def addSig(self, *args, **kwd):
if len(args) > 2:
args = args[0:2] + (allsamples.getmany(args[2]),) + args[3:]
elif 'samples' in kwd:
kwd['samples'] = allsamples.getmany(kwd['samples'])
self.sigGroups.append(GroupSpec(*args, **kwd))
def addBkg(self, *args, **kwd):
if len(args) > 2:
args = args[0:2] + allsamples.getmany(args[2]) + args[3:]
elif 'samples' in kwd:
kwd['samples'] = allsamples.getmany(kwd['samples'])
if len(args) < 4 and 'region' not in kwd:
kwd['region'] = self.obs.region
self.bkgGroups.append(GroupSpec(*args, **kwd))
def addBkg(self, *args, **kwd):
if len(args) > 2:
args = args[0:2] + allsamples.getmany(args[2]) + args[3:]
elif 'samples' in kwd:
if type(kwd['samples'][0]) == tuple:
kwd['samples'] = [ allsamples.get(sname) for (sname, _) in kwd['samples'] ]
else:
kwd['samples'] = allsamples.getmany(kwd['samples'])
self.bkgGroups.append(GroupSpec(*args, **kwd))
def __init__(self, name, obsSamples = []):
self.name = name # name serves as the default region selection (e.g. monoph)
self.baseline = '1'
self.fullSelection = ''
self.obs = GroupSpec('data_obs', 'Observed', samples = allsamples.getmany(obsSamples))
self.prescales = dict([(s, 1) for s in self.obs.samples])
self.sigGroups = []
self.signalPoints = []
self.bkgGroups = []
self.plots = []
self.treeMaker = ''
def processSampleNames(_inputNames, _selectorKeys, _plotConfig = ''):
snames = []
if _plotConfig:
# if a plot config is specified, use the samples for that
snames = plotconfig.getConfig(_plotConfig).samples()
else:
snames = _inputNames
# handle special group names
if 'all' in snames:
snames.remove('all')
snames = _selectorKeys
if 'data16' in snames:
snames.remove('data16')
snames += [key for key in _selectorKeys if '16' in key and allsamples[key].data]
if 'bkgd' in snames:
snames.remove('bkgd')
snames += [key for key in _selectorKeys if not allsamples[key].data and not key.startswith('dm') and not key.startswith('add') and not key.endswith('-d')]
if 'dmfs' in snames:
snames.remove('dmfs')
snames += [key for key in _selectorKeys if key.startswith('dm') and key[3:5] == 'fs']
if 'dm' in snames:
snames.remove('dm')
snames += [key for key in _selectorKeys if key.startswith('dm') and not key[3:5] == 'fs']
if 'add' in snames:
snames.remove('add')
snames += [key for key in _selectorKeys if key.startswith('add')]
if 'fs' in snames:
snames.remove('fs')
snames += [key for key in _selectorKeys if 'fs' in key]
# filter out empty samples
for name in list(snames):
if '*' in name: # wild card
snames.remove(name)
snames.extend([s.name for s in allsamples.getmany(name)])
try:
samp = allsamples[name]
except KeyError:
print name, "is not in datasets.csv. Removing it from the list of samples to run over."
snames.remove(name)
snames = [name for name in snames if allsamples[name].sumw != 0.]
return snames
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
TEMPLATEONLY = True
FITPSEUDODATA = False
from datasets import allsamples
from plotstyle import SimpleCanvas
import config
import utils
ROOT.RooMsgService.instance().setGlobalKillBelow(ROOT.RooFit.ERROR)
ROOT.gROOT.LoadMacro(basedir + '/../common/MultiDraw.cc+')
#targs = allsamples.getmany(['sph-16b-m', 'sph-16c-m', 'sph-16d-m'])
targs = allsamples.getmany(['sph-16*-m'])
dataLumi = sum(s.lumi for s in targs)
### Canvas
from plotstyle import SimpleCanvas
canvas = SimpleCanvas()
canvas.lumi = dataLumi
### Make templates
# Visualization
def plotHist(hist):
canvas.addHistogram(hist)
canvas.xtitle = '#phi\''
canvas.printWeb('monophoton/halo', hist.GetName(), logy=False)
canvas.Clear()
if isSensitive or outFile:
usedPoints = []
for sspec in plotConfig.signalPoints:
usedPoints.append(sspec.name)
hist = groupHist(sspec.group, vardef, plotConfig, args.skimDir, samples = [sspec.name], name = sspec.name, lumi = lumi, outFile = outFile)
if vardef.name == 'count' or vardef.name == args.bbb or vardef.name == args.chi2:
counters[sspec.name] = hist
elif plotDir:
canvas.addSignal(hist, title = sspec.title, color = sspec.color, drawOpt = drawOpt)
# write out all signal distributions if asked for
if args.allSignal:
for group in plotConfig.sigGroups:
for sample in allsamples.getmany(group.samples):
if sample.name not in usedPoints:
usedPoints.append(sample.name)
groupHist(group, vardef, plotConfig, args.skimDir, samples = [sample.name], name = sample.name, lumi = lumi, outFile = outFile)
if args.asimov == '':
print 'data_obs'
if args.saveTrees:
obshist = []
else:
obshist = vardef.makeHist('data_obs', outDir = outFile)
for sname in plotConfig.obs.samples:
if args.saveTrees:
obshist += getHist(sname, allsamples[sname], plotConfig, vardef, args.skimDir, prescale = plotConfig.prescales[sname], outDir = sampleDir)
else:
data.Add('/mnt/hadoop/scratch/yiiyama/monophoton/skim/sph-16*-m_tpeg.root')
outputFile.cd()
data.Draw('probes.sieie>>hdata', selection, 'goff')
hmc.Scale(hdata.GetSumOfWeights() / hmc.GetSumOfWeights())
ratio = hdata.Clone('ratio')
ratio.Divide(hmc)
line = ROOT.TF1('line', '[0] + [1] * x', 0.0085, 0.0105)
line.SetParameters(1.5, -100.)
ratio.Fit(line)
outputFile.cd()
hmc.Write()
hdata.Write()
ratio.Write()
line.Write('fit')
# visualize
canvas = DataMCCanvas(lumi=sum(s.lumi
for s in allsamples.getmany('sph-16*-m')))
canvas.legend.setPosition(0.7, 0.7, 0.9, 0.9)
canvas.addStacked(hmc, title='MC', color=ROOT.kBlue, style=3003)
canvas.addObs(hdata, title='Data')
canvas.printWeb('purity', 'sieie_ratio', logy=False)
import sys
import os
import math
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
import ROOT
from datasets import allsamples
file1 = ROOT.TFile("../data/muo_muon_idsf_2016_bcdef.root")
file2 = ROOT.TFile("../data/muo_muon_idsf_2016_gh.root")
lumi1 = sum(s.lumi for s in allsamples.getmany("sph-16{b,c,d,e,f}-m"))
lumi2 = sum(s.lumi for s in allsamples.getmany("sph-16{g,h}-m"))
lids = ['Loose', 'Tight']
outfile = ROOT.TFile("../data/muo_muon_idsf_2016.root", "RECREATE")
unc = 0.01
for lid in lids:
folder = "MC_NUM_" + lid + "ID_DEN_genTracks_PAR_pt_eta"
axes = "pt_abseta"
sfhistname = folder + "/" + axes + "_ratio"
sfhist1 = file1.Get(sfhistname)
sfhist2 = file2.Get(sfhistname)
return (obs, exp)
###======================================================================================
### Make Datacards and compute limits
###======================================================================================
classes = {} # "dmv" : ( [mMed], [mDM] )
modelList = []
if opts.models == []:
opts.models = ['add', 'dmv', 'dmvfs', 'dma', 'dmafs', 'dmewk', 'zgr']
for model in opts.models:
samples = allsamples.getmany(model + '-*')
snames = [s.name for s in samples]
modelList += snames
for sname in snames:
(name, xval, yval) = sname.split('-')
if not name in classes.keys():
classes[name] = ([], [])
if not xval in classes[name][0]:
classes[name][0].append(xval)
if not yval in classes[name][1]:
classes[name][1].append(yval)
LimitToolDir = os.path.join(os.environ['CMSSW_BASE'],
'src/HiggsAnalysis/CombinedLimit')
cardDir = os.path.join(LimitToolDir, 'data/monoph')
('w{m,p}lnuewk', mc_wlnu),
('znn{,n}-*', mc_cand),
('dyn-50', applyMod(mc_cand + mc_lep + mc_dilep + [tpegLowPt, tpmgLowPt, 'tp2e', 'tp2m'], s.addGenPhotonVeto)),
('dyn-50@', applyMod(mc_cand + mc_lep + mc_dilep + [tpegLowPt, tpmgLowPt, 'tp2e', 'tp2m'], s.addGenPhotonVeto, s.genBosonPtTruncator(maximum = 50.))),
('dyn-50-*', applyMod(mc_cand + mc_lep + mc_dilep + [tpegLowPt, tpmgLowPt, 'tp2e', 'tp2m'], s.addGenPhotonVeto)),
('zewk', applyMod(mc_cand + mc_lep + mc_dilep + [tpegLowPt, tpmgLowPt, 'tp2e', 'tp2m'], s.addGenPhotonVeto)),
('qcd-*', mc_cand + mc_qcd + mc_dilep + mc_lep),
('add-*', mc_sig),
('dm*', mc_sig),
('dph*', mc_sig),
('hbb-nlo-125', mc_sig)
"""
allSelectors = {}
for pat, sels in allSelectors_byPattern:
samples = allsamples.getmany(pat)
for sample in samples:
if sample in allSelectors:
raise RuntimeError('Duplicate skim config for ' + sample.name)
sampleSelectors = {}
for sel in sels:
# sel has to be either a selector function name or a tuple of form (region, selector[, modifiers])
if type(sel) is str:
sampleSelectors[sel] = getattr(s, sel)
elif len(sel) == 2:
sampleSelectors[sel[0]] = sel[1]
else:
sampleSelectors[sel[0]] = sel[1:]
allSelectors[sample] = sampleSelectors
import ROOT
mask = collections.defaultdict(set)
if args.mask:
with open(args.mask) as source:
for run, ranges in json.load(source).items():
for begin, end in ranges:
mask[int(run)] |= set(range(begin, end + 1))
allLumis = collections.defaultdict(set)
sampleRuns = collections.defaultdict(set)
if not args.list:
print 'Calculating integrated luminosity for', args.snames, 'from', config.ntuplesDir
for sample in allsamples.getmany(args.snames):
dname = config.ntuplesDir + '/' + sample.book + '/' + sample.fullname
for fname in os.listdir(dname):
if not fname.endswith('.root'):
continue
path = dname + '/' + fname
source = ROOT.TFile.Open(path)
if not source:
continue
gr = source.Get('ProcessedRunsLumis')
if gr:
for iP in range(gr.GetN()):
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
from datasets import allsamples
import config
try:
os.makedirs(config.histDir + '/trigger')
except OSError:
pass
obj = sys.argv[1]
snames = sys.argv[2:]
samples = allsamples.getmany(snames)
print 'Skimming samples'
print [s.name for s in samples]
ROOT.gSystem.Load(config.libsimpletree)
ROOT.gSystem.AddIncludePath('-I' + config.dataformats + '/interface')
ROOT.gROOT.LoadMacro(thisdir + '/Skimmer.cc+')
# available object - sample - skim type configurations
configs = {
'photon': {'sph': ROOT.kDiphoton, 'sel': ROOT.kElectronPhoton, 'smu': ROOT.kMuonPhoton, 'jht': ROOT.kJetHT},
'muon': {'smu': ROOT.kDimuon},
'electron': {'sel': ROOT.kDielectron},
'met': {'sel': ROOT.kElectronMET}
return (obs, exp)
###======================================================================================
### Make Datacards and compute limits
###======================================================================================
classes = {} # "dmv" : ( [mMed], [mDM] )
modelList = []
if opts.models == []:
opts.models = ['add', 'dmv', 'dmvfs', 'dma', 'dmafs', 'dmewk', 'zgr']
for model in opts.models:
samples = allsamples.getmany(model + '-*')
snames = [s.name for s in samples]
modelList += snames
for sname in snames:
(name, xval, yval) = sname.split('-')
if not name in classes.keys():
classes[name] = ( [], [] )
if not xval in classes[name][0]:
classes[name][0].append(xval)
if not yval in classes[name][1]:
classes[name][1].append(yval)
LimitToolDir = os.path.join(os.environ['CMSSW_BASE'], 'src/HiggsAnalysis/CombinedLimit')
cardDir = os.path.join(LimitToolDir, 'data/monoph')
if not os.path.exists(cardDir):
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)
if args.mask:
with open(args.mask) as source:
for run, ranges in json.load(source).items():
for begin, end in ranges:
mask[int(run)] |= set(range(begin, end + 1))
allLumis = collections.defaultdict(set)
sampleRuns = collections.defaultdict(set)
if not args.list:
print 'Calculating integrated luminosity for', args.snames
arun = array.array('I', [0])
alumi = array.array('I', [0])
for sample in allsamples.getmany(args.snames):
print sample.name
for path in sample.files():
source = ROOT.TFile.Open(path)
if not source:
print 'Cannot open', path
continue
tree = source.Get('lumiSummary')
if not tree:
print path, 'does not have lumiSummary. Extracting the lumi list from the events tree.'
tree = source.Get('events')
tree.SetBranchStatus('*', False)
tree.SetBranchStatus('runNumber', True)
import array
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
from plotstyle import DataMCCanvas
from datasets import allsamples
from main.plotutil import PlotDef
from main.plotconfig import getConfig
import config
import ROOT as r
r.gROOT.SetBatch(True)
lumi = sum([s.lumi for s in allsamples.getmany('sph-16*-m')])
canvas = DataMCCanvas(lumi=lumi)
varDef = PlotDef('phoPt',
'E_{T}^{#gamma}',
'photons.scRawPt[0]',
[175.0, 200., 250., 300., 400., 600., 1000.0],
unit='GeV',
overflow=True)
gj = ['gj-100', 'gj-200', 'gj-400', 'gj-600']
wlnun = [
'wlnun-0', 'wlnun-50', 'wlnun-100', 'wlnun-250', 'wlnun-400', 'wlnun-600'
]
top = ['ttg', 'tg']
gg = ['gg-40', 'gg-80']
import ROOT
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
from datasets import allsamples
import config
ROOT.gSystem.Load(config.libsimpletree)
ROOT.gSystem.AddIncludePath('-I' + config.dataformats + '/interface')
skim = sys.argv[1]
snames = sys.argv[2:]
puSource = ROOT.TFile.Open(basedir + '/data/pileup.root')
puweight = puSource.Get('puweight')
ROOT.gROOT.LoadMacro(thisdir + '/monoph.cc+')
ROOT.gROOT.LoadMacro(thisdir + '/dimu.cc+')
for sample in allsamples.getmany(snames):
source = ROOT.TChain('events')
source.Add(config.ntuplesDir + '/' + sample.book + '/' + sample.fullname + '/*.root')
outName = config.histDir + '/veto_eff/' + skim +'_' + sample.name + '.root'
if sample.data:
getattr(ROOT, skim)(source, outName)
else:
getattr(ROOT, skim)(source, outName, sample.crosssection / sample.sumw, puweight)
# garbage collection
if subdet == 'barrel':
template = ROOT.TH1D('template', ';#sigma_{i#etai#eta}', 44, 0.004, 0.015)
else:
template = ROOT.TH1D('template', ';#sigma_{i#etai#eta}', 45, 0.015, 0.06)
ratioTemplate = ROOT.TH1D('ratio', ';I_{CH} (GeV)', 3, array.array('d', [0., chIsoCuts[isub][1], 3.5, 5.]))
ptTotal = ROOT.TH1D('ptTotal', '', 1, 0., 1.)
outputFile = ROOT.TFile.Open(outputFileName, 'recreate')
# target and hadron templates
dataPlotter = ROOT.multidraw.MultiDraw()
dataPlotter.setWeightBranch('')
lumi = 0.
for sample in allsamples.getmany(fakeParams.measurementDataset):
utils.addSkimPaths(dataPlotter, sample.name, fakeParams.measurementSelection)
lumi += sample.lumi
if subdet == 'barrel':
dataPlotter.setFilter('t1Met.pt < 100. && photons.isEB[0]')
else:
dataPlotter.setFilter('t1Met.pt < 100. && !photons.isEB[0]')
mcPlotter = ROOT.multidraw.MultiDraw()
for sample in allsamples.getmany(fakeParams.measurementMC):
utils.addSkimPaths(mcPlotter, sample.name, fakeParams.measurementSelection)
if subdet == 'barrel':
mcPlotter.setFilter('t1Met.pt < 100. && photons.isEB[0]')
else:
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()
def addObs(self, snames, prescale=1):
samples = allsamples.getmany(snames)
for sample in samples:
self.obs.samples.append(sample)
self.prescales[sample] = prescale
sys.path.append(basedir)
from plotstyle import SimpleCanvas, RatioCanvas
from datasets import allsamples
import purity.selections as selections
import config
import utils
ROOT.gROOT.SetBatch(True)
###########################
# Setup and configuration #
###########################
snames = ['sph-16*-m']
lumi = sum(s.lumi for s in allsamples.getmany(snames))
canvas = SimpleCanvas(lumi=lumi)
rcanvas = RatioCanvas(lumi=lumi)
binning = array.array('d', map(float, selections.photonPtBinning))
pid = 'medium'
tune = 'Spring16' # 'GJetsCWIso'
extras = 'pixel-chargedpf' # -monoph'
suffix = '_Spring16' # '_GJetsCWIso'
itune = selections.Tunes.index(tune)
inputFile = ROOT.TFile.Open(basedir + '/data/impurity_' + tune + '.root')
impurityGraph = inputFile.Get("barrel-" + pid + "-" + extras + "-Met0to60")
outputFile = ROOT.TFile.Open(
names = ' '.join(sorted(sampleIds.keys()))
print names
sys.exit(0)
elif targets[0] == 'all':
targets = sampleIds.keys()
# if target contains mc / eldata / mudata
for confName in skimConfig:
if confName in targets:
targets.remove(confName)
for sname in skimConfig[targets][0]:
if sname not in targets:
targets.append(sname)
samples = allsamples.getmany(targets)
suffix = {'kEG': 'eg', 'kMG': 'mg', 'kMMG': 'mmg'}
puSource = ROOT.TFile.Open(basedir + '/data/pileup.root')
if not puSource:
print 'NPV reweight absent - run monophoton/ssw2.py'
sys.exit(1)
reweight = puSource.Get('puweight')
reweight.SetDirectory(ROOT.gROOT)
puSource.Close()
#npvSource = ROOT.TFile.Open('npvreweight.root')
#reweight = npvSource.Get('reweight')
for omname in omnames:
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)
import os
import sys
import shutil
import ROOT
NENTRIES = -1
thisdir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(thisdir)
sys.path.append(basedir)
from datasets import allsamples
import config
sphSamples = allsamples.getmany('sph-16*')
tree = ROOT.TChain('skimmedEvents')
for sample in sphSamples:
tree.Add('/data/t3home000/yiiyama/studies/monophoton/efake_skim/' +
sample.name + '_eg.root')
rhoBins = [0., 5., 7., 9.] + [10. + x
for x in range(8)] + [18., 20., 23., 30., 35.]
outputFile = ROOT.TFile.Open(config.histDir + '/worstIsoEA.root', 'recreate')
graph = ROOT.TGraphErrors(len(rhoBins) - 1)
for iP in range(len(rhoBins) - 1):
rhoMin = rhoBins[iP]
rhoMax = rhoBins[iP + 1]