead:
#hist['BkgSum'] = file[back[0]].Get("shapes_fit_b/"+channel+"/"+"total_background")
tmphist = file[back[0]].Get("shapes_prefit/"+channel+"/"+"total_background")
hist['PreFit'] = hist['BkgSum'].Clone("PreFit")
for i in range(tmphist.GetNbinsX()+1): hist['PreFit'].SetBinContent(i+1, tmphist.GetBinContent(i+1))
hist['PreFit'].SetLineStyle(2)
hist['PreFit'].SetLineColor(923)
hist['PreFit'].SetLineWidth(3)
hist['PreFit'].SetFillStyle(0)
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
# Create data and Bkg sum histograms
# if options.blind or 'SR' in channel:
# hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
# hist['data_obs'].Reset("MICES")
# Set histogram style
hist[data[0]].SetMarkerStyle(20)
hist[data[0]].SetMarkerSize(1.25)
for i, s in enumerate(data+back+sign+['BkgSum']): addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign): hist[s].SetLineWidth(3)
#for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2])
if norm:
for i, s in enumerate(sign):
hist[s].Scale(hist['BkgSum'].Integral()/hist[s].Integral())
# for i, s in enumerate(back):
# hist[s].SetFillStyle(3005)
# hist[s].SetLineWidth(2)
# #for i, s in enumerate(sign):
# # hist[s].SetFillStyle(0)
# if not var=="Events":
# sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral()
# print "Applying SF:", sfnorm
# for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm)
# Create stack
bkg = THStack("Bkg", ";"+hist['BkgSum'].GetXaxis().GetTitle()+";Events")
for i, s in enumerate(back): bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum']+back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if 'PreFit' in hist: leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9-leg.GetNRows()*0.05)
# --- Display ---
c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = ("log" in hist['BkgSum'].GetZaxis().GetTitle())
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist[data[0]].Draw("SAME, PE") # data
#data_graph.Draw("SAME, PE")
if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']: hist[s].Draw("SAME, HIST") # signals Normalized, hist[s].Integral()*sample[s]['weight']
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.SetMaximum((5. if log else 1.25)*max(bkg.GetMaximum(), hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin())+hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin())))
if len(sign) > 0 and bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(max(min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin()), hist[data[0]].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
leg.Draw()
drawCMS(LUMI, "Preliminary")
drawRegion(channel, True)
drawAnalysis("DM"+channel[:2])
drawOverflow()
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX()+1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(i, hist['BkgSum'].GetBinError(i)/hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist[data[0]].Clone("Residues")
for i in range(0, res.GetNbinsX()+1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(i, res.GetBinContent(i)/hist['BkgSum'].GetBinContent(i))
res.SetBinError(i, res.GetBinError(i)/hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
if 'PreFit' in hist:
respre = hist['PreFit'].Clone("ResiduesPreFit")
respre.Divide(hist['BkgSum'])
respre.Draw("SAME, HIST")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if gROOT.IsBatch() and options.saveplots: # and (treeRead and channel in selection.keys()):
if not os.path.exists("plots_"+options.name+"/"+plotdir): os.makedirs("plots_"+options.name+"/"+plotdir)
c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+".png")
c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if gROOT.IsBatch() and not fileRead and (var == 'MET_pt' or (channel.startswith('SL') and var == 'MET_sign') or (channel.endswith('ZR') and var == 'FakeMET_pt')):
saveHist(hist, channel+binName)
def efficiency(stype, Zlep=True):
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500]
eff = {}
channels = [x for x in channelList if len(x)<5]
for channel in channels:
treeSign = {}
ngenSign = {}
nevtSign = {}
eff[channel] = TGraphErrors()
for i, m in enumerate(genPoints):
signName = "%s_M%d" % (stype, m) #"%s_M%d" % (channel[:3], m)
ngenSign[m] = 0.
nevtSign[m] = 0.
for j, ss in enumerate(sample[signMass]['files']):
if 'nn' in channel and not 'Zinv' in ss: continue
if ('en' in channel or 'mn' in channel) and not 'Wlep' in ss: continue
if ('ee' in channel or 'mm' in channel) and not 'Zlep' in ss: continue
if Zlep and 'Zinv' in ss: continue
if not Zlep and 'Zlep' in ss: continue
sfile = TFile(NTUPLEDIR + ss + ".root", "READ")
if not sfile.Get("Events")==None:
ngenSign[m] += sfile.Get("Events").GetEntries()
# From trees
treeSign[m] = sfile.Get("tree")
nevtSign[m] += treeSign[m].GetEntries(selection[channel] + selection['SR'])
else:
ngenSign[m] = -1
print "Failed reading file", NTUPLEDIR + ss + ".root"
sfile.Close()
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
# Gen Br
n = eff[channel].GetN()
eff[channel].SetPoint(n, m, nevtSign[m]/ngenSign[m])
eff[channel].SetPointError(n, 0, math.sqrt(nevtSign[m])/ngenSign[m])
eff[channel].SetMarkerColor(color[channel])
eff[channel].SetMarkerStyle(20)
eff[channel].SetLineColor(color[channel])
eff[channel].SetLineWidth(2)
if channel.count('b')==1: eff[channel].SetLineStyle(3)
n = max([eff[x].GetN() for x in channels])
maxEff = 0.
# Total efficiency
eff["sum"] = TGraphErrors(n)
eff["sum"].SetMarkerStyle(24)
eff["sum"].SetMarkerColor(1)
eff["sum"].SetLineWidth(2)
for i in range(n):
tot, mass = 0., 0.
for channel in channels:
if eff[channel].GetN() > i:
tot += eff[channel].GetY()[i]
mass = eff[channel].GetX()[i]
if tot > maxEff: maxEff = tot
eff["sum"].SetPoint(i, mass, tot)
leg = TLegend(0.15, 0.60, 0.95, 0.8)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(len(channels)/4)
for i, channel in enumerate(channels):
if eff[channel].GetN() > 0: leg.AddEntry(eff[channel], getChannel(channel), "pl")
leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])/2.*0.045)
legS = TLegend(0.55, 0.85-0.045, 0.95, 0.85)
legS.SetBorderSize(0)
legS.SetFillStyle(0) #1001
legS.SetFillColor(0)
legS.AddEntry(eff['sum'], "Total efficiency", "pl")
c1 = TCanvas("c1", "Signal Efficiency", 1200, 800)
c1.cd(1)
eff['sum'].Draw("APL")
for i, channel in enumerate(channels): eff[channel].Draw("SAME, PL")
leg.Draw()
legS.Draw()
setHistStyle(eff["sum"], 1.1)
eff["sum"].SetTitle(";m_{"+stype[1]+"'} (GeV);Acceptance #times efficiency")
eff["sum"].SetMinimum(0.)
eff["sum"].SetMaximum(max(1., maxEff*1.5)) #0.65
eff["sum"].GetXaxis().SetTitleSize(0.045)
eff["sum"].GetYaxis().SetTitleSize(0.045)
eff["sum"].GetYaxis().SetTitleOffset(1.1)
eff["sum"].GetXaxis().SetTitleOffset(1.05)
eff["sum"].GetXaxis().SetRangeUser(750, 5500)
if stype=='XWH' or (stype=='XZH' and Zlep): line = drawLine(750, 2./3., 4500, 2./3.)
drawCMS(-1,YEAR, "Simulation") #Preliminary
drawAnalysis("ZH")
suffix = ""
if stype=='XZH' and Zlep: suffix = "ll"
elif stype=='XZH' and not Zlep: suffix = "nn"
elif stype=='XWH': suffix = "ln"
c1.Print("plotsSignal/Efficiency/"+stype+suffix+".pdf")
c1.Print("plotsSignal/Efficiency/"+stype+suffix+".png")
# print
print "category",
for m in range(0, eff["sum"].GetN()):
print " & %d" % int(eff["sum"].GetX()[m]),
print "\\\\", "\n\\hline"
for i, channel in enumerate(channels+["sum"]):
if channel=='sum': print "\\hline"
print getChannel(channel).replace("high ", "H").replace("low ", "L").replace("purity", "P").replace("b-tag", ""),
for m in range(0, eff[channel].GetN()):
print "& %.1f" % (100.*eff[channel].GetY()[m]),
print "\\\\"
def dijet(category):
channel = 'bb'
stype = channel
isSB = True # relict from using Alberto's more complex script
isData = not ISMC
nTupleDir = NTUPLEDIR
samples = data if isData else back
pd = []
for sample_name in samples:
if YEAR == 'run2':
pd += sample[sample_name]['files']
else:
pd += [x for x in sample[sample_name]['files'] if YEAR in x]
print "datasets:", pd
if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR)
if BIAS: print "Running in BIAS mode"
order = 0
RSS = {}
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
weight = RooRealVar("MANtag_weight", "", -1.e9, 1.e9)
variables = RooArgSet(X_mass)
variables.add(RooArgSet(weight))
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(RooBinning(len(abins_narrow) - 1, abins_narrow))
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
baseCut = ""
print stype, "|", baseCut
print " - Reading from Tree"
treeBkg = TChain("tree")
for ss in pd:
if os.path.exists(nTupleDir + ss + "_" + BTAGGING + ".root"):
treeBkg.Add(nTupleDir + ss + "_" + BTAGGING + ".root")
else:
print "found no file for sample:", ss
setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables,
RooFit.Cut(baseCut), RooFit.WeightVar(weight),
RooFit.Import(treeBkg))
nevents = setData.sumEntries()
dataMin, dataMax = array('d', [0.]), array('d', [0.])
setData.getRange(X_mass, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = X_mass.getMax()
if VARBINS:
for b in narrow_bins:
if b > xmax:
lastBin = b
break
print "Imported", (
"data" if isData else "MC"
), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax)
#xmax = xmax+binsXmass.averageBinWidth() # start form next bin
# 1 parameter
print "fitting 1 parameter model"
p1_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p1_1", "p1", 7.0, 0.,
2000.)
modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)",
"1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1))
normzBkg1 = RooRealVar(
modelBkg1.GetName() + "_norm", "Number of background events", nevents,
0., 5. * nevents) #range dependent of actual number of events!
modelExt1 = RooExtendPdf(modelBkg1.GetName() + "_ext",
modelBkg1.GetTitle(), modelBkg1, normzBkg1)
fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes1.Print()
RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass,
[fitRes1], normzBkg1.getVal())
# 2 parameters
print "fitting 2 parameter model"
p2_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_1", "p1", 0., -100.,
1000.)
p2_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_2", "p2",
p1_1.getVal(), -100., 600.)
modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2)",
RooArgList(X_mass, p2_1, p2_2))
normzBkg2 = RooRealVar(modelBkg2.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt2 = RooExtendPdf(modelBkg2.GetName() + "_ext",
modelBkg2.GetTitle(), modelBkg2, normzBkg2)
fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes2.Print()
RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass,
[fitRes2], normzBkg2.getVal())
# 3 parameters
print "fitting 3 parameter model"
p3_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_1", "p1",
p2_1.getVal(), -2000., 2000.)
p3_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_2", "p2",
p2_2.getVal(), -400., 2000.)
p3_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_3", "p3", -2.5,
-500., 500.)
modelBkg3 = RooGenericPdf(
"Bkg3", "Bkg. fit (4 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))",
RooArgList(X_mass, p3_1, p3_2, p3_3))
normzBkg3 = RooRealVar(modelBkg3.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt3 = RooExtendPdf(modelBkg3.GetName() + "_ext",
modelBkg3.GetTitle(), modelBkg3, normzBkg3)
fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes3.Print()
RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass,
[fitRes3], normzBkg3.getVal())
# 4 parameters
print "fitting 4 parameter model"
p4_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_1", "p1",
p3_1.getVal(), -2000., 2000.)
p4_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_2", "p2",
p3_2.getVal(), -2000., 2000.)
p4_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_3", "p3",
p3_3.getVal(), -50., 50.)
p4_4 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_4", "p4", 0.1, -50.,
50.)
modelBkg4 = RooGenericPdf(
"Bkg4", "Bkg. fit (5 par.)",
"pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))",
RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4))
normzBkg4 = RooRealVar(modelBkg4.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt4 = RooExtendPdf(modelBkg4.GetName() + "_ext",
modelBkg4.GetTitle(), modelBkg4, normzBkg4)
fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes4.Print()
RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass,
[fitRes4], normzBkg4.getVal())
# Normalization parameters are should be set constant, but shape ones should not
# if BIAS:
# p1_1.setConstant(True)
# p2_1.setConstant(True)
# p2_2.setConstant(True)
# p3_1.setConstant(True)
# p3_2.setConstant(True)
# p3_3.setConstant(True)
# p4_1.setConstant(True)
# p4_2.setConstant(True)
# p4_3.setConstant(True)
# p4_4.setConstant(True)
normzBkg1.setConstant(True)
normzBkg2.setConstant(True)
normzBkg3.setConstant(True)
normzBkg4.setConstant(True)
#*******************************************************#
# #
# Fisher #
# #
#*******************************************************#
# Fisher test
with open(PLOTDIR + "/Fisher_" + category + ".tex", 'w') as fout:
fout.write(r"\begin{tabular}{c|c|c|c|c}")
fout.write("\n")
fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
CL_high = False
for o1 in range(1, 5):
o2 = min(o1 + 1, 5)
fout.write("%d par & %.2f & %.2f & %d & " %
(o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"],
RSS[o1]["nbins"] - RSS[o1]["npar"]))
if o2 > len(RSS):
fout.write(r"\\")
fout.write("\n")
continue #order==0 and
CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1 + 1., o2 + 1.,
RSS[o1]["nbins"])
fout.write("CL=%.3f " % (CL))
if CL > 0.10: # The function with less parameters is enough
if not CL_high:
order = o1
#fout.write( "%d par are sufficient " % (o1+1))
CL_high = True
else:
#fout.write( "%d par are needed " % (o2+1))
if not CL_high:
order = o2
fout.write(r"\\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
fout.write(r"\end{tabular}")
print "saved F-test table as", PLOTDIR + "/Fisher_" + category + ".tex"
#print "-"*25
#print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\"
#print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\"
#print "\\hline"
#CL_high = False
#for o1 in range(1, 5):
# o2 = min(o1 + 1, 5)
# print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]),
# if o2 > len(RSS):
# print "\\\\"
# continue #order==0 and
# CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
# print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL),
# if CL > 0.10: # The function with less parameters is enough
# if not CL_high:
# order = o1
# print "%d par are sufficient" % (o1+1),
# CL_high=True
# else:
# print "%d par are needed" % (o2+1),
# if not CL_high:
# order = o2
# print "\\\\"
#print "\\hline"
#print "-"*25
#print "@ Order is", order, "("+category+")"
#order = min(3, order)
#order = 2
if order == 1:
modelBkg = modelBkg1 #.Clone("Bkg")
modelAlt = modelBkg2 #.Clone("BkgAlt")
normzBkg = normzBkg1 #.Clone("Bkg_norm")
fitRes = fitRes1
elif order == 2:
modelBkg = modelBkg2 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg2 #.Clone("Bkg_norm")
fitRes = fitRes2
elif order == 3:
modelBkg = modelBkg3 #.Clone("Bkg")
modelAlt = modelBkg4 #.Clone("BkgAlt")
normzBkg = normzBkg3 #.Clone("Bkg_norm")
fitRes = fitRes3
elif order == 4:
modelBkg = modelBkg4 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg4 #.Clone("Bkg_norm")
fitRes = fitRes4
else:
print "Functions with", order + 1, "or more parameters are needed to fit the background"
exit()
modelBkg.SetName("Bkg_" + YEAR + "_" + category)
modelAlt.SetName("Alt_" + YEAR + "_" + category)
normzBkg.SetName("Bkg_" + YEAR + "_" + category + "_norm")
print "-" * 25
# Generate pseudo data
setToys = RooDataSet()
setToys.SetName("data_toys")
setToys.SetTitle("Data (toys)")
if not isData:
print " - Generating", nevents, "events for toy data"
setToys = modelBkg.generate(RooArgSet(X_mass), nevents)
#setToys = modelAlt.generate(RooArgSet(X_mass), nevents)
print "toy data generated"
if VERBOSE: raw_input("Press Enter to continue...")
#*******************************************************#
# #
# Plot #
# #
#*******************************************************#
print "starting to plot"
c = TCanvas("c_" + category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = X_mass.frame()
setPadStyle(frame, 1.25, True)
if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
signal = getSignal(
category, stype,
2000) #replacing Alberto's getSignal by own dummy function
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning),
RooFit.Scaling(False), RooFit.Invisible())
modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False),
RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("FL"),
RooFit.Name("1sigma"))
modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("L"),
RooFit.Name(modelBkg.GetName()))
modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613),
RooFit.FillColor(609), RooFit.FillStyle(1001),
RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName()))
if not isSB and signal[0] is not None: # FIXME remove /(2./3.)
signal[0].plotOn(
frame,
RooFit.Normalization(signal[1] * signal[2], RooAbsReal.NumEvent),
RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629),
RooFit.DrawOption("L"), RooFit.Name("Signal"))
graphData = setData.plotOn(
frame, RooFit.Binning(plot_binning), RooFit.Scaling(False),
RooFit.XErrorSize(0 if not VARBINS else 1),
RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2),
RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True)
chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True)
#setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2))
frame.GetYaxis().SetTitle("Events / ( 100 GeV )")
frame.GetYaxis().SetTitleOffset(1.05)
frame.Draw()
#print "frame drawn"
# Get Chi2
# chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName())
# chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName())
# chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName())
# chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName())
frame.SetMaximum(frame.GetMaximum() * 10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-1))
c.GetPad(1).SetLogy()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
leg = TLegend(0.575, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(setData.GetName(),
setData.GetTitle() + " (%d events)" % nevents, "PEL")
leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(),
"FL") #.SetTextColor(629)
leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L")
if not isSB and signal[0] is not None:
leg.AddEntry("Signal", signal[0].GetTitle(), "L")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextFont(42)
if not isSB:
latex.DrawLatex(leg.GetX1() * 1.16,
leg.GetY1() - 0.04, "HVT model B (g_{V}=3)")
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb")
c.cd(2)
frame_res = X_mass.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.3)
frame_res.Draw()
fixData(pulls, False, True, False)
drawChi2(RSS[order]["chi2"], RSS[order]["nbins"] - (order + 1), True)
line = drawLine(X_mass.getMin(), 0, lastBin, 0)
if VARBINS:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
else:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if BIAS:
gSystem.Load("libHiggsAnalysisCombinedLimit.so")
from ROOT import RooMultiPdf
cat = RooCategory("pdf_index", "Index of Pdf which is active")
pdfs = RooArgList(modelBkg, modelAlt)
roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs)
normulti = RooRealVar("roomultipdf_norm",
"Number of background events", nevents, 0., 1.e6)
normzBkg.setConstant(
False
) ## newly put here to ensure it's freely floating in the combine fit
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
# Dataset
if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs"))
else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
#getattr(w, "import")(setData, RooFit.Rename("data_obs"))
if BIAS:
getattr(w, "import")(cat, RooFit.Rename(cat.GetName()))
getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName()))
getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName()))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile(WORKDIR + "%s_%s.root" % (DATA_TYPE + "_" + YEAR, category),
True)
print "Workspace", WORKDIR + "%s_%s.root" % (
DATA_TYPE + "_" + YEAR, category), "saved successfully"
if VERBOSE: raw_input("Press Enter to continue...")
def addPlots(plots):
from ROOT import RooCurve, RooHist, TLine, TLegend, RooPlot, RooAbsData, \
SetOwnership, TGraphErrors
#print plots
outplot = plots[0].emptyClone(plots[0].GetName())
newMax = 0.
for item in range(0, int(plots[0].numItems())):
itemName = plots[0].nameOf(item)
firstItem = plots[0].getObject(item)
if (type(firstItem) == RooCurve):
fullCurve = clipCurve(firstItem)
for plot in range(1, len(plots)):
nextCurve = clipCurve(plots[plot].getCurve(itemName))
fullCurve = RooCurve(fullCurve.GetName(), fullCurve.GetTitle(),
fullCurve, nextCurve)
fullCurve.addPoint(fullCurve.GetX()[fullCurve.GetN() - 1], 0)
fullCurve.addPoint(fullCurve.GetX()[0], 0)
fullCurve.SetLineColor(firstItem.GetLineColor())
fullCurve.SetLineStyle(firstItem.GetLineStyle())
fullCurve.SetFillColor(firstItem.GetFillColor())
fullCurve.SetFillStyle(firstItem.GetFillStyle())
outplot.addPlotable(fullCurve,
plots[0].getDrawOptions(itemName).Data())
SetOwnership(fullCurve, False)
if (type(firstItem) == RooHist):
fullHist = firstItem
for plot in range(1, len(plots)):
nextHist = plots[plot].getHist(itemName)
fullHist = addHists(fullHist, nextHist)
fullHist.SetName(itemName)
fullHist.SetTitle(firstItem.GetTitle())
outplot.addPlotable(fullHist,
plots[0].getDrawOptions(itemName).Data())
SetOwnership(fullHist, False)
if (type(firstItem) == TGraphErrors):
fullErrors = firstItem
for plot in range(1, len(plots)):
nextErrors = plots[plot].findObject(itemName)
fullErrors = addErrors(fullErrors, nextErrors)
fullErrors.SetName(itemName)
fullErrors.SetTitle(firstItem.GetTitle())
outplot.addObject(fullErrors,
plots[0].getDrawOptions(itemName).Data())
SetOwnership(fullErrors, False)
if (type(firstItem) == TLine):
newLine = TLine(firstItem)
newLine.SetY2(outplot.GetMaximum())
SetOwnership(newLine, False)
outplot.addObject(newLine)
pass
if (type(firstItem) == TLegend):
newLeg = TLegend(firstItem)
newLeg.SetY1NDC(0.92 - \
0.04*newLeg.GetListOfPrimitives().GetSize() - \
0.02)
newLeg.SetY1(newLeg.GetY1NDC())
SetOwnership(newLeg, False)
outplot.addObject(newLeg)
for plot in plots:
newMax += plot.GetMaximum()
outplot.SetMaximum(outplot.GetMaximum() * 1.3)
outplot.GetYaxis().SetTitle("Events / GeV")
## outplot.SetMaximum(newMax)
## outplot.Print("v")
return outplot
def plot(var, cut, signal):
sign = signal
### Preliminary Operations ###
treeRead = not 'cutflow' in var
#treeRead = False
channel = cut
if 'inc' in cut:
eventWeightLuminame = 'eventWeightLumi_nobtag'
else:
eventWeightLuminame = 'eventWeightLumi'
if "SB" in cut or "SR" in cut:
channel_name = cut[:-2]
else:
channel_name = cut
if year in ['2016', '2017', '2018']:
NTUPLEDIR = "/work/pbaertsc/heavy_resonance/Ntuples%s/" % (year)
else:
NTUPLEDIR = "/work/pbaertsc/heavy_resonance/"
#showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
if var in [
'dijet_VBF_mass', 'deltaR_VBF', 'deltaR_HVBFjet1',
'deltaR_HVBFjet2'
]:
showSignal = False
else:
showSignal = True
if treeRead:
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut: cut = cut.replace(k, selection[k])
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
tree[sign] = TChain("tree")
for j, ss in enumerate(sample[sign]['files']):
tree[sign].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
hist[sign] = TH1F(
sign,
";" + title + ";Events;" + ('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist[s] = TH1F(sign, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[sign].Sumw2()
cutstring = "%s" % eventWeightLuminame + ("*(" + cut + ")")
tree[sign].Project(sign, var, cutstring)
if not tree[sign].GetTree() == None:
hist[sign].SetOption("%s" % tree[sign].GetTree().GetEntriesFast())
hist[sign].SetFillColor(sample[sign]['fillcolor'])
hist[sign].SetFillStyle(sample[sign]['fillstyle'])
hist[sign].SetLineColor(sample[sign]['linecolor'])
hist[sign].SetLineStyle(sample[sign]['linestyle'])
hist[sign].SetLineWidth(3)
sample[sign]['plot'] = True
# Legend
leg = TLegend(0.6, 0.6, 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(hist[sign], sample[sign]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800)
c1.SetTopMargin(0.06)
c1.SetRightMargin(0.05)
c1.SetTicks(1, 1)
hist[sign].Draw("HIST")
leg.Draw()
drawCMS(LUMI, year, "Preliminary")
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
c1.Update()
if gROOT.IsBatch():
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists(OUTPUTDIR + channel_name):
os.makedirs(OUTPUTDIR + channel_name)
c1.Print("%s" % OUTPUTDIR + channel_name + "/" + varname + sign +
".png")
c1.Print("%s" % OUTPUTDIR + channel_name + "/" + varname + sign +
".pdf")
