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
"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',
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
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))
