def plotAll():
gROOT.SetBatch(True)
hists = {}
# file = TFile(NTUPLEDIR + "DYJetsToLL.root", 'READ')
# file.cd()
# # Looping over file content
# for key in file.GetListOfKeys():
# obj = key.ReadObj()
# # Histograms
# if obj.IsA().InheritsFrom('TH1'): continue
# # hists.append(obj.GetName())
# # Tree
# elif obj.IsA().InheritsFrom('TTree'): continue
# # Directories
# if obj.IsFolder() and obj.GetName() in ['0l', '1e', '1m', '2e', '2m', '1e1m']:
# subdir = obj.GetName()
# hists[subdir] = []
# file.cd(subdir)
# for subkey in file.GetDirectory(subdir).GetListOfKeys():
# subobj = subkey.ReadObj()
# if subobj.IsA().InheritsFrom('TH1'):
# hists[subdir].append(subobj.GetName())
# file.cd('..')
# file.Close()
#
# for c, l in hists.iteritems():
# #if not os.path.exists("plots/"+c):
# # os.mkdir("plots/"+c)
# for h in l:
# plot(h, c)
for r in selection.keys():
#for v in ['Lepton1_pt', 'Lepton1_pfIso', 'Lepton2_pt', 'Lepton2_pfIso', 'Jet1_pt', 'Jet1_csv', 'Jet2_pt', 'Jet2_csv', 'Jet3_pt', 'Jet3_csv', 'Jet4_pt', 'Jet4_csv', 'JetF_pt', 'mZ', 'V_pt', 'mT', 'mT2', 'MinDPhi', 'MinDPhi12', 'MET_pt', 'MET_sign', 'FakeMET_pt', 'nPV', 'nJets', 'nForwardJets', 'nBTagJets', 'nElectrons', 'nMuons', 'nTaus', 'HT', 'ST']:
for v in [
'MET_pt', 'MET_sign', 'MinDPhi12', 'mTb', 'mT',
'ratio_jet1pt_HT', 'mT2'
]:
#for v in ['MET_pt','MET_sign', 'FakeMET_pt']:
# plot(v, r)
p = multiprocessing.Process(target=plot, args=(
v,
r,
))
jobs.append(p)
p.start()
p.join()
def plot(var, cut, nm1=False):
### Preliminary Operations ###
treeRead = True
channel = cut
BTagAK4deepup = False
BTagAK4deepdown = False
BTagAK4deep = False
BTagAK8deepup = False
BTagAK8deepdown = False
BTagAK8deep = False
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
isBlind = BLIND and 'SR' in channel
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 "cutflow" in var:
showSignal = False
if var in [
'X_mass_jesUp', 'X_mass_jesDown', 'X_mass_jerUp', 'X_mass_jerDown',
'X_mass_nom', 'X_mass_MET_jesUp', 'X_mass_MET_jesDown',
'X_mass_MET_jerUp', 'X_mass_MET_jerDown', 'X_mass_MET_nom',
'H_mass_jmsUp', 'H_mass_jmsDown', 'H_mass_jmrUp', 'H_mass_jmrDown',
'H_mass_nom'
]:
back = ["VV"]
else:
back = [
"VV", "ST", "TTbarSL", "WJetsToLNu_HT", "DYJetsToNuNu_HT",
"DYJetsToLL_HT"
]
stype = "HVT model B"
if channel.endswith('up'):
TopBTagAK4deepup = True
channel = channel[:-2]
cut = channel
if channel.endswith('down'):
TopBTagAK4deepdown = True
channel = channel[:-4]
cut = channel
if treeRead:
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut: cut = cut.replace(k, selection[k])
# Determine Primary Dataset
pd = []
if var == 'BTagAK4Weightdeep_up':
var = 'X_mass'
BTagAK4deepup = True
elif var == 'BTagAK4Weight_deep_down':
var = 'X_mass'
BTagAK4deepdown = True
elif var == 'BTagAK4Weight_deep':
var = 'X_mass'
BTagAK4deep = True
elif var == 'BTagAK8Weight_deep_up':
var = 'X_mass'
BTagAK8deepup = True
elif var == 'BTagAK8Weight_deep_down':
var = 'X_mass'
BTagAK8deepdown = True
elif var == 'BTagAK8Weight_deep':
var = 'X_mass'
BTagAK8deep = True
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back + sign + sign_VBF):
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):
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
if 'X_mass' in var:
min_value = 0.0
max_value = 6000.
else:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
hist[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "%s" % eventWeightLuminame + ("*(" + cut + ")")
if var == 'LeptonWeightUp':
cutstring = "(%s * LeptonWeightUp/LeptonWeight)" % eventWeightLuminame + (
"*(" + cut + ")")
elif var == 'LeptonWeightDown':
cutstring = "(%s * LeptonWeightDown/LeptonWeight)" % eventWeightLuminame + (
"*(" + cut + ")")
elif var == 'TriggerWeightUp':
cutstring = "(%s * TriggerWeightUp/TriggerWeight)" % eventWeightLuminame + (
"*(" + cut + ")")
elif var == 'TriggerWeightDown':
cutstring = "(%s * TriggerWeightDown/TriggerWeight)" % eventWeightLuminame + (
"*(" + cut + ")")
#division by BTagAk8Weight_deep is because the weighted samples are used
elif BTagAK4deep:
cutstring = "(%s * BTagAK4Weight_deep)" % eventWeightLuminame + (
"*(" + cut + ")")
elif BTagAK4deepup:
cutstring = "(%s * BTagAK4Weight_deep_up)" % eventWeightLuminame + (
"*(" + cut + ")")
elif BTagAK4deepdown:
cutstring = "(%s * BTagAK4Weight_deep_down)" % eventWeightLuminame + (
"*(" + cut + ")")
elif BTagAK8deepup:
cutstring = "(%s * BTagAK8Weight_deep_up/BTagAK8Weight_deep)" % eventWeightLuminame + (
"*(" + cut + ")")
elif BTagAK8deepdown:
cutstring = "(%s * BTagAK8Weight_deep_down/BTagAK8Weight_deep)" % eventWeightLuminame + (
"*(" + cut + ")")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
#print tree[s].GetTree().GetEntriesFast()
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'])
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])
# Set histogram style
for i, s in enumerate(back + sign + sign_VBF + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign + sign_VBF):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign + sign_VBF):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
return hist
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, 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 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'
]) # Read from tree
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 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
print "preparing cut grid"
# fillCutValues(0.,0.2,4,1,
# 0.,0.4,4,1,
# 0.,0.5,5,1,
# 0.,1.,5,1,
# 0.,0.2,4,1
# )
# fillCutValues(0.,3.2,4,2,
# 0.,3.2,4,1,
# 0.,3.2,4,2,
# 0.,3.2,4,1,
# 0.,1.,5,2
# )
# fillCutValues(0.,3.2,4,1,
# 0.,3.2,4,1,
# 0.,3.2,4,2,
# 0.,3.2,4,2,
# 0.,400.,4,2
# )
fillCutValues(100., 400, 30, 1, 0., 0., 1, 1, 0., 0., 1, 1, 0., 0., 1, 1,
0., 0., 1, 1)
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
#variables = ["mTb","mT2","mT","MinLepMetDPhi","MinLepJetDPhi"]
#'mTb','topMass','WMass','HT','Dphi_topb','Dphi_Wb','Dphi_topMET','Dphi_WMET'
#variables = ["mTb","WdRMass","topdRMass","MET_pt","MET_pt"]
#variables = ["Dphi_topb","Dphi_Wb","Dphi_topMET","Dphi_WMET","MET_pt"]
#variables = ["aplanarityFT","CFT","circularityFT","sphericity_AZFT","DFT"]
#variables = ["Dphi_topb","Dphi_topMET","Dphi_Wb","Dphi_WMET","Jet1_pt/HT"]
#variables = ["MaxBJetMetDPhi","MinBJetMetDPhi","minDphiJet1BJet","minDphiJet2BJet","pt_bjet"]
#variables = ["cosTheta_bb","topdRMass","topdRMass","WdRMass","WdRMass"]
#variables = ["Jet1_pt/HT","MET_pt","MET_pt","MET_pt","MET_pt"]
#variables = ["MinDPhi12","MET_pt","MET_pt","MET_pt","MET_pt"]
variables = ["mT2", "MET_pt", "MET_pt", "MET_pt", "MET_pt"]
#variables = ["MET_pt","MET_pt","MET_pt","MET_pt","MET_pt"]
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
print "reading 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 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()
cutstring = "(" + weight + ")" + ("*(" + cut +
")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
if s.startswith("tDM") or s.startswith("ttDM"):
isSignal = True
fillSigBkgArrays(isSignal, variables, tree[s], weight, s, cut)
elif not (s.startswith("data")):
isSignal = False
fillSigBkgArrays(isSignal, variables, tree[s], weight, s, cut)
print "searching for best cuts"
searchBestCut()
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")
def systematics(sys, isShape=False, antiCorr=False):
treeRead = True
file = {}
hist = {}
tree = {}
histUp = {}
histDown = {}
up = {}
down = {}
gUp = TGraph()
gDown = TGraph()
gUp.SetLineWidth(3)
gDown.SetLineWidth(3)
gUp.SetLineColor(632)
gDown.SetLineColor(602)
BTagAK4deepup = False
BTagAK4deepdown = False
BTagAK4deep = False
BTagAK8deepup = False
BTagAK8deepdown = False
BTagAK8deep = False
# g.SetMarkerStyle(20)
# g.SetMarkerColor(418)
# g.SetMarkerSize(1.25)
var = sys
cut = 'nnbbSR'
if var == 'BTagAK4Weight_deep_up':
var = 'X_mass'
BTagAK4deepup = True
elif var == 'BTagAK4Weight_deep_down':
var = 'X_mass'
BTagAK4deepdown = True
elif var == 'BTagAK4Weight_deep':
var = 'X_mass'
BTagAK4deep = True
elif var == 'BTagAK8Weight_deep_up':
var = 'X_mass'
BTagAK8deepup = True
elif var == 'BTagAK8Weight_deep_down':
var = 'X_mass'
BTagAK8deepdown = True
elif var == 'BTagAK8Weight_deep':
var = 'X_mass'
BTagAK8deep = True
upAvg, downAvg, upMin, downMin, upMax, downMax = 0., 0., 2., 2., 0., 0.
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(k, selection[k])
for i, s in enumerate(sign):
if '_MZ' in s: m = int((s.split('_MZ')[1]).split('_MA')[0])
elif '_M' in s: m = int(s.split('_M')[1])
else: m = 0
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):
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
if 'isZtoNN' in cut:
if var == 'MET':
min_value = 200
max_value = 2000
elif var == 'DPhi':
title = "#Delta #varphi (AK8 jet-#slash{E}_{T})"
elif var == 'VH_deltaR':
title = "#Delta R (#slash{E}_{T}, AK8 jet)"
hist[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "(eventWeightLumi)" + ("*(" + cut + ")")
if var == 'LeptonWeightUp':
cutstring = "(eventWeightLumi * LeptonWeightUp/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'LeptonWeightDown':
cutstring = "(eventWeightLumi * LeptonWeightDown/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightUp':
cutstring = "(eventWeightLumi * TriggerWeightUp/TriggerWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightDown':
cutstring = "(eventWeightLumi * TriggerWeightDown/TriggerWeight)" + (
"*(" + cut + ")")
#division by BTagAk4Weight_deep is because the weighted samples are used
elif BTagAK4deepup:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_up/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK4deepdown:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_down/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deep:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deepup:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_up)" + (
"*(" + cut + ")")
elif BTagAK8deepdown:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_down)" + (
"*(" + cut + ")")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
"""
for j, ss in enumerate(sample[s]['files']):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "READ")
tmp = file[ss].Get("Sys/"+sys)
if tmp == None: continue
if not s in hist.keys(): hist[s] = tmp
else:
if antiCorr:
for x in range(1, hist[s].GetNbinsX()+1): hist[s].SetBinContent(x, hist[s].GetBinContent(x)+tmp.GetBinContent(tmp.GetNbinsX()+1-x))
else: hist[s].Add( tmp )
if isShape:
tmp = file[ss].Get("Sys/"+sys+"_up")
print "Sys/"+sys+"_up"
if tmp == None: continue
if not s in histUp.keys(): histUp[s] = tmp
else: histUp[s].Add( tmp )
#
tmp = file[ss].Get("Sys/"+sys+"_down")
if tmp == None: continue
if not s in histDown.keys(): histDown[s] = tmp
else: histDown[s].Add( tmp )
if 'accept' in sys:
norm = None
for j, ss in enumerate(sample[s]['files']):
tmp = file[ss].Get("Sys/PDF_scale")
if tmp == None: continue
if norm == None: norm = tmp
else: norm.Add( tmp )
hist[s].Divide(norm)
"""
if (isShape):
shape = TF1("shape", "gaus", 0, 5000)
shapeUp = TF1("shapeUp", "gaus", 0, 5000)
shapeDown = TF1("shapeDown", "gaus", 0, 5000)
hist[s].Fit(shape, "Q0", "")
histUp[s].Fit(shapeUp, "Q0", "")
histDown[s].Fit(shapeDown, "Q0", "")
if 'scale' in sys or 'unc' in sys:
up[s] = histUp[s].GetMean() / hist[s].GetMean()
down[s] = histDown[s].GetMean() / hist[s].GetMean()
# up[s] = shapeUp.GetParameter(1)/shape.GetParameter(1)
# down[s] = shapeDown.GetParameter(1)/shape.GetParameter(1)
elif 'res' in sys:
up[s] = histUp[s].GetRMS() / hist[s].GetRMS()
down[s] = histDown[s].GetRMS() / hist[s].GetRMS()
# up[s] = shapeUp.GetParameter(2)/shape.GetParameter(2)
# down[s] = shapeDown.GetParameter(2)/shape.GetParameter(2)
else:
up[s] = hist[s].GetBinContent(
hist[s].FindBin(+1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
down[s] = hist[s].GetBinContent(
hist[s].FindBin(-1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
gUp.SetPoint(i, m, up[s])
gDown.SetPoint(i, m, down[s])
#if mass < 1000: continue
upAvg += up[s]
downAvg += down[s]
if abs(up[s]) > upMax: upMax = abs(up[s])
if abs(up[s]) < upMin: upMin = abs(up[s])
if abs(down[s]) > downMax: downMax = abs(down[s])
if abs(down[s]) < downMin: downMin = abs(down[s])
upAvg /= len(sign)
downAvg /= len(sign)
print " ---", sys, "--- | up: %.3f, down: %.3f, average: %.3f" % (
upAvg, downAvg, abs(upAvg - 1 + 1. - downAvg) /
2.), "|", "^{%.1f-%.1f}_{%.1f-%.1f}" % (100. * (1. - upMin), 100. *
(1. - upMax), 100. *
(downMin - 1.), 100. *
(downMax - 1.))
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
c1.GetPad(0).SetTicky(2)
gUp.Draw("AL")
gDown.Draw("SAME, L")
gUp.GetYaxis().SetRangeUser(0.65, 1.35)
gUp.GetXaxis().SetTitle("m_{X} (GeV)")
gUp.GetYaxis().SetTitle("Uncertainty")
leg = TLegend(0.5, 0.90 - 0.20, 0.9, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader(sys.replace('_', ' '))
leg.AddEntry(gUp, "+1 s. d. (%.1f%%)" % (100. * (upAvg - 1.)), "l")
leg.AddEntry(gDown, " -1 s. d. (%.1f%%)" % (100. * (1. - downAvg)), "l")
leg.Draw()
drawCMS(-1, "Simulation", False)
c1.Update()
filename = sys
if sys.startswith('W_mass') or sys.startswith('Z_mass'):
filename += "_" + sign[0][:3]
# c1.Print("plots/Systematics/"+filename+".png")
# c1.Print("plots/Systematics/"+filename+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'doubleB' in sys or 'subjet' in sys or 'mass' in sys:
print sys + " = {",
for m in [
800, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
2000, 2500, 3000, 3500, 4000, 4500
]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'extr' in sys or 'tagging' in sys:
print sys + " = {",
for m in [800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'QCD_scale' in sys or 'PDF_scale' in sys:
print sys + " = {",
for m in range(800, 4500 + 1, 10):
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
def plot(var, cut):
### 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
cut = 'mmincSB_noisocut'
isBlind = BLIND and 'SR' in channel
if year in ['2016', '2017', '2018']:
NTUPLEDIR = "/work/pbaertsc/heavy_resonance/Ntuples%s/" % (year)
else:
NTUPLEDIR = "/work/pbaertsc/heavy_resonance/"
stype = "HVT model B"
if treeRead:
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut: cut = cut.replace(k, selection[k])
# Determine Primary Dataset
pd = []
if any(w in cut for w in ['mn', 'mm', 'isZtoMM', 'isWtoMN']):
pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x]
if any(w in cut
for w in ['en', 'ee', 'isWtoEN', 'isZtoEE', 'emqq', 'isTtoEM']):
pd += [
x for x in sample['data_obs']['files']
if ('SingleElectron' in x or 'EGamma' in x)
]
if any(w in cut for w in ['nn', '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?")
if var == "PrefireWeight":
pd = []
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
### Create and fill MC histograms ###
# Create dict
file = {}
tree_noisocut = {}
tree_isocut = {}
hist_noisocut = {}
hist_isocut = {}
cutstring_noisocut = "%s" % eventWeightLuminame + ("*(Mu1_highPtId==2 &&" +
cut + ")")
cutstring_isocut = "%s" % eventWeightLuminame + (
"*(Mu1_relIso!=-1. && Mu1_relIso < 0.1 && Mu1_highPtId==2 &&" + cut +
")")
#cutstring_noisocut = "%s" % eventWeightLuminame + ("*(Mu1_highPtId==1 &&"+cut+")")
#cutstring_isocut = "%s" % eventWeightLuminame + ("*(Mu1_relIso!=-1. && Mu1_relIso < 0.1 && Mu1_highPtId==1 &&"+cut+")")
"""
if var == 'Mu1_pt':
cutstring_noisocut = "%s" % eventWeightLuminame + ("*(Mu1_highPtId==2 &&"+cut+")")
cutstring_isocut = "%s" % eventWeightLuminame + ("*(Mu1_relIso!=-1. && Mu1_relIso < 0.1 && Mu1_highPtId==2 &&"+cut+")")
elif var == 'Mu2_pt':
cutstring_noisocut = "%s" % eventWeightLuminame + ("*(Mu2_highPtId==1 &&"+cut+")")
cutstring_isocut = "%s" % eventWeightLuminame + ("*(Mu2_relIso!=-1. && Mu2_relIso < 0.1 && Mu2_highPtId==1 &&"+cut+")")
"""
### Create and fill MC histograms ###
for i, s in enumerate(sign):
if treeRead: # Project from tree
tree_noisocut[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_noisocut[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
hist_noisocut[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist_noisocut[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist_noisocut[s].Sumw2()
tree_noisocut[s].Project(s, var, cutstring_noisocut)
hist_noisocut[s].Scale(
sample[s]['weight'] if hist_noisocut[s].Integral() >= 0 else 0)
for i, s in enumerate(sign):
if treeRead: # Project from tree
tree_isocut[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_isocut[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
hist_isocut[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist_isocut[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist_isocut[s].Sumw2()
tree_isocut[s].Project(s, var, cutstring_isocut)
hist_isocut[s].Scale(
sample[s]['weight'] if hist_isocut[s].Integral() >= 0 else 0)
for i, s in enumerate(sign):
addOverflow(hist_noisocut[s], False) # Add overflow
for i, s in enumerate(sign):
addOverflow(hist_isocut[s], False) # Add overflow
for i, s in enumerate(sign):
hist_noisocut[s].SetLineWidth(3)
for i, s in enumerate(sign):
hist_isocut[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
# Create stack
SigSum_noisocut = hist_noisocut[sign[0]].Clone("SigSum_noisocut")
SigSum_noisocut.Reset("MICES")
for i, s in enumerate(sign):
SigSum_noisocut.Add(hist_noisocut[s], 1)
SigSum_isocut = hist_isocut[sign[0]].Clone("SigSum_isocut")
SigSum_isocut.Reset("MICES")
for i, s in enumerate(sign):
SigSum_isocut.Add(hist_isocut[s], 1)
SigSum_noisocut.Divide(SigSum_isocut)
SigSum_noisocut.GetYaxis().SetRangeUser(0.98, 1.05)
c1 = TCanvas("c1", "Efficiency Mu Iso", 800, 800)
SigSum_noisocut.Draw("HIST")
drawCMS(LUMI, year, "Preliminary")
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
c1.Print("plotsSF/MuIsoEff_highPtId.png")
c1.Print("plotsSF/MuIsoEff_highPtId.pdf")