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Plot.py
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Plot.py
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#! /usr/bin/env python
###
### Macro for plotting the distribution of variables in the MC abckground, signal and data, as well as signal efficiency and acceptance.
###
import global_paths
import os, multiprocessing
import copy
import math
from array import array
from ROOT import ROOT, gROOT, gStyle, gRandom, TSystemDirectory
from ROOT import TFile, TChain, TTree, TCut, TF1, TH1F, TH2F, THStack
from ROOT import TGraph, TGraphErrors, TGraphAsymmErrors, TVirtualFitter, TMultiGraph
from ROOT import TStyle, TCanvas, TPad
from ROOT import TLegend, TLatex, TText, TLine
from samples import sample
from variables import variable
from aliases import alias, aliasSM, working_points, dijet_bins
from aliases import additional_selections as SELECTIONS
from utils import *
import sys
########## SETTINGS ##########
import optparse
usage = "usage: %prog [options]"
parser = optparse.OptionParser(usage)
parser.add_option("-v", "--variable", action="store", type="string", dest="variable", default="")
parser.add_option("-c", "--cut", action="store", type="string", dest="cut", default="")
parser.add_option("-y", "--year", action="store", type="string", dest="year", default="run2")
parser.add_option("-b", "--btagging", action="store", type="string", dest="btagging", default="medium")
parser.add_option("-n", "--norm", action="store_true", default=False, dest="norm")
parser.add_option("-B", "--blind", action="store_true", default=False, dest="blind")
parser.add_option("-f", "--final", action="store_true", default=False, dest="final")
parser.add_option("-e", "--efficiency", action="store_true", default=False, dest="efficiency")
parser.add_option("-s", "--selection", action="store", type="string", dest="selection", default="")
parser.add_option("-a", "--acceptance", action="store_true", default=False, dest="acceptance")
parser.add_option("-t", "--trigger", action="store_true", default=False, dest="trigger")
parser.add_option("-S", "--signal_only", action="store_true", default=False, dest="signal_only")
parser.add_option("", "--fraction_above", action="store", type="float", dest="fraction_above", default=0.)
parser.add_option("", "--separate", action="store_true", default=False, dest="separate")
parser.add_option("", "--btagging_eff", action="store_true", default=False, dest="btagging_eff")
parser.add_option("", "--sync", action="store_true", default=False, dest="sync")
parser.add_option("", "--recal", action="store_true", default=False, dest="recal")
parser.add_option("", "--muon_eff", action="store_true", default=False, dest="muon_eff")
parser.add_option("", "--s_over_b", action="store_true", default=False, dest="s_over_b")
parser.add_option("", "--widejet_efficiency", action="store_true", default=False, dest="widejet_efficiency")
(options, args) = parser.parse_args()
########## SETTINGS ##########
gROOT.SetBatch(True)
gStyle.SetOptStat(0)
BTAGGING = options.btagging
NTUPLEDIR = global_paths.SKIMMEDDIR
OUTDIR = "./"
## FIXME temorary FIXME
#NTUPLEDIR = "/eos/home-j/jkiesele/DeepNtuples/Zbb/"
#OUTDIR = "/eos/user/m/msommerh/jan/"
ACCEPTANCEDIR = "acceptance/"
TRIGGERDIR = global_paths.SKIMMEDDIR+"TriggerStudy"
SIGNAL = 1 # Signal magnification factor
RATIO = 4 # 0: No ratio plot; !=0: ratio between the top and bottom pads
#RATIO = 0
if options.blind: RATIO = 0
NORM = options.norm
PARALLELIZE = False
BLIND = False
LUMI = {"run2" : 137190, "2016" : 35920, "2017" : 41530, "2018" : 59740}
ADDSELECTION= options.selection!=""
SYNC = options.sync
RECAL = options.recal
BTAGGEFFVARS= ["jCSV", "jdeepCSV", "jdeepFlavour"]
SEPARATE = options.separate
SIGNAL_ONLY = options.signal_only
if SIGNAL_ONLY: RATIO = 0
color = {'none': 920, 'qq': 1, 'bq': 632, 'bb': 600, 'mumu': 418}
color_shift = {'none': 2, 'qq': 922, 'bq': 2, 'bb': 2, 'mumu':2}
if options.selection not in SELECTIONS.keys():
print "invalid selection!"
sys.exit()
btag_colors = {"jdeepFlavour":801, "jdeepCSV":6, "jCSV":4}
btag_titles = {"jCSV": "CSVv2", "jdeepCSV": "DeepCSV", "jdeepFlavour": "DeepJet"}
########## SAMPLES ##########
if SIGNAL_ONLY:
data = []
back = []
else:
data = ["data_obs"]
back = ["TTbar", "QCD"]
#sign = ['ZpBB_M2000', 'ZpBB_M4000', 'ZpBB_M6000']#, 'ZpBB_M8000']
sign = ['ZpBB_M2000', 'ZpBB_M4000', 'ZpBB_M6000', 'ZpBB_M8000']
########## ######## ##########
if BTAGGING not in ['tight', 'medium', 'loose', 'semimedium']:
print "unknown btagging requirement:", BTAGGING
sys.exit()
jobs = []
def plot(var, cut, year, norm=False, fraction_above=0):
### 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:
if RECAL and 'data' in s:
tree[s].Add(NTUPLEDIR + "Recal/" + ss + "_Recal.root")
else:
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])
if SIGNAL_ONLY:
hist['BkgSum'] = None
else:
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 and not SIGNAL_ONLY:
for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral()/hist['BkgSum'].Integral())
# Create data and Bkg sum histograms
if not SIGNAL_ONLY:
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
else:
for i, s in enumerate(sign):
if not "jmuon_gen_reco_dR" in var:
addOverflow(hist[s], False)
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 not SIGNAL_ONLY:
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")
if not SIGNAL_ONLY:
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 not SIGNAL_ONLY:
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)
#else:
# c1.GetPad(0).SetTopMargin(0.06)
# c1.GetPad(0).SetRightMargin(0.05)
# c1.GetPad(0).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
if not SIGNAL_ONLY:
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")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
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)
if not SIGNAL_ONLY:
bkg.GetYaxis().SetTitleOffset(0.9)
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())))
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.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
if log: bkg.SetMinimum(1)
else:
hist[sign[0]].GetYaxis().SetTitleOffset(0.9)
signal_maxima = []
for sig in sign:
signal_maxima.append(hist[sig].GetMaximum())
hist[sign[0]].SetMaximum(1.1*max(signal_maxima))
#hist[sign[0]].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())))
#hist[sign[0]].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)
hist[sign[0]].GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
if log: hist[sign[0]].SetMinimum(1)
leg.Draw()
drawCMS(LUMI[year], "Preliminary")
drawRegion('XVH'+channel, True)
drawAnalysis(channel)
if not SIGNAL_ONLY:
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO and not SIGNAL_ONLY:
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(OUTDIR+"plots/"+channel+"/"+varname.replace("/","_")+"_"+year+suffix+".png")
c1.Print(OUTDIR+"plots/"+channel+"/"+varname.replace("/","_")+"_"+year+suffix+".pdf")
# Print table
if not SIGNAL_ONLY:
printTable(hist, sign, fraction_above=fraction_above)
# if True:
# sFile = TFile("sync/data_2016.root", "RECREATE")
# sFile.cd()
# hist['data_obs'].
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
########## ######## ##########
def efficiency(year):
SINGLE_LEGEND = True
import numpy as np
from root_numpy import tree2array, fill_hist
from aliases import AK8veto, electronVeto, muonVeto
genPoints = [1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000]
eff = {}
vetoes = {"AK8":AK8veto, "electron": electronVeto, "muon": muonVeto, "lepton": electronVeto+muonVeto}
VETO = "AK8" ##could change the veto to investigate here
#VETO = "lepton"
if SEPARATE: eff_add = {}
#channels = ['none', 'qq', 'bq', 'bb', 'mumu']
channels = ['qq', 'bq', 'bb', 'mumu']
for channel in channels:
treeSign = {}
ngenSign = {}
nevtSign = {}
eff[channel] = TGraphErrors()
if SEPARATE:
nevtSign_add = {}
eff_add[channel] = TGraphErrors()
for i, m in enumerate(genPoints):
signName = "ZpBB_M"+str(m)
ngenSign[m] = 0.
nevtSign[m] = 0.
if SEPARATE: nevtSign_add[m] = 0.
for j, ss in enumerate(sample[signName]['files']):
if year=="run2" or year in ss:
print "opening " + NTUPLEDIR + ss + ".root"
sfile = TFile(NTUPLEDIR + ss + ".root", "READ")
ngenSign[m] += sfile.Get("Events").GetBinContent(1)
treeSign[m] = sfile.Get("tree")
if BTAGGING=='semimedium':
#if SEPARATE:
# temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel].replace(vetoes[VETO], ""))
#else:
temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel])
temp_hist = TH1F('pass', 'pass', 1,0,1)
fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array=None; temp_hist.Reset()
if SEPARATE:
temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel].replace(vetoes[VETO], ""))
temp_hist = TH1F('pass', 'pass', 1,0,1)
fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array=None; temp_hist.Reset()
else:
#if SEPARATE:
# temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format(WP=working_points[BTAGGING]).replace(vetoes[VETO], ""))
#else:
temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format(WP=working_points[BTAGGING]))
temp_hist = TH1F('pass', 'pass', 1,0,1)
fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array)
nevtSign[m] += temp_hist.GetBinContent(1)
temp_array=None; temp_hist.Reset()
if SEPARATE:
temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format(WP=working_points[BTAGGING]).replace(vetoes[VETO], ""))
temp_hist = TH1F('pass', 'pass', 1,0,1)
fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array)
nevtSign_add[m] += temp_hist.GetBinContent(1)
temp_array=None; temp_hist.Reset()
sfile.Close()
#print channel, ss, ":", nevtSign[m], "/", ngenSign[m], "=", nevtSign[m]/ngenSign[m]
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
n = eff[channel].GetN()
eff[channel].SetPoint(n, m, nevtSign[m]/ngenSign[m])
eff[channel].SetPointError(n, 0, math.sqrt(nevtSign[m])/ngenSign[m])
if SEPARATE:
eff_add[channel].SetPoint(n, m, nevtSign_add[m]/ngenSign[m])
eff_add[channel].SetPointError(n, 0, math.sqrt(nevtSign_add[m])/ngenSign[m])
eff[channel].SetMarkerColor(color[channel])
eff[channel].SetMarkerStyle(20)
eff[channel].SetLineColor(color[channel])
eff[channel].SetLineWidth(3)
if SEPARATE:
eff_add[channel].SetMarkerColor(color[channel]+color_shift[channel])
eff_add[channel].SetMarkerStyle(21)
eff_add[channel].SetLineColor(color[channel]+color_shift[channel])
eff_add[channel].SetLineWidth(3)
eff_add[channel].SetLineStyle(7)
if channel=='qq' or channel=='none': eff[channel].SetLineStyle(3)
n = max([eff[x].GetN() for x in channels])
maxEff = 0.
# Total efficiency
eff["sum"] = TGraphErrors(n)
eff["sum"].SetMarkerStyle(24)
eff["sum"].SetMarkerColor(1)
eff["sum"].SetLineWidth(3)
if SEPARATE:
eff_add["sum"] = TGraphErrors(n)
eff_add["sum"].SetMarkerStyle(25)
eff_add["sum"].SetMarkerColor(1)
eff_add["sum"].SetLineWidth(3)
eff_add["sum"].SetLineStyle(7)
for i in range(n):
tot, mass = 0., 0.
if SEPARATE: tot_add = 0.
for channel in channels:
if channel=='qq' or channel=='none': continue #not sure if I should include 2mu category in sum
if eff[channel].GetN() > i:
tot += eff[channel].GetY()[i]
if SEPARATE: tot_add += eff_add[channel].GetY()[i]
mass = eff[channel].GetX()[i]
if tot > maxEff: maxEff = tot
eff["sum"].SetPoint(i, mass, tot)
if SEPARATE: eff_add["sum"].SetPoint(i, mass, tot_add)
if not SINGLE_LEGEND:
if SEPARATE:
leg = TLegend(0.15, 0.50, 0.95, 0.8)
legS = TLegend(0.5, 0.8-0.045, 0.9, 0.85)
else:
leg = TLegend(0.15, 0.60, 0.95, 0.8)
legS = TLegend(0.5, 0.85-0.045, 0.9, 0.85)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(len(channels)/4)
legS.SetBorderSize(0)
legS.SetFillStyle(0) #1001
legS.SetFillColor(0)
legS.SetMargin(0.15)
if SEPARATE:
leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])*0.045)
else:
leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])/2.*0.045)
legS.AddEntry(eff['sum'], "Total b tag efficiency (1 b tag + 2 b tag + 1 #mu)", "pl")
for i, channel in enumerate(channels):
if eff[channel].GetN() > 0:
leg.AddEntry(eff[channel], getChannel(channel), "pl")
if SEPARATE: leg.AddEntry(eff_add[channel], getChannel(channel)+" no "+VETO+"-veto", "pl")
if SEPARATE: legS.AddEntry(eff_add['sum'], "Total b tag efficiency, no "+VETO+"-veto", "pl")
else:
#leg = TLegend(0.25, 0.48, 0.87, 0.85)
leg = TLegend(0.45, 0.63, 0.9, 0.93)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetTextSize(0.045)
leg.SetMargin(0.15)
## same vertical order as in the low mass region as prescribed by CCLE
leg.AddEntry(eff[channels[0]], "Acceptance prior to b tagging", "pl")
leg.AddEntry(eff['sum'], "Total (1 b tag + 2 b tag + 1 #mu)", "pl")
for i, channel in enumerate(channels):
if i==0: continue
if eff[channel].GetN() > 0:
leg.AddEntry(eff[channel], getChannel(channel), "pl")
c1 = TCanvas("c1", "Signal Efficiency", 1200, 800)
c1.cd(1)
eff['sum'].Draw("APL")
if SEPARATE: eff_add['sum'].Draw("SAME, PL")
for i, channel in enumerate(channels):
eff[channel].Draw("SAME, PL")
if SEPARATE: eff_add[channel].Draw("SAME, PL")
leg.Draw()
if not SINGLE_LEGEND:
legS.Draw()
setHistStyle(eff["sum"], 1.1)
eff["sum"].SetTitle(";Z' mass (GeV);Acceptance #times efficiency")
eff["sum"].SetMinimum(0.)
eff["sum"].SetMaximum(max(1., maxEff*1.5)) #0.65
if SEPARATE:
eff_add["sum"].SetTitle(";Z' mass (GeV);Acceptance #times efficiency")
eff_add["sum"].SetMinimum(0.)
eff_add["sum"].SetMaximum(1.)
eff["sum"].GetXaxis().SetTitleSize(0.055)
eff["sum"].GetYaxis().SetTitleSize(0.055)
#eff["sum"].GetYaxis().SetTitleOffset(1.1)
eff["sum"].GetYaxis().SetTitleOffset(0.85)
#eff["sum"].GetXaxis().SetTitleOffset(1.05)
eff["sum"].GetXaxis().SetTitleOffset(1.03)
eff["sum"].GetXaxis().SetRangeUser(1800, 8000)
eff["sum"].GetYaxis().SetRangeUser(0,0.6)
c1.SetTopMargin(0.05)
c1.SetBottomMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
drawCMS(-1, "Simulation", year='')
#drawCMS(-1, "Simulation Preliminary", year='')
#drawCMS(-1, "Simulation Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
#drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
if SEPARATE:
c1.Print("plots/Efficiency/"+year+"_"+BTAGGING+"_no"+VETO+"veto.pdf")
c1.Print("plots/Efficiency/"+year+"_"+BTAGGING+"_no"+VETO+"veto.png")
else:
c1.Print("plots/Efficiency/"+year+"_"+BTAGGING+".pdf")
c1.Print("plots/Efficiency/"+year+"_"+BTAGGING+".png")
# print
print "category",
for m in range(0, eff["sum"].GetN()):
print " & %d" % int(eff["sum"].GetX()[m]),
print "\\\\", "\n\\hline"
for i, channel in enumerate(channels+["sum"]):
if channel=='sum': print "\\hline"
print getChannel(channel).replace("high ", "H").replace("low ", "L").replace("purity", "P").replace("b-tag", ""),
for m in range(0, eff[channel].GetN()):
print "& %.1f" % (100.*eff[channel].GetY()[m]),
print "\\\\"
def acceptance(year):
genPoints = [1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000]
treeSign = {}
ngenSign = {}
nevtSign = {}
nevtSign_eta = {}
nevtSign_dEta = {}
eff = TGraphErrors()
eff_eta = TGraphErrors()
eff_dEta = TGraphErrors()
for i, m in enumerate(genPoints):
ngenSign[m] = 0.
nevtSign[m] = 0.
nevtSign_eta[m] = 0.
nevtSign_dEta[m] = 0.
if year == "run2":
years = ['2016', '2017', '2018']
else:
years = [year]
for yr in years:
signName = "MC_signal_{}_M{}".format(yr, m)
sfile = TFile(ACCEPTANCEDIR + signName + "_acceptanceHist.root", "READ")
ngenSign[m] += sample["ZpBB_M"+str(m)]['genEvents'][yr]
#all_events_hist = sfile.Get('all_events')
#nEvents = all_events_hist.GetBinContent(1)
#ngenSign[m] += nEvents
passing_events_hist = sfile.Get('passing')
eta_flag_hist = sfile.Get('eta_flag')
dEta_flag_hist = sfile.Get('dEta_flag')
nEvents = passing_events_hist.GetBinContent(1)
nEvents_eta = eta_flag_hist.GetBinContent(1)
nEvents_dEta = dEta_flag_hist.GetBinContent(1)
nevtSign[m] += nEvents
nevtSign_eta[m] += nEvents_eta
nevtSign_dEta[m] += nEvents_dEta
sfile.Close()
print m, ":", nevtSign[m], "/", ngenSign[m], "=", nevtSign[m]/ngenSign[m]
if nevtSign[m] == 0 or ngenSign[m] < 0: continue
n = eff.GetN()
eff.SetPoint(n, m, nevtSign[m]/ngenSign[m])
eff.SetPointError(n, 0, math.sqrt(nevtSign[m])/ngenSign[m])
eff_eta.SetPoint(n, m, nevtSign_eta[m]/ngenSign[m])
eff_eta.SetPointError(n, 0, math.sqrt(nevtSign_eta[m])/ngenSign[m])
eff_dEta.SetPoint(n, m, nevtSign_dEta[m]/ngenSign[m])
eff_dEta.SetPointError(n, 0, math.sqrt(nevtSign_dEta[m])/ngenSign[m])
eff.SetMarkerColor(4)
eff.SetMarkerStyle(24)
eff.SetMarkerSize(2)
eff.SetLineColor(4)
eff.SetLineWidth(3)
eff_eta.SetMarkerColor(2)
eff_eta.SetMarkerStyle(23)
eff_eta.SetMarkerSize(2)
eff_eta.SetLineColor(2)
eff_eta.SetLineWidth(2)
eff_eta.SetLineStyle(2)
eff_dEta.SetMarkerColor(418)
eff_dEta.SetMarkerStyle(23)
eff_dEta.SetMarkerSize(2)
eff_dEta.SetLineColor(418)
eff_dEta.SetLineWidth(2)
eff_dEta.SetLineStyle(2)
n = eff.GetN()
maxEff = 0.
leg = TLegend(0.15, 0.7, 0.95, 0.8)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
#leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])/2.*0.045)
leg.AddEntry(eff, "total")
leg.AddEntry(eff_eta, "|#eta|<2.5")
leg.AddEntry(eff_dEta, "#Delta#eta<1.1")
#legS = TLegend(0.5, 0.85-0.045, 0.9, 0.85)
#legS.SetBorderSize(0)
#legS.SetFillStyle(0) #1001
#legS.SetFillColor(0)
#legS.AddEntry(eff['sum'], "Total efficiency (1 b tag + 2 b tag)", "pl")
c1 = TCanvas("c1", "Signal Acceptance", 1200, 800)
c1.cd(1)
eff.Draw("APL")
eff_eta.Draw("SAME, PL")
eff_dEta.Draw("SAME, PL")
leg.Draw()
#legS.Draw()
#setHistStyle(eff["sum"], 1.1)
eff.SetTitle(";m_{Z'} (GeV);Acceptance")
eff.SetMinimum(0.)
eff.SetMaximum(max(1.5, maxEff*1.5)) #0.65
eff.GetXaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleOffset(1.1)
eff.GetXaxis().SetTitleOffset(1.05)
eff.GetXaxis().SetRangeUser(1500, 8000)
c1.SetTopMargin(0.05)
drawCMS(-1, "Simulation Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
#drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
c1.Print("plots/Efficiency/"+year+"_Acceptance.pdf")
c1.Print("plots/Efficiency/"+year+"_Acceptance.png")
def trigger_efficiency(year, separate=False):
from root_numpy import root2array, fill_hist
from aliases import triggers, triggers_PFHT, triggers_Jet, triggers_BTag
import numpy as np
#spec_triggers = {"PFHT": triggers_PFHT, "Jet": triggers_Jet, "BTag": triggers_BTag}
spec_triggers = {"HT/Jet": "("+triggers_PFHT+" || "+triggers_Jet+")", "BTag": triggers_BTag}
spec_triggers_colors = {"PFHT": 418, "Jet": 4, "BTag": 6, "HT/Jet": 4, "total": 2}
hist_pass = TH1F("pass", "pass", 100, 0., 10000.)
if separate:
hist_pass_spec = {}
for trig in spec_triggers.keys(): hist_pass_spec[trig] = TH1F("pass_"+trig, "pass_"+trig, 100, 0., 10000.)
hist_all = TH1F("all", "all", 100, 0., 10000.)
file_list = []
data_2016_letters = ["B", "C", "D", "E", "F", "G", "H"]
data_2017_letters = ["B", "C", "D", "E", "F"]
data_2018_letters = ["A", "B", "C", "D"]
sample_names = []
if year=='2016':
letters = data_2016_letters
elif year=='2017':
letters = data_2017_letters
elif year=='2018':
letters = data_2018_letters
else:
print "unknown year"
sys.exit()
for letter in letters:
#dir_content = os.listdir(TRIGGERDIR+"/SingleMuon_{}_{}/".format(year, letter)) ## intended to run directly on ntuples
#for entry in dir_content:
# if "_flatTuple" in entry: file_list.append(TRIGGERDIR+"/SingleMuon_{}_{}/".format(year, letter)+entry)
file_list.append(TRIGGERDIR+"/SingleMuon_{}_{}.root".format(year, letter))
print "appending:",TRIGGERDIR+"/SingleMuon_{}_{}.root".format(year, letter)
for file_name in file_list:
temp_array = root2array(file_name, treename='tree', branches='jj_mass_widejet', selection="jj_deltaEta_widejet<1.1")
fill_hist(hist_all, temp_array)
temp_array = root2array(file_name, treename='tree', branches='jj_mass_widejet', selection="jj_deltaEta_widejet<1.1 && "+triggers)
fill_hist(hist_pass, temp_array)
if separate:
for trig in spec_triggers.keys():
temp_array = root2array(file_name, treename='tree', branches='jj_mass_widejet', selection="jj_deltaEta_widejet<1.1 && "+spec_triggers[trig])
fill_hist(hist_pass_spec[trig], temp_array)
temp_array=None
import array
from aliases import dijet_bins
binning=[]
for num in dijet_bins:
if num<=10000: binning.append(num)
#binning = range(0,1500,100)+range(1500,2000,100)+range(2000,3100,150)+range(3100,10000,300)
binning_ = array.array('d', binning)
hist_pass2 = hist_pass.Rebin(len(binning_)-1, "hist_pass_rebinned", binning_)
hist_all2 = hist_all.Rebin(len(binning_)-1, "hist_all_rebinned", binning_)
if separate:
hist_pass_spec2 = {}
for trig in spec_triggers.keys():
hist_pass_spec2[trig] = hist_pass_spec[trig].Rebin(len(binning_)-1, "hist_pass_"+trig+"_rebinned", binning_)
hist_pass2.Sumw2()
hist_all2.Sumw2()
eff = TGraphAsymmErrors()
eff.Divide(hist_pass2, hist_all2)
eff.SetMarkerColor(spec_triggers_colors["total"])
if separate:
eff.SetMarkerStyle(5)
else:
eff.SetMarkerStyle(1)
eff.SetLineColor(spec_triggers_colors["total"])
eff.SetLineWidth(2)
if separate:
eff_spec = {}
for trig in spec_triggers.keys():
hist_pass_spec2[trig].Sumw2()
eff_spec[trig] = TGraphAsymmErrors()
eff_spec[trig].Divide(hist_pass_spec2[trig], hist_all2)
eff_spec[trig].SetMarkerColor(spec_triggers_colors[trig])
eff_spec[trig].SetMarkerStyle(1)
eff_spec[trig].SetLineColor(spec_triggers_colors[trig])
eff_spec[trig].SetLineWidth(2)
one_line = TGraph()
one_line.SetPoint(0, 0., 1.)
one_line.SetPoint(1, 10000., 1.)
one_line.SetLineStyle(2)
c1 = TCanvas("c1", "Trigger Efficiency", 800, 800)
c1.cd(1)
eff.Draw("AP")
one_line.Draw("L")
eff.SetTitle(";m_{jj} (GeV);trigger efficiency")
eff.SetMinimum(0.)
eff.SetMaximum(1.4) #0.65
## new
dijet_bin_centers = []
for b, lthr in enumerate(dijet_bins[:-1]):
if lthr<1200 or lthr > 2500: continue
dijet_bin_centers.append(0.5*(dijet_bins[b]+dijet_bins[b+1]))
print "total trigger efficiency:"
for cval in dijet_bin_centers:
print cval,":", eff.Eval(cval)
## end new
if separate:
leg = TLegend(0.65,0.75, 0.9, 0.95)
leg.AddEntry(eff, "total")
for trig in spec_triggers.keys():
leg.AddEntry(eff_spec[trig], trig+"-based")
eff_spec[trig].Draw("P SAME")
## new
print trig, "trigger efficiency"
for cval in dijet_bin_centers:
print cval,":", eff_spec[trig].Eval(cval)
## end new
leg.Draw()
eff.GetXaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleOffset(1.1)
eff.GetXaxis().SetTitleOffset(1.05)
eff.GetXaxis().SetLimits(700., 5000.)
c1.SetTopMargin(0.05)
drawCMS(-1, "Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
#drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
suffix = ""
if separate: suffix = "_sep"
c1.Print("plots/Efficiency/trigger_"+year+suffix+".pdf")
c1.Print("plots/Efficiency/trigger_"+year+suffix+".png")
def btag_efficiency(cut, year, pT_range=None):
### Preliminary Operations ###
from root_numpy import hist2array
from sklearn.metrics import roc_curve
import numpy as np
genPoints = [1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000]
signal = ['ZpBB_M'+str(x) for x in genPoints]
btag_vars = BTAGGEFFVARS
nbins = 100
min_, max_ = 0., 1.
treeRead = True
channel = cut
isAH = False
for k in sorted(alias.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(k, aliasSM[k])
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back+signal):
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if year=="run2" or year in ss:
### to run on big ntuples:
#k = 0
#while True:
# if os.path.exists("/eos/user/m/msommerh/Zprime_to_bb_analysis/" + ss + "/" + ss+ "_flatTuple_{}.root".format(k)):
# tree[s].Add("/eos/user/m/msommerh/Zprime_to_bb_analysis/" + ss + "/" + ss + "_flatTuple_{}.root".format(k))
# k += 1
# else:
# print "found {} files for sample:".format(k), ss
# break
#if k == 0:
# print ' WARNING: files for sample', ss , 'do not exist, continuing'
# return True
### end big ntuples
tree[s].Add(NTUPLEDIR + ss + ".root") ## to run on skimmed ntuples
for var in btag_vars:
hist[s+"_"+var] = TH1F(s+"_"+var, ";efficiency; mistag rate", nbins, min_, max_)
for suf in ["_1", "_2"]:
temp_hist = TH1F(s+"_"+var+suf, ";efficiency; mistag rate", nbins, min_, max_)
temp_hist.Sumw2()
if pT_range is not None:
pT_cut = " && jpt"+suf+">="+str(pT_range[0])+" && jpt"+suf+"<"+str(pT_range[1])
else:
pT_cut = ""
if s in signal:
flavourcut = " && abs(jflavour"+suf+")==5"
else:
flavourcut = " && (abs(jflavour"+suf+")<4 || abs(jflavour"+suf+")==9 || abs(jflavour"+suf+")==21)" ## FIXME this currently excludes charms
if len(cut)==0: flavourcut = flavourcut[4:]
cutstring = "(eventWeightLumi)" + "*("+cut+flavourcut+pT_cut+")"
tree[s].Project(s+"_"+var+suf, var+suf, cutstring)
if not tree[s].GetTree()==None: hist[s+"_"+var].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
#hist[s][var+suf].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s+"_"+var].Add(temp_hist)
temp_hist.Delete()
fpr = {}
tpr = {}
thr = {}
for var in btag_vars:
hist_sig = TH1F(var+"_sig", ";efficiency; mistag rate", nbins, min_, max_)
hist_bkg = TH1F(var+"_bkg", ";efficiency; mistag rate", nbins, min_, max_)
for i, s in enumerate(signal):
hist_sig.Add(hist[s+"_"+var])
for i, s in enumerate(back):
hist_bkg.Add(hist[s+"_"+var])
sig_arr, sig_edges = hist2array(hist_sig, return_edges=True)
bkg_arr, bkg_edges = hist2array(hist_bkg, return_edges=True)
assert len(sig_edges[0]) == len(bkg_edges[0])
vals = []
sig_weights = []
bkg_weights = []
for j, entry in enumerate(sig_edges[0][:-1]):
vals.append(0.5*(sig_edges[0][j]+sig_edges[0][j+1]))
sig_weights.append(sig_arr[j])
bkg_weights.append(bkg_arr[j])
sig_labels = np.ones(len(sig_weights))
bkg_labels = np.zeros(len(bkg_weights))
fpr[var], tpr[var], thr[var] = roc_curve(np.concatenate((sig_labels, bkg_labels)), np.array(vals+vals), sample_weight=np.concatenate((sig_weights,bkg_weights)))
canv = TCanvas('c', 'c', 500, 650)
canv.SetGrid()