Example #1
0
import os
from ROOT import *
from array import array
from JPsi.MuMu.common.pmvTrees import getChains

#weight = {
    #"data": 1.,
    #"z"  : 0.030541912803076,
    #"qcd": 0.10306919044126,
    #"w"  : 0.074139194512438,
    #"tt" : 0.005083191122289,
#}

tree = getChains('v15reco')

canvases = []
graphs = []

## Set TDR style
macroPath = "tdrstyle.C"
if os.path.exists(macroPath):
    gROOT.LoadMacro(macroPath)
    ROOT.setTDRStyle()
    gROOT.ForceStyle()

gStyle.SetPadRightMargin(0.05)
gStyle.SetPadTopMargin(0.05)
wWidth = 600
wHeight = 600
canvasDX = 20
canvasDY = 20
Example #2
0
    "qcd" : "QCD",
    "tt"  : "t#bar{t}",
    "w"   : "W",
}

histograms = {}

## gStyle.SetPadRightMargin(0.05)
#print 'PadTopMargin:', gStyle.GetPadTopMargin()
#gStyle.SetPadTopMargin(0.15)
#print 'PadTopMargin:', gStyle.GetPadTopMargin()

latexLabel = TLatex()
latexLabel.SetNDC()

chains = pmvTrees.getChains('v19')
tree = {}
for tag in 'data z qcd w tt'.split():
  tree[tag] = chains[tag]

#______________________________________________________________________________
def get_selection():
    '''
    Return the TTree selection expression based on the name.
    '''
    if 'EB' in name:
        selection = '&'.join([
            'phoIsEB',
            'phoPt > 25',
            'scEt > 10',
            'phoHoE < 0.5',
Example #3
0
latexLabel = TLatex()
latexLabel.SetNDC()

## open files
# file = {}
# for tag, name in fileName.items():
#     file[tag] = TFile(os.path.join(path, name))

## get trees
tree = {}
# for tag, f in file.items():
#     tree[tag] = f.Get("pmvTree/pmv")
# import JPsi.MuMu.common.energyScaleChains as esChains
import JPsi.MuMu.common.pmvTrees as pmvTrees

chains = pmvTrees.getChains("v15")
tree = {}
for tag in "data z qcd w tt".split():
    tree[tag] = chains[tag]

## make histos of pmv vs mmgMass

# ebSelection = "phoIsEB & abs(mmgMass-90)<15 & (minDEta > 0.04 | minDPhi > 0.3)"
# eeSelection = "!phoIsEB & abs(mmgMass-90)<15 & (minDEta > 0.08 | minDPhi > 0.3)"
selection = "scEt > 10 && phoHoE < 0.5 && mmMass < 80"
# selection = 'phoIsEB'
# selection = '!phoIsEB'

###############################################################################
# Plot a quantity in data for EB
yRange = (1e-4, 7000.0)
sys.argv.append( '-b' )

import ROOT
import JPsi.MuMu.common.pmvTrees as pmvTrees

## Configuration
# sample = "data39x"
sample, version = 'z', 'v12'
# sample, version = 'data2011', 'v9'
# sample = "zg"
# sample = "qcd"

ROOT.gROOT.LoadMacro("resolutionErrors.C++")
ROOT.gROOT.LoadMacro("res/tools.C++")

chain = pmvTrees.getChains(version)[sample]

## Apply run-based energy scale correction to MC
## Store the pileup weight for MC, dummy weight for real data
if sample == 'data2011':
    outputVars = """
        mmMass
        scaledMmgMass3(corrByRun(id.run,scEta,phoR9),mmgMass,mmMass)
        phoPt*corrByRun(id.run,scEta,phoR9)
        scEta
        phoR9
        1
        """.split()
else:
    outputVars = """
        mmMass
Example #5
0
latexLabel = TLatex()
latexLabel.SetNDC()

## open files
file = {}
for tag, name in fileName.items():
    file[tag] = TFile(os.path.join(path, name))

## get trees
tree = {}
for tag, f in file.items():
    tree[tag] = f.Get("pmvTree/pmv")

import JPsi.MuMu.common.pmvTrees as pmvtrees
treev15reco = pmvtrees.getChains('v15reco')
tree['z'] = treev15reco['z']
tree['data'] = treev15reco['data']

## make histos of pmv vs deta

###############################################################################
# Plot PMV eff. vs photon pt in data for EB
c1 = TCanvas()
canvases.append(c1)

#xbins = [0., 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 2.0, 2.5]
xbins = [0.1 * i for i in range(26)]

h_Eta = TH1F("h_Eta_data_eb", "#eta^#gamma", len(xbins)-1, array("d", xbins))