import math
import ROOT as r
basedir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(basedir)
from plotstyle import SimpleCanvas, RatioCanvas, DataMCCanvas
from datasets import allsamples
import config
from pprint import pprint
outputFile = r.TFile.Open(basedir + '/data/gjetsTFactor.root', 'recreate')
lumi = min(
config.jsonLumi, allsamples['sph-16b2-d'].lumi +
allsamples['sph-16c2-d'].lumi + allsamples['sph-16d2-d'].lumi)
canvas = DataMCCanvas(lumi=lumi)
dtree = r.TChain('events')
dtree.Add(config.skimDir + '/sph-16*2-d_monoph.root')
print dtree.GetEntries()
btree = r.TChain('events')
btree.Add(config.skimDir + '/sph-16*2-d_hfake.root')
btree.Add(config.skimDir + '/sph-16*2-d_efake.root')
bmctree = r.TChain('events')
# bmctree.Add(config.skimDir + '/znng-130_monoph.root')
# bmctree.Add(config.skimDir + '/wnlg-130_monoph.root')
# bmctree.Add(config.skimDir + '/wg_monoph.root') # NLO sample to get around pT/MET > 130 GeV cut on LO sample
bmctree.Add(config.skimDir + '/wglo_monoph.root')
bmctree.Add(config.skimDir + '/wlnu-*_monoph.root')
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)
histFile = ROOT.TFile.Open(args.histFile)
# closes if not args.replot
####################
## DRAW / ANALYZE ##
####################
if args.plotDir == '-' and not ('count' in plotNames or args.bbb
or args.chi2):
# nothing to do
sys.exit(0)
print 'Drawing plots..'
canvas = DataMCCanvas()
nentries = (1 + len(plotConfig.bkgGroups) + len(plotConfig.signalPoints))
ncolumns = math.ceil(float(nentries) / 5.)
xmin = 0.35 if ncolumns > 2 else 0.55
canvas.legend.setPosition(xmin, SimpleCanvas.YMAX - 0.01 - 0.035 * 5, 0.92,
SimpleCanvas.YMAX - 0.01)
if args.plotDir:
if args.plotDir == '-':
plotDir = ''
else:
plotDir = args.plotDir
if plotDir.startswith(plotstyle.WEBDIR):
plotDir = plotDir.replace(plotstyle.WEBDIR, '')
else:
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.plotconfig import VariableDef
import config
import ROOT as r
r.gROOT.SetBatch(True)
smuLumi = sum(allsamples[s].lumi for s in [
'smu-16b-r', 'smu-16c-r', 'smu-16d-r', 'smu-16e-r', 'smu-16f-r',
'smu-16g-r', 'smu-16h'
])
canvas = DataMCCanvas(lumi=smuLumi)
probePixel = '!probe.pixelVeto'
zMassCut = 'z.mass > 81. && z.mass < 101.'
dPhiCut = 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - jets.phi[0])) > 3.'
dPhiMetCut = 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - t1Met.phi)) < 0.5'
jetPtCut = 'jets.pt[0] > 100.'
dPhiJetCut = 't1Met.minJetDPhi > 0.5'
monojetCut = 'jets.size == 1'
njetsCut = 'jets.size < 3'
variables = [
VariableDef('met',
'E_{T}^{miss}',
't1Met.met',
[25 * x
