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...")
pad = c1.cd(1)
pad.SetPad(0, 0.3, 1, 1)
pad.SetTopMargin(0.1)
pad.SetBottomMargin(0.005)
t = pad.GetTopMargin()
# prepare the 2nd pad
pad = c1.cd(2)
pad.SetPad(0, 0.0, 1, 0.3)
pad.SetTopMargin(0.06)
pad.SetBottomMargin(0.4)
pad.SetTickx(1)
pad.SetTicky(1)
c1.cd(1)
hs.SetMaximum(hs.GetMaximum() * 5)
hs.SetMinimum(0.1)
gPad.SetLogy()
hs.Draw("Hist")
h_bkg.Draw("e2 same")
h_data.Draw("same")
for ihist in reversed(templates[5:8]):
print 'overlaying, ', ihist.GetName()
ihist.Draw("ehist same")
xTitle = h_top.GetXaxis().GetTitle()
yTitle = h_top.GetYaxis().GetTitle()
setTitle(hs, xTitle)
def SaveHisto1D(HIST,
suffix,
output,
snorm=1,
ratio=0,
poisson=True,
logy=False,
isVal=False):
# only data and mc
# SUFFIX = 2016_ss_lep1_pt
bkgsum = 'BkgSum_%s' % (suffix)
HIST[bkgsum] = HIST['DATA_%s' % (suffix)].Clone("BkgSum") if 'DATA_%s' % (
suffix) in HIST else HIST['MC_%s' % (suffix)].Clone("BkgSum")
HIST[bkgsum].Reset("MICES")
HIST[bkgsum].SetFillStyle(3003)
HIST[bkgsum].SetFillColor(1)
HIST[bkgsum].SetMarkerStyle(0)
HIST[bkgsum].Add(HIST['MC_%s' % (suffix)])
HIST['DATA_%s' % (suffix)].SetMarkerStyle(20)
HIST['DATA_%s' % (suffix)].SetMarkerSize(1.25)
HIST['DATA_%s' % (suffix)].SetFillColor(418)
HIST['DATA_%s' % (suffix)].SetFillStyle(1001)
HIST['DATA_%s' % (suffix)].SetLineColor(1)
HIST['DATA_%s' % (suffix)].SetLineStyle(1)
HIST['DATA_%s' % (suffix)].SetLineWidth(2)
HIST['MC_%s' % (suffix)].SetFillColor(418)
HIST['MC_%s' % (suffix)].SetFillStyle(1001)
HIST['MC_%s' % (suffix)].SetLineColor(418)
HIST['MC_%s' % (suffix)].SetLineStyle(1)
HIST['MC_%s' % (suffix)].SetLineWidth(2)
for i, s in enumerate(HIST):
addOverflow(HIST[s], False) # Add overflow
#Stack
bkg = THStack(
'bkg', ";" + HIST[bkgsum].GetXaxis().GetTitle() + ";" +
HIST[bkgsum].GetYaxis().GetTitle())
bkg.Add(HIST['MC_%s' % (suffix)]) # ADD ALL BKG
#Legend
n = len(HIST)
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetTextSize(0.03)
leg.AddEntry(HIST['DATA_%s' % (suffix)],
'Data [%.1f]' % (HIST['DATA_%s' % (suffix)].Integral()), "pl")
leg.AddEntry(HIST['MC_%s' % (suffix)],
'DY [%.1f]' % (HIST['MC_%s' % (suffix)].Integral()), "f")
#if isFake: leg.AddEntry(HIST['FAKE_%s' %(suffix)], 'Fake [%.1f]' %(HIST['FAKE_%s' %(suffix)].Integral()), "f")
leg.AddEntry(HIST[bkgsum], 'BkgSum [%.1f]' % (HIST[bkgsum].Integral()),
"f")
c1 = TCanvas("c1",
HIST.values()[-1].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
#Ratio pad
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)
if logy:
c1.GetPad(bool(ratio)).SetLogy()
#Draw
bkg.Draw("HIST") # stack
HIST[bkgsum].Draw("SAME, E2") # sum of bkg
HIST['DATA_%s' % (suffix)].Draw("SAME, PE") # data
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1) #1.075
bkg.SetMaximum((6.0 if logy else 1.5) * max(
bkg.GetMaximum(),
HIST['DATA_%s' %
(suffix)].GetBinContent(HIST['DATA_%s' %
(suffix)].GetMaximumBin()) +
HIST['DATA_%s' %
(suffix)].GetBinError(HIST['DATA_%s' %
(suffix)].GetMaximumBin())))
bkg.SetMinimum(
max(
min(HIST[bkgsum].GetBinContent(HIST[bkgsum].GetMinimumBin(
)), HIST['DATA_%s' %
(suffix)].GetMinimum()), 5.e-1) if logy else 0.)
#bkg.SetMinimum(1.0)
leg.Draw()
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)
errLine.SetLineColor(1)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'DATA_%s' % (suffix) in HIST:
res = HIST['DATA_%s' % (suffix)].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)
res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(HIST['DATA_%s' % (suffix)], HIST[bkgsum])
drawKolmogorov(HIST['DATA_%s' % (suffix)], HIST[bkgsum])
#drawRelativeYield(HIST['DATA_%s' %(suffix)], HIST[bkgsum])
else:
res = None
c1.cd(1)
if '2016' in output:
drawCMS("35.87", "Object Study")
elif '2017' in output:
drawCMS("41.53", "Object Study")
elif '2018' in output:
drawCMS("59.74", "Object Study")
if 'os' in suffix:
drawRegion('Opposite Sign')
elif 'ss' in suffix:
drawRegion('Same Sign')
c1.Update()
c1.Print('%s/hstack_%s.png' % (output, suffix))
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
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize * t)
posX_ = 0
posY_ = 1 - t - relPosY * (1 - t - b)
latex.SetTextFont(cmsTextFont)
latex.SetTextSize(0.8 * cmsTextSize * t)
latex.SetTextAlign(align_)
extraTextSize = extraOverCmsTextSize * cmsTextSize
latex.SetTextFont(extraTextFont)
latex.SetTextAlign(align_)
latex.SetTextSize(0.8 * extraTextSize * t)
hs.SetMaximum(1.6 * hs.GetMaximum())
fPads1.cd()
hs.Draw("HIST")
hs.GetYaxis().SetTitle("Events/bin")
hs.GetYaxis().SetTitleSize(0.04)
hs.GetYaxis().SetTickLength(0.02)
hs.GetYaxis().SetTitleOffset(1.2)
hs.GetXaxis().SetTitleOffset(1.3)
hs.GetXaxis().SetLabelSize(0.0)
hs.GetXaxis().SetTitle("M_{jj} [GeV]")
bin_width_signal = ['500', '600', '700', '1000', 'inf']
bin_width_control = ['200', '300', '400', '500']
hs.Print("all")
x1 = array('d', (4, 4))
SipMCTTbar[i].SetFillColor(kAzure - 3)
SipMCTTbar[i].SetLineColor(kBlack)
hs.Add(SipMCTTbar[i])
#MC DY hist
SipMCDY[i].Scale(norm) # MC normalization
SipMCDY[i].SetFillColor(kAzure + 6)
SipMCDY[i].SetLineColor(kBlack)
hs.Add(SipMCDY[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.3 * max(hs.GetMaximum(), SipDATA[i].GetMaximum()))
SipDATA[i].SetMaximum(1.3 * max(hs.GetMaximum(), SipDATA[i].GetMaximum()))
hs.Draw("histo")
SipDATA[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle("SIP")
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
def draw(hist, data, back, sign, snorm=1, ratio=0, poisson=False, log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Some style
for i, s in enumerate(data):
hist[s].SetMarkerStyle(21)
hist[s].SetMarkerSize(1.25)
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
# Poisson error bars for data
if poisson:
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(
i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L,
U - N)
res_graph.SetPoint(i,
hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if samples[x]['plot']])
for i, s in enumerate(sign):
if 'sublabel' in samples[s]: n += 1
if 'subsublabel' in samples[s]: n += 1
#leg = TLegend(0.68, 0.9-0.05*n, 0.93, 0.9)
leg = TLegend(0.68 - 0.05, 0.9 - 0.05 * n, 0.93, 0.9) #DCMS
leg.SetTextSize(0.03) #DCMS
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("Signal x-sec=%.0f pb" % (1 * snorm))
if len(data) > 0:
leg.AddEntry(hist[data[0]], samples[data[0]]['label'], "ple1")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], samples[s]['label'], "f")
for i, s in enumerate(sign):
leg.AddEntry(hist[s], samples[s]['label'], "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[-1].GetXaxis().GetTitle(), 1000,
800 if ratio else 700)
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)
if log:
c1.GetPad(bool(ratio)).SetLogy()
#c1.GetPad(bool(ratio)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if poisson: data_graph.Draw("SAME, PE")
elif len(data) > 0: hist['data_obs'].Draw("SAME, PE")
for i, s in enumerate(sign):
if samples[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
# Determine range
if 'data_obs' in hist:
bkg.SetMaximum((2.5 if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(
hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
else:
bkg.SetMaximum(bkg.GetMaximum() * (2.5 if log else 1.2))
bkg.SetMinimum(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)
#drawAnalysis("LL")
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(2)
errLine.SetFillStyle(0)
errLine.SetLineColor(2) #L#
errLine.SetLineStyle(2) #L#
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'data_obs' in hist:
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)
if poisson: res_graph.Draw("SAME, PE0")
else: res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
else:
res = None
c1.Update()
# return list of objects created by the draw() function
return [
c1, bkg, leg, err if ratio else None, errLine if ratio else None,
res if ratio else None, data_graph if poisson else None,
res_graph if poisson else None
]
def DrawSplotHistograms(DataFileName,
DataTreeName,
SFileName,
STreeName,
noStack=True):
fData = TFile(DataFileName)
tData = fData.Get(DataTreeName)
tData.AddFriend(STreeName, SFileName)
histoListe = setupHistograms()
HistDic = {}
for i, h in enumerate(histoListe):
legende = TLegend(0.75, 0.83, 0.97, 0.97)
CanvasName = "c" + str(i)
CanvasName = TCanvas(CanvasName, 'Defining histogram size')
g = THStack("", "")
#tMC.Draw(histoListe[i][3],CutMC)
#hist_MC = tMC.GetHistogram().Clone("hist_MC")
#hist_MC.SetLineColor(6)
#hist_MC.SetLineWidth(1)
##hist_MC.SetMarkerStyle(21)
#legende.AddEntry(hist_MC, "MC", "lp")
#hist_MC.GetXaxis().SetTitle(h[1])
#hist_MC.GetYaxis().SetTitle(h[2])
#hist_MC.Scale(1/hist_MC.Integral())
#MaxMC = hist_MC.GetMaximum()
#g.Add(hist_MC)
tData.Draw(histoListe[i][3], "B_M>5300")
hist_data = tData.GetHistogram().Clone("hist_data")
hist_data.SetLineColor(5)
hist_data.SetLineWidth(1)
#hist_data.SetMarkerStyle(21)
legende.AddEntry(hist_data, "Data", "lp")
hist_data.GetXaxis().SetTitle(h[1])
hist_data.GetYaxis().SetTitle(h[2])
MaxData = hist_data.GetMaximum()
g.Add(hist_data)
tData.Draw(histoListe[i][3], "nsig_sw")
hist_sw = tData.GetHistogram().Clone("hist_sw")
hist_sw.SetLineColor(4)
hist_sw.SetLineWidth(1)
##hist_RS.SetMarkerStyle(21)
legende.AddEntry(hist_sw, "sweighted Data", "lp")
MaxSW = hist_sw.GetMaximum()
g.Add(hist_sw)
if noStack:
if (MaxSW > MaxData):
g.SetMaximum(MaxSW * 1.2)
else:
g.SetMaximum(MaxData * 1.2)
g.Draw("nostackhist")
else:
g.SetMaximum(g.GetMaximum() * 1.2)
g.Draw("hist")
legende.Draw()
g.GetXaxis().SetTitle(h[1])
g.GetYaxis().SetTitle(h[2])
g.GetYaxis().SetTitleOffset(1.4)
g.GetXaxis().SetTitleOffset(1.1)
my_Latex = TLatex()
my_Latex.SetTextSize(0.04)
my_Latex.DrawLatexNDC(0.13, 0.85, "LHCb Data 2012")
CanvasName.Update()
outDir = '/sps/lhcb/volle/FitBMass/v1_Min5120'
if not os.path.isdir(outDir):
os.makedirs(outDir)
CanvasName.SaveAs("{}/{}_both.pdf".format(outDir,
histoListe[i][0])) #_both
CanvasName.SaveAs("{}/{}_both.png".format(outDir,
histoListe[i][0])) #_both
def main(argv):
P0DBANFFStyle = INTERFACE.GetThisStyle()
INTERFACE.SetStyle(P0DBANFFStyle.GetName())
OUTPUTFILENAME = "BANFFReactionCodeStacksOfficial_Joint2018_Numode_Prefit_P0DOnly_Momentum.pdf"
inputFile = TFile(sys.argv[0])
dataFile = TFile(sys.argv[1])
dummyPage = TCanvas()
dummyPage.Print(OUTPUTFILENAME + "[")
sampleNames = [
"P0D_Air_NuMu_CC_CC1Track", "P0D_Air_NuMu_CC_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
fileNameRoots = [
"P0D_Air_NuMu_CC1Track", "P0D_Air_NuMu_CCNTracks",
"P0D_Water_NuMu_CC1Track", "P0D_Water_NuMu_CCNTracks"
]
stackHistos = [
"#nu CCQE", "#nu CC 2p-2h", "#nu CC Res 1#pi", "#nu CC Coh 1#pi",
"#nu CC Other", "#nu NC modes", "#bar{#nu} modes"
]
P0DBANFFStyle.SetTitleBorderSize(0)
P0DBANFFStyle.SetTitleX(0.02)
P0DBANFFStyle.SetTitleY(0.95)
P0DBANFFStyle.SetTitleW(0.3)
P0DBANFFStyle.SetTitleH(0.07)
# Align left, centered in y)
P0DBANFFStyle.SetTitleAlign(11)
stackColours = [1300, 1302, 1303, 1304, 1305, 1308, 1310]
stackFillStyle = [1001, 1001, 1001, 1001, 1001, 1001, 1001]
"""
The graphs will be of the form <sampleName>_rxnPredMC_<rxnCode+100>
So for each sample, we'll loop from 0-200, and if the THnD is not NULL when we
try and get it, add it to a list for stacking.
for sampleName in sampleNames:
"""
for ii in xrange(0, len(sampleNames)):
sampleName = sampleNames[ii]
# Total the number of events for this sample going into the histogram.
# (Remember: Sand is excluded here.)
mcSampleTotal = 0.0
# Now make the set of histograms to show, which combine several interaction
# types.
# Load in the data histogram and use it to assemble the TH2Ds for the MC, as
# well as making it one of the histograms on the stack.
dataHist = dataFile.Get(sampleName + "_data").Projection(1, 0)
dataMomProj = dataFile.Get(sampleName + "_data").Projection(
1, 0).ProjectionX("dataratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
dataMomProj.SetDirectory(0)
dataMomProj.SetTitle("")
prefitMomProj = inputFile.Get(sampleName + "_prefit").Projection(
1, 0).ProjectionX("prefitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
prefitMomProj.SetDirectory(0)
postfitMomProj = inputFile.Get(sampleName + "_postfit_0_0").Projection(
1, 0).ProjectionX("postfitratio" + sampleName + "_px", 1,
dataHist.GetNbinsX())
postfitMomProj.SetDirectory(0)
for ibx in xrange(1, dataMomProj.GetNbinsX() + 1):
dataMomProj.SetBinContent(
ibx,
dataMomProj.GetBinContent(ibx) / prefitMomProj.GetBinContent(ibx))
dataMomProj.SetBinError(
ibx,
ROOT.Math.sqrt(dataMomProj.GetBinContent(ibx)) /
prefitMomProj.GetBinContent(ibx))
print "Data = " + str(
dataMomProj.GetBinContent(ibx)) + " MC = " + str(
prefitMomProj.GetBinContent(ibx)) + " Ratio = " + str(
dataMomProj.GetBinContent(ibx) /
prefitMomProj.GetBinContent(ibx))
dataMomProj.GetYaxis().SetRangeUser(0.75, 1.25)
dataMomProj.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
dataMomProj.GetXaxis().SetTitle("Reconstructed muon momentum (MeV/c)")
dataMomProj.GetYaxis().SetTitle("Data / Sim.")
# There is only one data plot for each stack, so:
# Momentum projection
# Assemble them here.
dataOneDimPlots = []
dataOneDimPlots.append(
dataHist.ProjectionX("data" + sampleName + "_px", 1,
dataHist.GetNbinsY()))
dataNXBins = dataHist.GetXaxis().GetNbins()
dataxarray = dataHist.GetXaxis().GetXbins().GetArray()
# Now, loop through all the theta bins and add the slices in.
for iby in xrange(1, dataHist.GetNbinsY() + 1):
thetaLow = dataHist.GetYaxis().GetBinLowEdge(iby)
thetaHigh = dataHist.GetYaxis().GetBinUpEdge(iby)
thetaString = " < cos# theta < "
thetaString = str(thetaLow) + thetaString + str(thetaHigh)
dataOneDimPlots.append(
TH1D("dataslice_" + sampleName + str(iby), thetaString, dataNXBins,
dataxarray))
# Now, go through the x bins in this slice and fill the plot we
# just created.
for ibx in xrange(1, dataHist.GetNbinsX() + 1):
# Get the bin dimensions for dividing purposes to area
# normalize.
dataOneDimPlots[len(dataOneDimPlots) - 1].SetBinContent(
ibx, dataHist.GetBinContent(ibx, iby))
# Now that the dataOneDimPlots array is filled, loop through a area
# normalize the bins.
for dataOneDimHist in dataOneDimPlots:
for ibx in xrange(1, dataOneDimHist.GetNbinsX() + 1):
mpdim = (dataOneDimHist.GetXaxis().GetBinUpEdge(ibx) -
dataOneDimHist.GetXaxis().GetBinLowEdge(ibx)) / 100.0
dataOneDimHist.SetBinError(
ibx,
math.sqrt(dataOneDimHist.GetBinContent(ibx)) / mpdim)
dataOneDimHist.SetBinContent(
ibx,
dataOneDimHist.GetBinContent(ibx) / mpdim)
histosToStack = []
for histo in stackHistos:
histosToStack.append(
TH2D(sampleName + "_" + histo, sampleName + "_" + histo,
dataHist.GetXaxis().GetNbins(),
dataHist.GetXaxis().GetXbins().GetArray(),
dataHist.GetYaxis().GetNbins(),
dataHist.GetYaxis().GetXbins().GetArray()))
# Now pull the histograms from the file, and add their content to the
# relevant combined histogram.
for i in xrange(0, 200):
if inputFile.Get(sampleName + "_rxnPredMC_" + str(i)):
histosToStack[CorrespondingIndex(i)].Add(
inputFile.Get(sampleName + "_rxnPredMC_" + str(i)).Projection(
1, 0))
"""
# OK, so now we have an array of 2D histograms for this sample that we'd
# like to stack. Need to divide it up into individual ones. Do Momentum
# Projection, then momentum in theta slices. Normalize by bin area.
# For storing all the one dimensional plots for this sample.
"""
oneDimPlots = []
# Add nothing for the momentum projection, will append the thetaStrings as
# needed.
stackTitles = []
for i in xrange(0, len(stackHistos)):
# For storing all the one dimensional plots for this reaction code.
oneDimPlotsThisReac = []
nameBase = histosToStack[i].GetTitle()
oneDimPlotsThisReac.append(histosToStack[i].ProjectionX(
nameBase + "_px", 1, histosToStack[i].GetNbinsY()))
stackTitles.append("Momentum projection")
# Area normalize this now.
NXBins = histosToStack[i].GetXaxis().GetNbins()
for ibx in xrange(1, NXBins + 1):
mpdim = (histosToStack[i].GetXaxis().GetBinUpEdge(ibx) -
histosToStack[i].GetXaxis().GetBinLowEdge(ibx)) / 100.0
oneDimPlotsThisReac[0].SetBinContent(
ibx, oneDimPlotsThisReac[0].GetBinContent(ibx) / mpdim)
mcSampleTotal += oneDimPlotsThisReac[0].GetSumOfWeights()
# Now that the x bins are filled, loop on to the next histogram.
# With all the y-slices done, oneDimPlotsThisReac is now full. Append
# it to oneDimPlots.
oneDimPlots.append(oneDimPlotsThisReac)
""""
At this point in time, oneDimPlots has a momentum projection and theta
slices for each reaction code grouping in this sample. So now we just need
to make TStacks for them and print them out to PDF.
The stack contains the plot from the same index of each entry of
oneDimPlots.
They should all have the same length, so use the first one to loop
through (number of slices plus 1 full momenutm projection.)
"""
for odi in xrange(0, len(oneDimPlots[0])):
# For each one create a TStack
sampleStack = THStack(
"mpStack_" + sampleName + str(odi),
";Reconstructed muon momentum (MeV/c);Events/(100 MeV/c)")
leg = TLegend(0.7, 0.15, 0.94, 0.93)
leg.SetFillColor(0)
# Make data the first entry.
leg.AddEntry(dataOneDimPlots[odi], "Data", "LEP")
dataOneDimPlots[odi].SetMarkerStyle(20)
dataOneDimPlots[odi].SetMarkerColor(kBlack)
dataOneDimPlots[odi].SetLineColor(kBlack)
for odj in xrange(0, len(oneDimPlots)):
# Apply the appropriate plot options from the beginning.
oneDimPlots[odj][odi].SetFillColor(stackColours[odj])
oneDimPlots[odj][odi].SetFillStyle(stackFillStyle[odj])
oneDimPlots[odj][odi].SetMarkerStyle(1) # Don't want P0DBANFFStyle to interfere.
sampleStack.Add(oneDimPlots[odj][odi])
print oneDimPlots[odj][odi].GetTitle() + " " + str(
oneDimPlots[odj][odi].GetSumOfWeights())
leg.AddEntry(oneDimPlots[odj][odi], stackHistos[odj], "F")
c1 = TCanvas()
upperPad = TPad("upperPad", "upperPad", .05, .35, .96, .95)
lowerPad = TPad("lowerPad", "lowerPad", .05, .1, .96, .35)
upperPad.Draw()
lowerPad.Draw()
upperPad.cd()
gPad.SetBottomMargin(1e-5)
c1.SetTicks(1, 1)
sampleStack.Draw("")
dataOneDimPlots[odi].Draw("PEsame")
sampleStack.SetMaximum(
max(sampleStack.GetMaximum(),
FindPlotMax(dataOneDimPlots[odi], dataOneDimPlots[odi])))
leg.Draw()
sampleStack.GetXaxis().SetRangeUser(MINMOMENTUM, MAXMOMENTUM)
sampleStack.GetXaxis().SetTitle("")
sampleStack.GetXaxis().SetTickLength(0)
sampleStack.GetXaxis().SetLabelSize(0)
sampleStack.SetTitle("# nu-mode")
lowerPad.cd()
ROOT.gPad.SetTopMargin(1e-5)
ROOT.gPad.SetBottomMargin(0.35)
ROOT.gPad.SetTickx()
dataMomProj.SetLineColor(2)
dataMomProj.SetMarkerColor(2)
dataMomProj.GetYaxis().SetNdivisions(8, 1)
dataMomProj.GetYaxis().SetLabelSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.07)
dataMomProj.GetYaxis().SetTitleSize(
dataMomProj.GetYaxis().GetLabelSize() + 0.05)
dataMomProj.GetYaxis().SetTitleOffset(0.335)
dataMomProj.GetXaxis().SetLabelSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.08)
dataMomProj.GetXaxis().SetTitleSize(
dataMomProj.GetXaxis().GetLabelSize() + 0.04)
dataMomProj.GetXaxis().SetTitleOffset(0.9)
dataMomProj.GetXaxis().SetTickLength(0.04)
dataMomProj.Draw("PE")
line = TLine(0.0, 1.0, 5000.0, 1.0)
line.SetLineWidth(2)
line.SetLineColor(1)
line.Draw("SAME")
c1.Modified()
c1.Update()
c1.Print(OUTPUTFILENAME)
outFileName = fileNameRoots[sampleNames.index(
sampleName)] + "_mumom_rxn_postfit"
c1.Print(outFileName + ".pdf")
c1.Print(outFileName + ".png")
c1.Print(outFileName + ".eps")
dummyPage.Print(OUTPUTFILENAME + ']')
def 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)
for group in groups :
histo[group] = f.Get("h{0:s}_{1:s}_Mtt".format(group,cat))
#print("In makeStack(): histo={0:s} type(histo)={1:s}".format(str(histo[group]),type(histo[group])))
if dndm : convertToDNDM(histo[group])
if group == 'data' :
applyDATAStyle(histo[group])
elif group == 'Signal' :
applySignalStyle(histo[group])
else :
applyStyle(histo[group],colors[group],1,1001)
if group != 'data' : hs.Add(histo[group])
exit()
hMax= 1.2*max(histo['data'].GetMaximum(),hs.GetMaximum())
print("hMax={0:f}".format(hMax))
hs.SetMaximum(hMax)
hs.Draw("HIST")
histo['data'].Draw("e,SAME")
exit()
if doRatio :
hs.GetXaxis().SetLabelSize(0)
else :
if units!="" :
hs.GetXaxis().SetTitle(labelX+" ["+units+"]")
else :
hs.GetXaxis().SetTitle(labelX)
hs.GetYaxis().SetTitle("Events")
print integral
histos_HH4lbb[i].Scale(100.)
# ALL DATA
histos_AllData[i].SetMarkerColor(kBlack)
histos_AllData[i].SetLineColor(kBlack)
histos_AllData[i].SetMarkerStyle(20)
# histos_AllData[i].Rebin(6)
# --- upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
# Draw all
hs.SetMaximum(1.5 * max(hs.GetMaximum(), histos_AllData[i].GetMaximum()))
hs.Draw('histo')
histos_HH4lbb[i].Draw('histosame')
histos_AllData[i].Draw('samep')
# print "-----------------"
# print "fondi: " , integral_fondi
# print "dati: ", histos_AllData[i].Integral()
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetXaxis().SetTitle(histos_HH4lbb[i].GetXaxis().GetTitle())
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.4)
hs.GetYaxis().SetLabelFont(43)
def makePlot(hName, CR, isQCDMC, isData, isLog, isRatio):
'''
We first draw stacked histograms then data then unc band.
The ratio of data and background is drawn next in a separate
pad.
'''
#Divide canvas for the ratio plot
canvas = TCanvas()
if isRatio and isData:
canvas.Divide(1,2)
canvas.cd(1)
gPad.SetRightMargin(0.03);
gPad.SetPad(xPadRange[0],yPadRange[2],xPadRange[1],yPadRange[3]);
gPad.SetTopMargin(0.09);
gPad.SetBottomMargin(padGap);
#gPad.SetTickx(0);
gPad.RedrawAxis();
if isLog:
gPad.SetLogy(True);
#Get nominal histograms
#dataHist, bkgHists, qcdMCHist, qcdDDHist = getBaseHists(fileDict, hName, CR)
dataHist, bkgHists, qcdMCHist = getBaseHists(fileDict, hName, CR)
hSumOtherBkg = bkgHists[0].Clone("hSumOtherBkg")
hSumOtherBkg.Reset()
hAllBkgs = bkgHists
for bkgHist in bkgHists:
hColor = Samples[bkgHist.GetName().split("_")[0]][1]
hSumOtherBkg.Add(bkgHist)
hSumAllBkg = hSumOtherBkg.Clone("hSumAllBkg")
if isQCDMC:
hSumAllBkg.Add(qcdMCHist[0])
hAllBkgs.append(qcdMCHist[0])
#else:
# hSumAllBkg.Add(qcdDDHist[0])
# hAllBkgs.append(qcdDDHist[0])
#Stack nominal hists
xTitle = histograms[hName][0]
yTitle = histograms[hName][1]
hStack = THStack(hName,hName)
hForStack = sortHists(hAllBkgs, False)
for h in hForStack:
sampleName = h.GetName().split("_")[0]
decoHist(h, xTitle, yTitle, Samples[sampleName][1])
hStack.Add(h)
hStack.SetMinimum(1.0)
if isLog:
hStack.SetMaximum(100*hStack.GetMaximum())
else:
hStack.SetMaximum(1.3*hStack.GetMaximum())
hStack.Draw("HIST")
decoHistStack(hStack, xTitle, yTitle)
#Get histograms for the difference between nominal and syst up/down
hSumOtherBkgUps, hQCDUps = getSystHists(fileDict, hName, CR, "Up")
hSumOtherBkgDowns, hQCDDowns = getSystHists(fileDict, hName, CR, "Down")
hDiffUp = hSumOtherBkg.Clone("hDiffUp")
hDiffUp.Reset()
hDiffDown = hSumOtherBkg.Clone("hDiffDown")
hDiffDown.Reset()
for hUp in hSumOtherBkgUps:
hDiff = hUp.Clone("hDiff")
hDiff.Add(hSumOtherBkg, -1)
hDiffUp.Add(hDiff)
print "hDiffUp = ", hDiffUp.Integral()
for hDown in hSumOtherBkgDowns:
hDiff = hSumOtherBkg.Clone("hDiff")
hDiff.Add(hDown, -1)
hDiffDown.Add(hDiff)
if isQCDMC:
for hUp in hQCDUps:
hDiff = hUp.Clone("hDiff")
hDiff.Add(qcdMCHist[0], -1)
hDiffUp.Add(hDiff)
for hDown in hQCDDowns:
hDiff = qcdMCHist[0].Clone("hDiff")
hDiff.Add(hDown, -1)
hDiffDown.Add(hDiff)
#Get unc band for the top plot
uncGraphTop = getUncBand(hSumAllBkg, hDiffUp, hDiffDown,False)
uncGraphTop.SetFillColor(2);
uncGraphTop.SetFillStyle(3001);
uncGraphTop.Draw(" E2 same ");
#Draw data
decoHist(dataHist[0], xTitle, yTitle, Samples["Data"][1])
dataHist[0].SetMarkerStyle(20)
if isData:
dataHist[0].Draw("EPsame")
#Draw legend
hForLegend = sortHists(hAllBkgs, True)
plotLegend = getLegend(dataHist, hForLegend, uncGraphTop)
plotLegend.Draw()
#Draw CMS, Lumi, channel
if channel in ["mu", "Mu", "m"]:
chName = "#mu + jets"
else:
chName = "e + jets"
crName = formatCRString(CR)
if CR=="":
chName = "%s, SR"%chName
else:
chName = "%s, CR"%chName
chCRName = "#splitline{#font[42]{%s}}{#font[42]{%s}}"%(chName, crName)
extraText = "#splitline{Preliminary}{%s}"%chCRName
#CMS_lumi(canvas, iPeriod, iPosX, extraText)
lumi_13TeV = "35.9 fb^{-1}"
if "16" in year:
lumi_13TeV = "35.9 fb^{-1} (2016)"
if "17" in year:
lumi_13TeV = "41.5 fb^{-1} (2017)"
if "18" in year:
lumi_13TeV = "59.7 fb^{-1} (2018)"
CMS_lumi(lumi_13TeV, canvas, iPeriod, iPosX, extraText)
#Draw the ratio of data and all background
if isData and isRatio:
canvas.cd(2)
gPad.SetTopMargin(padGap);
gPad.SetBottomMargin(0.30);
gPad.SetRightMargin(0.03);
#gPad.SetTickx(0);
gPad.SetPad(xPadRange[0],yPadRange[0],xPadRange[1],yPadRange[2]);
gPad.RedrawAxis();
hRatio = dataHist[0].Clone("hRatio")
hRatio.Divide(hSumAllBkg)
decoHistRatio(hRatio, xTitle, "Obs./Exp.", 1)
hRatio.Draw()
uncGraphRatio = getUncBand(hSumAllBkg, hDiffUp, hDiffDown,True)
uncGraphRatio.SetFillColor(2);
uncGraphRatio.SetFillStyle(3001);
uncGraphRatio.Draw("E2same");
baseLine = TF1("baseLine","1", -100, 2000);
#baseLine.SetLineColor(kRed+1);
baseLine.SetLineColor(3);
baseLine.Draw("SAME");
hRatio.Draw("same")
#canvas.SaveAs("%s/%s.pdf"%(outPlotFullDir, hName))
canvas.SaveAs("%s/%s_%s_%s.png"%(outPlotFullDir, hName, year, channel))
sigmagraph.SetPoint(pointno, pointno + 1, fitarray[pointno][2])
sigmagraph.SetPointError(pointno, 0, fitarray[pointno][5])
sigmahist.Fill(fitarray[pointno][2])
chisquaregraph.SetPoint(pointno, pointno + 1, fitarray[pointno][6])
chisquaregraph.SetPointError(pointno, 0, 0)
# iterator.ls()
for i in range(0, 3):
fill2d(fitarray[:, i], mapsa_mat, objarr2d[i])
fill2d(fitarray[:, 6], mapsa_mat, objarr2d[3])
g.cd(str(outfile))
g.mkdir(str(outfile) + "/Channels")
g.cd(str(outfile) + "/Channels")
iterator.Write()
# iterator.First().Print("all")
Maximum = TMath.Power(10, (round(TMath.Log10(stack.GetMaximum())) - 1))
ROOT.gStyle.SetLabelSize(0.06, "xyz")
ROOT.gStyle.SetTitleSize(0.06, "xyz")
ROOT.gStyle.SetTitleOffset(1.2, "y")
ROOT.gStyle.SetTitleOffset(.825, "x")
ROOT.gStyle.SetPadGridX(1)
ROOT.gStyle.SetPadGridY(1)
ROOT.gStyle.SetOptStat(0)
# ROOT.gStyle.SetPadLeftMargin(.2);
# ROOT.gStyle.SetPadRightMargin(.1);
c1.cd(1)
stack.Draw("nostack hist e1 x0")
stack.GetXaxis().SetRangeUser(0, 256)
def makeDiTauStack(outDir,
inFile,
rootDir,
s,
labelX,
units="GeV",
left=False,
channel="",
json="Golden",
log=False,
dndm=False,
doRatio=False,
year=2017,
sign='OS',
LTcut=0.,
cat='mmtt',
wait='wait'):
tdrstyle.setTDRStyle()
writeExtraText = True # if extra text
extraText = "Preliminary" # default extra text is "Preliminary"
lumi_sqrtS = "13 TeV"
if json == "Golden": lumi_13TeV = channel + " 41.8 fb^{-1}, 2017"
iPeriod = 4 # 1=7TeV, 2=8TeV, 3=7+8TeV, 7=7+8+13TeV
xR = 0.65 #legend parameters
xR = 0.2 #legend parameters
H = 600
W = 600
H_ref = 600
W_ref = 600
# references for T, B, L, R
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.16 * W_ref
R = 0.04 * W_ref
#margins for inbetween the pads in a ratio plot
B_ratio = 0.1 * H_ref
T_ratio = 0.03 * H_ref
#margin required for lebal on bottom of raito plot
B_ratio_label = 0.3 * H_ref
c = TCanvas('c1', 'c1', 50, 50, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
if not doRatio:
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.cd()
ratioPad = TPad("pad2", "", 0.0, 0.0, 1.0, 0.29)
plotPad = TPad("pad1", "", 0.0016, 0.291, 1.0, 1.0)
if doRatio:
plotPad.SetTicks(0, 0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B_ratio / H)
plotPad.SetFillColor(0)
plotPad.SetBottomMargin(0)
ratioPad.SetLeftMargin(L / W)
ratioPad.SetRightMargin(R / W)
ratioPad.SetTopMargin(T_ratio / H)
ratioPad.SetTopMargin(0.007)
ratioPad.SetBottomMargin(B_ratio_label / H)
ratioPad.SetGridy(1)
ratioPad.SetFillColor(4000)
else:
plotPad = TPad("pad1", "", 0.0, 0.03, 1.0, 1.0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B / H)
plotPad.Draw()
plotPad.cd()
print("In makeStack inFile={0:s}".format(inFile))
f = TFile(inFile)
groups = ['data', 'Reducible', 'Rare', 'ZZ4L', 'Signal']
histo = {}
colors = {
'data': 0,
'Reducible': kMagenta - 10,
'Rare': kBlue - 8,
'ZZ4L': kAzure - 9,
'Signal': kRed
}
hs = THStack("hs", "")
for group in groups:
if len(cat) == 4:
histo[group] = f.Get("h{0:s}_{1:s}_Mtt".format(group, cat))
else:
histo[group] = f.Get("h{0:s}_ee{1:s}_Mtt".format(group, cat))
histo2 = f.Get("h{0:s}_mm{1:s}_Mtt".format(group, cat))
histo[group].Add(histo2)
if dndm: convertToDNDM(histo[group])
if group == 'data':
applyDATAStyle(histo[group])
else:
applyStyle(histo[group], colors[group], 1, 1001)
if group != 'data': hs.Add(histo[group])
try:
hMax = 1.2 * max(histo['data'].GetMaximum(), hs.GetMaximum())
except KeyError:
hMax = 1.2 * hs.GetMaximum()
hs.SetMaximum(hMax)
hs.Draw("HIST")
try:
histo['data'].Draw("e,SAME")
except KeyError:
pass
if doRatio:
hs.GetXaxis().SetLabelSize(0)
else:
if units != "":
hs.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
hs.GetXaxis().SetTitle(labelX)
hs.GetYaxis().SetTitle("Events")
hs.GetYaxis().SetTitleOffset(1)
if dndm: hs.GetYaxis().SetTitle("dN/d" + labelX)
c.cd()
if doRatio:
data2 = histo['data'].Clone("data")
mc = histo['Rare']
mc.Add(histo['Reducible'])
mc.Add(histo['ZZ4L'])
xmin = mc.GetXaxis().GetXmin()
xmax = mc.GetXaxis().GetXmax()
line = TLine(xmin, 1.0, xmax, 1.0)
line.SetLineWidth(1)
line.SetLineColor(kBlack)
ratioPad.Draw()
ratioPad.cd()
data2.Divide(data2, mc)
data2.SetMarkerStyle(20)
data2.SetTitleSize(0.12, "Y")
data2.SetTitleOffset(0.40, "Y")
data2.SetTitleSize(0.12, "X")
data2.SetLabelSize(0.10, "X")
data2.SetLabelSize(0.08, "Y")
data2.GetYaxis().SetRangeUser(0.62, 1.38)
data2.GetYaxis().SetNdivisions(305)
data2.GetYaxis().SetTitle("Obs/Exp ")
if units != "":
data2.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
data2.GetXaxis().SetTitle(labelX)
data2.Draw("P")
line.Draw()
c.cd()
plotPad.cd()
l = TLegend(xR, 0.55, xR + 0.28, 0.9)
for group in groups:
try:
l.AddEntry(histo[group], group, "F")
except KeyError:
continue
l.SetBorderSize(0)
l.SetFillColor(0)
l.SetFillStyle(0)
l.SetTextSize(0.04)
l.Draw()
lTex1 = TLatex(120., 0.97 * hMax, 'Preliminary {0:d}'.format(year))
lTex1.SetTextSize(0.04)
lTex1.Draw()
signText = 'Same Sign'
if sign == 'OS': signText = 'Opposite Sign'
lTex2 = TLatex(120., 0.90 * hMax, '{0:s}'.format(signText))
lTex2.SetTextSize(0.04)
lTex2.Draw()
if len(cat) == 4:
lt = {
'eeet': '#it{l}#it{l}#it{e}#tau',
'eemt': '#it{l}#it{l}#mu#tau',
'eett': '#it{l}#it{l}#tau#tau',
'mmet': '#mu#mu#it{e}#tau',
'mmmt': '#mu#mu#mu#tau',
'mmtt': '#mu#mu#tau#tau'
}
else:
lt = {
'et': '#it{l}#it{l}#it{e}#tau',
'mt': '#it{l}#it{l}#mu#tau',
'tt': '#it{l}#it{l}#tau#tau'
}
lTex3 = TLatex(120., 0.83 * hMax,
'{0:s} H_LT > {1:d} '.format(lt[cat], int(LTcut)))
lTex3.SetTextSize(0.04)
lTex3.Draw()
plotPad.Draw()
#CMS_lumi(plotPad,4,11)
outFileBase = "Stack_{0:d}_{1:s}_{2:s}_LT{3:02d}".format(
year, cat, sign, int(LTcut))
c.SaveAs("{0:s}.png".format(outFileBase))
c.SaveAs("{0:s}.root".format(outFileBase))
if wait == 'wait':
raw_input()
else:
import time
time.sleep(5.)
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 read_file(arr, meas_type, mapsa_fitter_inst, path):
if not os.path.isfile(str(path + arr[0])):
print "Root file not found at", str(path + arr[0])
#sys.exit(1)
return
f = TFile(str(path + arr[0]), 'READ')
if (f.IsZombie()):
print "Error opening file"
return
else:
print "Reading File ", arr[0]
#f.ls()
tree = f.Get('tree')
f_GlobalData_Map = ROOT.TMap()
f_GlobalData_Map.Add(ROOT.TObjString("tree"), tree)
#tree.Print()
outfile = TString(arr[0])
outfile.ReplaceAll(".root", "")
outfile.ReplaceAll(" ", "")
# print outfile
if (meas_type == 0):
outfile = "pedestal"
if (not g.FindKey(str(outfile))):
g.mkdir(str(outfile))
g.cd(str(outfile))
else:
return
channels = 288
if (arr[2] == 'inv'):
channels = 96
mapsa_mat = [[1, 0, 0], [1, 0, 0]]
elif (arr[2] == 'norm'):
channels = 288
mapsa_mat = [[1, 1, 1], [1, 0, 1]]
#print "channels", channels
#print "mapsa_mat", mapsa_mat
c1 = TCanvas('c1', 'Pixel Monitor ', 700, 900)
c2 = TCanvas('c2', 'Pixel Monitor ', 500, 500)
c3 = TCanvas('c3', 'Pixel Monitor ', 1280, 720)
c4 = TCanvas('c4', 'Pixel Monitor ', 500, 500)
c5 = TCanvas('c5', 'Pixel Monitor ', 500, 500)
f_GlobalData_Map.Add(ROOT.TObjString("c1"), c1)
f_GlobalData_Map.Add(ROOT.TObjString("c2"), c2)
f_GlobalData_Map.Add(ROOT.TObjString("c3"), c3)
f_GlobalData_Map.Add(ROOT.TObjString("c4"), c4)
f_GlobalData_Map.Add(ROOT.TObjString("c5"), c5)
# c2.Divide(2,1)
#c2.cd(0)
c1.Divide(3, 2)
for i in range(1, 7):
c1.cd(i)
ROOT.gPad.SetGridx()
ROOT.gPad.SetGridy()
# channelcounts = TH2I('HitMap','Counts; Channel; DAC Value (1.456 mV)', 288, .5,288.5,256, .5, 256.5)
channelcounts = TH2I('HitMap', 'Counts; Channel; DAC Value (a.u.)', 288,
.5, 288.5, 256, .5, 256.5)
channelcounts_norm = TH2F('HitMap_norm',
'Occupancy ; Channel; DAC Value (a.u.)', 288, .5,
288.5, 256, .5, 256.5)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts_norm)
norm_2d = TH2F('Norm2D', 'Normalization; Column; Row', 48, .5, 48.5, 6, .5,
6.5)
mean_2d = TH2F('Mean2D', 'Mean; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
sigma_2d = TH2F('Sigma2D', 'Sigma; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
chisquare = TH2F('Chisquare2D', 'Chisquare; Column; Row', 48, .5, 48.5, 6,
.5, 6.5)
f_GlobalData_Map.Add(ROOT.TObjString("Norm2D"), norm_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Mean2D"), mean_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Sigma2D"), sigma_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Chisquare2D"), chisquare)
objarr2d = []
objarr2d.append(norm_2d)
objarr2d.append(mean_2d)
objarr2d.append(sigma_2d)
objarr2d.append(chisquare)
normgraph = TGraphErrors()
meangraph = TGraphErrors()
sigmagraph = TGraphErrors()
chisquaregraph = TGraphErrors()
mean_corrgraph = TGraphErrors()
f_GlobalData_Map.Add(ROOT.TObjString("normgraph "), normgraph)
f_GlobalData_Map.Add(ROOT.TObjString("meangraph "), meangraph)
f_GlobalData_Map.Add(ROOT.TObjString("sigmagraph "), sigmagraph)
f_GlobalData_Map.Add(ROOT.TObjString("chisquaregraph"), chisquaregraph)
f_GlobalData_Map.Add(ROOT.TObjString("mean_corrgraph"), mean_corrgraph)
meanhist = TH1F('meanhist', 'Mean DAC; DAC Value (a.u.); counts', 2560, 0,
255)
sigmahist = TH1F('sigmahist', 'Sigma DAC; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_std = TH1F('meanhist_std',
'Mean DAC Standard; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_std = TH1F('sigmahist_std',
'Sigma DAC Standard; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double = TH1F('meanhist_double',
'Mean DAC Double; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_double = TH1F('sigmahist_double',
'Sigma DAC Double; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double_neighbour = TH1F(
'meanhist_double_neighbour',
'Mean DAC Double Neighbour; DAC Value (a.u.); counts', 2560, 0, 255)
sigmahist_double_neighbour = TH1F(
'sigmahist_double_neighbour',
'Sigma DAC Double Neighbour; DAC Value (a.u.); counts', 100, 0, 10)
objarr = []
objarr.append(normgraph)
objarr.append(meangraph)
objarr.append(sigmagraph)
objarr.append(chisquaregraph)
objarr.append(mean_corrgraph)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist'), meanhist)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist'), sigmahist)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_std'), meanhist_std)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_std'), sigmahist_std)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double'), meanhist_double)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double'), sigmahist_double)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double_neighbour'),
meanhist_double_neighbour)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double_neighbour'),
sigmahist_double_neighbour)
objarr.append(meanhist)
objarr.append(sigmahist)
objarr.append(meanhist_std)
objarr.append(sigmahist_std)
objarr.append(meanhist_double)
objarr.append(sigmahist_double)
objarr.append(meanhist_double_neighbour)
objarr.append(sigmahist_double_neighbour)
for objs in objarr:
objs.SetMarkerColor(2)
objs.SetMarkerStyle(20)
objs.SetMarkerSize(1)
normgraph.SetName('normgraph')
meangraph.SetName('meangraph')
sigmagraph.SetName('sigmagraph')
chisquaregraph.SetName('chisquare')
mean_corrgraph.SetName('mean_corr')
normgraph.SetTitle('Normalization; Channel; Normalization')
meangraph.SetTitle('Mean; Channel; DAC Value (a.u.)')
sigmagraph.SetTitle('Sigma; Channel; DAC Value (a.u.)')
chisquaregraph.SetTitle('Chisquared/NDF_gr; Channel; Chisquared/NDF ')
ROOT.gStyle.SetOptFit(1111)
stack = THStack('a', ';DAC Value (a.u.); Occupancy')
f_GlobalData_Map.Add(ROOT.TObjString("stack"), stack)
fitfuncs = []
fitparams = []
gr1 = []
for pixel in range(0, channels):
gr1.append(
TH1D(
str(pixel).zfill(3),
str(pixel + 1).zfill(3) + ';DAC Value (a.u.); Occupancy ', 256,
0.5, 256.5))
f_GlobalData_Map.Add(ROOT.TObjString(str(pixel).zfill(3)), gr1[pixel])
#gr2.append(TH1F('th1f_'+str(pixel).zfill(3),str(pixel+1).zfill(3)+';DAC Value (a.u.); Occupancy',256,0.5,256.5))
color = pixel % 8 + 1
formating_th1(gr1[pixel], color)
if (meas_type == 0):
fitfuncs.append(
TF1('gauss' + str(pixel + 1).zfill(3), 'gaus(0)', 0, 256))
fitfuncs[pixel].SetNpx(256)
fitfuncs[pixel].SetLineColor(color)
f_GlobalData_Map.Add(
ROOT.TObjString('gauss' + str(pixel).zfill(3)),
fitfuncs[pixel])
#Here we read the data and fill the histogram
for event in tree:
eventstr = []
for counter, vals in enumerate(tree.AR_MPA):
#eventstr.append(vals)
channelcounts.Fill(counter, tree.THRESHOLD, vals)
if (counter < channels):
gr1[counter].Fill(tree.THRESHOLD, vals)
#if tree.THRESHOLD%20==0 and tree.REPETITION==0:
#print eventstr
#print ("Threshold %d Repetion %d" % (tree.THRESHOLD,tree.REPETITION))
#print tree.AR_MPA
#now we make a small analysis of the curves fitting different functions to it:
print "Finished Reading the Tree\n Normalization of Histograms\n"
for pixel in range(0, channels):
#gr1[pixel].ResetStats()
for j in range(0, gr1[pixel].GetXaxis().GetNbins() + 1):
gr1[pixel].SetBinError(
gr1[pixel].GetBin(j),
TMath.Sqrt(gr1[pixel].GetBinContent(gr1[pixel].GetBin(j))))
#if(pixel==0):
#gr1[pixel].Print("all")
#gr1[pixel].Sumw2(ROOT.kTRUE)
gr1[pixel].Scale(1 / arr[1])
#if(pixel==0):
#gr1[pixel].Print("all")
stack.Add(gr1[pixel])
#first create a THStack with histograms:
iterator = stack.GetHists()
if (meas_type == 0):
for idx, it in enumerate(iterator):
fitparams.append([])
if (it.Integral() > 0):
if (idx < channels):
#fitfuncs.append(TF1('combined'+str(idx),combined, 0,256,5))
#fitfuncs.append(TF1('combined_same_mean'+str(idx),combined_mean, 0,256,4))
#fitfuncs.append(TF1('double_gauss'+str(idx),'gaus(0)+gaus(3)',0,256))
#fitfuncs.append(TF1('gauss'+str(idx),'gaus(0)',0,256))
#fitfuncs.append(TF1('double_gauss_same_mean'+str(idx),double_gauss, 0,256,5))
#print it.GetName(), idx
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean()+1,it.GetRMS(),it.GetMean()-1,it.GetRMS());
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean(),it.GetRMS()*0.1,it.GetRMS()*0.1);
#print ("Channels %f\t%f\t%f\n" % (it.GetMaximum(),it.GetMean(),it.GetRMS()))
fitfuncs[idx].SetParameters(it.GetMaximum(), it.GetMean(),
it.GetRMS())
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),it.GetMean(),10*it.GetRMS());
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),10*it.GetRMS());
#it.Fit(fitfuncs[idx],'lr0 rob=0.95','same',0,256)
#it.Fit(fitfuncs[idx],'lr0q ','',0,256)
it.Fit(fitfuncs[idx], 'r0q ', '', 0, 256)
fitparams[idx].append(fitfuncs[idx].GetParameter(0))
fitparams[idx].append(fitfuncs[idx].GetParameter(1))
fitparams[idx].append(fitfuncs[idx].GetParameter(2))
fitparams[idx].append(fitfuncs[idx].GetParError(0))
fitparams[idx].append(fitfuncs[idx].GetParError(1))
fitparams[idx].append(fitfuncs[idx].GetParError(2))
if (fitfuncs[idx].GetNDF() > 0):
fitparams[idx].append(fitfuncs[idx].GetChisquare() /
fitfuncs[idx].GetNDF())
else:
for kk in range(0, 7):
fitparams[idx].append(0)
#print "fitparamarray"
fitarray = np.array(fitparams)
## print fitarray
for pointno, it in enumerate(fitarray):
if (fitarray[pointno][0] > 0):
normgraph.SetPoint(pointno, pointno + 1, fitarray[pointno][0])
normgraph.SetPointError(pointno, 0, fitarray[pointno][3])
meangraph.SetPoint(pointno, pointno + 1, fitarray[pointno][1])
meangraph.SetPointError(pointno, 0, fitarray[pointno][4])
meanhist.Fill(fitarray[pointno][1])
sigmagraph.SetPoint(pointno, pointno + 1, fitarray[pointno][2])
sigmagraph.SetPointError(pointno, 0, fitarray[pointno][5])
sigmahist.Fill(fitarray[pointno][2])
chisquaregraph.SetPoint(pointno, pointno + 1,
fitarray[pointno][6])
chisquaregraph.SetPointError(pointno, 0, 0)
## iterator.ls()
# Map the data to the pixel layout:
tmp_objarr = []
tmp_objarr.extend(
[meanhist_std, meanhist_double, meanhist_double_neighbour])
tmp_objarr.extend(
[sigmahist_std, sigmahist_double, sigmahist_double_neighbour])
for i in tmp_objarr:
print str(i.GetName())
fill2d(fitarray[:, 0], mapsa_mat, objarr2d[0])
fill2d(fitarray[:, 1], mapsa_mat, objarr2d[1])
fill2d(fitarray[:, 2], mapsa_mat, objarr2d[2])
fill2d(fitarray[:, 6], mapsa_mat, objarr2d[3])
fill1d_edges(objarr2d[1], tmp_objarr[0:3])
fill1d_edges(objarr2d[2], tmp_objarr[3:])
g.cd(str(outfile))
mapsa_fitter_inst.Make_dirs()
mapsa_fitter_inst.Set_run_no(outfile)
if (meas_type == 1):
for idx, it in enumerate(iterator):
if (it.Integral() > 0):
if (idx < channels):
mapsa_fitter_inst.Find_signal(it, idx, 0.0025, 3)
g.cd()
#g.mkdir(str(outfile)+"/Channels")
#g.cd(str(outfile)+"/Channels")
#iterator.Write()
g.cd(str(outfile))
g.mkdir(str(outfile) + "/Overview")
## iterator.First().Print("all")
Maximum = TMath.Power(10, (round(TMath.Log10(stack.GetMaximum())) - 1))
#Minimum = TMath.Power(10,(round(TMath.Log10(stack.GetMinimum()))+1))
Minimum = .1
ROOT.gStyle.SetLabelSize(0.06, "xyz")
ROOT.gStyle.SetTitleSize(0.06, "xyz")
ROOT.gStyle.SetTitleOffset(1.2, "y")
ROOT.gStyle.SetTitleOffset(.825, "x")
ROOT.gStyle.SetPadGridX(1)
ROOT.gStyle.SetPadGridY(1)
ROOT.gStyle.SetOptStat(0)
# ROOT.gStyle.SetPadLeftMargin(.2);
# ROOT.gStyle.SetPadRightMargin(.1);
c1.cd(1)
stack.Draw("nostack hist e1 x0")
stack.GetXaxis().SetRangeUser(0, 256)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLogy()
c2.cd(0)
stack.Draw("nostack hist e1 x0")
#if(outfile.Contains("SR_90_on_top")):
#stack.GetXaxis().SetRangeUser(0,256)
#else:
#stack.GetXaxis().SetRangeUser(0,100)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
ROOT.gPad.SetLogy()
ROOT.gPad.Update()
#for idx, it in enumerate(fitfuncs):
## if idx>0 and idx<7:
#c1.cd(1)
#fitfuncs[idx].Draw("same")
#c2.cd(0)
#fitfuncs[idx].DrawCopy("psame")
## it.SetLineColor(idx%9+1)
## it.Draw("same")
#g.cd(str(outfile)+"/Channels")
#it.Write("HitMap_py_"+str(idx+1)+"_fit")
g.cd(str(outfile) + "/Overview")
c1.cd(2)
chisquaregraph.Draw("ap")
c1.cd(3)
normgraph.Draw("ap")
c1.cd(4)
sigmagraph.Draw("ap")
sigmagraph.GetYaxis().SetRangeUser(0, 5)
sigmagraph.GetXaxis().SetRangeUser(0, channels + 1)
c2.cd(2)
sigmagraph.Draw("ap")
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
c1.cd(5)
meangraph.Draw("ap")
c1.cd(6)
channelcounts.Draw("colz")
channelcounts.GetXaxis().SetRangeUser(0, channels + 1)
# c2.cd(3)
c3.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy = channelcounts.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy"),copy)
#if(outfile.Contains("SR_90_on_top")):
#copy.SetMaximum(100)
#copy.SetMinimum(1)
copy.GetYaxis().SetTitle("DAC Value (a.u.)")
c4.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy1"),copy1)
copy1.GetZaxis().SetTitle("Sigma (a.u.)")
copy1.GetZaxis().SetTitleOffset(1.2)
ROOT.gPad.SetRightMargin(.2)
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c5.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
#copy1 = chisquare.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1.GetZaxis().SetTitle("sigma (a.u.)")
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c1.Update()
c1.Modified()
c2.Update()
c2.Modified()
c3.Update()
c3.Modified()
c4.Update()
c4.Modified()
c5.Update()
c5.Modified()
## c1.SaveAs("double_gauss_same_mean.pdf")
## time.sleep(2)
g.cd(str(outfile) + "/Overview")
#for objs in objarr:
#objs.Write(objs.GetName())
#norm_2d.GetZaxis().SetRangeUser(1E5,2E6)
#mean_2d.GetZaxis().SetRangeUser(54,64)
## norm_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3))-2)), TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3)))-1)))
## mean_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(mean_2d.mean_2d.GetStdDev(3)))-1))-5,TMath.Power(10,(round(TMath.Log10(mean_2d.GetStdDev(3)))-1))+5)
#sigma_2d.GetZaxis().SetRangeUser(0,5)
#chisquare.GetZaxis().SetRangeUser(0,10000 )
#for objs in objarr2d:
#objs.Write(objs.GetName())
#c1.Write("c1")
#outfile1=outfile+TString(".pdf")
#c2.SaveAs(str(outfile1))
#c2.Write("c2")
#c3.SaveAs("c3"+str(outfile1))
#c3.Write("c3")
#c4.SaveAs("c4"+str(outfile1))
#c4.Write("c4")
## while (TObject(iterator.Next())):
## print iterator.Next().Title()
#stack.Write("stack")
#g.cd(str(outfile))
#channelcounts.Write(str(outfile))
#f.Close()
c1.Close()
c2.Close()
c3.Close()
c4.Close()
c5.Close()
f_GlobalData_Map.DeleteAll()
f.Close()
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)
sigma1, sigma2, sigma3 = ("nan", "nan", "nan")
string_sigma = "muta, tata, nunu sigma = " + str(sigma1) + " " + str(
sigma2) + " " + str(sigma3) + '\n'
fNevt.write(string_sigma)
#Draw
if nsig1 * nsig2 * nsig3 > 0:
h_sig1.Scale(ntotalbkg / (nsig1 * 10000))
h_sig2.Scale(ntotalbkg / (nsig2 * 10000))
h_sig3.Scale(ntotalbkg / (nsig3 * 10000))
c = TCanvas("c_" + "{}".format(i), "c", 1)
if log and h_bkg.Integral() > 0:
c.SetLogy()
max_hs = max([
hs.GetMaximum(),
h_sig1.GetMaximum(),
h_sig2.GetMaximum(),
h_sig3.GetMaximum()
])
maxfrac = 0.5
if log:
if max_hs > 100000:
maxfrac = 1000
else:
maxfrac = 100
#hs.SetMaximum(max_hs+max_hs*maxfrac)
h_sig1.SetMaximum(max_hs + max_hs * maxfrac)
#if log: hs.SetMinimum(10)
if log: h_sig1.SetMinimum(10)
h_sig1.Draw("hist same")
DATA_list[i].SetMarkerSize(0.6)
#DATA_list[i].Rebin(4)
#MC DY hist
MCDY_list[i].Scale(norm) # MC normalization
MCDY_list[i].SetFillColor(kAzure - 3)
MCDY_list[i].SetLineColor(kBlack)
#MCDY_list[i].Rebin(4)
hs.Add(MCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.6 * max(hs.GetMaximum(), DATA_list[i].GetMaximum()))
DATA_list[i].SetMaximum(1.6 *
max(hs.GetMaximum(), DATA_list[i].GetMaximum()))
hs.SetMinimum(10) #EF for ele BDT
hs.Draw("histo")
DATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle(x_axis_list[i])
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.2)
hs.GetYaxis().SetLabelFont(43)
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)
c.cd() # returns to main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0.0, 0.0, 1, 0.28)
pad2.SetBottomMargin(0.3)
pad2.SetTopMargin(0.02)
pad2.SetGridx()
pad2.SetGridy()
pad2.Draw()
#Draw each plot
pad1.cd()
if log:
pad1.SetLogy()
h_data.SetMarkerStyle(20)
h_data.SetMarkerSize(0.5)
max_data = h_data.GetMaximum()
max_hs = hs.GetMaximum()
sigmaximums = [
hs_Hct.GetMaximum(),
hs_Hut.GetMaximum(),
hs_stHct.GetMaximum(),
hs_stHut.GetMaximum()
]
max_sig = max(sigmaximums)
maxfrac = 0.5
if (max_hs > max_sig):
if log:
if max_data > 100000:
maxfrac = 1000
else:
maxfrac = 100
if max_hs > max_data:
pad2.SetFrameFillStyle(0)
pad2.SetFrameBorderMode(0)
pad2.SetTickx(0)
pad2.SetTicky(0)
pad1.Draw()
pad2.Draw()
noData = False
oneLine = TF1("oneline", "1", -9e9, 9e9)
oneLine.SetLineColor(kBlack)
oneLine.SetLineWidth(1)
oneLine.SetLineStyle(2)
maxVal = stack.GetMaximum()
if not noData:
maxVal = max(rebinnedData.GetMaximum(), maxVal)
minVal = 0
# minVal = max(stack.GetStack()[0].GetMinimum(),1)
stack.SetMaximum(1.25 * maxVal)
stack.SetMinimum(minVal)
errorband = stack.GetStack().Last().Clone("error")
errorband.Sumw2()
errorband.SetLineColor(kBlack)
errorband.SetFillColor(kBlack)
errorband.SetFillStyle(3245)
errorband.SetMarkerSize(0)
def draw(hist,
fit,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
setHistStyle(hist['BkgSum'], 1.1 if ratio else 1.0)
# Create stack
bkg = THStack(
"Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";" +
hist['BkgSum'].GetYaxis().GetTitle())
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.69, 0.86 - 0.04 * n, 0.95, 0.86)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
### data/bkg ratio and systematics
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("data / bkg")
if fit == "prefit":
err.SetFillColor(ROOT.kOrange - 2)
elif fit == "postfit":
err.SetFillColor(ROOT.kBlue)
err.SetFillStyle(3001)
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))
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))
# Legend
leg1 = TLegend(0.12, 0.45, 0.25, 0.5)
leg1.SetBorderSize(0)
leg1.SetFillStyle(0)
leg1.SetFillColor(0)
leg1.SetTextSize(0.05)
leg1.AddEntry(err, "systematic uncertainty (" + fit + ")", "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
gStyle.SetOptStat(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(0.08)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
# hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
pass
bkg.SetMaximum((2. if log else 1.1) * max(
bkg.GetMaximum(), hist['data_obs'].GetMaximum() +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 1.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
setHistStyle(bkg, 1.1 if ratio else 1.0)
leg.Draw()
# drawCMS(lumi, "Preliminary",True)
drawCMS(lumi, "Private", True)
drawRegion(channel)
drawAnalysis(channel)
drawFit(fit)
if ratio:
c1.cd(2)
setBotStyle(err, 3, True)
setBotStyle(res)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
res.Draw("SAME, PE0")
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
leg1.Draw()
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, leg1, err, errLine, res]
inMCTTbar_list[i].SetFillColor(kAzure - 2)
inMCTTbar_list[i].SetLineColor(kBlack)
hs.Add(inMCTTbar_list[i])
#MC DY hist
inMCDY_list[i].Scale(norm) #normalize MC
inMCDY_list[i].SetFillColor(kOrange - 3)
inMCDY_list[i].SetLineColor(kBlack)
hs.Add(inMCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.3 * max(hs.GetMaximum(), inDATA_list[i].GetMaximum()))
inDATA_list[i].SetMaximum(
1.3 * max(hs.GetMaximum(), inDATA_list[i].GetMaximum()))
hs.Draw("histo")
inDATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle(inDATA_list[i].GetTitle())
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 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'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- 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)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# 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")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
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)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
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 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
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_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'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
def plotter(plotdir,
plot,
xTitle,
logY,
channels=['VV'],
includeData=False,
scaleSignal=0,
UserRange=[None, None, None, None],
initPath=''):
# channelTex={'WPWP':'W^{+}W^{+}', 'WPWM':'W^{+}W^{-}','WMWM':'W^{-}W^{-}','WPZ':'W^{+}Z','WMZ':'W^{-}Z','ZZ':'ZZ'}
channelTex = {'ZZ': 'ZZ'}
# plotstyle=[(1,1),(1,2),(2,1),(2,2),(4,1),(4,2)]
plotstyle = [(1, 1)]
# 0 1 2 3 4 5 6
# Backgrounds=['QCD', 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'TT', 'WW', 'WZ', 'ZZ']
# BGColors= [rt.kAzure+7, rt.kRed-4, rt.kOrange-2, rt.kGreen+2, rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
# BGTeX= ['QCD', 'W+JetsToQQ', 'Z+JetsToQQ', 'TTbar'], 'WW', 'WZ', 'ZZ']
#stackOrder= [4,5,6,2,1,3,0]
Backgrounds = [
'QCD'
] # 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'ZZ'] 'TT', 'WW', 'WZ', 'ZZ']
BGColors = [
rt.kAzure + 7
] #, rt.kOrange-2, rt.kGreen+2], rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
BGTeX = ['QCD'] #
stackOrder = [0]
PreSelection = [
'nocuts', 'common', 'corrections', 'cleaner', 'softdropmassCorr',
'AK4pfidfilter', 'AK8pfidfilter', 'invMAk8sel', 'detaAk8sel',
'AK8N2sel'
]
Selection = [
'preselection', 'softdropAK8sel', 'tau21sel', 'deltaR48', 'VVRegion',
'AK4N2sel', 'OpSignsel', 'detaAk4sel', 'invMAk4sel_1p0', 'Kin_AK8'
]
cutnames = [
'cleaner', 'AK8N2sel', 'invMAk8sel', 'detaAk8sel', 'softdropAK8sel',
'tau21sel', 'AK4cleaner', 'AK4N2sel', 'OpSignsel', 'detaAk4sel',
'invMAk4sel_1p0'
]
cuts = {
'cleaner':
'#splitline{p_{T-AK8} > 200 GeV, |#eta_{AK8}| < 2.5}{p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0}',
'AK8N2sel': 'N_{AK8} #geq 2',
'invMAk8sel': 'M_{jj-AK8} > 1050 GeV',
'detaAk8sel': '|#Delta#eta_{jj-AK8}|<1.3',
'softdropAK8sel': '65 GeV <M_{SD}< 105 GeV',
'tau21sel': '0 #leq #tau_{2}/#tau_{1}<0.45',
# 'AK4cleaner':'p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0',
'AK4cleaner': '',
'AK4N2sel': 'N_{AK4} #geq 2',
'OpSignsel': '#eta_{1-AK4} #eta_{2-AK4} < 0',
'detaAk4sel': '|#Delta#eta_{jj-AK4}| > 3.0',
'invMAk4sel_1p0': 'M_{jj-AK4} > 1.0 TeV'
}
VV = ('VV' in channels)
seperate = (not VV)
if VV:
#channels=["WPWP","WPWM","WMWM","WPZ","WMZ","ZZ"]
channels = ["ZZ"]
plottitle = plotdir + '_' + plot
lumi = 36.814
xLabelSize = 18.
yLabelSize = 18.
xTitleSize = 20.
yTitleSize = 22.
xTitleOffset = 4.
yTitleOffset = 1.3
printout = False
Portrait = True
cutname = False
ratio = includeData
if ('highbin' in plot):
binning = 'dijetbinning'
else:
binning = 'default'
if (Portrait):
canvX = 600
canvY = 600
else:
canvX = 900
canvY = 675
if (plotdir in PreSelection):
region = 'PreSelection'
initPath = ''
referenceHistPath = 'detaAk8sel/N_pv'
if (PreSelection.index(plotdir) < 4 and ('softdrop' in plot)):
return 'SofdropMass not filled yet!'
else:
region = 'SignalRegion'
referenceHistPath = 'tau21sel/N_pv'
referenceHistPath = plotdir + '/' + plot
if (initPath == ''):
path = '/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/%s' % region
else:
path = initPath
outputPath = path.replace(
'/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/SignalRegion',
'plots/')
if (plotdir in PreSelection):
CutNumber = PreSelection.index(plotdir)
else:
CutNumber = Selection.index(plotdir)
outputPath = outputPath + '/%02i_%s' % (CutNumber, plotdir) + '/'
if (printout):
print('InputPath:', path)
print('OutputPath:', outputPath)
#check if OutputPath exists - and if not create it!
if not os.path.exists(outputPath):
os.makedirs(outputPath)
# path='/home/albrec/Master/signal/'
scaleVV = (scaleSignal != 0)
VVScale = scaleSignal
if (UserRange[2] == None or UserRange[3] == None):
YRangeUser = False
Ymin = UserRange[2]
Ymax = UserRange[3]
else:
YRangeUser = True
Ymin = UserRange[2]
Ymax = UserRange[3]
if (UserRange[0] == None or UserRange[1] == None):
XRangeUser = False
Xmin = UserRange[0]
Xmax = UserRange[1]
else:
XRangeUser = True
Xmin = UserRange[0]
Xmax = UserRange[1]
# YRangeUser=False
# Ymin=0.11
# Ymax=9*10**3
# XRangeUser=False
# Xmin=0
# Xmax=6000.
gROOT.ProcessLine("gErrorIgnoreLevel = 2001;")
SFiles = []
for i in range(len(channels)):
SFiles.append(
TFile(
path +
"/uhh2.AnalysisModuleRunner.MC.MC_aQGC_%sjj_hadronic_2016v3.root"
% channels[i]))
#uhh2.AnalysisModuleRunner.MC.MC_aQGC_ZZjj_hadronic_2016v3.root
##Open Files to get BackgroundHist:
BFiles = []
for i in range(len(Backgrounds)):
BFiles.append(
TFile(path +
"/uhh2.AnalysisModuleRunner.MC.MC_%s.root" % Backgrounds[i]))
#Open File to get DataHist:
# DataFile = TFile(path+"/uhh2.AnalysisModuleRunner.Data.DATA.root")
#gROOT.ProcessLine( "gErrorIgnoreLevel = 0;")
if (includeData == True):
#calculate QCDscale with Integrals from the following Histogram:
# referenceHistPath = 'tau21sel/N_AK4'
# referenceHistPath = 'detaAk8sel/N_pv'
# referenceHistPath = 'tau21sel/met_pt_over_sumptAK8_2'
QCDscale = float(DataFile.Get(referenceHistPath).Integral())
QCDNorm = 1
for i in range(len(BFiles)):
if ('QCD' in BFiles[i].GetName()):
QCDNorm = float(BFiles[i].Get(referenceHistPath).Integral())
else:
QCDscale -= float(BFiles[i].Get(referenceHistPath).Integral())
QCDscale /= QCDNorm
else:
QCDscale = 1.0
if (printout):
print('using QCDscale:', QCDscale)
SHists = []
for i in range(len(channels)):
SHists.append(SFiles[i].Get(plotdir + '/' + plot))
BHists = []
for i in range(len(BFiles)):
BHists.append(BFiles[i].Get(plotdir + '/' + plot))
if ('QCD' in BFiles[i].GetName()):
BHists[-1].Scale(QCDscale)
#if(includeData):
# DataHist=DataFile.Get(plotdir+'/'+plot)
if (binning == 'dijetbinning'):
fitbinning = array('d')
binwidth = 200
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
NBins = int(NBins)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
for i in range(len(channels)):
SHists[i] = SHists[i].Rebin(NBins, "new binning", fitbinning)
for i in range(len(Backgrounds)):
BHists[i] = BHists[i].Rebin(NBins, "new binning", fitbinning)
if (includeData):
DataHist = DataHist.Rebin(NBins, "new binning", fitbinning)
canv = TCanvas(plottitle, plottitle, canvX, canvY)
yplot = 0.7
yratio = 0.3
ymax = 1.0
xmax = 1.0
xmin = 0.0
if (ratio):
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot, xmax, ymax)
ratiopad = TPad("ratiopad", "Ratio", xmin, ymax - yplot - yratio, xmax,
ymax - yplot)
else:
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot - yratio, xmax,
ymax)
plotpad.SetTopMargin(0.08)
plotpad.SetLeftMargin(0.1)
plotpad.SetRightMargin(0.05)
plotpad.SetTicks()
plotpad.Draw()
if (ratio):
plotpad.SetBottomMargin(0.016)
ratiopad.SetTopMargin(0.016)
ratiopad.SetBottomMargin(0.35)
ratiopad.SetLeftMargin(0.1)
ratiopad.SetRightMargin(0.05)
ratiopad.SetTicks()
ratiopad.Draw()
else:
plotpad.SetBottomMargin(0.1)
if (logY):
plotpad.SetLogy()
canv.SetLogy()
if ('-logX' in xTitle):
plotpad.SetLogx()
if (ratio):
ratiopad.SetLogx()
canv.SetLogx()
drawOptions = "HE"
stack = THStack(plottitle, plottitle)
BHist = THStack(plottitle, plottitle)
# for i in range(len(Backgrounds)):
for i in stackOrder:
# BHists[i].SetFillColor(BGColors[i])
BHists[i].SetLineColor(BGColors[i])
BHist.Add(BHists[i], 'Hist')
BHist.SetTitle(plottitle)
BHistErr = BHists[0].Clone()
for i in range(1, len(Backgrounds)):
BHistErr.Add(BHists[i])
BHistErr.SetFillStyle(3204)
BHistErr.SetFillColor(rt.kGray + 2)
BHistErr.SetLineColor(1)
BGMax = BHist.GetMaximum()
SIGMax = 0
#if(VV): #new
# SIGMax=VVsum.GetMaximum()
#else:
for i in range(len(channels)):
tmpmax = SHists[i].GetMaximum()
if (tmpmax > SIGMax):
SIGMax = tmpmax
if (scaleVV):
SIGMax = SIGMax * VVScale
if (logY):
MAX = 0.9 * float(10**(magnitude(max(BGMax, SIGMax)) + 1))
MIN = float(10**(magnitude(max(BGMax, SIGMax)) - 5))
MIN += float(10**(magnitude(MIN)))
legendMIN = math.log(max(BGMax, SIGMax)) / math.log(MAX)
else:
MAX = (1.0 / 0.8) * max(BGMax, SIGMax)
legendMIN = 0.7
MIN = 0.
legendMIN = (legendMIN * 0.7) + 0.3 - 0.016
legend = TLegend(0.5, 0.75, 0.85, 0.89)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
legend.SetNColumns(2)
legend.SetColumnSeparation(0.3)
if (includeData):
DataHist.SetMarkerStyle(8)
DataHist.SetLineColor(1)
DataHist.SetTitle(plottitle)
if VV:
for i in range(len(channels)):
if (i == 0):
VVsum = SHists[i].Clone()
else:
VVsum.Add(SHists[i])
legentry = 'VVjj'
if (scaleVV):
VVsum.Scale(VVScale)
legentry += ' *%0.f' % VVScale
VVsum.SetLineColor(1)
VVsum.SetLineStyle(plotstyle[0][1])
VVsum.SetLineWidth(2)
legend.AddEntry(VVsum, legentry)
else:
for i in range(len(channels)):
SHists[i].SetLineColor(plotstyle[i][0])
SHists[i].SetLineStyle(plotstyle[i][1])
SHists[i].SetLineWidth(2)
legentry = "%sjj" % channelTex[channels[i]]
if (scaleVV):
SHists[i].Scale(VVScale)
legentry += ' *%.2E' % VVScale
legend.AddEntry(SHists[i], legentry)
for i in stackOrder:
legend.AddEntry(BHists[i], BGTeX[i], "f")
legend.AddEntry(BHistErr, "stat. Uncertainty", "f")
if (includeData):
legend.AddEntry(DataHist, "Data", "lep")
canv.SetTitle(plottitle)
BHistErr.GetYaxis().SetTitle('Events')
BHistErr.GetYaxis().SetRangeUser(MIN, MAX)
BHistErr.GetYaxis().SetTitleFont(43)
BHistErr.GetYaxis().SetTitleSize(yTitleSize)
BHistErr.GetYaxis().SetTitleOffset(yTitleOffset)
BHistErr.GetYaxis().SetLabelFont(43)
BHistErr.GetYaxis().SetLabelSize(yLabelSize)
if (ratio):
BHistErr.GetXaxis().SetTitleSize(0.0)
BHistErr.GetXaxis().SetLabelSize(0.0)
else:
BHistErr.GetXaxis().SetTitle(xTitle)
BHistErr.GetXaxis().SetTitleFont(43)
BHistErr.GetXaxis().SetTitleSize(xTitleSize)
# BHistErr.GetXaxis().SetTitleOffset(xTitleOffset)
BHistErr.GetXaxis().SetTitleOffset(1.2)
BHistErr.GetXaxis().SetLabelFont(43)
BHistErr.GetXaxis().SetLabelSize(xLabelSize)
# BHistErr.GetXaxis().SetTickLength(0.08)
# BHistErr.GetXaxis().SetNdivisions(506)
if (YRangeUser):
BHistErr.GetYaxis().SetRangeUser(Ymin, Ymax)
if (XRangeUser):
BHistErr.GetXaxis().SetRangeUser(Xmin, Xmax)
plotpad.cd()
BHistErr.Draw("E2")
BHist.Draw("HistSAME")
BHistErr.Draw("E2SAME")
if (VV):
VVsum.Draw("SAME" + drawOptions)
elif ('-noSig' not in xTitle):
for i in range(len(channels)):
SHists[i].Draw("SAME" + drawOptions)
if (includeData):
DataHist.Draw("APE1SAME")
plotpad.RedrawAxis()
if (ratio):
ratiopad.cd()
if (includeData):
ratioHist = DataHist.Clone()
else:
ratioHist = BHistErr.Clone()
ratioHist.SetLineColor(rt.kBlack)
# ratioHist.Sumw2()
ratioHist.SetStats(0)
ratioHist.Divide(BHistErr)
ratioHist.SetMarkerStyle(21)
ratioHist.SetMarkerSize(0.7)
#Yaxis
ratioHist.GetYaxis().SetRangeUser(0.3, 1.7)
ratioHist.GetYaxis().SetTitle("Data/BG")
ratioHist.GetYaxis().CenterTitle()
ratioHist.GetYaxis().SetTitleFont(43)
ratioHist.GetYaxis().SetTitleSize(yTitleSize)
ratioHist.GetYaxis().SetTitleOffset(yTitleOffset)
ratioHist.GetYaxis().SetLabelFont(43)
ratioHist.GetYaxis().SetLabelSize(yLabelSize)
ratioHist.GetYaxis().SetNdivisions(506)
#Xaxis
ratioHist.GetXaxis().SetTitle(xTitle)
ratioHist.GetXaxis().SetTitleFont(43)
ratioHist.GetXaxis().SetTitleSize(xTitleSize)
ratioHist.GetXaxis().SetTitleOffset(xTitleOffset)
ratioHist.GetXaxis().SetLabelFont(43)
ratioHist.GetXaxis().SetLabelSize(xLabelSize)
ratioHist.GetXaxis().SetTickLength(0.08)
ratioHist.GetXaxis().SetNdivisions(506)
# if(YRangeUser):
# ratioHist.GetYaxis().SetRangeUser(Ymin,Ymax)
if (XRangeUser):
ratioHist.GetXaxis().SetRangeUser(Xmin, Xmax)
ratioXMin = Xmin
ratioXMax = Xmax
else:
ratioXMin = ratioHist.GetXaxis().GetXmin()
ratioXMax = ratioHist.GetXaxis().GetXmax()
ratioHist.Draw("ep")
zeropercent = TLine(ratioXMin, 1, ratioXMax, 1)
zeropercent.Draw()
plus10percent = TLine(ratioXMin, 1.1, ratioXMax, 1.1)
plus10percent.SetLineStyle(rt.kDashed)
plus10percent.Draw()
minus10percent = TLine(ratioXMin, 0.9, ratioXMax, 0.9)
minus10percent.SetLineStyle(rt.kDashed)
minus10percent.Draw()
canv.cd()
gPad.RedrawAxis()
legend.Draw()
latex = TLatex()
latex.SetNDC(kTRUE)
latex.SetTextSize(20)
latex.DrawLatex(0.69, 0.953, "%.2f fb^{-1} (13 TeV)" % lumi)
latex.DrawLatex(0.1, 0.953, "private work")
lastcut = 'nocuts'
for cut in cutnames:
if cut in plotdir:
lastcut = cut
if (not (lastcut == 'nocuts') and cutname):
# latex.SetTextSize(0.03)
latex.SetTextSize(15)
for l in range(cutnames.index(lastcut) + 1):
latex.DrawLatex(0.12, 0.8 - l * 0.04, cuts[cutnames[l]])
canv.Update()
canv.Print(outputPath + '/%s_%s.pdf' % (plotdir, plot))
#prevents memory leak in Canvas Creation/Deletion
#see: https://root.cern.ch/root/roottalk/roottalk04/2484.html
gSystem.ProcessEvents()
if (ratio):
del ratiopad
del plotpad, canv
# gc.collect()
return 'done!'
class Plot_Multi_TH1:
def __init__(self,filepath,filename,tree,list_variable,weight,list_cut,list_legend,list_color,name,bins,xmin,xmax,title,xlabel,ylabel,option='hist',logx=False,logy=False,legend_pos=[0.5,0.5,0.9,0.85],stack=False,norm=False):
self.filepath = filepath
self.filename = filename
self.tree = tree
self.weight = weight
self.name = name
self.bins = bins
self.xmin = xmin
self.xmax = xmax
self.title = title
self.xlabel = xlabel
self.ylabel = ylabel
self.legend_pos = legend_pos
self.logx = logx
self.logy = logy
self.option = option
self.stack = stack
self.norm = norm
if not isinstance(list_cut,list):
list_cut = [list_cut]
if not isinstance(list_variable,list):
list_variable = [list_variable]
if not isinstance(list_legend,list):
list_legend = [list_legend]
if len(list_variable) == 1 and len(list_cut) > 1:
logging.debug('\tOnly one variable but several cuts')
self.list_variable = list_variable*len(list_cut)
self.list_cut = list_cut
elif len(list_variable) > 1 and len(list_cut) == 1:
logging.debug('\tOnly one cut but several variables')
self.list_cut = list_cut*len(list_variable)
self.list_variable = list_variable
elif len(list_variable) == 1 and len(list_cut) == 1:
logging.warning('\tWhy do you even need to stack ?')
elif len(list_variable) != len(list_cut):
raise RuntimeError('Inconsistent number of variables and cuts')
print (len(list_variable))
print (len(list_cut))
else:
self.list_variable = list_variable
self.list_cut = list_cut
if len(list_legend) != max(len(list_variable),len(list_cut)):
raise RuntimeError('Inconsistent number of legends compared to variables and cuts')
else:
self.list_legend = list_legend
if len(list_color) != len(list_legend):
raise RuntimeError('Inconsistent number of colors compared to legends')
else:
self.list_color = list_color
for i in range(len(self.list_color)):
if isinstance(self.list_color[i],str):
self.list_color[i] = ROOT.TColor.GetColor(self.list_color[i])
def MakeHisto(self):
self.list_obj = []
for i in range(0,len(self.list_variable)):
instance = Plot_TH1(filepath = self.filepath,
filename = self.filename,
tree = self.tree,
variable = self.list_variable[i],
weight = self.weight,
cut = self.list_cut[i],
name = self.name,
bins = self.bins,
xmin = self.xmin,
xmax = self.xmax,
title = self.title,
xlabel = self.xlabel,
ylabel = self.ylabel,
logx = self.logx,
logy = self.logy)
instance.MakeHisto()
self.list_obj.append(copy.deepcopy(instance.histo))
def PlotOnCanvas(self,pdf_name):
tdrstyle.setTDRStyle()
# Canvas #
canvas = TCanvas("c", "c", 600, 600)
canvas.SetTopMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetLeftMargin(0.15)
if self.title == '':
canvas.SetTopMargin(0.05)
if self.logx:
canvas.SetLogx()
if self.logy:
canvas.SetLogy()
self.list_obj[0].SetMinimum(10)
# Norm #
if self.norm:
for obj in self.list_obj:
if obj.Integral() != 0:
obj.Scale(1./obj.Integral())
# Axes #
maxY = max([h.GetMaximum() for h in self.list_obj])
if self.logy:
maxY *= 2
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
self.list_obj[0].GetXaxis().SetLabelSize(0.03)
self.list_obj[0].GetXaxis().SetTitleSize(0.05)
if len(self.xlabel) > 30:
self.list_obj[0].GetXaxis().SetTitleSize(0.035)
if len(self.xlabel) > 60:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
if "frac" in self.xlabel:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
self.list_obj[0].GetXaxis().SetTitleOffset(1.9)
else:
self.list_obj[0].GetXaxis().SetTitleOffset(1.6)
self.list_obj[0].GetYaxis().SetTitleOffset(1.5)
self.list_obj[0].GetYaxis().SetLabelSize(0.03)
self.list_obj[0].GetYaxis().SetTitleSize(0.05)
self.list_obj[0].GetYaxis().SetNdivisions(10)
if len(self.ylabel) > 30:
self.list_obj[0].GetYaxis().SetTitleSize(0.035)
if len(self.ylabel) > 60:
self.list_obj[0].GetYaxis().SetTitleSize(0.03)
# Stacking #
if self.stack:
self.stack_hist = THStack("hs","") # Needs to be in self, otherwise destroyed at end of function
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetFillColor(col)
obj.SetLineColor(col)
self.stack_hist.Add(obj)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
self.stack_hist.Draw(self.option+" same")
maxY = self.stack_hist.GetMaximum()
if self.logy:
maxY *= 10
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
# Regular plotting
else:
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetLineColor(col)
obj.SetLineWidth(2)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
# Legend #
legend = TLegend(*self.legend_pos)
for leg,obj in zip(self.list_legend,self.list_obj):
legend.AddEntry(obj,leg,"f" if self.stack else "l")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.Draw()
ROOT.SetOwnership(legend,0) # Otherwise goes out of scope and not printed
return canvas,self.filename
LepBDTMCTTbar_list[i].SetFillColor(kMagenta + 3)
LepBDTMCTTbar_list[i].SetLineColor(kBlack)
hs.Add(LepBDTMCTTbar_list[i])
#MC DY hist
LepBDTMCDY_list[i].Scale(norm) # MC normalization
LepBDTMCDY_list[i].SetFillColor(kMagenta - 6)
LepBDTMCDY_list[i].SetLineColor(kBlack)
hs.Add(LepBDTMCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
LepBDTDATA_list[i].SetMaximum(
1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
hs.SetMinimum(10) #EF for ele BDT
hs.Draw("histo")
LepBDTDATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle("BDT score")
hs.GetXaxis().SetRangeUser(0.75, 1) #EF for ele BDT
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
def make_ratioplot(name,
ttbar_file=0,
qcd_file=0,
signal_files=[],
histo=0,
rebin=1,
minx=0,
maxx=0,
miny=0,
maxy=0,
logy=False,
xtitle='',
ytitle='',
textsizefactor=1,
signal_legend=[],
outfile=0,
signal_colors=[],
signal_zoom=1,
qcd_zoom=1,
ttbar_zoom=1,
ttbar_legend='t#bar{t}',
qcd_legend='QCD from MC',
dosys=False,
docms=True,
legendtitle=''):
###canvas setting up
canvas = 0
canvas = TCanvas(name, '', 0, 0, 600, 600)
canvas.SetLeftMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetTopMargin(0.10)
canvas.SetBottomMargin(0.10)
charsize = 0.04
offset = 1.9
###latex label
latex = 0
latex = TLatex(0.6, 0.7, '13 TeV, 2.69 fb^{-1}')
latex.SetTextSize(charsize)
latex.SetNDC(1)
latex.SetTextFont(42)
###legend setting up
#legend=TLegend(0.0,0.75,0.99,1.04)
legend = TLegend(0.4, 0.6, 0.94, 0.95)
legend.SetNColumns(2)
legend.SetHeader('')
legend.SetFillStyle(0)
legend.SetBorderSize(0)
###mc stack
stack = THStack(name + '_stack', '')
qcd_histo = qcd_file.Get(histo).Clone(name + '_make_plot')
qcd_histo.Rebin(rebin)
ttbar_histo = ttbar_file.Get(histo).Clone()
ttbar_histo.Rebin(rebin)
ttbar_histo.SetFillColor(kRed - 9)
ttbar_histo.SetLineColor(kRed - 9)
ttbar_histo.SetMarkerColor(kRed - 9)
if ttbar_zoom != 1:
ttbar_histo.Scale(ttbar_zoom)
legend.AddEntry(ttbar_histo, ttbar_legend, 'f')
qcd_histo.SetFillColor(kOrange - 5)
qcd_histo.SetLineColor(kOrange - 5)
qcd_histo.SetMarkerColor(kOrange - 5)
if qcd_zoom != 1:
qcd_histo.Scale(qcd_zoom)
legend.AddEntry(qcd_histo, qcd_legend, 'f')
sum_mc = qcd_histo.Clone(histo + 'tmp')
sum_mc.Add(ttbar_histo)
stack.Add(ttbar_histo)
stack.Add(qcd_histo)
sum_mc.SetLineColor(kBlack)
sum_mc.SetFillStyle(0)
err = TGraphAsymmErrors(sum_mc)
legend.AddEntry(err, 'Total uncertainty', 'f')
if legendtitle == '':
legend.AddEntry(0, "", '')
legend.AddEntry(0, "g_{RS} #rightarrow t#bar{t} (2pb)", '')
else:
legend.AddEntry(0, "", '')
legend.AddEntry(0, legendtitle, '')
###signal setting up
signal_histos = []
colors = [
kBlack, kRed, kOrange, kBlue, kGreen + 3, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, 60
]
styles = [
1, 3, 5, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1
]
if signal_colors != []:
colors = signal_colors
for i in range(len(signal_files)):
signal_histos.append(signal_files[i].Get(histo).Clone())
signal_histos[i].SetLineWidth(3)
signal_histos[i].SetLineStyle(styles[i])
signal_histos[i].SetLineColor(colors[i])
signal_histos[i].SetMarkerColor(colors[i])
signal_histos[i].Rebin(rebin)
if signal_zoom != 1:
signal_histos[i].Scale(signal_zoom)
legend.AddEntry(signal_histos[i], signal_legend[i], 'l')
###mc shape line
ttbar_line = 0
ttbar_line = ttbar_histo.Clone()
ttbar_line.SetLineColor(kBlack)
ttbar_line.SetFillStyle(0)
###mc errors
if dosys:
sys_diff_qcd = []
sys_diff_ttbar = []
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
sys_diff_qcd.append([])
sys_diff_ttbar.append([])
#adding stat uncertainties <--removed
# for imtt in range(1,ttbar_histo.GetNbinsX()+1):
# sys_diff_ttbar[imtt-1].append(ttbar_histo.GetBinError(imtt))
# sys_diff_ttbar[imtt-1].append(-ttbar_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(qcd_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(-qcd_histo.GetBinError(imtt))
#adding flat uncertainties
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
#ttbar
for i in [
2.4, #pdf
10.0, #mu
3.0, #xsec
6.0, #toppt
1.0, #lumi
3.5, #jec
3.0, #jer
10.0, #btag
#3.0,#trig
10.0, #toptag
3.0
]: #pileup
sys_diff_ttbar[imtt - 1].append(
i / 100.0 * ttbar_histo.GetBinContent(imtt))
sys_diff_ttbar[imtt - 1].append(
-i / 100.0 * ttbar_histo.GetBinContent(imtt))
closureunc = 5.0
# if '1b' in histo:
# closureunc=5.0
# elif '2b' in histo:
# closureunc=10.0
for i in [
2.0, #modmass
closureunc
]: #closure
sys_diff_qcd[imtt - 1].append(i / 100.0 *
qcd_histo.GetBinContent(imtt))
sys_diff_qcd[imtt - 1].append(-i / 100.0 *
qcd_histo.GetBinContent(imtt))
# #3% trigger
# sys_diff_ttbar[imtt-1].append(0.03*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.03*ttbar_histo.GetBinContent(imtt))
# #2.7% lumi
# sys_diff_ttbar[imtt-1].append(0.023*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.023*ttbar_histo.GetBinContent(imtt))
# #15% ttbar
# #sys_diff_ttbar[imtt-1].append(0.15*ttbar_histo.GetBinContent(imtt))
# #sys_diff_ttbar[imtt-1].append(-0.15*ttbar_histo.GetBinContent(imtt))
# #2.8% QCD
# sys_diff_qcd[imtt-1].append(0.028*qcd_histo.GetBinContent(imtt))
# sys_diff_qcd[imtt-1].append(-0.028*qcd_histo.GetBinContent(imtt))
#combining uncertainties
sys_tot_ttbar = []
sys_tot_qcd = []
sys_tot = []
sys_global_ttbar = [0.0, 0.0]
sys_global_qcd = [0.0, 0.0]
nevt_global = [0.0, 0.0, 0.0]
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
uperr_qcd = 0
downerr_qcd = 0
uperr_ttbar = 0
downerr_ttbar = 0
for error in sys_diff_ttbar[imtt - 1]:
if error < 0:
downerr_ttbar = downerr_ttbar + error * error
else:
uperr_ttbar = uperr_ttbar + error * error
for error in sys_diff_qcd[imtt - 1]:
if error < 0:
downerr_qcd = downerr_qcd + error * error
else:
uperr_qcd = uperr_qcd + error * error
sys_tot_ttbar.append(
[math.sqrt(downerr_ttbar),
math.sqrt(uperr_ttbar)])
sys_tot_qcd.append([math.sqrt(downerr_qcd), math.sqrt(uperr_qcd)])
sys_tot.append([
math.sqrt(downerr_qcd + downerr_ttbar),
math.sqrt(uperr_qcd + uperr_ttbar)
])
sys_global_qcd[0] = sys_global_qcd[0] + downerr_qcd
sys_global_qcd[1] = sys_global_qcd[1] + uperr_qcd
sys_global_ttbar[0] = sys_global_ttbar[0] + downerr_ttbar
sys_global_ttbar[1] = sys_global_ttbar[1] + uperr_ttbar
# nevt_global[0]=nevt_global[0]+data_histo.GetBinContent(imtt)
nevt_global[1] = nevt_global[1] + qcd_histo.GetBinContent(imtt)
nevt_global[2] = nevt_global[2] + ttbar_histo.GetBinContent(imtt)
#print 'ttbar+qcd',math.sqrt(uperr_qcd+uperr_ttbar),math.sqrt(downerr_qcd+downerr_ttbar)
#print 'qcd',math.sqrt(uperr_qcd),math.sqrt(downerr_qcd)
#print 'ttbar',math.sqrt(uperr_ttbar),math.sqrt(downerr_ttbar)
err.SetPointEYhigh(imtt - 1, math.sqrt(uperr_qcd + uperr_ttbar))
err.SetPointEYlow(imtt - 1, math.sqrt(downerr_qcd + downerr_ttbar))
sys_global = [0.0, 0.0]
sys_global[0] = math.sqrt(sys_global_qcd[0] + sys_global_ttbar[0])
sys_global[1] = math.sqrt(sys_global_qcd[1] + sys_global_ttbar[1])
sys_global_qcd[0] = math.sqrt(sys_global_qcd[0])
sys_global_qcd[1] = math.sqrt(sys_global_qcd[1])
sys_global_ttbar[0] = math.sqrt(sys_global_ttbar[0])
sys_global_ttbar[1] = math.sqrt(sys_global_ttbar[1])
# print name
# print "\hline"
# print "Multijet QCD & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1],sys_global_qcd[1],sys_global_qcd[0])
# print "SM ttbar & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[2],sys_global_ttbar[1],sys_global_ttbar[0])
# print "\hline"
# print "Total background & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1]+nevt_global[2],sys_global[1],sys_global[0])
# print 'DATA & %.0f' %nevt_global[0]
err.SetFillStyle(3145)
err.SetFillColor(kGray + 1)
###drawing top
canvas.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(ttbar_histo.GetXaxis().GetTitle())
stack.GetYaxis().SetTitle(ttbar_histo.GetYaxis().GetTitle())
stack.GetXaxis().SetLabelSize(charsize)
stack.GetXaxis().SetTitleSize(charsize)
stack.GetYaxis().SetLabelSize(charsize)
stack.GetYaxis().SetTitleSize(charsize)
stack.GetYaxis().SetTitleOffset(offset)
if minx != 0 or maxx != 0:
stack.GetXaxis().SetRangeUser(minx, maxx)
#else:
# stack.GetXaxis().SetRangeUser(0,4000)
if miny != 0 or maxy != 0:
stack.SetMaximum(maxy)
stack.SetMinimum(miny)
else:
if logy:
stack.SetMaximum(stack.GetMaximum() * 10)
stack.SetMinimum(0.2)
else:
stack.SetMaximum(stack.GetMaximum() * 2.0)
stack.SetMinimum(0.001)
err.Draw('2')
sum_mc.Draw('samehist')
if ttbar_file != 0:
ttbar_line.Draw('samehist')
for i in signal_histos:
i.Draw('samehist')
if logy:
canvas.SetLogy()
legend.Draw()
latex2text = ''
if 'ldy_0b' in name:
latex2text = '#Deltay < 1; 0 b tag'
elif 'ldy_1b' in name:
latex2text = '#Deltay < 1; 1 b tag'
elif 'ldy_2b' in name:
latex2text = '#Deltay < 1; 2 b tag'
elif 'hdy_0b' in name:
latex2text = '#Deltay > 1; 0 b tag'
elif 'hdy_1b' in name:
latex2text = '#Deltay > 1; 1 b tag'
elif 'hdy_2b' in name:
latex2text = '#Deltay > 1; 2 b tag'
latex2 = TLatex(0.19, 0.7, latex2text)
latex2.SetTextSize(0.03)
latex2.SetNDC(1)
latex2.SetTextFont(42)
latex2.Draw()
if docms:
if '3000' in name:
CMS_lumi.CMS_lumi(canvas, 3, 11)
elif '1000' in name:
CMS_lumi.CMS_lumi(canvas, 2, 11)
elif '300' in name:
CMS_lumi.CMS_lumi(canvas, 1, 11)
elif '36' in name:
CMS_lumi.CMS_lumi(canvas, 0, 11)
###saving
canvas.SaveAs('pdf/' + name + '.pdf')
if outfile != 0:
canvas.Write()
