Exemplos de WeightInfo.get_weight_func em Python

Exemplo n.º 1

Arquivo: skim_plots.py Projeto: llechner/TTXPheno

#    samples.append(sample)
#    samples[-1].name+='_%i'%i_param
#    print param
#    samples[-1].style = styles.lineStyle( param['color'] )
#    samples[-1].texName = param['legendText']
#    #samples[-1].ttreeFormula = ROOT.TTreeFormula("weightstring_%i"%i_param,  w.get_weight_string(**param['WC']), samples[-1].chain )
#    samples[-1].paramWeightFunc =  w.get_weight_func(**param['WC'])

#def getWeightFunc(sample):
#    formula = sample.ttreeFormula
#    def rw(event, sample):
#        return formula.EvalInstance()
#    return rw 

stack = Stack(*[ [ sample ] for param in params ] )
weight= [ [ w.get_weight_func(**param['WC']) ] for param in params ]
#print weight

##
## Text on the plots
##
def drawObjects( hasData = False ):
    tex = ROOT.TLatex()
    tex.SetNDC()
    tex.SetTextSize(0.04)
    tex.SetTextAlign(11) # align right
    lines = [
      (0.15, 0.95, 'CMS Preliminary' if hasData else "Robert\'s CMS Simulation"), 
      #(0.45, 0.95, 'L=%3.1f fb{}^{-1} (13 TeV) Scale %3.2f'% ( lumi_scale, dataMCScale ) ) if plotData else (0.45, 0.95, 'L=%3.1f fb{}^{-1} (13 TeV)' % lumi_scale)
    ]
    return [tex.DrawLatex(*l) for l in lines] 

Exemplo n.º 2

Arquivo: ttz_dataset.py Projeto: schoef/Kullback-Leibner-divergence-Criterion

    sample.reduceFiles( to = 10 )


output_dir = os.path.join(tmp_directory, args.version)
if not os.path.exists(output_dir):
    os.makedirs( output_dir )

# lumi
lumi = 150

# Polynomial parametrization
w = WeightInfo(sample.reweight_pkl)
w.set_order(2)

# function that evaluates the weight of the SM hypothesis
sm_weight = w.get_weight_func()
# function that evaluates the weight of the BSM hypothesis
bsm_weight = w.get_weight_func(ctGI=10)

selectionString = "Sum$(genLep_pt>10&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)==3&&Sum$(genLep_pt>20&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)>=2&&Sum$(genLep_pt>40&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)>=1&&abs(genZ_mass-91.2)<=10&&Sum$(genJet_pt>30&&abs(genJet_eta)<2.4)>=3&&Sum$(genJet_pt>30&&genJet_matchBParton>=1&&abs(genJet_eta)<2.4)>=1&&genZ_pt>=0"
sample.setSelectionString( selectionString ) 

# Define variables
file_read_variables = [ "genZ_pt/F", "genZ_eta/F", "genZ_phi/F", "genZ_mass/F", "genZ_cosThetaStar/F", "ref_lumiweight1fb/F" ]
read_variables = map( TreeVariable.fromString, file_read_variables)
read_variables.append( VectorTreeVariable.fromString('p[C/F]', nMax=2000) )

training_variables = {
    'genZ_pt/F' : lambda event: event.genZ_pt,
    'genZ_cosThetaStar/F' : lambda event: event.genZ_cosThetaStar }

Exemplo n.º 3

Arquivo: ttz_dataset_root.py Projeto: schoef/Kullback-Leibner-divergence-Criterion

# Root_numpy
from root_numpy import root2array

# make small
sample.reduceFiles(to=1)

# lumi
lumi = 150

# Polynomial parametrization
w = WeightInfo(sample.reweight_pkl)
w.set_order(2)

# function that evaluates the weight of the SM hypothesis
sm_weight = w.get_weight_func()
# function that evaluates the weight of the BSM hypothesis
bsm_weight = w.get_weight_func(ctZ=4)

selectionString = "Sum$(genLep_pt>10&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)==3&&Sum$(genLep_pt>20&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)>=2&&Sum$(genLep_pt>40&&(abs(genLep_pdgId)==11||abs(genLep_pdgId)==13)&&abs(genLep_eta)<2.5)>=1&&abs(genZ_mass-91.2)<=10&&Sum$(genJet_pt>30&&abs(genJet_eta)<2.4)>=3&&Sum$(genJet_pt>30&&genJet_matchBParton>=1&&abs(genJet_eta)<2.4)>=1&&genZ_pt>=0"

print sample.files

X = root2array(sample.files,
               branches=[
                   "genZ_pt", "genZ_eta", "genZ_phi", "genZ_mass",
                   "genZ_cosThetaStar", "ref_lumiweight1fb"
               ],
               selection=selectionString)

print X