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bs_test_lpc.py
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bs_test_lpc.py
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# Goal:
# (1) Plot N-1 for tagging variables
# (2) Calculate HEM corrected mt vs mtw distribution
# (3) Save a ttree as a snapshot for isolating signal after successful top tag
from HAMMER.Analyzer import *
from HAMMER.Tools.Common import *
from optparse import OptionParser
import ROOT
import multiprocessing, time, glob
parser = OptionParser()
parser.add_option('-i', '--input', metavar='FILE', type='string', action='store',
default = '',
dest = 'input',
help = 'A root file or text file with multiple root file locations to analyze')
parser.add_option('-y', '--year', metavar='FILE', type='string', action='store',
default = '',
dest = 'year',
help = 'Year')
parser.add_option('-c', '--config', metavar='FILE', type='string', action='store',
default = 'bstar_config.json',
dest = 'config',
help = 'Configuration file in json format with xsecs, cuts, etc that is interpreted as a python dictionary')
parser.add_option('--HEM',metavar='BOOL',action='store_true',
default = False,
dest = 'HEM',
help = 'Use HEM corrected pt for testing')
parser.add_option('--EXCESSISO',metavar='BOOL',action='store_true',
default = False,
dest = 'EXCESSISO',
help = 'Make plots to isolate excess')
(options, args) = parser.parse_args()
start_time = time.time()
cc = CommonCscripts()
CompileCpp(cc.deltaPhi)
CompileCpp(cc.vector)
CompileCpp(cc.invariantMass)
CompileCpp('bstar.cc')
#########
# Setup #
#########
def main(options):
ROOT.ROOT.EnableImplicitMT(4)
a = analyzer(options.input)
setname = options.input.split('/')[-1].replace('.txt','').replace('.root','').replace('_loc','')
print 'Setname: %s'%setname
if options.HEM: hem_str = '_HEM'
else: hem_str = ''
config = openJSON(options.config)
xsec, lumi = 1., 1.
if setname in config['XSECS'].keys() and not a.isData:
xsec = config['XSECS'][setname]
lumi = config['lumi']
if "16" in options.year: year = "16"
elif "17" in options.year: year = "17"
elif "18" in options.year: year = "18"
cuts = config['CUTS'][year]
if not a.isData: norm = (xsec*lumi)/a.genEventCount
else: norm = 1.
flags = ["Flag_goodVertices",
"Flag_globalTightHalo2016Filter",
"Flag_eeBadScFilter",
"Flag_HBHENoiseFilter",
"Flag_HBHENoiseIsoFilter",
"Flag_ecalBadCalibFilter",
"Flag_EcalDeadCellTriggerPrimitiveFilter"]
flags = a.FilterColumnNames(flags)
flag_string = '('
for f in flags: flag_string += f +' && '
flag_string = flag_string[:-4] + ') == 1'
##################################
# Start an initial group of cuts #
##################################
# Trigger
if a.isData:
if year == '16': trigs = ["HLT_PFHT800","HLT_PFHT900","HLT_PFJet450"]
else: trigs = ["HLT_PFHT1050","HLT_PFJet500","HLT_AK8PFJet380_TrimMass30","HLT_AK8PFJet400_TrimMass30"]
trigs = a.FilterColumnNames(trigs)
trig_string = ''
for t in trigs: trig_string += t + ' && '
a.Cut('trigger',trig_string[:-4])
# Filters and nJets
a.Cut("filters",flag_string)
a.Cut("nFatJets_cut","nFatJet > 1")
# HEM re-ordering if needed
if options.HEM:
a.Define('HEMstuff','HEMreweight(FatJet_phi,FatJet_eta,FatJet_pt)') # Returns a vector of the indices of jets after pt reweighting
a.Define('myJet_pt','UnpackHEMpt(HEMstuff)')
a.Define('HEM_index','UnpackHEMidx(HEMstuff)')
a.Define("jetIdx","hemispherize(FatJet_phi, FatJet_jetId, HEM_index)")
else:
a.Define('myJet_pt','FatJet_pt')
a.Define("jetIdx","hemispherize(FatJet_phi, FatJet_jetId)")
a.Cut("hemispherize","(jetIdx[0] != -1)&&(jetIdx[1] != -1)")
a.Cut("pt_cut","myJet_pt[jetIdx[0]] > 400 && myJet_pt[jetIdx[1]] > 400")
a.Cut("eta_cut","abs(FatJet_eta[jetIdx[0]]) < 2.4 && abs(FatJet_eta[jetIdx[1]]) < 2.4")
branches_to_save = [
'nFatJet',
'FatJet_pt',
'myJet_pt',
'FatJet_eta',
'FatJet_phi',
'FatJet_msoftdrop',
'FatJet_tau.',
'FatJet_subJetIdx.',
'nSubJet',
'SubJet_btagDeepB',
'\bjetIdx'
]
#################################
# Build some variables for jets #
#################################
# Wtagging decision logic
# Returns 0 for no tag, 1 for lead tag, 2 for sublead tag, and 3 for both tag (which is physics-wise equivalent to 2)
wtag_str = "1*Wtag(FatJet_tau2[jetIdx[0]]/FatJet_tau1[jetIdx[0]],0,{0}, FatJet_msoftdrop[jetIdx[0]],65,105) + 2*Wtag(FatJet_tau2[jetIdx[1]]/FatJet_tau1[jetIdx[1]],0,{0}, FatJet_msoftdrop[jetIdx[1]],65,105)".format(cuts['tau21'])
jets = VarGroup('jets')
jets.Add('wtag_bit', wtag_str)
jets.Add('top_bit', '(wtag_bit & 2)? 0: (wtag_bit & 1)? 1: -1') # (if wtag==3 or 2 (subleading w), top_index=0) else (if wtag==1, top_index=1) else (-1)
jets.Add('top_index', 'top_bit >= 0 ? jetIdx[top_bit] : -1')
jets.Add('w_index', 'top_index == 0 ? jetIdx[1] : top_index == 1 ? jetIdx[0] : -1')
# Calculate some new comlumns that we'd like to cut on (that were costly to do before the other filtering)
jets.Add("lead_vect", "analyzer::TLvector(myJet_pt[jetIdx[0]],FatJet_eta[jetIdx[0]],FatJet_phi[jetIdx[0]],FatJet_msoftdrop[jetIdx[0]])")
jets.Add("sublead_vect","analyzer::TLvector(myJet_pt[jetIdx[1]],FatJet_eta[jetIdx[1]],FatJet_phi[jetIdx[1]],FatJet_msoftdrop[jetIdx[1]])")
jets.Add("deltaY", "lead_vect.Rapidity()-sublead_vect.Rapidity()")
jets.Add("mtw", "analyzer::invariantMass(lead_vect,sublead_vect)")
noJetTagging = a.Apply([jets])
#########
# N - 1 #
#########
plotting_vars = VarGroup('plotting_vars') # assume leading is top and subleading is W
plotting_vars.Add("mtop", "FatJet_msoftdrop[jetIdx[0]]")
plotting_vars.Add("mW", "FatJet_msoftdrop[jetIdx[1]]")
plotting_vars.Add("tau32", "FatJet_tau3[jetIdx[0]]/FatJet_tau2[jetIdx[0]]")
plotting_vars.Add("subjet_btag", "max(SubJet_btagDeepB[FatJet_subJetIdx1[jetIdx[0]]],SubJet_btagDeepB[FatJet_subJetIdx2[jetIdx[0]]])")
plotting_vars.Add("tau21", "FatJet_tau2[jetIdx[1]]/FatJet_tau1[jetIdx[1]]")
N_cuts = CutGroup('Ncuts') # cuts
N_cuts.Add("deltaY_cut", "abs(deltaY)<1.6")
N_cuts.Add("mtop_cut", "(mtop > 105.)&&(mtop < 220.)")
N_cuts.Add("mW_cut", "(mW > 65.)&&(mW < 105.)")
N_cuts.Add("tau32_cut", "(tau32 > 0.0)&&(tau32 < %s)"%(cuts['tau32']))
N_cuts.Add("subjet_btag_cut", "(subjet_btag > %s)&&(subjet_btag < 1.)"%(cuts['sjbtag']))
N_cuts.Add("tau21_cut", "(tau21 > 0.0)&&(tau21 < %s)"%(cuts['tau21']))
# Organize N-1 of tagging variables when assuming top is always leading
nodeToPlot = a.Apply([plotting_vars])
nminus1Nodes = a.Nminus1(nodeToPlot,N_cuts)
nminus1Hists = HistGroup('nminus1Hists')
binning = {
'mtop': [25,50,300],
'mW': [25,30,270],
'tau32': [20,0,1],
'tau21': [20,0,1],
'subjet_btag': [20,0,1],
'deltaY': [40,-2.0,2.0]
}
# Add hists to group and write out
for nkey in nminus1Nodes.keys():
if nkey == 'full': continue
var = nkey.replace('_cut','').replace('minus_','')
#hist = nminus1Nodes[nkey].DataFrame.Histo1D((var,var,binning[var][0],binning[var][1],binning[var][2]),var)
#nminus1Hists.Add(var,hist)
a.SetActiveNode(noJetTagging)
# # Snapshot while we're here
#branches_to_save.extend(plotting_vars.keys()+jets.keys())
#noJetTagging.Snapshot(branches_to_save,'rootfiles/%s_%s_nojettag%s.root'%(setname,year,hem_str),'snapshot')
########################
# Cut on new variables #
########################
# Select real W and top
tagging_vars = VarGroup('tagging_vars')
tagging_vars.Add("mtop", "top_index > -1 ? FatJet_msoftdrop[top_index] : -10")
tagging_vars.Add("mW", "w_index > -1 ? FatJet_msoftdrop[w_index]: -10")
tagging_vars.Add("tau32", "top_index > -1 ? FatJet_tau3[top_index]/FatJet_tau2[top_index]: -1")
tagging_vars.Add("subjet_btag", "top_index > -1 ? max(SubJet_btagDeepB[FatJet_subJetIdx1[top_index]],SubJet_btagDeepB[FatJet_subJetIdx2[top_index]]) : -1")
tagging_vars.Add("tau21", "w_index > -1 ? FatJet_tau2[w_index]/FatJet_tau1[w_index]: -1")
toptag_str = "TopTag(tau32,0,{0}, subjet_btag,{1},1, mtop,50,1000)==1".format(cuts['tau32'],cuts['sjbtag'])
tagging_vars.Add("wtag",'wtag_bit>0')
tagging_vars.Add("top_tag",toptag_str)
a.Apply([tagging_vars])
branches_to_save.extend(tagging_vars.keys()+jets.keys())
snapshot_node = a.GetActiveNode()
#snapshot_node.Snapshot(branches_to_save,'rootfiles/%s_%s_presel%s.root'%(setname,year,hem_str),'snapshot',lazy=True if options.EXCESSISO else False)
#f_out = ROOT.TFile.Open('rootfiles/%s_%s_presel%s.root'%(setname,year,hem_str),'UPDATE')
#nminus1Hists.Do('Write')
#f_out.Close()
# Write cut on new column
jet_sel = CutGroup('jet_sel')
jet_sel.Add('wtag_cut','wtag')
jet_sel.Add("mtw_cut","mtw>1000.")
jet_sel.Add('deltaY_cut','abs(deltaY)<1.6')
#########
# Apply #
#########
a.Apply([jet_sel])
# Finally discriminate on top tag
final = a.Discriminate("top_tag_cut","top_tag==1")
#CutflowTxt('txtfiles/%s_%s.txt'%(setname,year),final["pass"])
# h = final["pass"].DataFrame.Histo2D(('MtwvMtPass','MtwvMtPass',60, 50, 350, 70, 500, 4000),'mtop','mtw')
# h.Draw('lego')
####################
# Signal isolation #
####################
if options.EXCESSISO:
a.SetActiveNode(final['pass'])
a.Cut('signal_iso','mtop > 160. && mtop < 190. && mtw > 2000 && mtw < 3000')
kin_binning = {
'myJet_pt[top_index]': ROOT.RDF.TH1DModel('pt_top','p_{T}_{top}',32,400,2000),
'myJet_pt[w_index]': ROOT.RDF.TH1DModel('pt_w','p_{T}_{W}',32,400,2000),
'FatJet_eta[top_index]':ROOT.RDF.TH1DModel('eta_top','#eta_{top}',48,-2.4,2.4),
'FatJet_eta[w_index]': ROOT.RDF.TH1DModel('eta_w','#eta_{W}',48,-2.4,2.4),
'FatJet_phi[top_index]':ROOT.RDF.TH1DModel('phi_top','#phi_{top}',50,-3.1415,3.1415),
'FatJet_phi[w_index]': ROOT.RDF.TH1DModel('phi_w','#phi_{W}',50,-3.1415,3.1415),
'mtw': ROOT.RDF.TH1DModel('mtw','m_{tW}',50,2000,3000),
'mtop': ROOT.RDF.TH1DModel('mtop','m_{t}',60,160,190),
'mW': ROOT.RDF.TH1DModel('mW','m_{W}',25,30,270)
}
for k in kin_binning:
if '[' in k:
a.Define(kin_binning[k].fName,k)
node = a.GetActiveNode()
excessHists = HistGroup('excessHists')
for k in kin_binning:
binning = kin_binning[k]
if '[' in k: var = binning.fName
else: var = k
hist = node.DataFrame.Histo1D(binning,var)
excessHists.Add(var,hist)
f_out = ROOT.TFile.Open('rootfiles/%s_%s%s.root'%(setname,year,hem_str),'UPDATE')
excessHists.Do('Write')
f_out.Close()
# a.PrintNodeTree('test',verbose=False)
#########################
# Setup multiprocessing #
#########################
# ex. 4 processes (from multiprocessing) with 4 threads each (EnableImplicitMT) for 16 thread processor
inputs = args
if len(inputs) > 0:
# Will use args as input for multiprocessing instead of options.input (for single process)
print('Using multiprocessing')
pool = multiprocessing.Pool(processes=min(4,len(inputs)))
process_args = []
for i in inputs:
sub_options = copy.deepcopy(options)
sub_options.input = i
process_args.append(sub_options)
pool.map(main,process_args)
else:
main(options)
print "Total time: "+str((time.time()-start_time)/60.) + ' min'