if args.asimov != '' and args.saveTrees:
print 'Cannot use --save-trees together with --asimov.'
sys.exit(1)
fullLumi = 0.
effLumi = 0.
for sName in plotConfig.obs.samples:
fullLumi += allsamples[sName].lumi
effLumi += allsamples[sName].lumi / plotConfig.prescales[sName]
# from config import jsonLumi, jsonLumiBlinded
# fullLumi = min(jsonLumi, fullLumi)
# effLumi = min(jsonLumiBlinded, effLumi)
canvas = DataMCCanvas()
if args.plotDir:
if args.plotDir == '-':
plotDir = ''
else:
plotDir = args.plotDir
else:
plotDir = 'monophoton/' + args.config
if plotDir and args.clearDir:
for plot in os.listdir(WEBDIR + '/' + plotDir):
os.remove(WEBDIR + '/' + plotDir + '/' + plot)
print "Starting plot making for %s." % plotConfig.name
import math
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.plotconfig import VariableDef
import config
import ROOT as r
r.gROOT.SetBatch(True)
lumi = min(config.jsonLumi, allsamples['smu-16b2-d'].lumi + allsamples['smu-16c2-d'].lumi + allsamples['smu-16d2-d'].lumi)
canvas = DataMCCanvas(lumi = lumi)
probePixel = '!probe.pixelVeto'
zMassCut = 'z.mass > 81. && z.mass < 101.'
dPhiCut = 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - jets.phi[0])) > 3.'
metCut = 't1Met.met > 50.'
dPhiMetCut = 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - t1Met.phi)) < 0.5'
jetPtCut = 'jets.pt[0] > 100.'
dPhiJetCut = 't1Met.minJetDPhi > 0.5'
njetsCut = 'jets.size == 1'
variables = [ VariableDef('Met', 'E_{T}^{miss}', 't1Met.met', [10 * x for x in range(0, 10)] + [100 + 20 * x for x in range(0,5)] + [200, 250, 300], unit = 'GeV', overflow = True),
# VariableDef('dPhi', '#Delta#phi(Z, jet)', 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - jets.phi))', (15, 0., math.pi) ),
# VariableDef('dPhiJetMet', '#Delta#phi(E_{T}^{miss}, jet)', 'TMath::Abs(TVector2::Phi_mpi_pi(t1Met.phi - jets.phi))', (15, 0., math.pi) ),
VariableDef('dPhiZMet', '#Delta#phi(Z, E_{T}^{miss})', 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - t1Met.phi))', (15, 0., math.pi) ),
# VariableDef('jetEta', '|#eta_{j}|', 'TMath::Abs(jets.eta[0])', (10, 0., 5.), applyFullSel = True),
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')
"""
binnings = {
'Low': array.array('d', [0. + 10. * x for x in range(10)] + [100., 120., 140., 170.]
+ [200. + 50. * x for x in range(9)] ),
'High': array.array('d', [0. + 10. * x for x in range(10)] + [100., 120., 140., 170.]
+ [200. + 50. * x for x in range(9)] )
}
dmets = []
bmets = []
gmets = []
mcmets = []
lumi = allsamples['sph-d3'].lumi + allsamples['sph-d4'].lumi
canvas = DataMCCanvas(lumi = lumi)
for region, sel in regions:
binning = binnings[region]
dname = 'dmet'+region
dmet = r.TH1D(dname, ';E_{T}^{miss} (GeV); Events / GeV', len(binning) - 1, binning)
dmet.SetMinimum(0.002)
dmet.Sumw2()
dtree.Draw('t1Met.met>>'+dname, sel, 'goff')
bname = 'bmet'+region
bmet = r.TH1D(bname, ';E_{T}^{miss} (GeV); Events / GeV', len(binning) - 1, binning)
bmet.SetMinimum(0.002)
bmet.Sumw2()
btree.Draw('t1Met.met>>'+bname, 'weight * '+sel, 'goff')
samples = [('zllg', r.TColor.GetColor(0xff, 0x99, 0x33)),
('ttg', r.TColor.GetColor(0xbb, 0xaa, 0xff)),
('wlnu', r.TColor.GetColor(0xff, 0xee, 0x99)),
('wg', r.TColor.GetColor(0x99, 0xee, 0xff)),
('tt', r.TColor.GetColor(0xff, 0xaa, 0xcc)),
('dy-50', r.TColor.GetColor(0x99, 0xff, 0xaa))]
mcTrees = []
for sample, color in samples:
mcTree = r.TChain('skim')
mcTree.Add('/scratch5/ballen/hist/monophoton/phoMet/skim_' + sample +
'*.root')
mcTrees.append((sample, color, mcTree))
lumi = allsamples['sel-d3'].lumi + allsamples['sel-d4'].lumi
canvas = DataMCCanvas(lumi=lumi)
varString = '(probe.ptReco / probe.ptCorr)'
probeEtaCuts = [('TMath::Abs(probe.eta) < 0.8', 'low'),
('TMath::Abs(probe.eta > 0.8)', 'high')]
recoilCuts = [('recoil.pt > %i.' % pt, 'recoil' + str(pt))
for pt in [170, 200, 250, 300, 400]]
njetsCut = 'njets < 2'
tagEtaCut = 'TMath::Abs(tag.eta) < 0.2'
cuts = []
for recoilCut in recoilCuts:
allCuts = cuts + [recoilCut[0]]
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']
zllg = ['zllg-130-o', 'zllg-300-o']
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:
import math
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.plotconfig import VariableDef
import config
import ROOT as r
r.gROOT.SetBatch(True)
lumi = min(config.jsonLumi, allsamples['smu-16b2-d'].lumi + allsamples['smu-16c2-d'].lumi + allsamples['smu-16d2-d'].lumi)
canvas = DataMCCanvas(lumi = lumi)
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 for x in range(2, 4)] + [100 + 50 * x for x in range(0, 8)], unit = 'GeV', overflow = True, logy = True),
# VariableDef('dPhi', '#Delta#phi(Z, jet)', 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - jets.phi))', (15, 0., math.pi) ),
# VariableDef('dPhiJetMet', '#Delta#phi(E_{T}^{miss}, jet)', 'TMath::Abs(TVector2::Phi_mpi_pi(t1Met.phi - jets.phi))', (15, 0., math.pi) ),
VariableDef('dPhiZMet', '#Delta#phi(Z, E_{T}^{miss})', 'TMath::Abs(TVector2::Phi_mpi_pi(z.phi - t1Met.phi))', (15, 0., math.pi) ),
VariableDef('jetPt', 'p_{T}^{j}', 'jets.pt[0]', (20, 0., 1000.), unit = 'GeV', applyFullSel = True, logy = True),
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)
("zllg", r.TColor.GetColor(0xFF, 0x99, 0x33)),
("ttg", r.TColor.GetColor(0xBB, 0xAA, 0xFF)),
("wlnu", r.TColor.GetColor(0xFF, 0xEE, 0x99)),
("wg", r.TColor.GetColor(0x99, 0xEE, 0xFF)),
("tt", r.TColor.GetColor(0xFF, 0xAA, 0xCC)),
("dy-50", r.TColor.GetColor(0x99, 0xFF, 0xAA)),
]
mcTrees = []
for sample, color in samples:
mcTree = r.TChain("skim")
mcTree.Add("/scratch5/ballen/hist/monophoton/phoMet/skim_" + sample + "*.root")
mcTrees.append((sample, color, mcTree))
lumi = allsamples["sel-d3"].lumi + allsamples["sel-d4"].lumi
canvas = DataMCCanvas(lumi=lumi)
varString = "(probe.ptReco / probe.ptCorr)"
probeEtaCuts = [("TMath::Abs(probe.eta) < 0.8", "low"), ("TMath::Abs(probe.eta > 0.8)", "high")]
recoilCuts = [("recoil.pt > %i." % pt, "recoil" + str(pt)) for pt in [170, 200, 250, 300, 400]]
njetsCut = "njets < 2"
tagEtaCut = "TMath::Abs(tag.eta) < 0.2"
cuts = []
for recoilCut in recoilCuts:
allCuts = cuts + [recoilCut[0]]
for probeEtaCut in probeEtaCuts:
finalCuts = allCuts + [probeEtaCut[0]]
import ROOT
ROOT.gROOT.SetBatch(True)
fitDiagnostics = sys.argv[1]
plots = sys.argv[2] # plots with random fakemet
name = sys.argv[3]
sigScale = float(sys.argv[4])
fakeNorm = float(sys.argv[5])
originalMu = 0.1
dist = 'mtPhoMet'
signal = 'dph-nlo-125'
region = 'gghg'
canvas = DataMCCanvas()
source = ROOT.TFile.Open(fitDiagnostics)
bkg = source.Get('shapes_fit_s/gghg/total_background')
fake = source.Get('shapes_fit_s/gghg/fakemet')
sig = source.Get('shapes_fit_s/gghg/total_signal')
data = source.Get('shapes_fit_s/gghg/data')
bkg.Add(fake, -1.)
plotsSource = ROOT.TFile.Open(plots)
bkgTrue = plotsSource.Get(dist + '/bkgtotal')
fakeTrue = plotsSource.Get(dist + '/fakemet')
sigTrue = plotsSource.Get(dist + '/samples/' + signal + '_' + region)
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
