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, 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 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...")
def btag_efficiency(cut, year, pT_range=None):
### Preliminary Operations ###
from root_numpy import hist2array
from sklearn.metrics import roc_curve
import numpy as np
genPoints = [
1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000,
8000
]
signal = ['ZpBB_M' + str(x) for x in genPoints]
btag_vars = BTAGGEFFVARS
nbins = 100
min_, max_ = 0., 1.
treeRead = True
channel = cut
isAH = False
for k in sorted(alias.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(k, aliasSM[k])
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back + signal):
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if year == "run2" or year in ss:
### to run on big ntuples:
#k = 0
#while True:
# if os.path.exists("/eos/user/m/msommerh/Zprime_to_bb_analysis/" + ss + "/" + ss+ "_flatTuple_{}.root".format(k)):
# tree[s].Add("/eos/user/m/msommerh/Zprime_to_bb_analysis/" + ss + "/" + ss + "_flatTuple_{}.root".format(k))
# k += 1
# else:
# print "found {} files for sample:".format(k), ss
# break
#if k == 0:
# print ' WARNING: files for sample', ss , 'do not exist, continuing'
# return True
### end big ntuples
tree[s].Add(NTUPLEDIR + ss +
".root") ## to run on skimmed ntuples
for var in btag_vars:
hist[s + "_" + var] = TH1F(s + "_" + var,
";efficiency; mistag rate", nbins, min_,
max_)
for suf in ["_1", "_2"]:
temp_hist = TH1F(s + "_" + var + suf,
";efficiency; mistag rate", nbins, min_, max_)
temp_hist.Sumw2()
if pT_range is not None:
pT_cut = " && jpt" + suf + ">=" + str(
pT_range[0]) + " && jpt" + suf + "<" + str(pT_range[1])
else:
pT_cut = ""
if s in signal:
flavourcut = " && abs(jflavour" + suf + ")==5"
else:
flavourcut = " && (abs(jflavour" + suf + ")<4 || abs(jflavour" + suf + ")==9 || abs(jflavour" + suf + ")==21)" ## FIXME this currently excludes charms
if len(cut) == 0: flavourcut = flavourcut[4:]
cutstring = "(eventWeightLumi)" + "*(" + cut + flavourcut + pT_cut + ")"
tree[s].Project(s + "_" + var + suf, var + suf, cutstring)
if not tree[s].GetTree() == None:
hist[s + "_" + var].SetOption(
"%s" % tree[s].GetTree().GetEntriesFast())
#hist[s][var+suf].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s + "_" + var].Add(temp_hist)
temp_hist.Delete()
fpr = {}
tpr = {}
thr = {}
for var in btag_vars:
hist_sig = TH1F(var + "_sig", ";efficiency; mistag rate", nbins, min_,
max_)
hist_bkg = TH1F(var + "_bkg", ";efficiency; mistag rate", nbins, min_,
max_)
for i, s in enumerate(signal):
hist_sig.Add(hist[s + "_" + var])
for i, s in enumerate(back):
hist_bkg.Add(hist[s + "_" + var])
sig_arr, sig_edges = hist2array(hist_sig, return_edges=True)
bkg_arr, bkg_edges = hist2array(hist_bkg, return_edges=True)
assert len(sig_edges[0]) == len(bkg_edges[0])
vals = []
sig_weights = []
bkg_weights = []
for j, entry in enumerate(sig_edges[0][:-1]):
vals.append(0.5 * (sig_edges[0][j] + sig_edges[0][j + 1]))
sig_weights.append(sig_arr[j])
bkg_weights.append(bkg_arr[j])
sig_labels = np.ones(len(sig_weights))
bkg_labels = np.zeros(len(bkg_weights))
fpr[var], tpr[var], thr[var] = roc_curve(np.concatenate(
(sig_labels, bkg_labels)),
np.array(vals + vals),
sample_weight=np.concatenate(
(sig_weights,
bkg_weights)))
canv = TCanvas('c', 'c', 500, 650)
canv.SetGrid()
graphs = {}
for j, var in enumerate(btag_vars):
graphs[var] = TGraph(len(tpr[var]), tpr[var], fpr[var])
graphs[var].SetLineColor(btag_colors[var])
graphs[var].SetMarkerStyle(1)
graphs[var].SetMarkerColor(btag_colors[var])
graphs[var].SetLineWidth(2)
graphs[var].SetTitle(";b tagging efficiency;mistag rate (udsg jets)")
graphs[var].GetXaxis().SetLimits(0., 1.)
graphs[var].GetHistogram().SetMinimum(1e-4)
graphs[var].GetHistogram().SetMaximum(1.)
graphs[var].GetYaxis().SetTitleOffset(1.4)
leg = TLegend(0.65, 0.15, 0.9, 0.35)
for j, var in enumerate(btag_vars):
leg.AddEntry(graphs[var], btag_titles[var])
if j == 0:
graphs[var].Draw("APL")
else:
graphs[var].Draw("PL SAME")
latex = TLatex(0.05, 0.5,
str(pT_range[0]) + '<p_{T}<' + str(pT_range[1]) + ' GeV')
latex.SetTextSize(0.043)
latex.Draw()
leg.Draw()
canv.SetLogy()
if pT_range is not None:
pt_suff = "pT{}to{}".format(pT_range[0], pT_range[1])
else:
pt_suff = "incl_pT"
canv.Print("plots/btag_eff/ROC_{}_{}.png".format(year, pt_suff))
canv.Print("plots/btag_eff/ROC_{}_{}.pdf".format(year, pt_suff))