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getCutFlow.py
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getCutFlow.py
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#!/usr/bin/env python
# Modules imported
from __future__ import print_function
import sys
import argparse
import numpy
from os import path, listdir
# Argument parser setup
parser = argparse.ArgumentParser()
parser.add_argument("--part", help="1= prod and reduced level, 2= object selection, 3= mass windows, 999= get everything together", nargs='?', const=2, type=int, default=2)
parser.add_argument("--doBackgrounds", help="do the plot for backgrounds instead of Higgses samples", nargs='?', const=0, type=int, default=0)
#parser.add_argument("--selectionVersion", help="selection version", nargs='?', const="v16_beta", type=str, default="v16_beta")
parser.add_argument("--selectionFolder", help="selection folder", nargs='?', const="./", type=str, default="./")
parser.add_argument("--plotName", help="plot name", nargs='?', const="flow", type=str, default="flow")
args = parser.parse_args()
# ROOT setup
import ROOT
from ROOT import gROOT, gStyle
from ROOT import TChain, TCanvas, TGraph, TLatex, TLegend, TPad, TGaxis
gROOT.Reset()
gROOT.SetBatch()
if args.part == 1:
print("part 1: preselection {produced, reduced, optree}-level")
# produced
eos_prod = "/store/group/phys_higgs/resonant_HH/V15_00_12/"
eos_pro2 = "/store/user/hebda/h2gglobe/"
eos_pro3 = "/store/user/zghiche/NR_HHTo2G2B/8TeV_BigNtuples/"
# reduced
eos_redu = "/store/user/hebda/h2gglobe/reduced/radion_reduction_v12/mc/"
eos_red2 = "/store/group/phys_higgs/resonant_HH/V15_00_12/reduced/radion_reduction_v12/mc/"
eos_red3 = "/store/user/zghiche/hbbhgg/H2GGLOBE/Radion/reduced/radion_reduction_v12/mc/"
# optree
eos_tree = "/store/user/acarvalh/hh_gamgambb_8TeV/trees/radion_redu_12_tree_10/"
samples = []
if args.doBackgrounds == 0:
samples.append(["Radion_m270", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-270_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m270", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-270_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m270", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m270_8TeV"])
samples.append(["Radion_m300", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-300_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m300", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-300_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m300", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m300_8TeV"])
samples.append(["Radion_m300", "prod", eos_pro2, "RadionToHHTo2G2B_M-300_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m300", "redu", eos_redu, "RadionToHHTo2G2B_M-300_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m350", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-350_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m350", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-350_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m350", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m350_8TeV"])
samples.append(["Radion_m400", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-400_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m400", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-400_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m400", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m400_8TeV"])
samples.append(["Radion_m450", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-450_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m450", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-450_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m450", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m450_8TeV"])
samples.append(["Radion_m500", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-500_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m500", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-500_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m500", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m500_8TeV"])
samples.append(["Radion_m500", "prod", eos_pro2, "RadionToHHTo2G2B_M-500_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m500", "redu", eos_redu, "RadionToHHTo2G2B_M-500_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m550", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-550_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m550", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-550_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m550", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m550_8TeV"])
samples.append(["Radion_m600", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-600_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m600", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-600_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m600", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m600_8TeV"])
samples.append(["Radion_m650", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-650_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m650", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-650_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m650", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m650_8TeV"])
samples.append(["Radion_m700", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-700_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m700", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-700_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m700", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m700_8TeV"])
samples.append(["Radion_m700", "prod", eos_pro2, "RadionToHHTo2G2B_M-700_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m700", "redu", eos_redu, "RadionToHHTo2G2B_M-700_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m800", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-800_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m800", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-800_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m800", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m800_8TeV"])
samples.append(["Radion_m900", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-900_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m900", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-900_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m900", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m900_8TeV"])
samples.append(["Radion_m1000", "prod", eos_pro2, "RadionToHHTo2G2B_M-1000_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m1000", "redu", eos_redu, "RadionToHHTo2G2B_M-1000_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m1000", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1000_8TeV"])
samples.append(["Radion_m1100", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-1100_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1100", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-1100_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1100", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1100_8TeV"])
samples.append(["Radion_m1200", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-1200_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1200", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-1200_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1200", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1200_8TeV"])
samples.append(["Radion_m1300", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-1300_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1300", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-1300_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1300", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1300_8TeV"])
samples.append(["Radion_m1400", "prod", eos_pro2, "RadionToHH_2Gamma_2b_M-1400_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1400", "redu", eos_redu, "RadionToHH_2Gamma_2b_M-1400_TuneZ2star_8TeV-Madgraph_pythia6", "*root", "event"])
samples.append(["Radion_m1400", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1400_8TeV"])
samples.append(["Radion_m1500", "prod", eos_pro2, "RadionToHHTo2G2B_M-1500_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m1500", "redu", eos_redu, "RadionToHHTo2G2B_M-1500_TuneZ2star_8TeV-nm-madgraph", "*root", "event"])
samples.append(["Radion_m1500", "tree", eos_tree, "", "tree_XHH_v2.root", "Radion_m1500_8TeV"])
samples.append(["ggH", "prod", eos_prod, "GluGluToHToGG_M-125_8TeV-powheg-pythia6", "*root", "event"])
samples.append(["ggH", "redu", eos_red2, "GluGluToHToGG_M-125_8TeV-powheg-pythia6", "*root", "event"])
samples.append(["ggH", "tree", eos_tree, "", "tree_SMHiggs.root", "ggh_m125_powheg_8TeV"])
samples.append(["VBF", "prod", eos_prod, "VBF_HToGG_M-125_8TeV-powheg-pythia6", "*root", "event"])
samples.append(["VBF", "redu", eos_red2, "VBF_HToGG_M-125_8TeV-powheg-pythia6", "*root", "event"])
samples.append(["VBF", "tree", eos_tree, "", "tree_SMHiggs.root", "vbf_m125_8TeV"])
samples.append(["WH_", "prod", eos_prod, "WH_ZH_HToGG_M-125_8TeV-pythia6", "*root", "event"])
samples.append(["WH_", "redu", eos_red2, "WH_ZH_HToGG_M-125_8TeV-pythia6_wh", "*root", "event"])
samples.append(["WH_","tree", eos_tree, "", "tree_SMHiggs.root", "wzh_m125_8TeV_wh"])
samples.append(["ZH_","prod", eos_prod, "WH_ZH_HToGG_M-125_8TeV-pythia6", "*root", "event"])
samples.append(["ZH_","redu", eos_red2, "WH_ZH_HToGG_M-125_8TeV-pythia6_zh", "*root", "event"])
samples.append(["ZH_","tree", eos_tree, "", "tree_SMHiggs.root", "wzh_m125_8TeV_zh"])
samples.append(["ttH","prod", eos_prod, "TTH_HToGG_M-125_8TeV-pythia6", "*root", "event"])
samples.append(["ttH","redu", eos_red2, "TTH_HToGG_M-125_8TeV-pythia6", "*root", "event"])
samples.append(["ttH","tree", eos_tree, "", "tree_SMHiggs.root", "tth_m125_8TeV"])
samples.append(["bbH","prod", eos_prod, "bbH_HToGG_M-125_8TeV-Madgraph_LO_Summer12_DR53X-PU_RD1_START53_V7N-v1_AODSIM/bbHtoGG_AODSIM_RD", "*root", "event"])
samples.append(["bbH","redu", eos_red2, "bbHtoGG_RD", "*root", "event"])
samples.append(["bbH","tree", eos_tree, "", "tree_SMHiggs.root", "bbh_m125_8TeV"])
samples.append(["ggHH","prod", eos_pro3, "HH_bbaa_8TeV_Lam_1d0_Yt_1d0_c2_0d0", "*root", "event"])
samples.append(["ggHH","redu", eos_red3, "HH_bbaa_8TeV_Lam_1d0_Yt_1d0_c2_0d0", "*root", "event"])
samples.append(["ggHH","tree", eos_tree, "", "ggHH_anomalous_v4.root", "HH_bbaa_8TeV_Lam_1d0_Yt_1d0_c2_0d0"])
# else: # background and data samples
#samples.append([displayName, level, eos_base, sample, file, tree])
# DATA
# samples.append(["data", "prod", eos_pro4, "Photon_Run2012A_22Jan2013-v1_AOD", "*root", "event"])
# samples.append(["data", "prod", eos_pro4, "DoublePhoton_Run2012B-22Jan2013-v1_AOD", "*root", "event"])
# samples.append(["data", "prod", eos_pro4, "DoublePhoton_Run2012C-22Jan2013-v2_AOD", "*root", "event"])
# samples.append(["data", "prod", eos_pro4, "DoublePhoton_Run2012D-22Jan2013-v1_v3", "*root", "event"])
# samples.append(["data", "redu", eos_red3, "Photon_Run2012A_22Jan2013-v1", "*root", "event"])
# samples.append(["data", "redu", eos_red3, "DoublePhoton_Run2012B-22Jan2013-v1", "*root", "event"])
# samples.append(["data", "redu", eos_red3, "DoublePhoton_Run2012C-22Jan2013-v2", "*root", "event"])
# samples.append(["data", "redu", eos_red3, "DoublePhoton_Run2012D-22Jan2013-v1_v3", "*root", "event"])
# samples.append(["data", "tree", eos_tree, "", "Data.root", "Data"])
# PP
# samples.append()
#diphojet_sherpa_8TeV
## PF
#qcd_30_8TeV_pf
#qcd_40_8TeV_pf
#gjet_20_8TeV_pf
#gjet_40_8TeV_pf
## FF
#qcd_30_8TeV_ff
#qcd_40_8TeV_ff
## DY
#DYJetsToLL
## OTHERS
#LNuGG_FSR_8TeV
#LNuGG_ISR_8TeV
#ttGG_8TeV
#tGG_8TeV
#TTGJets_8TeV
#ZGToLLG_8TeV
#
nprocessed = {}
nreduced = {}
ntrees = {}
treename = {}
for displayName, level, eos_base, sample, file, tree in samples:
nprocessed[displayName] = 0
nreduced[displayName] = 0
ntrees[displayName] = 0
treename[displayName] = ""
for displayName, level, eos_base, sample, file, tree in samples:
print("Now taking care of", displayName, "(", level, sample, ")", file=sys.stderr)
chain = TChain(tree)
files = "root://eoscms//eos/cms" + path.join(eos_base, sample, file)
chain.Add(files)
# print "displayName= ", displayName, "level= ", level, "chain.GetEntries()= ", chain.GetEntries()
if level == "prod":
nprocessed[displayName] += chain.GetEntries()
if level == "redu":
nreduced[displayName] += chain.GetEntries()
if level == "tree":
ntrees[displayName] += chain.GetEntries()
treename[displayName] = tree
# if level == "tree":
# nentries_w = 0
# for ievt in xrange(chain.GetEntries()):
# chain.GetEntry(ievt)
# nentries_w += chain.evweight
# print "displayName= ", displayName, "level= ", "trew", "chain.GetEntries()= ", nentries_w * nprocessed[displayName] / 19706.
del chain
# print "########################################################"
# print "treeName, nprocessed, nreduced, ntrees"
# print "########################################################"
displayNames = set([ x[0] for x in samples ])
for display in displayNames:
print(treename[display], nprocessed[display], nreduced[display], ntrees[display])
#for display in displayNames:
# print treename[display], nprocessed[display] / float(nprocessed[display]) * 100, nreduced[display] / float(nprocessed[display]) * 100, ntrees[display] / float(nprocessed[display]) * 100
if args.part == 2:
print("part 2: object selection")
nflow = {}
cutFlowDir = args.selectionFolder
cutFlowFiles = [ x for x in listdir(cutFlowDir) if path.isfile(path.join(cutFlowDir,x)) and "cutFlow" in x and ".dat" in x and "part" not in x]
for file in cutFlowFiles:
sample = file.replace(".dat", "").replace("cutFlow_", "")
nflow[sample] = []
with open( path.join(cutFlowDir,file) ) as data:
for line in data:
nflow[sample].append(line.split("\t")[1])
for sample in nflow:
print(sample, ", ".join(nflow[sample]))
if args.part == 3:
samples = []
eos_quic = "/afs/cern.ch/work/o/obondu/public/forRadion/limitTrees/v44"
samples.append(["Radion_m260", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "MSSM_m260_8TeV_m260.root", "TCVARS"])
samples.append(["Radion_m270", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "Radion_m270_8TeV_m270.root", "TCVARS"])
samples.append(["Radion_m300", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "Radion_m300_8TeV_m300.root", "TCVARS"])
samples.append(["Radion_m350", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "Radion_m350_8TeV_m350.root", "TCVARS"])
samples.append(["Radion_m400", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "Radion_m400_8TeV_m400.root", "TCVARS"])
samples.append(["Radion_m400", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m400_8TeV_m400.root", "TCVARS"])
samples.append(["Radion_m450", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m450_8TeV_m450.root", "TCVARS"])
samples.append(["Radion_m500", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m500_8TeV_m500.root", "TCVARS"])
samples.append(["Radion_m550", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m550_8TeV_m550.root", "TCVARS"])
samples.append(["Radion_m600", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m600_8TeV_m600.root", "TCVARS"])
samples.append(["Radion_m650", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m650_8TeV_m650.root", "TCVARS"])
samples.append(["Radion_m700", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m700_8TeV_m700.root", "TCVARS"])
samples.append(["Radion_m800", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m800_8TeV_m800.root", "TCVARS"])
samples.append(["Radion_m900", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m900_8TeV_m900.root", "TCVARS"])
samples.append(["Radion_m1000", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m1000_8TeV_m1000.root", "TCVARS"])
samples.append(["Radion_m1100", "limit", eos_quic, "v44_fitToMggjj_withKinFit", "Radion_m1100_8TeV_m1100.root", "TCVARS"])
samples.append(["ggH", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "ggh_m125_powheg_8TeV_m300.root", "TCVARS"])
samples.append(["VBF", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "vbf_m125_8TeV_m300.root", "TCVARS"])
samples.append(["WH_", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "wzh_m125_8TeV_wh_m300.root", "TCVARS"])
samples.append(["ZH_", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "wzh_m125_8TeV_zh_m300.root", "TCVARS"])
samples.append(["ttH", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "tth_m125_8TeV_m300.root", "TCVARS"])
samples.append(["bbH", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "bbh_m125_8TeV_m300.root", "TCVARS"])
samples.append(["ggHH", "limit", eos_quic, "v44_fitTo2D_resSearch_withRegKinFit", "ggHH_Lam_1d0_Yt_1d0_c2_0d0_8TeV_m300.root", "TCVARS"])
for displayName, level, eos_base, sample, file, tree in samples:
chain = TChain(tree)
chain.Add(path.join(eos_base, sample, file))
print(displayName, chain.GetEntries())
## francois_cuts_w_kinFit = True
# whichJet = "kin"
# print("part 3: mass windows")
# mggjj_cut = {}
# mggjj_cut[260] = [250, 270]
# mggjj_cut[270] = [260, 280]
# mggjj_cut[300] = [290, 310]
# mggjj_cut[350] = [330, 375]
# mggjj_cut[400] = [380, 435]
## if francois_cuts_w_kinFit:
## mggjj_cut[270] = [260, 280]
## mggjj_cut[300] = [290, 310]
## mggjj_cut[350] = [330, 375]
## mggjj_cut[400] = [380, 435]
# cutFlowDir = args.selectionFolder
# selectedFolder = args.selectionFolder
# cutFlowFiles = [ x for x in listdir(cutFlowDir) if path.isfile(path.join(cutFlowDir,x)) and "cutFlow_" in x and ".dat" in x and "part" not in x]
# for file in cutFlowFiles:
# sample = file.replace(".dat", "").replace("cutFlow_", "")
# # FIXME: for now just process SM Higgs and Radion samples
# if ("Radion" not in sample) and ("m125" not in sample) and ("ggHH" not in sample):
# continue
# if "Radion" in sample and ("m300" in sample or "m500" in sample or "m700" in sample or "m1000" in sample or "m1500" in sample):
# sample = sample.replace("_nm", "")
# try:
# mass = int(sample.split("_")[1].strip('m'))
# except ValueError:
# mass = 125
# chain = TChain(sample)
# files = path.join(selectedFolder, sample + "_noRegression_noMassCut_" + args.selectionVersion + ".root")
# chain.Add(files)
## FIXME
# if not "Radion" in sample:
# masses = [260, 270, 300, 350, 400]
# for mass in masses:
# print(sample + "_M" + str(mass), chain.GetEntries(), chain.GetEntries("jj_mass > 85 && jj_mass < 155"), chain.GetEntries("jj_mass > 85 && jj_mass < 155 && " + whichJet + "ggjj_mass > " + str(mggjj_cut[mass][0]) + " && " + whichJet + "ggjj_mass < " + str(mggjj_cut[mass][1])))
# else:
# if mass <= 400:
# print(sample, chain.GetEntries(), chain.GetEntries("jj_mass > 85 && jj_mass < 155"), chain.GetEntries("jj_mass > 85 && jj_mass < 155 && " + whichJet + "ggjj_mass > " + str(mggjj_cut[mass][0]) + " && " + whichJet + "ggjj_mass < " + str(mggjj_cut[mass][1])))
# if mass >= 400:
# print(sample, chain.GetEntries(), chain.GetEntries("jj_mass > 90 && jj_mass < 165"), chain.GetEntries("jj_mass > 90 && jj_mass < 165 && gg_mass > 120 && gg_mass < 130"))
#
oldPlot = False
showSingleHiggs = False
if args.part == 999:
print("part 999: get everything together")
# Get all numbers
flow_low = {}
flow_high = {}
flow_hgg = {}
was400lowDone = False
for ifile in range(1,4):
print(ifile)
file = path.join(args.selectionFolder, "cutFlow_part" + str(ifile) + ".dat")
with open(file) as data:
for line in data:
if ("Radion" not in line) and ("m125" not in line) and ("ggHH" not in line) or ("minlo" in line):
continue
if oldPlot and ("ggHH" in line or "bbh" in line):
continue
sline = line.replace(",", "").split()
# print(sline)
sample = sline[0]
# print len(sline), sample, sline
sample = sample.replace("_nm", "")
try:
mass = int(sample.split("_")[1].split("m")[1])
except IndexError:
mass = 125
# if "m125" in sample:
# process = sample.split("_m125")[0] + sample.split("m125")[1].split("8TeV")[1]
# base = sample.split("_M")[0]
# print sample, process
if mass > 1100:
continue
if mass == 125 and "Radion" not in sample and sample not in flow_hgg:
flow_hgg[sample] = []
if 200 < mass <= 400 and sample not in flow_low:
flow_low[sample] = []
if mass > 400 and sample not in flow_high:
flow_high[sample] = []
# if mass == 400:
# print(mass, sline, "was400lowDone", was400lowDone)
if mass == 125 and "Radion" not in sample:
for iflow_hgg in range(1, len(sline)):
if ifile == 2 and (iflow_hgg == 1 or iflow_hgg == 2 or iflow_hgg == 3 or iflow_hgg == 6 or iflow_hgg == 7 or iflow_hgg == 9):
continue
# if ifile == 3 and (iflow_hgg == 1 or iflow_hgg == 2):
# continue
flow_hgg[sample].append( int(sline[iflow_hgg]) )
if 200 < mass <= 400:
for iflow_low in range(1, len(sline)):
if ifile == 2 and (iflow_low == 1 or iflow_low == 2 or iflow_low == 3 or iflow_low == 6 or iflow_low == 7 or iflow_low == 9):
continue
# if ifile == 3 and (iflow_low == 1 or iflow_low == 2):
# continue
flow_low[sample].append( int(sline[iflow_low]) )
# print(mass, flow_low[sample])
# if mass == 400 and not was400lowDone and ifile == 3:
# was400lowDone = True
# continue
if mass > 400:
for iflow_high in range(1, len(sline)):
if ifile == 2 and (iflow_high == 1 or iflow_high == 2 or iflow_high == 3 or iflow_high == 6 or iflow_high == 7 or iflow_high == 9):
continue
# if ifile == 3 and (iflow_high == 1 or iflow_high == 2):
# continue
flow_high[sample].append( int(sline[iflow_high]) )
print("now organize all this")
# print(flow_low)
# print(flow_high)
# print(flow_hgg)
# organize the numbers
n_hgg = {}
x_hgg = {}
y_hgg = {}
gr_hgg = {}
n_low = {}
x_low = {}
y_low = {}
gr_low = {}
n_high = {}
x_high = {}
y_high = {}
gr_high = {}
for igraph in range(7):
n_hgg[igraph] = 0
x_hgg[igraph] = []
y_hgg[igraph] = []
n_low[igraph] = 0
x_low[igraph] = []
y_low[igraph] = []
n_high[igraph] = 0
x_high[igraph] = []
y_high[igraph] = []
base_set = []
# flow_hgg_new = {}
for key in flow_hgg:
base = key.split("_m")[0]
print(key, base, flow_hgg[key])
base_set.append(base)
# if key not in base:
# flow_hgg_new[base] = flow_hgg[key]
# flow_hgg[key] = flow_hgg[base] + flow_hgg[key]
# print(base, key, flow_hgg[key])
# base_set = set(base_set)
# for base in base_set:
# del flow_hgg[base]
# print(flow_hgg)
# print(flow_hgg_new)
offset = {}
offset["ggh"] = -125
offset["vbf"] = -124
offset["_wh"] = -123
offset["_zh"] = -122
offset["bbh"] = -121
offset["tth"] = -120
offset["ggHH"] = -119
sigma_wh = 0.7046
sigma_zh = 0.4153
w = {}
w["ggh"] = 1.
w["vbf"] = 1.
w["_wh"] = sigma_wh / (sigma_wh + sigma_zh)
w["_zh"] = sigma_zh / (sigma_wh + sigma_zh)
w["tth"] = 1.
w["bbh"] = 1.
w["ggHH"] = 1.
for key in flow_hgg:
# if "M300" not in key:
# continue
off = 0
wgt = 1.
for koff in offset:
if koff in key:
off = int(offset[koff])
wgt = w[koff]
print(koff, key, off, wgt)
try:
mass = int(key.split("_")[1].split("m")[1])
except IndexError:
mass = 125
print(key, off, flow_hgg[key])
for igraph in range(7):
x_hgg[igraph].append(mass + off )
eff = float(flow_hgg[key][igraph]) / float( flow_hgg[key][0] * wgt ) * 100.
y_hgg[igraph].append( eff )
n_hgg[igraph] += 1
for key in flow_low:
try:
mass = int(key.split("_")[1].split("m")[1])
except:
mass = 125
print(key, flow_low[key])
for igraph in range(7):
x_low[igraph].append(mass / 1000.)
eff = float(flow_low[key][igraph]) / float(flow_low[key][0]) * 100.
y_low[igraph].append( eff )
n_low[igraph] += 1
for key in flow_high:
mass = int(key.split("_")[1].split("m")[1])
print(key, flow_high[key])
for igraph in range(7):
x_high[igraph].append(mass / 1000.)
eff = float(flow_high[key][igraph]) / float(flow_high[key][0]) * 100.
y_high[igraph].append( eff )
n_high[igraph] += 1
for igraph in range(7):
y_hgg[igraph] = [b for (a,b) in sorted(zip(x_hgg[igraph], y_hgg[igraph]))]
x_hgg[igraph] = sorted(x_hgg[igraph])
x_hgg[igraph] = map(float, x_hgg[igraph])
y_hgg[igraph] = map(float, y_hgg[igraph])
x_hgg[igraph] = numpy.asarray(x_hgg[igraph], dtype='float')
y_hgg[igraph] = numpy.asarray(y_hgg[igraph], dtype='float')
y_low[igraph] = [b for (a,b) in sorted(zip(x_low[igraph], y_low[igraph]))]
x_low[igraph] = sorted(x_low[igraph])
x_low[igraph] = map(float, x_low[igraph])
y_low[igraph] = map(float, y_low[igraph])
x_low[igraph] = numpy.asarray(x_low[igraph], dtype='float')
y_low[igraph] = numpy.asarray(y_low[igraph], dtype='float')
y_high[igraph] = [b for (a,b) in sorted(zip(x_high[igraph], y_high[igraph]))]
x_high[igraph] = sorted(x_high[igraph])
x_high[igraph] = map(float, x_high[igraph])
y_high[igraph] = map(float, y_high[igraph])
x_high[igraph] = numpy.asarray(x_high[igraph], dtype='float')
y_high[igraph] = numpy.asarray(y_high[igraph], dtype='float')
# Now with the plot
xAxisMin = .250
xAxisMax = 1.140
canvasSplit = 35.
if oldPlot:
canvasSplit = 30.
wPad = .82
gROOT.ProcessLine(".x setTDRStyleMP.C")
TGaxis.SetMaxDigits(3)
color = [ROOT.kRed, ROOT.kMagenta, ROOT.kBlue, ROOT.kCyan+3, ROOT.kGreen+2, ROOT.kYellow+3]
c1 = TCanvas()
pad1 = TPad("pad1", "pad1", 0., 0., canvasSplit / 100., 1.)
pad2 = TPad("pad2", "pad2", canvasSplit / 100., 0., 1., 1.)
if showSingleHiggs:
pad1.Draw()
else:
pad2.SetPad(0., 0., 1., 1.)
pad2.ResizePad()
pad2.Draw()
# gStyle.SetPadBorderMode(0)
# gStyle.SetFrameBorderMode(0)
small = .01
# c1.Divide(2,1,small,small)
for igraph in range(1,7):
pad2.cd()
# c1.cd(2)
if showSingleHiggs: pad2.SetLeftMargin(small);
if showSingleHiggs: pad2.SetRightMargin(0.05 / (100. * wPad - canvasSplit) * 100.)
gr_low[igraph] = TGraph(n_low[igraph], x_low[igraph], y_low[igraph])
gr_low[igraph].SetName("low_" + str(igraph))
gr_low[igraph].SetMarkerStyle(20)
gr_low[igraph].SetMarkerColor( color[igraph -1] )
gr_low[igraph].SetLineColor( color[igraph -1] )
gr_low[igraph].SetTitle("")
gr_low[igraph].GetXaxis().SetTitle("m_{X} (TeV)")
if not showSingleHiggs: gr_low[igraph].GetYaxis().SetTitle("Signal selection efficiency (%)")
gr_low[igraph].GetYaxis().SetTickLength(0.03 / (100. - canvasSplit) * 100.) # due to assymmetric canvas...
if showSingleHiggs: gr_low[igraph].GetYaxis().SetLabelSize(0.)
gr_low[igraph].GetXaxis().SetLabelSize(25)
gr_low[igraph].SetMinimum(0.)
gr_low[igraph].SetMaximum(100.)
gr_low[igraph].GetXaxis().SetLimits(xAxisMin, xAxisMax)
if igraph == 1:
gr_low[igraph].Draw("ALP")
else:
gr_low[igraph].Draw("LP")
gr_high[igraph] = TGraph(n_high[igraph], x_high[igraph], y_high[igraph])
gr_high[igraph].SetName("low_" + str(igraph))
gr_high[igraph].SetMarkerStyle(21)
gr_high[igraph].SetMarkerColor( color[igraph -1] )
gr_high[igraph].SetLineColor( color[igraph -1] )
gr_high[igraph].SetTitle("")
gr_high[igraph].GetXaxis().SetTitle("m_{X} (TeV)")
if not showSingleHiggs: gr_high[igraph].GetYaxis().SetTitle("Signal selection efficiency (%)")
gr_high[igraph].GetXaxis().SetTickLength(0.03 / (100. - canvasSplit) * 100.) # due to assymmetric canvas...
gr_high[igraph].GetXaxis().SetNdivisions(510)
gr_high[igraph].GetYaxis().SetTickLength(0.03 / (100. - canvasSplit) * 100.) # due to assymmetric canvas...
gr_high[igraph].SetMinimum(0.)
gr_high[igraph].SetMaximum(100.)
gr_high[igraph].GetXaxis().SetLimits(xAxisMin, xAxisMax)
gr_high[igraph].Draw("LP")
# c1.cd(1)
pad1.cd()
pad1.SetRightMargin(small)
pad1.SetLeftMargin(0.13 / canvasSplit * 100.)
gr_hgg[igraph] = TGraph(n_hgg[igraph], x_hgg[igraph], y_hgg[igraph])
gr_hgg[igraph].SetName("low_" + str(igraph))
gr_hgg[igraph].SetMarkerStyle(20)
gr_hgg[igraph].SetMarkerColor( color[igraph -1] )
gr_hgg[igraph].SetLineColor( color[igraph -1] )
gr_hgg[igraph].SetTitle("")
gr_hgg[igraph].GetXaxis().SetLabelSize(19)
gr_hgg[igraph].GetXaxis().SetLimits(-.2, 6.2)
a = 15
b = 5
if oldPlot:
gr_hgg[igraph].GetXaxis().SetLimits(-.2, 4.2)
a = 21
b = 5
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 0) + b, "ggH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 1) + b, "qqH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 2) + b, "WH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 3) + b, "ZH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 4) + b, "ttH")
if not oldPlot:
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 4) + b, "bbH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 5) + b, "ttH")
gr_hgg[igraph].GetXaxis().SetBinLabel(a * (0 + 6) + b, "ggHH")
gr_hgg[igraph].GetYaxis().SetTitle("Signal selection efficiency (%)")
gr_hgg[igraph].GetYaxis().SetTitleOffset(1.04 / canvasSplit * 100.)
gr_hgg[igraph].GetYaxis().SetTickLength(0.03 / canvasSplit * 100.) # due to assymmetric canvas...
gr_hgg[igraph].SetMinimum(0.)
gr_hgg[igraph].SetMaximum(100.)
if igraph == 1:
gr_hgg[igraph].Draw("ALP")
else:
gr_hgg[igraph].Draw("LP")
c1.cd()
legend = TLegend(0.55, 0.16, 0.80, 0.36, "")
# legend.SetNColumns(2)
legend.SetTextSize(0.03)
# legend.SetTextFont(63) # precision 3
# legend.SetTextSize(18) # in pixel
legend.SetFillColor(ROOT.kWhite)
legend.SetLineColor(ROOT.kWhite)
legend.SetShadowColor(ROOT.kWhite)
legend.SetTextFont(42) # helvetica
legend.AddEntry(gr_low[1].GetName(), "# #gamma #geq 2, # jets #geq 2", "lp")
legend.AddEntry(gr_low[2].GetName(), "#gamma preselection + p_{T} cuts", "lp")
legend.AddEntry(gr_low[3].GetName(), "#gamma ID", "lp")
legend.AddEntry(gr_low[4].GetName(), "jet ID", "lp")
legend.AddEntry(gr_low[5].GetName(), "at least one bjet", "lp")
legend.AddEntry(gr_low[6].GetName(), "mass cuts", "lp")
legend.Draw()
latexLabel = TLatex()
# latexLabel.SetTextSize(0.03)
# latexLabel.SetTextFont(63)
# latexLabel.SetTextSize(18) # in pixel
latexLabel.SetTextSize(0.75 * c1.GetTopMargin())
latexLabel.SetNDC()
# latexLabel.DrawLatex(0.25, 0.96, "CMS work in progress #sqrt{s} = 8 TeV L = 19.7 fb^{-1}")
latexLabel.SetTextFont(42) # helvetica
latexLabel.DrawLatex(0.83, 0.96, "(8 TeV)")
latexLabel.SetTextFont(61) # helvetica bold face
latexLabel.DrawLatex(0.13, 0.96, "CMS")
latexLabel.SetTextFont(52) # helvetica italics
latexLabel.DrawLatex(0.22, 0.96, "Simulation Supplementary")
latexLabel.SetTextAngle(45)
c1.Print(path.join(args.selectionFolder, args.plotName + ".pdf"))
c1.Print(path.join(args.selectionFolder, args.plotName + ".png"))
c1.Print(path.join(args.selectionFolder, args.plotName + ".root"))
del c1