def __init__(self, name, snames, variable):
self.name = name
self.plotter = ROOT.MultiDraw()
for sname in snames:
if lowpt:
self.plotter.addInputPath(utils.getSkimPath(sname, 'ph75'))
else:
self.plotter.addInputPath(utils.getSkimPath(sname, 'emjet'))
self.variable = variable
self.categories = [] # [(name, title, sel)]
oname = omname[0]
mname = omname[1]
print oname, mname
snames, region, basesel, colname = measurements[(oname, mname)]
outputFile = ROOT.TFile.Open(
outDir + '/trigger_efficiency_%s_%s.root' % (oname, mname),
'recreate')
# fill the histograms
plotter = ROOT.MultiDraw()
plotter.setWeightBranch('')
for sample in allsamples.getmany(snames):
plotter.addInputPath(utils.getSkimPath(sample.name, region))
plotter.setBaseSelection(basesel)
# make an empty histogram for each (trigger, variable) combination
histograms = []
for tname, (passdef, commonsel, title,
variables) in confs[oname].items():
if len(omname) > 2 and tname != omname[2]:
continue
trigDir = outputFile.mkdir(tname)
passdef = passdef.format(col=colname)
commonsel = commonsel.format(col=colname)
# templates setup
egPlotter = ROOT.MultiDraw()
mgPlotter = ROOT.MultiDraw()
# egPlotter.setPrintLevel(2)
# mgPlotter.setPrintLevel(2)
if dataType == 'data':
egPlotter.setWeightBranch('')
mgPlotter.setWeightBranch('')
# target samples
if dataSource == 'smu':
for sname in skimConfig['mudata'][0]:
egPlotter.addInputPath(utils.getSkimPath(sname, 'tp2m'))
mgPlotter.addInputPath(utils.getSkimPath(sname, 'tp2m'))
egPlotter.setBaseSelection('tags.pt_ > 50. && probes.loose')
mgPlotter.setBaseSelection('tags.pt_ > 50.')
else:
if dataSource == 'sph':
egSamp = 'phdata'
mgSamp = 'phdata'
elif dataSource == 'sel':
egSamp = 'eldata'
mgSamp = 'mudata'
for sname in skimConfig[egSamp][0]:
egPlotter.addInputPath(utils.getSkimPath(sname, 'tpeg'))
nomSels['chIso'] = nomSels['chIso'].replace('chIso', 'chIsoMax')
tightSels['chIso'] = tightSels['chIso'].replace('chIso', 'chIsoMax')
looseSels['chIso'] = looseSels['chIso'].replace('chIso', 'chIsoMax')
# selections concatenated with chVeto
configurations = [('Nom', nomSels), ('Tight', tightSels), ('Loose', looseSels)]
#######################
# Plot all histograms #
#######################
plotter = ROOT.MultiDraw()
plotter.setPrintLevel(1)
for sample in allsamples.getmany(snames):
if selections.lowpt:
plotter.addInputPath(utils.getSkimPath(sample.name, 'ph75'))
else:
plotter.addInputPath(utils.getSkimPath(sample.name, 'emjet'))
plotter.setBaseSelection(' && '.join(baseSels.values()))
template = ROOT.TH1D('template', ';p_{T} (GeV)', len(binning) - 1, binning)
template.Sumw2()
histograms = {}
gname = 'gpt'
gpt = template.Clone(gname)
histograms[gname] = gpt
plotter.addPlot(gpt, 'photons.scRawPt[0]', ' && '.join(goodSels.values()))
#sname = 'sph'
#sample = ('sph-16*-m', 'monoph')
#boson = 'Photon'
#sname = 'smu'
#sample = ('smu-16*-m', 'zmumu')
#boson = 'Z'
#sname = 'znng'
#sample = ('znng-130-o', 'monoph')
#boson = 'Photon'
sname = 'wnlg'
sample = ('wnlg-130-o', 'monoph')
boson = 'Photon'
ROOT.gSystem.Load('libPandaTreeObjects.so')
e = ROOT.panda.Event
ROOT.gROOT.LoadMacro('jetbalance.cc+')
print utils.getSkimPath(*sample)
tree = ROOT.TChain('events')
tree.Add(utils.getSkimPath(*sample))
outputFile = ROOT.TFile.Open(
'/data/t3home000/yiiyama/monophoton/jetbalance_' + sname + '_' + boson +
'.root', 'recreate')
ROOT.jetbalance(tree, outputFile, getattr(ROOT, 'b' + boson))
outputFile.Close()
import os
import sys
import shutil
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
import config
import utils
from datasets import allsamples
import ROOT
for sample in allsamples.getmany('sph-16*'):
source = ROOT.TFile.Open(utils.getSkimPath(sample.name, 'offtime'))
tree = source.Get('events')
tmpname = '/tmp/' + os.environ[
'USER'] + '/' + sample.name + '_offtimeIso.root'
outputFile = ROOT.TFile.Open(tmpname, 'recreate')
newtree = tree.CopyTree(
'TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 175 && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 40.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026 && t1Met.pt > 170. && t1Met.photonDPhi > 0.5 && t1Met.minJetDPhi > 0.5'
)
newtree.Write()
source.Close()
outputFile.Close()
shutil.copy(tmpname,
config.skimDir + '/' + sample.name + '_offtimeIso.root')
os.remove(tmpname)
from datasets import allsamples
import ROOT
arun = array.array('I', [0])
alumi = array.array('I', [0])
aevent = array.array('I', [0])
aeta = array.array('f', [0.] * 10)
aphi = array.array('f', [0.] * 10)
positions = {}
#for sname in ['sph-16b-m', 'sph-16c-m', 'sph-16d-m', 'sph-16e-m', 'sph-16f-m', 'sph-16g-m', 'sph-16h-m']:
for sname in ['sph-16b-m', 'sph-16c-m', 'sph-16d-m']:
positions[sname] = {}
source = ROOT.TFile.Open(utils.getSkimPath(sname, 'monoph'))
tree = source.Get('events')
tree.Draw(
'>>elist',
'photons.scRawPt[0] > 175. && t1Met.pt > 170. && t1Met.photonDPhi > 0.5 && t1Met.minJetDPhi > 0.5',
'entrylist')
elist = ROOT.gDirectory.Get('elist')
tree.SetEntryList(elist)
tree.SetBranchAddress('runNumber', arun)
tree.SetBranchAddress('lumiNumber', alumi)
tree.SetBranchAddress('eventNumber', aevent)
tree.SetBranchAddress('photons.eta_', aeta)
tree.SetBranchAddress('photons.phi_', aphi)
ientry = 0
# garbage collection
histograms = []
template = ROOT.TH1D('template', ';#sigma_{i#etai#eta}', 44, 0.004, 0.015)
ratioTemplate = ROOT.TH1D(
'ratio', ';I_{CH} (GeV)', 3,
array.array('d', [0., getCut('chIsoCuts'), 3.5, 5.]))
ptTotal = ROOT.TH1D('ptTotal', '', 1, 0., 1.)
outputFile = ROOT.TFile.Open(basedir + '/hist/hadron_fake/direct_vbf.root',
'recreate')
# target and hadron templates
dataPlotter = ROOT.MultiDraw()
lumi = 0.
for sample in allsamples.getmany('sph-16*'):
dataPlotter.addInputPath(utils.getSkimPath(sample.name, 'vbfem'))
lumi += sample.lumi
dataPlotter.setBaseSelection('t1Met.pt < 100.')
mcPlotter = ROOT.MultiDraw()
for sample in allsamples.getmany('gj-*'):
mcPlotter.addInputPath(utils.getSkimPath(sample.name, 'vbfem'))
mcPlotter.setBaseSelection('t1Met.pt < 100.')
mcPlotter.setConstantWeight(lumi)
for ipt in range(len(ptBinning) - 1):
ptlow = ptBinning[ipt]
pthigh = ptBinning[ipt + 1]
def plotHist(hist):
canvas.addHistogram(hist)
canvas.xtitle = '#phi\''
canvas.printWeb('monophoton/halo', hist.GetName(), logy=False)
canvas.Clear()
outputFile = ROOT.TFile.Open(config.histDir + '/halo/phifit.root', 'recreate')
templates = {}
trees = {}
haloPlotter = ROOT.MultiDraw()
mcPlotter = ROOT.MultiDraw()
for sample in targs:
haloPlotter.addInputPath(utils.getSkimPath(sample.name, 'halo'))
mcPlotter.addInputPath(utils.getSkimPath('znng-130-o', 'monoph'))
mcPlotter.setConstantWeight(dataLumi)
haloPlotter.setBaseSelection(
'photons.scRawPt[0] > 175. && t1Met.pt > 170. && t1Met.minJetDPhi > 0.5 && t1Met.photonDPhi > 0.5'
)
mcPlotter.setBaseSelection(
'photons.scRawPt[0] > 175. && t1Met.pt > 170. && t1Met.minJetDPhi > 0.5 && t1Met.photonDPhi > 0.5'
)
foldedPhi = 'TMath::Abs(TVector2::Phi_mpi_pi(TVector2::Phi_mpi_pi(photons.phi_[0] + 0.005) - {halfpi})) - {halfpi}'.format(
halfpi=math.pi * 0.5)
empty = ROOT.TH1D('empty', ';#phi\'', 40, -0.5 * math.pi, 0.5 * math.pi)
import sys
import math
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
import config
import utils
from datasets import allsamples
import ROOT
offtime = ROOT.TChain('events')
for sample in allsamples.getmany('sph-16*'):
# offtime.Add(utils.getSkimPath(sample.name, 'offtime'))
offtime.Add(utils.getSkimPath(sample.name, 'offtimealt'))
# singlemu cannot be an alternative to sph because sph has severe trigger bias
#offtime = ROOT.TChain('events')
#for sample in allsamples.getmany('smu-16b-m'):
# offtime.Add(utils.getSkimPath(sample.name, 'offtime'))
trivial = ROOT.TChain('events')
for sample in allsamples.getmany('sph-16*'):
trivial.Add(utils.getSkimPath(sample.name, 'trivialShower'))
#offsel = 'TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 175 && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 5.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026 && t1Met.pt > 170. && t1Met.photonDPhi > 0.5 && t1Met.minJetDPhi > 0.5'
#offsel = 'TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 175 && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 5.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026'
#offsel = 'HLT_Photon135_PFMET100 && TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 140 && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 5.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026'
offsel = 'HLT_Photon120_R9Id90_HE10_IsoM && TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 130 && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 5.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026'
#offsel = 'TMath::Abs(photons.scEta) < 1.4442 && photons.scRawPt > 50. && photons.mipEnergy < 4.9 && photons.time > -15. && photons.time < -10. && (photons.mediumX[][2] || (photons.type == 2 && photons.trackIso < 5.)) && photons.sieie < 0.0104 && photons.hOverE < 0.026'
cuts.append('(genPhoton.pt > 170. && genMet.pt > 130.)')
genSel = ' && '.join(cuts)
weight = '(weight * ' + str(lumi) + ')'
genSelString = weight + ' * ' + genSel
recoSelString = weight + ' * ' + recoSel
expr = ptdef.expr + ':genPhoton.pt'
print expr
print genSelString
print recoSelString
for sample in samples:
sourceName = utils.getSkimPath(sample.name, region, config.skimDir, '')
dname = sample.name + '_' + region
print ' ', dname, '(%s)' % sourceName
if not os.path.exists(sourceName):
sys.stderr.write('File ' + sourceName + ' does not exist.\n')
raise RuntimeError('InvalidSource')
source = ROOT.TFile.Open(sourceName)
events = source.Get('events')
hist = ROOT.TH2D(dname, dname, len(xbins)-1, xbins, len(ybins)-1, ybins)
events.Draw(expr + '>>' + dname, genSelString, 'colz')
outFile.cd()
def plotiso(loc, pid, pt, met, tune):
inputKey = tune + '_' + loc + '_' + pid + '_PhotonPt' + pt + '_Met' + met
try:
ptSel = s.PhotonPtSels['PhotonPt' + pt]
except KeyError:
print 'Inputted pt range', pt, 'not found!'
print 'Not applying any pt selection!'
ptSel = '(1)'
try:
metSel = s.MetSels['Met' + met]
except KeyError:
print 'Inputted met range', met, 'not found!'
print 'Not applying any met selection!'
metSel = '(1)'
var = s.getVariable('chiso', tune, loc)
versDir = s.versionDir
WEBDIR = os.environ['HOME'] + '/public_html/cmsplots'
plotDir = os.path.join(WEBDIR, 'purity', s.Version, inputKey, 'chiso')
histDir = os.path.join(versDir, inputKey)
if not os.path.exists(plotDir):
os.makedirs(plotDir)
if not os.path.exists(histDir):
os.makedirs(histDir)
pids = pid.split('-')
pid = pids[0]
extras = pids[1:]
selections = s.getSelections(tune, loc, pid)
itune = s.Tunes.index(tune)
### Plot I_{CH} from sph data and gjets MC
# selection on H/E & INH & IPh
baseSel = ' && '.join([
ptSel, metSel, selections['fiducial'], selections['hovere'],
selections['nhiso'], selections['phiso']
])
if 'pixel' in extras:
baseSel = baseSel + ' && ' + s.Cuts['pixelVeto']
if 'monoph' in extras:
baseSel = baseSel + ' && ' + s.Cuts['monophId']
if 'max' in extras:
baseSel = baseSel.replace('chIso', 'chIsoMax')
var = s.Variable('TMath::Max(0., photons.chIsoMaxX[0][%d])' % itune,
*var[1:])
truthSel = '(photons.matchedGenId == -22)'
# output file
outName = 'chiso' # + inputKey
print 'plotiso writing to', histDir + '/' + outName + '.root'
outFile = ROOT.TFile(histDir + '/' + outName + '.root', 'RECREATE')
# don't use var.binning for binning
binning = [0., var.cuts[pid]] + [0.1 * x for x in range(20, 111, 5)]
# make the data iso distribution for reference
extractor = s.HistExtractor('sphData', s.Samples['sphData'], var)
print 'setBaseSelection(' + baseSel + ')'
extractor.plotter.setBaseSelection(baseSel)
extractor.categories.append(
('data', 'I_{CH} Distribution from SinglePhoton Data', ''))
hist = extractor.extract(binning)[0]
hist.Scale(1. / hist.GetSumOfWeights())
formatHist(hist, 'Events')
printHist([hist], 'HIST', outName + '_data', plotDir, logy=True)
extractor = s.HistExtractor('gjetsMc', s.Samples['gjetsMc'], var)
print 'setBaseSelection(' + baseSel + ' && ' + truthSel + ')'
extractor.plotter.setBaseSelection(baseSel + ' && ' + truthSel)
extractor.categories.append(
('rawmc', 'I_{CH} Distribution from #gamma+jets MC', ''))
raw = extractor.extract(binning)[0]
raw.Scale(1. / raw.GetSumOfWeights())
formatHist(raw, 'Events')
### Plot I_{CH} from sph data and dy MC Zee samples
eSelScratch = ' && '.join([
'tp.mass > 81 && tp.mass < 101', metSel, selections['fiducial'],
selections['hovere'], selections['nhiso'], selections['phiso'],
selections['sieie']
])
eSel = 'weight * (' + eSelScratch.replace("photons", "probes") + ')'
eExpr = var.expression.replace('photons', 'probes')
print 'Extracting electron MC distributions'
mcTree = ROOT.TChain('events')
for sample in allsamples.getmany('dy-50-*'):
mcTree.Add(utils.getSkimPath(sample.name, 'tpeg'))
print 'Draw(' + eExpr + ', ' + eSel + ')'
mcHist = raw.Clone("emc")
mcHist.Reset()
mcTree.Draw(eExpr + ">>emc", eSel, 'goff')
mcHist.Scale(1. / mcHist.GetSumOfWeights())
formatHist(mcHist, 'Events')
print 'Extracting electron data distributions'
dataTree = ROOT.TChain('events')
for sample in allsamples.getmany('sph-16*'):
dataTree.Add(utils.getSkimPath(sample.name, 'tpeg'))
print 'Draw(' + eExpr + ', ' + eSel + ')'
dataHist = raw.Clone("edata")
dataHist.Reset()
dataTree.Draw(eExpr + ">>edata", eSel, 'goff')
dataHist.Scale(1. / dataHist.GetSumOfWeights())
formatHist(dataHist, 'Events')
printHist([mcHist, dataHist],
'HIST',
outName + '_electrons',
plotDir,
logy=True)
scaled = raw.Clone("scaledmc")
scaleHist = raw.Clone("escale")
scaleHist.SetTitle("Data/MC Scale Factors from Electrons")
scaleHist.Reset()
for iBin in range(1, scaleHist.GetNbinsX() + 1):
dataValue = dataHist.GetBinContent(iBin)
dataError = dataHist.GetBinError(iBin)
mcValue = mcHist.GetBinContent(iBin)
mcError = mcHist.GetBinError(iBin)
if mcValue == 0 or dataValue == 0:
ratio = 1
error = 0.05
else:
ratio = dataValue / mcValue
error = ratio * ((dataError / dataValue)**2 +
(mcError / mcValue)**2)**(0.5)
scaleHist.SetBinContent(iBin, ratio)
scaled.SetBinContent(iBin, ratio * scaled.GetBinContent(iBin))
scaleHist.SetBinError(iBin, error)
scaled.Scale(1. / scaled.GetSumOfWeights())
formatHist(scaled, 'Events')
formatHist(scaleHist, 'Scale Factor', lstyle=ROOT.kSolid)
printHist([scaleHist], '', outName + '_scale', plotDir, logy=False)
printHist([raw, scaled], 'HIST', outName + '_photons', plotDir, logy=True)
outFile.Close()
ntotal = 0
sampleNames = []
tree = ROOT.TChain('cutflow')
for sample in allsamples.getmany(args.snames):
sampleNames.append(sample.name)
if sample.data:
data = True
if args.skimDir == config.skimDir:
# default skim directory -> assume nevents in DB is accurate
ntotal += sample.nevents
filePath = utils.getSkimPath(sample.name, args.region)
else:
# otherwise open the original files
for fname in sample.files():
source = ROOT.TFile.Open(fname)
counter = source.Get('counter')
if not counter:
source.Close()
continue
ntotal += counter.GetBinContent(1)
source.Close()
filePath = args.skimDir + '/' + sample.name + '_' + args.region + '.root'