def load(cms_base, energy):
print("load model py", energy)
model = NonResonantModel()
if energy == 13:
print("load model for 13 TeV")
dumb = model.ReadCoefficients(
cms_base +
"/src/HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M_costHHSim_19-4.txt"
)
if energy == 14:
print("load model for 14 TeV")
model.ReadCoefficients2(14, cms_base)
return model
'--verbose',
dest='verbose',
action='store_true',
default=False,
required=False,
help='R|Verbose output',
)
args = parser.parse_args()
era = args.era
analysis_type = args.type
if args.verbose:
logging.getLogger().setLevel(logging.DEBUG)
cmssw_base = os.environ['CMSSW_BASE']
model = NonResonantModel()
coef_file = os.path.join(
cmssw_base,
"src/HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M_costHHSim_19-4.txt"
)
assert (os.path.isfile(coef_file))
coefs = model.ReadCoefficients(coef_file)
hist_file = os.path.join(
cmssw_base,
"src/Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root")
assert (os.path.isfile(hist_file))
hist_title = "H1bin4"
norm = []
for scan_idx in range(len(klJHEP)):
norm.append(
def AddReWeightBranch(intree, newfilename, KL, CT, CG, C2G, C2T):
cmssw_base = os.environ['CMSSW_BASE']
outfile = TFile(newfilename, "RECREATE")
model = NonResonantModel()
# dumb = model.ReadCoefficients(cmssw_base+"/src/HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M.txt")
dumb = model.ReadCoefficients(
cmssw_base +
"/src/HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M_costHHSim_19-4.txt"
)
# histfilename=cmssw_base+"/src/HiggsAnalysis/bbggLimits/data/NR_AnalyticalWeight/Hist2DSum_V0_SM_box.root"
histfilename = cmssw_base + "/src/HHStatAnalysis/Support/NonResonant/HistSum2D_4b_rebin_SimCostHH_19-4.root"
# histtitle= "SumV0_AnalyticalBinExt"
histtitle = "SumV0_AnalyticalBinExtSimCostHH"
fileHH = TFile(histfilename)
sumHAnalyticalBin = fileHH.Get(histtitle)
print KL, CT, CG, C2G, C2T
#need to turn kappa top = 0 to kappa top = very small, otherwise weights are 0
if abs(CT) < 0.00001: CT = 0.00001
calcSumOfWeights = model.getNormalization(KL, CT, C2T, CG, C2G,
histfilename, histtitle)
outtree = intree.CloneTree(0)
new_evWeight = array('f', [0])
NR_weight = array('f', [0])
NR_kl = array('f', [0])
NR_ct = array('f', [0])
NR_cg = array('f', [0])
NR_c2g = array('f', [0])
NR_c2t = array('f', [0])
_new_evWeight = outtree.Branch('new_evWeight', new_evWeight,
'new_evWeight/F')
_NR_weight = outtree.Branch('NR_weight', NR_weight, 'NR_weight/F')
_NR_kl = outtree.Branch('NR_kl', NR_kl, 'NR_kl/F')
_NR_ct = outtree.Branch('NR_ct', NR_ct, 'NR_ct/F')
_NR_cg = outtree.Branch('NR_cg', NR_cg, 'NR_cg/F')
_NR_c2t = outtree.Branch('NR_c2t', NR_c2t, 'NR_c2t/F')
_NR_c2g = outtree.Branch('NR_c2g', NR_c2g, 'NR_c2g/F')
nentries = intree.GetEntries()
for i in range(0, nentries):
if i % 1000 == 0: print i
intree.GetEntry(i)
NR_kl[0] = KL
NR_ct[0] = CT
NR_cg[0] = CG
NR_c2t[0] = C2T
NR_c2g[0] = C2G
mhh = intree.gen_mHH
cost = intree.gen_cosTheta
mhhcost = [mhh, cost, 0, 0]
bmhh = sumHAnalyticalBin.GetXaxis().FindBin(mhh)
bcost = sumHAnalyticalBin.GetYaxis().FindBin(abs(cost))
effSumV0 = sumHAnalyticalBin.GetBinContent(bmhh, bcost)
#factor of 6 comes from the fact that the normalization of the weights has been calculated
#with 300k events per node point, in bbgg we only have 6, so we are off by a factor of 30/5=6
weight = 6 * model.getScaleFactor(mhh, cost, KL, CT, C2T, CG, C2G,
effSumV0, calcSumOfWeights)
NR_weight[0] = weight
if 'bb' in str(intree.GetName()):
new_evWeight[0] = (intree.genTotalWeight) * weight
else:
new_evWeight[0] = (
intree.evWeight
) * weight * 50000. #need to multiply by 50k since, in the limit trees, evWeight has been normalized by the #evs in MC
outtree.Fill()
outfile.cd()
outtree.Write()
outfile.Close()
def load(coefFile):
model = NonResonantModel()
model.ReadCoefficients(coefFile)
return model
import os, sys, time, math
import shutil, subprocess
from HHStatAnalysis.AnalyticalModels.NonResonantModel import NonResonantModel
# ROOT imports
import ROOT
from ROOT import TChain, TH1F, TFile, vector, gROOT, TTree
##################
# read the histos tree and contruct the tree of the relevant variables
#path = "/lustre/cmswork/hh/alp_baseSelector/"
path = "/afs/cern.ch/user/l/lcadamur/public/4Xanda/"
#path = "/afs/cern.ch/user/a/andrey/public/HH/LT-Jan25-APZ_LowMass/"
outpath = "/afs/cern.ch/user/a/acarvalh/public/toTATABB/"
data = "../../../HHStatAnalysis/AnalyticalModels/data/"
model = NonResonantModel()
# obtaining BSM/SM coeficients
dumb = model.ReadCoefficients(
"../../../HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_klkt.txt")
fileoutput = "events_SumV0_afterBaseline.root"
files = []
endfile = ".root"
files.append("tree_HH_SM_tautau")
#files.append("LT_output_GluGluToHHTo2B2G_node_box")
#for ifile in range(2,14) : files.append("LT_output_GluGluToHHTo2B2G_node_"+str(ifile))
# to analytical re
# We use only SM as input events by now
# read the 2D histo referent to the sum of events
fileHH = ROOT.TFile("../../../Support/NonResonant/HistSum2D_SMonly.root")
sumHAnalyticalBin = fileHH.Get("SumV0_AnalyticalBin")
if not os.path.isfile(input_fn):
raise ValueError("No such file: %s" % input_fn)
output_dir = os.path.dirname(output_fn)
if not os.path.isdir(output_dir):
logging.warning("Creating directory: {}".format(output_dir))
os.makedirs(output_dir)
input_file = ROOT.TFile.Open(input_fn, 'read')
assert (input_file)
input_tree = input_file.Get('Events')
mhh_br_name = 'mHH_lhe'
cost_br_name = 'cosThetaStar_lhe'
model = NonResonantModel()
denominator_process = Hist2D(model.binGenMHH,
model.binGenCostS,
name=process_name)
denominator_category = Hist2D(model.binGenMHH,
model.binGenCostS,
name=category_name)
nof_events = input_tree.GetEntries()
logging.debug("Input file {} has {} events".format(input_fn, nof_events))
has_evt_brs = not bool({mhh_br_name, cost_br_name} -
set(br.GetName()
for br in input_tree.GetListOfBranches()))
if has_evt_brs:
logging.debug(
def main():
#if 1 > 0 :
# declare the 2D ===> should be global variable
model = NonResonantModel()
# obtaining BSM/SM coeficients
#dumb = model.ReadCoefficients("../data/coefficientsByBin_A1A3A7.txt")
dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M_costHHSim_19-4.txt")
#dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M.txt")
counteventSM=0
sumWeight=0
# now loop over events, calculate weights using the coeffitients and plot histograms
# We sum SM + box + the benchmarks from 2-13
# read the 2D histo referent to the sum of events
#fileHH=ROOT.TFile("../../../Analysis/Support/NonResonant/Hist2DSum_V0_SM_box.root")
#sumHAnalyticalBin = fileHH.Get("SumV0_AnalyticalBin")
histfilename="../../../Analysis/Support/NonResonant/HistSum2D_4b_rebin_SimCostHH_19-4.root"
#histtitle= "SumV0_AnalyticalBinExt" #
histtitle= "SumV0_AnalyticalBinExtSimCostHH" #
fileHH=ROOT.TFile(histfilename)
sumHAnalyticalBin = fileHH.Get(histtitle)
calcSumOfWeights = model.getNormalization(kl, kt,c2,cg,c2g,histfilename,histtitle) # this input is flexible, tatabb may have only the SM
# print "sum of weights calculated" , calcSumOfWeights
# read the events
pathBenchEvents="/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" #"/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" # events to reweight
file=ROOT.TFile(pathBenchEvents+"events_SumV0.root")
tree=file.Get("treeout")
nev = tree.GetEntries()
# declare the histograms
CalcMhh = np.zeros((nev))
CalcCost = np.zeros((nev))
CalcPtH = np.zeros((nev))
CalcPtHH = np.zeros((nev))
CalcWeight = np.zeros((nev))
CalcPtHgg = np.zeros((nev))
CalcMhhReco = np.zeros((nev))
CalcMXReco = np.zeros((nev))
countevent = 0
#for kll in range(-5,5) : model.getNormalization(kll, kt,sumHBenchBin)
for iev in range(0,nev) :
tree.GetEntry(iev)
mhh = tree.Genmhh
cost = tree.GenHHCost
mhhcost= [mhh,cost,0,0] # to store [mhh , cost] of that event
# find the Nevents from the sum of events on that bin
bmhh = sumHAnalyticalBin.GetXaxis().FindBin(mhh)
bcost = sumHAnalyticalBin.GetYaxis().FindBin(abs(cost))
#print (tree.GenHHCost,cost,bcost)
effSumV0 = sumHAnalyticalBin.GetBinContent(bmhh,abs(bcost)) # quantity of simulated events in that bin (without cuts)
weight = model.getScaleFactor(mhh , cost,kl, kt,c2,cg,c2g, effSumV0 , calcSumOfWeights) # model.effSM,model.MHH,model.COSTS,model.A1,model.A3,model.A7, effSumV0)
#############################################
# fill histograms to test
#############################################
if weight > 0:
#print countevent
CalcMhh[countevent] = float(mhhcost[0])
CalcCost[countevent] = float(abs(mhhcost[1]))
CalcPtH[countevent] = float(mhhcost[2])
CalcPtHH[countevent] = float(mhhcost[3])
CalcWeight[countevent] = weight
countevent+=1
sumWeight+=weight
print "plotted histogram reweighted from ",countevent," events, ", float(100*(nev-countevent)/nev)," % of the events was lost in empty bins in SM simulation"
print "sum of weights",sumWeight
############################################################################################################################
# Draw test histos
###############################################
drawtest =0
nevtest=50000
if kl == 1 and kt == 1 and c2 ==0 and cg == 0 and c2g ==0 :
filne = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_0.lhe.decayed" # 0 is SM
nevtest = 100000
drawtest = 1
# BSM events
pathBSMtest="/afs/cern.ch/work/a/acarvalh/generateHH/asciiHH_tofit/GF_HH_toRecursive/" # events of file to superimpose a test
# see the translation of coefficients for this last on: If you make this script smarter (to only read files we ask to test) you can implement more
# https://github.com/acarvalh/generateHH/blob/master/fit_GF_HH_lhe/tableToFitA3andA7.txt
if kl == -10 and kt == 0.5 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_42.lhe.decayed"
if kl == 0.0001 and kt == 2.25 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_9.lhe.decayed"
if kl == 2.5 and kt == 1.0 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_60.lhe.decayed"
klJHEP=[1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0]
ktJHEP=[1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0]
c2JHEP=[0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0]
cgJHEP=[0.0, 0.0, 0.6, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0]
c2gJHEP=[0.0, 0.0, 1.0, -0.8, 0.0, -1.0, -0.2,-0.2, 1.0, 0.6, 0.0, -1.0, 0.0]
# python recastHH.py --kl 7.5 --kt 1 --c2 -1
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.5 --cg -0.8 --c2g 0.6
# python recastHH.py --kl 1.0 --kt 1.0 --c2 -1.5 --cg 0.0 --c2g -0.8
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.0 --cg 0.8 --c2g -1.0
# python recastHH.py --kl 1.0 --kt 1.0 --c2 1.0 --cg -0.6 --c2g 0.6
for sam in range(0,13):
#print (sam, ktJHEP[sam] , kt , klJHEP[sam] , c2 ,c2JHEP[sam] , cg , cgJHEP[sam] , c2g , c2gJHEP[sam])
if kl == klJHEP[sam] and kt == ktJHEP[sam] and c2 ==c2JHEP[sam] and cg == cgJHEP[sam] and c2g ==c2gJHEP[sam] :
print sam
filne="/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_"+str(sam)+".lhe.decayed"
nevtest=100000
drawtest = sam
############################################################################################################################
CalcMhhTest = np.zeros((nevtest))
CalcCostTest = np.zeros((nevtest))
CalcPtHTest = np.zeros((nevtest))
CalcPtHHTest = np.zeros((nevtest))
CalcPtHggTest = np.zeros((nevtest))
CalcMhhRecoTest = np.zeros((nevtest))
CalcMXRecoTest = np.zeros((nevtest))
if options.doPlot :
print "draw plain histogram to test"
model.LoadTestEvents(CalcMhhTest,CalcCostTest,CalcPtHTest,CalcPtHHTest,filne)
model.plotting(kl,kt,c2,cg,c2g,CalcMhh,CalcCost,CalcPtHgg,CalcPtHH,CalcMhhReco,CalcMXReco,CalcWeight,CalcMhhTest,CalcCostTest,CalcPtHggTest,CalcPtHHTest,CalcMhhRecoTest,CalcMXRecoTest,drawtest)
def __init__(self,
era,
use_lhe=True,
use_gen=False,
compute_weights=False):
self.use_gen = use_gen
self.use_lhe = use_lhe
assert (self.use_gen or self.use_lhe)
self.compute_weights = compute_weights
if self.use_gen:
print(
"Computing di-Higgs variables from generator-level Higgs bosons"
)
else:
print(
"NOT computing di-Higgs variables from generator-level Higgs bosons"
)
if self.use_lhe:
print(
"Computing di-Higgs variables from LHE parton-level Higgs bosons"
)
else:
print(
"NOT computing di-Higgs variables from LHE parton-level Higgs bosons"
)
if self.compute_weights:
print("Also computing the di-Higgs event-level weights")
else:
print("NOT computing the di-Higgs event-level weights")
mHH_name = "mHH"
cosThetaStar_name = "cosThetaStar"
gen_suffix = "gen"
lhe_suffix = "lhe"
self.mHH_genName = "{}_{}".format(mHH_name, gen_suffix)
self.cosThetaStar_genName = "{}_{}".format(cosThetaStar_name,
gen_suffix)
self.mHH_lheName = "{}_{}".format(mHH_name, lhe_suffix)
self.cosThetaStar_lheName = "{}_{}".format(cosThetaStar_name,
lhe_suffix)
weightBaseName = "HHWeight"
BMName = "BM"
scanName = "scan"
self.nofWeightsBMName = "n{}_{}".format(weightBaseName, BMName)
self.weightBM_genName = "{}_{}_{}".format(weightBaseName, BMName,
gen_suffix)
self.weightBM_lheName = "{}_{}_{}".format(weightBaseName, BMName,
lhe_suffix)
self.nofWeightsScanName = "n{}_{}".format(weightBaseName, scanName)
self.weightScan_genName = "{}_{}_{}".format(weightBaseName, scanName,
gen_suffix)
self.weightScan_lheName = "{}_{}_{}".format(weightBaseName, scanName,
lhe_suffix)
os.environ["MKL_NUM_THREADS"] = "1"
cmssw_base = os.path.join(os.environ['CMSSW_BASE'], "src")
coeffFile = os.path.join(
cmssw_base,
"HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M_costHHSim_19-4.txt"
)
self.model = None
if self.compute_weights:
assert (os.path.isfile(coeffFile))
self.model = NonResonantModel()
self.model.ReadCoefficients(coeffFile)
denominatorHistogramName = "denominator_reweighting"
denominatorFile = os.path.join(
cmssw_base,
"hhAnalysis/bbww/data/denominator_reweighting_bbvv_{}.root".format(
era))
self.denominatorFilePtr = None
self.denominatorHistogram = None
if self.compute_weights:
assert (os.path.isfile(denominatorFile))
self.denominatorFilePtr = ROOT.TFile.Open(denominatorFile, "READ")
assert (self.denominatorFilePtr)
assert (denominatorHistogramName in [
key.GetName()
for key in self.denominatorFilePtr.GetListOfKeys()
])
self.denominatorHistogram = self.denominatorFilePtr.Get(
denominatorHistogramName)
scanFile = os.path.join(cmssw_base, "hhAnalysis/bbww/data/kl_scan.dat")
self.nofWeightsScan = 0
self.klScan = []
self.ktScan = []
self.c2Scan = []
self.cgScan = []
self.c2gScan = []
self.BM_klScan = []
self.Norm_klScan = []
if self.compute_weights:
assert (os.path.isfile(scanFile))
with open(scanFile, 'r') as scanFileObj:
for line in scanFileObj:
line_split = line.rstrip('\n').split()
if len(line_split) != 7:
continue
self.klScan.append(float(line_split[0]))
self.ktScan.append(float(line_split[1]))
self.c2Scan.append(float(line_split[2]))
self.cgScan.append(float(line_split[3]))
self.c2gScan.append(float(line_split[4]))
self.BM_klScan.append(float(line_split[5]))
self.Norm_klScan.append(float(line_split[6]))
self.nofWeightsScan = len(self.klScan)
assert (len(self.ktScan) == self.nofWeightsScan
and len(self.c2Scan) == self.nofWeightsScan
and len(self.cgScan) == self.nofWeightsScan
and len(self.c2gScan) == self.nofWeightsScan
and len(self.BM_klScan) == self.nofWeightsScan
and len(self.Norm_klScan) == self.nofWeightsScan)
print("Using %d points to scan" % self.nofWeightsScan)
normFile = os.path.join(
cmssw_base,
"Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root")
normHist = "H1bin4"
self.nofWeightsBM = 13
self.klJHEP = [
1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0
]
self.ktJHEP = [
1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0
]
self.c2JHEP = [
0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0
]
self.cgJHEP = [
0.0, 0.0, -0.8, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0
]
self.c2gJHEP = [
0.0, 0.0, 0.6, -0.8, 0.0, -1.0, -0.2, -0.2, 1.0, 0.6, 0.0, -1.0,
0.0
]
assert (len(self.klJHEP) == self.nofWeightsBM
and len(self.ktJHEP) == self.nofWeightsBM
and len(self.c2JHEP) == self.nofWeightsBM
and len(self.cgJHEP) == self.nofWeightsBM
and len(self.c2gJHEP) == self.nofWeightsBM)
self.normJHEP = []
if self.compute_weights:
assert (os.path.isfile(normFile))
for idx in range(self.nofWeightsBM):
normJHEPval = self.model.getNormalization(
self.klJHEP[idx], self.ktJHEP[idx], self.c2JHEP[idx],
self.cgJHEP[idx], self.c2gJHEP[idx], normFile, normHist)
self.normJHEP.append(normJHEPval)
assert (len(self.normJHEP) == self.nofWeightsBM)
print("Using %d JHEP BMs" % self.nofWeightsBM)
class diHiggsVarProducer(Module):
def __init__(self,
era,
use_lhe=True,
use_gen=False,
compute_weights=False):
self.use_gen = use_gen
self.use_lhe = use_lhe
assert (self.use_gen or self.use_lhe)
self.compute_weights = compute_weights
if self.use_gen:
print(
"Computing di-Higgs variables from generator-level Higgs bosons"
)
else:
print(
"NOT computing di-Higgs variables from generator-level Higgs bosons"
)
if self.use_lhe:
print(
"Computing di-Higgs variables from LHE parton-level Higgs bosons"
)
else:
print(
"NOT computing di-Higgs variables from LHE parton-level Higgs bosons"
)
if self.compute_weights:
print("Also computing the di-Higgs event-level weights")
else:
print("NOT computing the di-Higgs event-level weights")
mHH_name = "mHH"
cosThetaStar_name = "cosThetaStar"
gen_suffix = "gen"
lhe_suffix = "lhe"
self.mHH_genName = "{}_{}".format(mHH_name, gen_suffix)
self.cosThetaStar_genName = "{}_{}".format(cosThetaStar_name,
gen_suffix)
self.mHH_lheName = "{}_{}".format(mHH_name, lhe_suffix)
self.cosThetaStar_lheName = "{}_{}".format(cosThetaStar_name,
lhe_suffix)
weightBaseName = "HHWeight"
BMName = "BM"
scanName = "scan"
self.nofWeightsBMName = "n{}_{}".format(weightBaseName, BMName)
self.weightBM_genName = "{}_{}_{}".format(weightBaseName, BMName,
gen_suffix)
self.weightBM_lheName = "{}_{}_{}".format(weightBaseName, BMName,
lhe_suffix)
self.nofWeightsScanName = "n{}_{}".format(weightBaseName, scanName)
self.weightScan_genName = "{}_{}_{}".format(weightBaseName, scanName,
gen_suffix)
self.weightScan_lheName = "{}_{}_{}".format(weightBaseName, scanName,
lhe_suffix)
os.environ["MKL_NUM_THREADS"] = "1"
cmssw_base = os.path.join(os.environ['CMSSW_BASE'], "src")
coeffFile = os.path.join(
cmssw_base,
"HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_extended_3M_costHHSim_19-4.txt"
)
self.model = None
if self.compute_weights:
assert (os.path.isfile(coeffFile))
self.model = NonResonantModel()
self.model.ReadCoefficients(coeffFile)
denominatorHistogramName = "denominator_reweighting"
denominatorFile = os.path.join(
cmssw_base,
"hhAnalysis/bbww/data/denominator_reweighting_bbvv_{}.root".format(
era))
self.denominatorFilePtr = None
self.denominatorHistogram = None
if self.compute_weights:
assert (os.path.isfile(denominatorFile))
self.denominatorFilePtr = ROOT.TFile.Open(denominatorFile, "READ")
assert (self.denominatorFilePtr)
assert (denominatorHistogramName in [
key.GetName()
for key in self.denominatorFilePtr.GetListOfKeys()
])
self.denominatorHistogram = self.denominatorFilePtr.Get(
denominatorHistogramName)
scanFile = os.path.join(cmssw_base, "hhAnalysis/bbww/data/kl_scan.dat")
self.nofWeightsScan = 0
self.klScan = []
self.ktScan = []
self.c2Scan = []
self.cgScan = []
self.c2gScan = []
self.BM_klScan = []
self.Norm_klScan = []
if self.compute_weights:
assert (os.path.isfile(scanFile))
with open(scanFile, 'r') as scanFileObj:
for line in scanFileObj:
line_split = line.rstrip('\n').split()
if len(line_split) != 7:
continue
self.klScan.append(float(line_split[0]))
self.ktScan.append(float(line_split[1]))
self.c2Scan.append(float(line_split[2]))
self.cgScan.append(float(line_split[3]))
self.c2gScan.append(float(line_split[4]))
self.BM_klScan.append(float(line_split[5]))
self.Norm_klScan.append(float(line_split[6]))
self.nofWeightsScan = len(self.klScan)
assert (len(self.ktScan) == self.nofWeightsScan
and len(self.c2Scan) == self.nofWeightsScan
and len(self.cgScan) == self.nofWeightsScan
and len(self.c2gScan) == self.nofWeightsScan
and len(self.BM_klScan) == self.nofWeightsScan
and len(self.Norm_klScan) == self.nofWeightsScan)
print("Using %d points to scan" % self.nofWeightsScan)
normFile = os.path.join(
cmssw_base,
"Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root")
normHist = "H1bin4"
self.nofWeightsBM = 13
self.klJHEP = [
1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0
]
self.ktJHEP = [
1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0
]
self.c2JHEP = [
0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0
]
self.cgJHEP = [
0.0, 0.0, -0.8, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0
]
self.c2gJHEP = [
0.0, 0.0, 0.6, -0.8, 0.0, -1.0, -0.2, -0.2, 1.0, 0.6, 0.0, -1.0,
0.0
]
assert (len(self.klJHEP) == self.nofWeightsBM
and len(self.ktJHEP) == self.nofWeightsBM
and len(self.c2JHEP) == self.nofWeightsBM
and len(self.cgJHEP) == self.nofWeightsBM
and len(self.c2gJHEP) == self.nofWeightsBM)
self.normJHEP = []
if self.compute_weights:
assert (os.path.isfile(normFile))
for idx in range(self.nofWeightsBM):
normJHEPval = self.model.getNormalization(
self.klJHEP[idx], self.ktJHEP[idx], self.c2JHEP[idx],
self.cgJHEP[idx], self.c2gJHEP[idx], normFile, normHist)
self.normJHEP.append(normJHEPval)
assert (len(self.normJHEP) == self.nofWeightsBM)
print("Using %d JHEP BMs" % self.nofWeightsBM)
def beginJob(self):
pass
def endJob(self):
pass
def beginFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
self.out = wrappedOutputTree
if self.use_gen:
self.out.branch(self.mHH_genName, "F")
self.out.branch(self.cosThetaStar_genName, "F")
if self.use_lhe:
self.out.branch(self.mHH_lheName, "F")
self.out.branch(self.cosThetaStar_lheName, "F")
if self.compute_weights:
self.out.branch(self.nofWeightsBMName, "I")
self.out.branch(self.nofWeightsScanName, "I")
if self.use_gen:
self.out.branch(self.weightBM_genName,
"F",
lenVar=self.nofWeightsBMName)
self.out.branch(self.weightScan_genName,
"F",
lenVar=self.nofWeightsScanName)
if self.use_lhe:
self.out.branch(self.weightBM_lheName,
"F",
lenVar=self.nofWeightsBMName)
self.out.branch(self.weightScan_lheName,
"F",
lenVar=self.nofWeightsScanName)
def endFile(self, inputFile, outputFile, inputTree, wrappedOutputTree):
if self.denominatorFilePtr:
self.denominatorFilePtr.Close()
def get_denominator(self, mHH, cosThetaStar):
assert (self.denominatorHistogram)
mHH_bin = self.denominatorHistogram.GetXaxis().FindBin(mHH)
cosThetaStar_bin = self.denominatorHistogram.GetYaxis().FindBin(
abs(cosThetaStar))
return self.denominatorHistogram.GetBinContent(mHH_bin,
cosThetaStar_bin)
def compute(self, higgses):
higgs_lead_p4, higgs_sublead_p4 = get_p4_pair(higgses)
higgs_pair_p4 = higgs_lead_p4 + higgs_sublead_p4
mHH = higgs_pair_p4.M()
# boost leading or subleading Higgs -- doesn't matter
higgs_lead_p4.Boost(-higgs_pair_p4.BoostVector())
cosThetaStar = abs(higgs_lead_p4.CosTheta())
denominator = self.get_denominator(
mHH, cosThetaStar) if self.compute_weights else 1.
weightsBM = [0.] * self.nofWeightsBM
if self.compute_weights:
weightsBM = []
for bmIdx in range(self.nofWeightsBM):
weightBM = self.model.getScaleFactor(
mhh=mHH,
cost=cosThetaStar,
kl=self.klJHEP[bmIdx],
kt=self.ktJHEP[bmIdx],
c2=self.c2JHEP[bmIdx],
cg=self.cgJHEP[bmIdx],
c2g=self.c2gJHEP[bmIdx],
effSumV0=denominator,
Cnorm=self.normJHEP[bmIdx],
)
weightsBM.append(weightBM)
weightsScan = [0.] * self.nofWeightsScan
if self.compute_weights:
weightsScan = []
for scanIdx in range(self.nofWeightsScan):
weightScan = self.model.getScaleFactor(
mhh=mHH,
cost=cosThetaStar,
kl=self.klScan[scanIdx],
kt=self.ktScan[scanIdx],
c2=self.c2Scan[scanIdx],
cg=self.cgScan[scanIdx],
c2g=self.c2gScan[scanIdx],
effSumV0=denominator,
Cnorm=self.Norm_klScan[scanIdx],
)
weightsScan.append(weightScan)
return mHH, cosThetaStar, weightsBM, weightsScan
def analyze(self, event):
weightsBM_gen = [0.] * self.nofWeightsBM
weightsScan_gen = [0.] * self.nofWeightsScan
mHH_gen, cosThetaStar_gen = -1., -2.
if self.use_gen:
genParticles = Collection(event, "GenPart")
higgses_gen = sorted(
filter(
lambda genPart:
genPart.pdgId == 25 and \
(genParticles[genPart.genPartIdxMother].pdgId != 25 if genPart.genPartIdxMother >= 0 else True),
genParticles
),
key = lambda genHiggs: genHiggs.pt,
reverse = True
)
nofHiggs_gen = len(higgses_gen)
if nofHiggs_gen == 2:
mHH_gen, cosThetaStar_gen, weightsBM_gen, weightsScan_gen = self.compute(
higgses_gen)
else:
print(
"Found an event that has not exactly two Higgs bosons at the generator level"
)
weightsBM_lhe = [0.] * self.nofWeightsBM
weightsScan_lhe = [0.] * self.nofWeightsScan
mHH_lhe, cosThetaStar_lhe = -1., -2.
if self.use_lhe:
lheParticles = Collection(event, "LHEPart")
higgses_lhe = sorted(
filter(lambda lhePart: lhePart.pdgId == 25, lheParticles),
key=lambda lheHiggs: get_p4(lheHiggs).E(
), # sort by energy b/c pT is the same
reverse=True)
nofHiggs_lhe = len(higgses_lhe)
if nofHiggs_lhe == 2:
mHH_lhe, cosThetaStar_lhe, weightsBM_lhe, weightsScan_lhe = self.compute(
higgses_lhe)
if self.use_gen:
assert (len(weightsBM_gen) == len(weightsBM_lhe))
assert (len(weightsScan_gen) == len(weightsScan_lhe))
else:
print(
"Found an event that has not exactly two Higgs bosons at the LHE parton level"
)
if self.use_gen:
self.out.fillBranch(self.mHH_genName, mHH_gen)
self.out.fillBranch(self.cosThetaStar_genName, cosThetaStar_gen)
if self.use_lhe:
self.out.fillBranch(self.mHH_lheName, mHH_lhe)
self.out.fillBranch(self.cosThetaStar_lheName, cosThetaStar_lhe)
if self.compute_weights:
self.out.fillBranch(self.nofWeightsBMName, len(weightsBM_gen))
self.out.fillBranch(self.nofWeightsScanName, len(weightsScan_gen))
if self.use_gen:
self.out.fillBranch(self.weightBM_genName, weightsBM_gen)
self.out.fillBranch(self.weightScan_genName, weightsScan_gen)
if self.use_lhe:
self.out.fillBranch(self.weightBM_lheName, weightsBM_lhe)
self.out.fillBranch(self.weightScan_lheName, weightsScan_lhe)
return True
def main():
model = NonResonantModel()
# obtaining BSM/SM coeficients
#dumb = model.ReadCoefficients("../data/coefficientsByBin_A1A3A7.txt")
#dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M.txt")
dumb = model.ReadCoefficients(
"../data/coefficientsByBin_extended_3M_costHHSim_19-4.txt")
# We sum SM + box + the benchmarks from 2-13
# read the 2D histo referent to the sum of events
"""
fileHH=ROOT.TFile("../../../Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV.root")
sumJHEPAnalyticalBin = fileHH.Get("H1bin2")
SMAnalyticalBin = fileHH.Get("H0bin2")
"""
fileHH = ROOT.TFile(
"../../../Analysis/Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root"
) #Distros_5p_SM3M_sumBenchJHEP_13TeV.root") #Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root") #
#fileHH=ROOT.TFile("../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root")
#sumJHEPAnalyticalBin = fileHH.Get("H1bin3")
histfile = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root" #Distros_5p_SM3M_sumBenchJHEP_13TeV.root" # #Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
#histfile = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
histtitle = "H1bin4"
sumJHEPAnalyticalBin = fileHH.Get(histtitle)
SMAnalyticalBin = fileHH.Get("H0bin4")
#fileHHname = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
calcSumOfWeights = model.getNormalization(
kl, kt, c2, cg, c2g, histfile,
histtitle) # this input is flexible, tatabb may have only the SM
#print ("normalization is: ",calcSumOfWeights)
#xaxis = sumJHEPAnalyticalBin.GetXaxis()
#yaxis = sumJHEPAnalyticalBin.GetYaxis()
#print "Sum hist ",sumJHEPAnalyticalBin.GetNbinsX(),sumJHEPAnalyticalBin.GetNbinsY(),sumJHEPAnalyticalBin.Integral(),sumJHEPAnalyticalBin.GetXaxis().GetBinLowEdge(1),sumJHEPAnalyticalBin.GetXaxis().GetBinUpEdge(xaxis.GetNbins())
#print SMAnalyticalBin.GetBinContent(4,4)
# now loop over events, calculate weights using the coeffitients and plot histograms
# events to reweights, in text format (for testing only)
pathBenchEvents = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" # events to reweight
# declare the histograms
CalcMhh = np.zeros((1200000))
CalcCost = np.zeros((1200000))
CalcPtH = np.zeros((1200000))
CalcPtHgg = np.zeros((1200000))
CalcPtHH = np.zeros((1200000))
CalcWeight = np.zeros((1200000))
CalcMhhReco = np.zeros((1200000))
CalcMXReco = np.zeros((1200000))
##########################################
# initialize tables of coefficients by bins
# calculate mhh and cost* and find the bin
# initialize events reading
countline = 0
countevent = 0
counteventSM = 0
# read events as text files for events to test
# particuliarity of the text file with events = each 2 lines are one event there
# save the information of the two Higgses
Px = np.zeros((2))
Py = np.zeros((2))
Pz = np.zeros((2))
En = np.zeros((2))
for sam in range(1, 13): # read events from the list of 12 benchmarks
filne = pathBenchEvents + "GF_HH_" + str(
sam
) + ".lhe.decayed" # 0 is SM = here it does not enter in the events to be reweighted in this version
f = open(filne, 'r+')
lines = f.readlines() # get all lines as a list (array)
countline = 0 # particuliarity of the text file with events = each 2 lines are one event there
for line in lines:
model.ReadLine(line, countline, Px, Py, Pz, En)
#print countline
countline += 1
mhhcost = [0, 0, 0,
0] # to store [mhh , cost,ptH , ptHH] of that event
if countline == 2: # if read 2 lines
model.CalculateMhhCost(mhhcost, countline, Px, Py, Pz,
En) # ==> adapt to your input
bmhh = sumJHEPAnalyticalBin.GetXaxis().FindBin(mhhcost[0])
bcost = sumJHEPAnalyticalBin.GetYaxis().FindBin(abs(
mhhcost[1]))
#print (mhhcost[1],bcost)
effSumV0 = sumJHEPAnalyticalBin.GetBinContent(
bmhh, bcost
) # quantity of simulated events in that bin (without cuts)
#weight = model.getScaleFactor(mhhcost,kl, kt,0,model.effSM,model.effSum,model.MHH,model.COSTS,model.A1,model.A3,model.A7,0)
#print mhhcost[1],bcost
#print effSumV0
weight = model.getScaleFactor(mhhcost[0], mhhcost[1], kl, kt,
c2, cg, c2g, effSumV0,
calcSumOfWeights)
countline = 0
#############################################
# fill histograms
#############################################
if weight > 0:
#print countevent
CalcMhh[countevent] = float(mhhcost[0])
CalcCost[countevent] = float(mhhcost[1])
CalcPtH[countevent] = float(mhhcost[2])
CalcPtHH[countevent] = float(mhhcost[3])
CalcWeight[countevent] = weight
countevent += 1
#else : print ("neg weight ",weight,mhhcost[0],mhhcost[1],bmhh,bcost,kl, kt,c2,cg,c2g,effSumV0,calcSumOfWeights)
f.close()
print "plotted hostogram reweighted from ", countevent, " events, ", float(
100 * (1200000 - countevent) /
1200000), " % of the events was lost in empty bins in SM simulation"
print kl, kt, c2, cg, c2g, "Sum of weights:", CalcWeight.sum(
), " correction ", calcSumOfWeights
############################################################################################################################
# Draw test histos
###############################################
drawtest = -1
nevtest = 50000
#if kl == 1 and kt == 1 and c2 ==0 and cg == 0 and c2g ==0 :
# filne = pathBenchEvents+"GF_HH_0.lhe.decayed" # 0 is SM
# nevtest = 100000
# drawtest = 1
# BSM events
pathBSMtest = "/afs/cern.ch/work/a/acarvalh/generateHH/asciiHH_tofit/GF_HH_toRecursive/" # events of file to superimpose a test
# see the translation of coefficients for this last on: If you make this script smarter (to only read files we ask to test) you can implement more
# https://github.com/acarvalh/generateHH/blob/master/fit_GF_HH_lhe/tableToFitA3andA7.txt
if kl == -10 and kt == 0.5 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_42.lhe.decayed"
if kl == 0.0001 and kt == 2.25 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_9.lhe.decayed"
if kl == 0.0001 and kt == 1.0 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_4.lhe.decayed"
if kl == 2.5 and kt == 1.0 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_60.lhe.decayed"
if kl == -15 and kt == 0.5 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_40.lhe.decayed"
if kl == 5 and kt == 1.5 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_74.lhe.decayed"
if kl == 7.5 and kt == 2.0 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_88.lhe.decayed"
if kl == 0.0001 and kt == 0.0001 and c2 == -3.0 and cg == 0.0 and c2g == -1.5:
drawtest = 1
filne = pathBSMtest + "GF_HH_281.lhe.decayed"
if kl == -10 and kt == 0.0 and c2 == 1.0 and cg == 1.0 and c2g == 1.0:
drawtest = 1
filne = pathBSMtest + "GF_HH_280.lhe.decayed"
#########################################################
klJHEP = [
1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0
]
ktJHEP = [1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0]
c2JHEP = [
0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0
]
cgJHEP = [
0.0, 0.0, 0.6, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0
]
c2gJHEP = [
0.0, 0.0, 1.0, -0.8, 0.0, -1.0, -0.2, -0.2, 1.0, 0.6, 0.0, -1.0, 0.0
]
# python recastHH.py --kl 7.5 --kt 1 --c2 -1
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.5 --cg -0.8 --c2g 0.6
# python recastHH.py --kl 1.0 --kt 1.0 --c2 -1.5 --cg 0.0 --c2g -0.8
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.0 --cg 0.8 --c2g -1.0
# python recastHH.py --kl 1.0 --kt 1.0 --c2 1.0 --cg -0.6 --c2g 0.6
for sam in range(0, 13):
#print (sam, ktJHEP[sam] , kt , klJHEP[sam] , c2 ,c2JHEP[sam] , cg , cgJHEP[sam] , c2g , c2gJHEP[sam])
if kl == klJHEP[sam] and kt == ktJHEP[sam] and c2 == c2JHEP[
sam] and cg == cgJHEP[sam] and c2g == c2gJHEP[sam]:
print sam
filne = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_" + str(
sam) + ".lhe.decayed"
nevtest = 100000
drawtest = sam
############################################################################################################################
CalcMhhTest = np.zeros((nevtest))
CalcCostTest = np.zeros((nevtest))
CalcPtHTest = np.zeros((nevtest))
CalcPtHHTest = np.zeros((nevtest))
CalcPtHggTest = np.zeros((nevtest))
CalcMhhRecoTest = np.zeros((nevtest))
CalcMXRecoTest = np.zeros((nevtest))
if options.doPlot and drawtest > 0:
print "draw plain histogram to test"
model.LoadTestEvents(CalcMhhTest, CalcCostTest, CalcPtHTest,
CalcPtHHTest, filne)
model.plotting(kl, kt, c2, cg, c2g, CalcMhh, CalcCost, CalcPtHgg, CalcPtHH,
CalcMhhReco, CalcMXReco, CalcWeight, CalcMhhTest,
CalcCostTest, CalcPtHggTest, CalcPtHHTest, CalcMhhRecoTest,
CalcMXRecoTest, drawtest)
def main():
model = NonResonantModel()
# obtaining BSM/SM coeficients
#dumb = model.ReadCoefficients("../data/coefficientsByBin_A1A3A7.txt")
#dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M.txt")
dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M_costHHSim_19-4.txt")
# We sum SM + box + the benchmarks from 2-13
# read the 2D histo referent to the sum of events
"""
fileHH=ROOT.TFile("../../../Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV.root")
sumJHEPAnalyticalBin = fileHH.Get("H1bin2")
SMAnalyticalBin = fileHH.Get("H0bin2")
"""
fileHH=ROOT.TFile("../../../Analysis/Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root") #Distros_5p_SM3M_sumBenchJHEP_13TeV.root") #Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root") #
#fileHH=ROOT.TFile("../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root")
#sumJHEPAnalyticalBin = fileHH.Get("H1bin3")
histfile = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV_19-4.root" #Distros_5p_SM3M_sumBenchJHEP_13TeV.root" # #Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
#histfile = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
histtitle = "H1bin4"
sumJHEPAnalyticalBin = fileHH.Get(histtitle)
SMAnalyticalBin = fileHH.Get("H0bin4")
#fileHHname = "../../../Analysis/Support/NonResonant/Distros_5p_SM3M_rebin_sumBenchJHEP_5D_13TeV.root"
calcSumOfWeights = model.getNormalization(kl, kt,c2,cg,c2g,histfile,histtitle) # this input is flexible, tatabb may have only the SM
#print ("normalization is: ",calcSumOfWeights)
#xaxis = sumJHEPAnalyticalBin.GetXaxis()
#yaxis = sumJHEPAnalyticalBin.GetYaxis()
#print "Sum hist ",sumJHEPAnalyticalBin.GetNbinsX(),sumJHEPAnalyticalBin.GetNbinsY(),sumJHEPAnalyticalBin.Integral(),sumJHEPAnalyticalBin.GetXaxis().GetBinLowEdge(1),sumJHEPAnalyticalBin.GetXaxis().GetBinUpEdge(xaxis.GetNbins())
#print SMAnalyticalBin.GetBinContent(4,4)
# now loop over events, calculate weights using the coeffitients and plot histograms
# events to reweights, in text format (for testing only)
pathBenchEvents="/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" # events to reweight
# declare the histograms
CalcMhh = np.zeros((1200000))
CalcCost = np.zeros((1200000))
CalcPtH = np.zeros((1200000))
CalcPtHgg = np.zeros((1200000))
CalcPtHH = np.zeros((1200000))
CalcWeight = np.zeros((1200000))
CalcMhhReco = np.zeros((1200000))
CalcMXReco = np.zeros((1200000))
##########################################
# initialize tables of coefficients by bins
# calculate mhh and cost* and find the bin
# initialize events reading
countline=0
countevent=0
counteventSM=0
# read events as text files for events to test
# particuliarity of the text file with events = each 2 lines are one event there
# save the information of the two Higgses
Px = np.zeros((2))
Py = np.zeros((2))
Pz = np.zeros((2))
En = np.zeros((2))
for sam in range(1,13): # read events from the list of 12 benchmarks
filne = pathBenchEvents+"GF_HH_"+str(sam)+".lhe.decayed" # 0 is SM = here it does not enter in the events to be reweighted in this version
f = open(filne, 'r+')
lines = f.readlines() # get all lines as a list (array)
countline = 0 # particuliarity of the text file with events = each 2 lines are one event there
for line in lines:
model.ReadLine(line, countline,Px,Py,Pz,En)
#print countline
countline+=1
mhhcost= [0,0,0,0] # to store [mhh , cost,ptH , ptHH] of that event
if countline==2 : # if read 2 lines
model.CalculateMhhCost(mhhcost,countline,Px,Py,Pz,En) # ==> adapt to your input
bmhh = sumJHEPAnalyticalBin.GetXaxis().FindBin(mhhcost[0])
bcost = sumJHEPAnalyticalBin.GetYaxis().FindBin(abs(mhhcost[1]))
#print (mhhcost[1],bcost)
effSumV0 = sumJHEPAnalyticalBin.GetBinContent(bmhh,bcost) # quantity of simulated events in that bin (without cuts)
#weight = model.getScaleFactor(mhhcost,kl, kt,0,model.effSM,model.effSum,model.MHH,model.COSTS,model.A1,model.A3,model.A7,0)
#print mhhcost[1],bcost
#print effSumV0
weight = model.getScaleFactor(mhhcost[0],mhhcost[1],kl, kt,c2,cg,c2g, effSumV0,calcSumOfWeights)
countline=0
#############################################
# fill histograms
#############################################
if weight > 0:
#print countevent
CalcMhh[countevent] = float(mhhcost[0])
CalcCost[countevent] = float(mhhcost[1])
CalcPtH[countevent] = float(mhhcost[2])
CalcPtHH[countevent] = float(mhhcost[3])
CalcWeight[countevent] = weight
countevent+=1
#else : print ("neg weight ",weight,mhhcost[0],mhhcost[1],bmhh,bcost,kl, kt,c2,cg,c2g,effSumV0,calcSumOfWeights)
f.close()
print "plotted hostogram reweighted from ",countevent," events, ", float(100*(1200000-countevent)/1200000)," % of the events was lost in empty bins in SM simulation"
print kl, kt,c2,cg,c2g, "Sum of weights:", CalcWeight.sum()," correction ",calcSumOfWeights
############################################################################################################################
# Draw test histos
###############################################
drawtest =-1
nevtest=50000
#if kl == 1 and kt == 1 and c2 ==0 and cg == 0 and c2g ==0 :
# filne = pathBenchEvents+"GF_HH_0.lhe.decayed" # 0 is SM
# nevtest = 100000
# drawtest = 1
# BSM events
pathBSMtest="/afs/cern.ch/work/a/acarvalh/generateHH/asciiHH_tofit/GF_HH_toRecursive/" # events of file to superimpose a test
# see the translation of coefficients for this last on: If you make this script smarter (to only read files we ask to test) you can implement more
# https://github.com/acarvalh/generateHH/blob/master/fit_GF_HH_lhe/tableToFitA3andA7.txt
if kl == -10 and kt == 0.5 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_42.lhe.decayed"
if kl == 0.0001 and kt == 2.25 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_9.lhe.decayed"
if kl == 0.0001 and kt == 1.0 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_4.lhe.decayed"
if kl == 2.5 and kt == 1.0 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_60.lhe.decayed"
if kl == -15 and kt == 0.5 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_40.lhe.decayed"
if kl == 5 and kt == 1.5 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_74.lhe.decayed"
if kl == 7.5 and kt == 2.0 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_88.lhe.decayed"
if kl == 0.0001 and kt == 0.0001 and c2 ==-3.0 and cg == 0.0 and c2g ==-1.5 :
drawtest =1
filne = pathBSMtest+"GF_HH_281.lhe.decayed"
if kl == -10 and kt == 0.0 and c2 ==1.0 and cg == 1.0 and c2g ==1.0 :
drawtest =1
filne = pathBSMtest+"GF_HH_280.lhe.decayed"
#########################################################
klJHEP=[1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0]
ktJHEP=[1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0]
c2JHEP=[0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0]
cgJHEP=[0.0, 0.0, 0.6, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0]
c2gJHEP=[0.0, 0.0, 1.0, -0.8, 0.0, -1.0, -0.2,-0.2, 1.0, 0.6, 0.0, -1.0, 0.0]
# python recastHH.py --kl 7.5 --kt 1 --c2 -1
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.5 --cg -0.8 --c2g 0.6
# python recastHH.py --kl 1.0 --kt 1.0 --c2 -1.5 --cg 0.0 --c2g -0.8
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.0 --cg 0.8 --c2g -1.0
# python recastHH.py --kl 1.0 --kt 1.0 --c2 1.0 --cg -0.6 --c2g 0.6
for sam in range(0,13):
#print (sam, ktJHEP[sam] , kt , klJHEP[sam] , c2 ,c2JHEP[sam] , cg , cgJHEP[sam] , c2g , c2gJHEP[sam])
if kl == klJHEP[sam] and kt == ktJHEP[sam] and c2 ==c2JHEP[sam] and cg == cgJHEP[sam] and c2g ==c2gJHEP[sam] :
print sam
filne="/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_"+str(sam)+".lhe.decayed"
nevtest=100000
drawtest = sam
############################################################################################################################
CalcMhhTest = np.zeros((nevtest))
CalcCostTest = np.zeros((nevtest))
CalcPtHTest = np.zeros((nevtest))
CalcPtHHTest = np.zeros((nevtest))
CalcPtHggTest = np.zeros((nevtest))
CalcMhhRecoTest = np.zeros((nevtest))
CalcMXRecoTest = np.zeros((nevtest))
if options.doPlot and drawtest >0 :
print "draw plain histogram to test"
model.LoadTestEvents(CalcMhhTest,CalcCostTest,CalcPtHTest,CalcPtHHTest,filne)
model.plotting(kl,kt,c2,cg,c2g,CalcMhh,CalcCost,CalcPtHgg,CalcPtHH,CalcMhhReco,CalcMXReco,CalcWeight,CalcMhhTest,CalcCostTest,CalcPtHggTest,CalcPtHHTest,CalcMhhRecoTest,CalcMXRecoTest,drawtest)
def main():
#if 1 > 0 :
# declare the 2D ===> should be global variable
model = NonResonantModel()
# obtaining BSM/SM coeficients
#dumb = model.ReadCoefficients("../data/coefficientsByBin_A1A3A7.txt")
dumb = model.ReadCoefficients(
"../data/coefficientsByBin_extended_3M_costHHSim_19-4.txt")
#dumb = model.ReadCoefficients("../data/coefficientsByBin_extended_3M.txt")
counteventSM = 0
sumWeight = 0
# now loop over events, calculate weights using the coeffitients and plot histograms
# We sum SM + box + the benchmarks from 2-13
# read the 2D histo referent to the sum of events
#fileHH=ROOT.TFile("../../../Analysis/Support/NonResonant/Hist2DSum_V0_SM_box.root")
#sumHAnalyticalBin = fileHH.Get("SumV0_AnalyticalBin")
histfilename = "../../../Analysis/Support/NonResonant/HistSum2D_4b_rebin_SimCostHH_19-4.root"
#histtitle= "SumV0_AnalyticalBinExt" #
histtitle = "SumV0_AnalyticalBinExtSimCostHH" #
fileHH = ROOT.TFile(histfilename)
sumHAnalyticalBin = fileHH.Get(histtitle)
calcSumOfWeights = model.getNormalization(
kl, kt, c2, cg, c2g, histfilename,
histtitle) # this input is flexible, tatabb may have only the SM
# print "sum of weights calculated" , calcSumOfWeights
# read the events
pathBenchEvents = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" #"/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/" # events to reweight
file = ROOT.TFile(pathBenchEvents + "events_SumV0.root")
tree = file.Get("treeout")
nev = tree.GetEntries()
# declare the histograms
CalcMhh = np.zeros((nev))
CalcCost = np.zeros((nev))
CalcPtH = np.zeros((nev))
CalcPtHH = np.zeros((nev))
CalcWeight = np.zeros((nev))
CalcPtHgg = np.zeros((nev))
CalcMhhReco = np.zeros((nev))
CalcMXReco = np.zeros((nev))
countevent = 0
#for kll in range(-5,5) : model.getNormalization(kll, kt,sumHBenchBin)
for iev in range(0, nev):
tree.GetEntry(iev)
mhh = tree.Genmhh
cost = tree.GenHHCost
mhhcost = [mhh, cost, 0, 0] # to store [mhh , cost] of that event
# find the Nevents from the sum of events on that bin
bmhh = sumHAnalyticalBin.GetXaxis().FindBin(mhh)
bcost = sumHAnalyticalBin.GetYaxis().FindBin(abs(cost))
#print (tree.GenHHCost,cost,bcost)
effSumV0 = sumHAnalyticalBin.GetBinContent(bmhh, abs(
bcost)) # quantity of simulated events in that bin (without cuts)
weight = model.getScaleFactor(
mhh, cost, kl, kt, c2, cg, c2g, effSumV0, calcSumOfWeights
) # model.effSM,model.MHH,model.COSTS,model.A1,model.A3,model.A7, effSumV0)
#############################################
# fill histograms to test
#############################################
if weight > 0:
#print countevent
CalcMhh[countevent] = float(mhhcost[0])
CalcCost[countevent] = float(abs(mhhcost[1]))
CalcPtH[countevent] = float(mhhcost[2])
CalcPtHH[countevent] = float(mhhcost[3])
CalcWeight[countevent] = weight
countevent += 1
sumWeight += weight
print "plotted histogram reweighted from ", countevent, " events, ", float(
100 * (nev - countevent) /
nev), " % of the events was lost in empty bins in SM simulation"
print "sum of weights", sumWeight
############################################################################################################################
# Draw test histos
###############################################
drawtest = 0
nevtest = 50000
if kl == 1 and kt == 1 and c2 == 0 and cg == 0 and c2g == 0:
filne = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_0.lhe.decayed" # 0 is SM
nevtest = 100000
drawtest = 1
# BSM events
pathBSMtest = "/afs/cern.ch/work/a/acarvalh/generateHH/asciiHH_tofit/GF_HH_toRecursive/" # events of file to superimpose a test
# see the translation of coefficients for this last on: If you make this script smarter (to only read files we ask to test) you can implement more
# https://github.com/acarvalh/generateHH/blob/master/fit_GF_HH_lhe/tableToFitA3andA7.txt
if kl == -10 and kt == 0.5 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_42.lhe.decayed"
if kl == 0.0001 and kt == 2.25 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_9.lhe.decayed"
if kl == 2.5 and kt == 1.0 and c2 == 0 and cg == 0 and c2g == 0:
drawtest = 1
filne = pathBSMtest + "GF_HH_60.lhe.decayed"
klJHEP = [
1.0, 7.5, 1.0, 1.0, -3.5, 1.0, 2.4, 5.0, 15.0, 1.0, 10.0, 2.4, 15.0
]
ktJHEP = [1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 1.0, 1.0]
c2JHEP = [
0.0, -1.0, 0.5, -1.5, -3.0, 0.0, 0.0, 0.0, 0.0, 1.0, -1.0, 0.0, 1.0
]
cgJHEP = [
0.0, 0.0, 0.6, 0.0, 0.0, 0.8, 0.2, 0.2, -1.0, -0.6, 0.0, 1.0, 0.0
]
c2gJHEP = [
0.0, 0.0, 1.0, -0.8, 0.0, -1.0, -0.2, -0.2, 1.0, 0.6, 0.0, -1.0, 0.0
]
# python recastHH.py --kl 7.5 --kt 1 --c2 -1
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.5 --cg -0.8 --c2g 0.6
# python recastHH.py --kl 1.0 --kt 1.0 --c2 -1.5 --cg 0.0 --c2g -0.8
# python recastHH.py --kl 1.0 --kt 1.0 --c2 0.0 --cg 0.8 --c2g -1.0
# python recastHH.py --kl 1.0 --kt 1.0 --c2 1.0 --cg -0.6 --c2g 0.6
for sam in range(0, 13):
#print (sam, ktJHEP[sam] , kt , klJHEP[sam] , c2 ,c2JHEP[sam] , cg , cgJHEP[sam] , c2g , c2gJHEP[sam])
if kl == klJHEP[sam] and kt == ktJHEP[sam] and c2 == c2JHEP[
sam] and cg == cgJHEP[sam] and c2g == c2gJHEP[sam]:
print sam
filne = "/eos/user/a/acarvalh/asciiHH_tofit/GF_HH_BSM/GF_HH_" + str(
sam) + ".lhe.decayed"
nevtest = 100000
drawtest = sam
############################################################################################################################
CalcMhhTest = np.zeros((nevtest))
CalcCostTest = np.zeros((nevtest))
CalcPtHTest = np.zeros((nevtest))
CalcPtHHTest = np.zeros((nevtest))
CalcPtHggTest = np.zeros((nevtest))
CalcMhhRecoTest = np.zeros((nevtest))
CalcMXRecoTest = np.zeros((nevtest))
if options.doPlot:
print "draw plain histogram to test"
model.LoadTestEvents(CalcMhhTest, CalcCostTest, CalcPtHTest,
CalcPtHHTest, filne)
model.plotting(kl, kt, c2, cg, c2g, CalcMhh, CalcCost, CalcPtHgg,
CalcPtHH, CalcMhhReco, CalcMXReco, CalcWeight,
CalcMhhTest, CalcCostTest, CalcPtHggTest, CalcPtHHTest,
CalcMhhRecoTest, CalcMXRecoTest, drawtest)
def main():
model = NonResonantModel()
# obtaining BSM/SM coeficients
dumb = model.ReadCoefficients("../data/coefficientsByBin_klkt.txt",model.effSM,model.effSum,model.MHH,model.COSTS,model.A1,model.A3,model.A7)
# We sum SM + box + the benchmarks from 2-13
# read the 2D histo referent to the sum of events
fileHH=ROOT.TFile("../../Support/NonResonant/Distros_5p_SM3M_sumBenchJHEP_13TeV.root")
sumJHEPAnalyticalBin = fileHH.Get("H1bin2")
SMAnalyticalBin = fileHH.Get("H0bin2")
xaxis = sumJHEPAnalyticalBin.GetXaxis()
yaxis = sumJHEPAnalyticalBin.GetYaxis()
print "Sum hist ",sumJHEPAnalyticalBin.GetNbinsX(),sumJHEPAnalyticalBin.GetNbinsY(),sumJHEPAnalyticalBin.Integral(),sumJHEPAnalyticalBin.GetXaxis().GetBinLowEdge(1),sumJHEPAnalyticalBin.GetXaxis().GetBinUpEdge(xaxis.GetNbins())
#print SMAnalyticalBin.GetBinContent(4,4)
# now loop over events, calculate weights using the coeffitients and plot histograms
# events to reweights, in text format (for testing only)
pathBenchEvents="/afs/cern.ch/work/a/acarvalh/public/toAnamika/GF_HH_BSM/" # events to reweight
# declare the histograms
CalcMhh = np.zeros((1200000))
CalcCost = np.zeros((1200000))
CalcWeight = np.zeros((1200000))
##########################################
# initialize tables of coefficients by bins
# calculate mhh and cost* and find the bin
# initialize events reading
countline=0
countevent=0
counteventSM=0
# read events as text files for events to test
# particuliarity of the text file with events = each 2 lines are one event there
# save the information of the two Higgses
Px = np.zeros((2))
Py = np.zeros((2))
Pz = np.zeros((2))
En = np.zeros((2))
for sam in range(1,13): # read events from the list of 12 benchmarks
filne = pathBenchEvents+"GF_HH_"+str(sam)+".lhe.decayed" # 0 is SM = here it does not enter in the events to be reweighted in this version
f = open(filne, 'r+')
lines = f.readlines() # get all lines as a list (array)
countline = 0 # particuliarity of the text file with events = each 2 lines are one event there
for line in lines:
model.ReadLine(line, countline,Px,Py,Pz,En)
#print countline
countline+=1
mhhcost= [0,0] # to store [mhh , cost] of that event
if countline==2 : # if read 2 lines
model.CalculateMhhCost(mhhcost,countline,Px,Py,Pz,En) # ==> adapt to your input
bmhh = sumJHEPAnalyticalBin.GetXaxis().FindBin(mhhcost[0])
bcost = sumJHEPAnalyticalBin.GetYaxis().FindBin(mhhcost[1])
effSumV0 = sumJHEPAnalyticalBin.GetBinContent(bmhh,bcost) # quantity of simulated events in that bin (without cuts)
#weight = model.getScaleFactor(mhhcost,kl, kt,0,model.effSM,model.effSum,model.MHH,model.COSTS,model.A1,model.A3,model.A7,0)
#print mhhcost[1],bcost
#print effSumV0
weight = model.getScaleFactor(mhhcost,kl, kt,model.effSM,model.MHH,model.COSTS,model.A1,model.A3,model.A7,effSumV0)
countline=0
#############################################
# fill histograms
#############################################
if weight > 0:
#print countevent
CalcMhh[countevent] = float(mhhcost[0])
CalcCost[countevent] = float(mhhcost[1])
CalcWeight[countevent] = weight
countevent+=1
f.close()
print "plotted hostogram reweighted from ",countevent," events, ", float(100*(1200000-countevent)/1200000)," % of the events was lost in empty bins in SM simulation"
############################################################################################################################
# Draw test histos
###############################################
drawtest =0
nevtest=50000
if kl == 1 and kt == 1 and c2 ==0 and cg == 0 and c2g ==0 :
filne = pathBenchEvents+"GF_HH_0.lhe.decayed" # 0 is SM
nevtest = 100000
drawtest = 1
# BSM events
pathBSMtest="/afs/cern.ch/work/a/acarvalh/public/toAnamika/GF_HH_toRecursive/" # events of file to superimpose a test
# see the translation of coefficients for this last on: If you make this script smarter (to only read files we ask to test) you can implement more
# https://github.com/acarvalh/generateHH/blob/master/fit_GF_HH_lhe/tableToFitA3andA7.txt
if kl == -10 and kt == 0.5 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_42.lhe.decayed"
if kl == 0.0001 and kt == 2.25 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_9.lhe.decayed"
if kl == 2.5 and kt == 1.0 and c2 ==0 and cg == 0 and c2g ==0 :
drawtest =1
filne = pathBSMtest+"GF_HH_60.lhe.decayed"
############################################################################################################################
CalcMhhTest = np.zeros((nevtest))
CalcCostTest = np.zeros((nevtest))
if drawtest ==1 :
print "draw plain histogram to test"
model.LoadTestEvents(CalcMhhTest,CalcCostTest,filne)
model.plotting(kl,kt,CalcMhh,CalcCost,CalcWeight,CalcMhhTest,CalcCostTest,drawtest)
import shutil,subprocess
from HHStatAnalysis.AnalyticalModels.NonResonantModel import NonResonantModel
# ROOT imports
import ROOT
from ROOT import TChain, TH1F, TFile, vector, gROOT, TTree
##################
# read the histos tree and contruct the tree of the relevant variables
LM=1
if LM == 0 : path = "/afs/cern.ch/user/a/andrey/public/HH/LT-Jan25-APZ_HighMass/"
if LM == 1 : path = "/afs/cern.ch/user/a/andrey/public/HH/LT-Jan25-APZ_LowMass/"
if histdone==0 : path = "/afs/cern.ch/user/a/andrey/public/HH/GenTrees_for_Xandra/"
outpath="/afs/cern.ch/user/a/acarvalh/public/toAABB/"
data="../../../HHStatAnalysis/AnalyticalModels/data/"
model = NonResonantModel()
# obtaining BSM/SM coeficients
dumb = model.ReadCoefficients("../../../HHStatAnalysis/AnalyticalModels/data/coefficientsByBin_klkt.txt")
if LM == 0 : fileoutput = "events_SumV0_afterBaseline_HM.root"
if LM == 1 : fileoutput = "events_SumV0_afterBaseline_LM.root"
files = []
endfile = "_13TeV-madgraph.root"
if histdone==1 :
files.append("LT_output_GluGluToHHTo2B2G_node_SM")
files.append("LT_output_GluGluToHHTo2B2G_node_box")
for ifile in range(2,14) : files.append("LT_output_GluGluToHHTo2B2G_node_"+str(ifile))