def gethisto(tree, formula, binlo, binhi, additionalcut=None):
nbins = 50
h = root.TH1D('h', 'h', nbins, binlo, binhi)
s = Selector()
s.read_tree(tree, branches=[formula], cut=additionalcut)
h = s.draw(formula, hbase=h)
for ib in xrange(1, h.GetNbinsX() + 1):
v = h.GetBinContent(ib)
if v == 0:
h.SetBinContent(ib, 1)
else:
h.SetBinContent(ib, 1. / v)
return h
def infer(tt, ifile, payload, features, h5):
nent = int(tt.GetEntries())
if (0 != tt.FindBranch("dnnoutput")):
tt.SetBranchStatus("dnnoutput", 0)
output = ifile + "_tmp"
ofile = ROOT.TFile(output, "RECREATE")
otree = tt.CloneTree()
dnnoutput = array.array('f', [-99.0])
o_dnnoutput = otree.Branch("dnnoutput", AddressOf(treestruct, 'dnnoutput'),
"dnnoutput/F")
model = load_model(h5)
s = Selector()
s.read_tree(tt, branches=features)
for i in range(int(tt.GetEntries())):
tt.GetEntry(i)
treestruct.dnnoutput = -99.
if (i % (1 * nent / 100) == 0):
sys.stdout.write("\r[" + "=" * int(20 * i / nent) + " " +
str(round(100. * i / nent, 0)) + "% done")
sys.stdout.flush()
#treestruct.mT = 2*1.5*3*3
x = np.array([float(s[f][i]) for f in features])
x = x.reshape((1, x.shape[0]))
# print model.predict(x)
treestruct.dnnoutput = model.predict(x)[0, 0]
print treestruct.dnnoutput
o_dnnoutput.Fill()
ofile.Write()
ofile.Close()
os.system("mv -f %s %s" % (output, ifile))
binning = (40, 450, 1200)
n_partons = {
'Top' : 3,
'QCD' : 1,
'Higgs' : 2,
'W' : 2,
}
f = root.TFile(basedir + '/' + args.proc + '.root')
t = f.Get('events')
s = Selector()
s.read_tree(t, branches = ['fj1RawPt'], cut = 'fj1RawPt>450 && fj1RawPt<1200')
h = s.draw('fj1RawPt', fbins = binning)
h_inv = h.Clone()
for ib in xrange(1, h_inv.GetNbinsX()+1):
if h.GetBinContent(ib):
h_inv.SetBinContent(ib, 1)
h_inv.SetBinError(ib, 0)
h_inv.Divide(h)
for ib in xrange(1, h_inv.GetNbinsX()+1):
# print ib, h.GetBinContent(ib), h_inv.GetBinContent(ib)
if h.GetBinContent(ib) == 0:
h_inv.SetBinContent(ib, 0)
c.GetCanvas().SetBottomMargin(0.15)
c.cd()
args = parse('--outdir', '--proc')
f = root.TFile.Open(flatdir + '/%s.root' % args.proc)
t = f.Get('events')
sel = Selector()
is_top = not (args.proc == 'QCD')
cut = 'gen_pt>200 && fabs(gen_eta)<2.5 && clf_IsMatched'
if is_top:
cut = tAND(cut, 'gen_size<1.44 && fabs(gen_pdgid)==6')
sel.read_tree(t,
cut=cut,
branches=['clf_MSD', 'clf_Tau32', 'clf_Tau32SD', 'ptweight'])
nbins = 5
#hbase = root.TH2D('','',nbins,0,400,50,0.2,1.2)
hbase = root.TH2D('', '', nbins, 0.2, 1, 50, 0, 400)
hbase.GetYaxis().SetTitle('m_{SD} [GeV]')
hbase.GetXaxis().SetTitle('#tau_{32}')
hbase.GetXaxis().SetTitleOffset(1)
hbase.GetXaxis().SetTitleSize(0.06)
hbase.GetYaxis().SetTitleOffset(1)
hbase.GetYaxis().SetTitleSize(0.06)
def draw(key, color):
fields = ['clf_MSD', key]
f = root.TFile(args.ifile)
t = f.Get('events')
treestruct = TreeStruct()
nent = int(t.GetEntries())
#if t.FindBranch(args.bname):
# t.SetBranchStatus(args.bname, 0)
output = args.ifile + "_tmp"
ofile = root.TFile(output, "RECREATE")
otree = t.CloneTree()
s = Selector()
s.read_tree(t, branches=features)
model = load_model(args.h5)
print 'Loaded model...'
val = array('f', [-1])
o_val = otree.Branch(args.bname, val, args.bname + '/F')
for i in range(t.GetEntriesFast()):
val[0] = -1
x = np.array([float(s[f][i]) for f in features])
x = x.reshape((1, x.shape[0]))
# print model.predict(x)
val[0] = model.predict(x)[0, 0]
o_val.Fill()
ptarray = array('f', [30, 50, 80, 120, 160, 200, 250, 300, 500, 1000])
etaarray = array(
'f',
[-5, -4, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 4, 5])
uncarray = array('f', np.linspace(-30, 30, 200))
f = root.TFile.Open(args.infile)
t = f.Get('events')
ks = t.GetListOfBranches()
pt = args.jet + 'Pt'
rawpt = args.jet + 'RawPt'
eta = 'fabs(' + args.jet + 'Eta)'
corr = '100*(%s-%s)/%s' % (pt, rawpt, rawpt)
s = Selector()
s.read_tree(t, branches=[pt, rawpt, eta, corr], cut='%s>30' % (pt))
def draw(arr, xlabel, xvar, etabins):
plot.Reset()
plot.AddLumiLabel()
plot.GetCanvas().SetLogx('Pt' in xvar)
plot.cd()
# plot.Logy()
hbase = root.TH2D('', '', len(arr) - 1, arr, len(uncarray) - 1, uncarray)
h1s = []
for ie in xrange(len(etabins) - 1):
mask = np.logical_and(s[eta] > etabins[ie], s[eta] < etabins[ie + 1])
h2 = s.draw([xvar, corr], hbase=hbase, mask=mask)
h1 = h2.QuantilesX(0.5, 'eta_%i' % ie)
for i,s in enumerate(payload['samples']):
if s['name'] == args.name:
samples = s['samples']
y = i
break
else:
logger.error(sys.argv[0], 'Could not identify process '+args.name)
sys.exit(1)
s = Selector()
chain = root.TChain('Events')
for sample in samples:
chain.AddFile(basedir + '/' + sample + '.root')
logger.info(sys.argv[0], 'Reading files for process '+args.name)
s.read_tree(chain, branches=(features+substructure_vars+[weight]), cut=cut)
#s.read_tree(chain, branches=(features+[weight]), cut=cut)
#print s['fj_cpf_pt'].T[0:30,:].shape
sortinds = s['fj_cpf_pt'].T[0:30,:].argsort(axis=0)
print s['fj_cpf_pt'].T[0:30,:][:,sortinds[::-1]]
print sortinds
X = np.vstack([s[f].T[0:30,:] for f in features]).T
W = s[weight]
#W *= 1000 / W.sum()
Y = y * np.ones(shape=W.shape)
substructure_varss = np.vstack([s[var] for var in substructure_vars]).T
def save(arr, label):
fout = args.out+'/'+args.name+'_'+label+'.npy'
np.save(fout, arr)
logger.info(sys.argv[0], 'Saved to '+fout)
for i,s in enumerate(payload['samples']):
if s['name'] == args.name:
samples = s['samples']
y = i
break
else:
logger.error(sys.argv[0], 'Could not identify process '+args.name)
sys.exit(1)
s = Selector()
chain = root.TChain('events')
for sample in samples:
chain.AddFile(basedir + '/' + sample + '.root')
logger.info(sys.argv[0], 'Reading files for process '+args.name)
s.read_tree(chain, branches=(features+[weight]), cut=cut)
X = np.vstack([s[f] for f in features]).T
W = s[weight]
#W *= 1000 / W.sum()
Y = y * np.ones(shape=W.shape)
def save(arr, label):
fout = args.out+'/'+args.name+'_'+label+'.npy'
np.save(fout, arr)
logger.info(sys.argv[0], 'Saved to '+fout)
save(X, 'x')
save(Y, 'y')
save(W, 'w')
uncarray = array('f', np.linspace(0, 0.1, 200))
f = root.TFile.Open(args.infile)
t = f.Get('events')
ks = t.GetListOfBranches()
pt = args.jet + 'Pt'
pt_ = pt + '_'
uncs = []
for k in ks:
if k.GetName().startswith(pt_) and k.GetName().endswith('Up'):
uncs.append(k.GetName().replace(pt_, '')[:-2])
s = Selector()
branches = {u: 'fabs(%s-%s%sUp)/%s' % (pt, pt_, u, pt) for u in uncs}
all_branches = [pt, args.jet + 'Eta'] + branches.values()
s.read_tree(t, branches=all_branches, cut='%s>30' % (pt))
c2 = root.TCanvas()
majors = [
'Absolute', 'Relative', 'PileUp', 'SinglePion', 'Flavor', 'Fragmentation',
'Time', 'Total'
]
def draw(arr, xlabel, xvar):
plot.Reset()
plot.AddLumiLabel()
plot.GetCanvas().SetLogx('Pt' in xvar)
# plot.Logy()
hbase = root.TH2D('', '', len(arr) - 1, arr, len(uncarray) - 1, uncarray)
f = root.TFile(args.infile)
t = f.Get('events')
h_effs = {}
h_errs = {}
base_base_cut = 'partonPt>300 && partonPt<470 && fabs(partonEta)<2.4'
if args.isfake:
base_cut = tAND(base_base_cut, 'partonIsReco==1')
else:
base_cut = tAND(base_base_cut, 'partonIsReco==1 && partonSize<1.44')
s = Selector()
s.read_tree(t, branches=['partonPt', 'npv', '1'], cut=base_base_cut)
h_inc = {
'partonPt': s.draw('partonPt', '1', fbins=(nBins, 300, 470)),
'npv': s.draw('npv', '1', fbins=(nBins, 1, 41))
}
s.read_tree(t,
branches=['partonPt', 'npv', '1'],
cut=tAND(base_cut, 'top_ecf_bdt>0.43 && fj1MSD>110 && fj1MSD<210'))
h_bdt = {
'partonPt': s.draw('partonPt', '1', fbins=(nBins, 300, 470)),
'npv': s.draw('npv', '1', fbins=(nBins, 1, 41))
}
s.read_tree(t,
branches=['partonPt', 'npv', '1'],
load('PandaCoreDrawers')
load('PandaAnalysisUtilities')
f = root.TFile.Open(args.infile)
t = f.Get('events')
PFCand = root.panda.PFCand
hbase = root.TH1D('', '', 20, -5, 5)
nmax = root.std.numeric_limits('Short_t').max()
s = Selector()
cut = 'pa::PHS::up(pfCandidates.packedPt)>5'
eta = 'pfCandidates.packedEta * %f / %i' % (6, nmax)
ptype = 'pfCandidates.ptype'
s.read_tree(t, branches=[(eta, -9, 200), (ptype, -100, 200)], cut=cut)
s.flatten()
def get_hist(*ptypes):
masks = [s.data[ptype] == p for p in ptypes]
mask = np.zeros_like(s.data[eta]).astype(bool)
for p in ptypes:
mask = np.logical_or(mask, s.data[ptype] == p)
print ptypes
print s.data[eta][mask].min(), s.data[eta][mask].max()
h = s.draw(eta, hbase=hbase, mask=mask)
print h.Integral()
return h
#plot.SetAutoRange(False)
plot.InitLegend()
root.gStyle.SetOptStat(0)
nBins = 15
f = root.TFile(args.infile)
t = f.Get('events')
base_cut = 'partonPt>300 && partonPt<470 && fabs(partonEta)<2.4 && partonIsReco==1'
if not args.isfake:
base_cut = tAND(base_cut, 'partonSize<1.44')
s = Selector()
s.read_tree(t, branches=['fj1MSD', '1'], cut=base_cut)
h_inc = s.draw('fj1MSD', '1', fbins=(nBins, 40, 340))
s.read_tree(t,
branches=['fj1MSD', '1'],
cut=tAND(base_cut, 'top_ecf_bdt>0.43'))
h_bdt = s.draw('fj1MSD', '1', fbins=(nBins, 40, 340))
s.read_tree(t, branches=['fj1MSD', '1'], cut=tAND(base_cut, 'fj1Tau32SD<0.43'))
h_tau = s.draw('fj1MSD', '1', fbins=(nBins, 40, 340))
v = 'fj1MSD'
label = 'm_{SD} [GeV]'
h_inc.GetXaxis().SetTitle(label)
h_inc.GetYaxis().SetTitle('Arbitrary units')
y = i
break
else:
logger.error(sys.argv[0], 'Could not identify process ' + args.name)
sys.exit(1)
s = Selector()
chain = root.TChain('events')
for sample in samples:
chain.AddFile(basedir + '/' + sample + '.root')
logger.info(sys.argv[0], 'Reading files for process ' + args.name)
branches = [weight, 'hbbm_dreg']
branches += more_features
branches += [(f, -10, 6) for f in features]
s.read_tree(chain, branches=branches, cut=cut)
N, M = s['hbbjtidx'].shape
hbbidx = np.zeros_like(s[features[0]])
for k in xrange(M):
hbbidx[np.arange(N), s['hbbjtidx'][:, k]] = 1
# temporary, will eventually be jotPt-dimensional
breg = np.ones_like(hbbidx)
for k in xrange(s['jotBReg'].shape[1]):
breg[np.arange(N), s['hbbjtidx'][:, k]] = s['jotBReg'][:, k]
selected = []
for f in features:
arr = np.empty((N, M))
c = root.CanvasDrawer(800, 600)
c.SetTDRStyle()
c.GetCanvas().SetRightMargin(0.125)
c.GetCanvas().SetTopMargin(0.05)
c.GetCanvas().SetLeftMargin(0.13)
c.GetCanvas().SetBottomMargin(0.15)
c.cd()
args = parse('--outdir')
f = root.TFile.Open(flatdir + '/Top.root')
t = f.Get('events')
s = Selector()
s.read_tree(t,
branches=['ptweight', 'gen_pt', 'TMath::Sqrt(gen_size)'],
cut='fabs(gen_eta)<2.5')
max_x = 500
max_y = 3.14159
hbase = root.TH2D('', '', 50, 50, max_x, 50, 0, max_y)
h = s.draw(fields=['gen_pt', 'TMath::Sqrt(gen_size)'],
hbase=hbase,
weight='ptweight')
h.GetXaxis().SetTitle('Top p_{T} [GeV]')
h.GetYaxis().SetTitle("max#DeltaR_{qq}")
h.GetXaxis().SetTitleOffset(1)
h.GetXaxis().SetTitleSize(0.06)
h.GetYaxis().SetTitleOffset(1)
h.GetYaxis().SetTitleSize(0.06)
nBins = 10
f = root.TFile(args.infile)
t = f.Get('events')
h_effs = {}
h_errs = {}
if args.isfake:
base_cut = 'fj1MSD>110 && fj1MSD<210'
else:
base_cut = 'fj1MSD>110 && fj1MSD<210 && fj1IsMatched==1 && fj1GenSize<1.44'
s = Selector()
s.read_tree(t, branches=['fj1Pt', 'normalizedWeight'], cut=base_cut)
h_inc = s.draw('fj1Pt', 'normalizedWeight', fbins=(nBins, 250, 1000))
colors = [root.kRed, root.kRed + 3, root.kBlack]
counter = 10
for c, col in zip([0.45, 0.1, -0.5], colors):
s.read_tree(t,
branches=['fj1Pt', 'normalizedWeight'],
cut=tAND(base_cut, 'top_ecf_bdt>%f' % c))
h_effs[c] = s.draw('fj1Pt', 'normalizedWeight', fbins=(nBins, 250, 1000))
h_effs[c].Divide(h_inc)
h_effs[c].SetMaximum(1.4 * get_max(h_effs[c]))
h_effs[c].GetXaxis().SetTitle('fatjet p_{T} [GeV]')
h_effs[c].GetYaxis().SetTitle('Efficiency')
h_effs[c].SetLineColor(col)
plot.AddHistogram(h_effs[c], 'BDT > %.2f' % (c), counter, col, "hist")
plot.SetLumi(lumi / 1000.)
plot.DrawEmpty(True)
plot.AddCMSLabel()
plot.AddLumiLabel()
#plot.SetAutoRange(False)
plot.InitLegend()
root.gStyle.SetOptStat(0)
s = Selector()
f = root.TFile.Open(infile)
t = f.Get('events')
branches = [
'pfmet', 'jot12Mass', '1', 'fabs(genTauEta)', 'fabs(genMuonEta)',
'fabs(genElectronEta)', 'genTauPt', 'genElectronPt', 'genMuonPt', weight
]
s.read_tree(t, branches=branches, cut=base_cut)
t_mask = ((s['genTauPt'] > 1))
accp = ((s['genTauPt'] > 18) & (s['fabs(genTauEta)'] < 2.3))
t_in_mask = t_mask & accp
t_out_mask = t_mask & (~accp)
e_mask = ((s['genTauPt'] < 1) & (s['genElectronPt'] > 1))
accp = ((s['genElectronPt'] > 10) & (s['fabs(genElectronEta)'] < 2.6))
e_in_mask = e_mask & accp
e_out_mask = e_mask & (~accp)
m_mask = ((s['genTauPt'] < 1) & (s['genElectronPt'] < 1) &
(s['genMuonPt'] > 1))
accp = ((s['genMuonPt'] > 10) & (s['fabs(genMuonEta)'] < 2.4))
m_in_mask = m_mask & accp
baseDir = getenv('PANDA_FLATDIR')+'/'
args = parse('--outdir', ('--cut', {'default':'1==1'}))
Load('PandaCoreDrawers')
plot = root.HistogramDrawer()
plot.SetTDRStyle()
plot.InitLegend()
# plot.Logy(True)
s = Selector()
f = root.TFile.Open(baseDir + '/ZtoNuNu.root')
t = f.Get('events')
s.read_tree(t,
branches=['trueGenBosonPt',
'normalizedWeight',
'normalizedWeight*sf_qcdV',
'normalizedWeight*sf_qcdV*sf_ewkV'],
cut='trueGenBosonPt>160')
ptbins = (10,160,1600)
hlo = s.draw('trueGenBosonPt', weight='normalizedWeight', fbins=ptbins)
hqcd = s.draw('trueGenBosonPt', weight='normalizedWeight*sf_qcdV', fbins=ptbins)
hewk = s.draw('trueGenBosonPt', weight='normalizedWeight*sf_qcdV*sf_ewkV', fbins=ptbins)
for h in [hewk, hqcd, hlo]:
h.Divide(hlo)
for h in [hewk, hqcd, hlo]:
h.Smooth()
hlo.GetXaxis().SetTitle('p_{T}^{Z} [GeV]')
hlo.GetYaxis().SetTitle('k')
s = Selector()
s_nlo = Selector()
cut = 'trueGenBosonPt>50 && lheHT>100'
if args.proc == 'WJets':
cut = tAND(cut, 'genBosonMass>70 && genBosonMass<90')
else:
cut = tAND(cut, 'genBosonMass>80 && genBosonMass<100')
branches = ['normalizedWeight', '0.001*trueGenBosonPt', '0.001*lheHT']
infile = basedir + args.proc + '.root'
f = root.TFile.Open(infile)
t = f.Get('events')
print 'LO'
s.read_tree(t, branches=branches, cut=cut)
infile = basedir + args.proc + '_nlo.root'
f_nlo = root.TFile.Open(infile)
t_nlo = f_nlo.Get('events')
print 'NLO'
s_nlo.read_tree(t_nlo, branches=branches, cut=cut)
print 'done reading'
fout = root.TFile.Open('../../data/higgs/hbb_kfactors.root', 'UPDATE')
def draw(x, xbins, xlabel, y, ybins, ylabel):
x_ = '0.001*' + x
y_ = '0.001*' + y
plot.Reset()
#!/usr/bin/env python
from PandaCore.Tools.script import *
from PandaCore.Tools.root_interface import Selector
import numpy as np
from array import array
Load('PandaCoreTools')
args = parse('--infile')
bins = array('f', np.linspace(250, 1200, 50))
h = root.TH1D('h', 'h', len(bins) - 1, bins)
s = Selector()
f = root.TFile.Open(args.infile, 'update')
t = f.Get('events')
s.read_tree(t, branches=['gen_pt'], cut='clf_IsMatched==1')
h = s.draw(['gen_pt'], hbase=h)
norm = h.Integral() / (len(bins) - 1)
for ib in xrange(1, h.GetNbinsX() + 1):
val = h.GetBinContent(ib)
if val > 0:
h.SetBinContent(ib, norm / val)
ba = root.H1BranchAdder()
ba.setH(h)
ba.formulaX = 'gen_pt'
ba.newBranchName = 'weight'
ba.addBranch(t)
from PandaCore.Tools.Misc import *
from PandaCore.Tools.root_interface import Selector
Load('HistogramDrawer')
lumi=35900
import PandaAnalysis.VBF.PandaSelection as sel
cut = 'jot1Eta*jot2Eta<0 && jot1Pt>80 && jot2Pt>40 && fabs(jot1Eta)<4.7 && fabs(jot2Eta)<4.7 && nTau==0 && jetNMBtags==0 && jot1VBFID==1 && dphipfmet>0.5'
weight = 'normalizedWeight'
s = Selector()
f = root.TFile.Open(infile); t = f.Get('events')
branches = ['pfmet', 'jot12Mass',
weight]
s.read_tree(t, branches = branches, cut = cut)
met_bins = array('f',[50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 220, 240, 260, 280, 300, 340, 380, 420, 460, 500, 550, 600, 650, 700, 750, 800, 900, 1000])
mjj_bins = array('f',[200, 400, 600, 900, 1200, 1500, 2000, 2750, 3500, 5000])
h = root.TH2F('h','h',len(mjj_bins)-1, mjj_bins, len(met_bins)-1, met_bins)
h.GetXaxis().SetTitle('m_{jj} [GeV]')
h.GetYaxis().SetTitle('E_{T}^{miss} [GeV]')
h = s.draw(fields=['jot12Mass', 'pfmet'], weight=weight, hbase=h)
plot = root.CanvasDrawer()
plot.SetTDRStyle()
root.gStyle.SetPadRightMargin(0.15)
c = root.TCanvas()
c.cd()
c.SetLogx(); c.SetLogy(); c.SetLogz()
plot.AddCMSLabel(.16,.94)
elif ("hadmu" in args.out): cut+=" && fj_nProngs == 2 && fj_nEles == 0 && fj_nMus == 1"
elif ("-DYJetsToLL" in args.name):
cut+=" && fj_nProngs == 2"
print(cut)
s = Selector()
chain = root.TChain('Events')
for sample in samples:
chain.AddFile(basedir + '/' + sample + "-" + args.tag + '/out.root')
logger.info(sys.argv[0], 'Reading files for process '+args.name)
print("%i total events..."%chain.GetEntries())
for si,slim in enumerate(range(startstep*stepsize,chain.GetEntries(),stepsize)):
selargs = {"start":slim, "stop":slim+stepsize, "step":1}
print("\tRunning iteration %i"%(si+startstep))
#if ("JetHT" in args.name): selargs = {}
s.read_tree(chain, branches=(features+altfeatures+taufeatures+elecfeatures+muonfeatures+weight+ss), cut=cut, xkwargs=selargs)
print(s["PF_pt"][:,0])
X = np.concatenate([np.vstack([s[f].T for f in features]).T,np.vstack([s[f].T for f in altfeatures]).T,np.vstack([s[f].T for f in taufeatures]).T,np.vstack([s[f].T for f in elecfeatures]).T,np.vstack([s[f].T for f in muonfeatures]).T],axis=1)
W = np.vstack([s[var] for var in weight]).T
Y = y * np.ones(shape=W.shape[0])
print(Y)
ss_arr = np.vstack([s[var] for var in ss]).T
print(X.shape)
print(Y.shape)
print(W.shape)
print(ss_arr.shape)
save(X, 'x',si+startstep)
save(Y, 'y',si+startstep)
save(W, 'w',si+startstep)
save(ss_arr, 'ss',si+startstep)
n_partons = {
'Top_lo': 3,
'Top': 3,
'QCD': 1,
'Higgs': 2,
'W': 2,
}
n_parton = n_partons[args.proc]
f = root.TFile(basedir + '/' + args.proc + '.root')
t = f.Get('events')
s = Selector()
s.read_tree(t,
branches=['genFatJetPt'],
cut='genFatJetPt>450 && genFatJetPt<1200 && genFatJetNProngs==%i' %
n_parton)
h = s.draw('genFatJetPt', fbins=binning)
h_inv = h.Clone()
for ib in xrange(1, h_inv.GetNbinsX() + 1):
if h.GetBinContent(ib):
h_inv.SetBinContent(ib, 1)
h_inv.SetBinError(ib, 0)
h_inv.Divide(h)
h_inv.GetXaxis().SetTitle('jet p_{T}')
h_inv.GetYaxis().SetTitle('jet weight')
for ib in xrange(1, h_inv.GetNbinsX() + 1):
plot.SetCanvas(c)
s = Selector()
f = root.TFile.Open(basedir + '/' + 'VqqHbb' + '.root')
t = f.Get('events')
weight = sel.weights['signal'] % 40000
branches = [weight]
for b in ['Eta', 'Phi']:
for i, j in [('fjHiggsIdx', 'H'), ('fjVIdx', 'V')]:
branches.append('fjQ[%s]' % i)
branches.append('fj%s[%s]' % (b, i))
branches.append('gen%s%s' % (j, b))
branches.append('fj%s[%s] - gen%s%s' % (b, i, j, b))
s.read_tree(t, branches=branches, cut=tAND(sel.cuts['signal'], args.cut))
hbase = root.TH2D('h', 'h', 20, -2.5, 2.5, 20, -3.14159, 3.14159)
def draw(fmt, label_fmt, fname):
plot.Reset()
h = s.draw([fmt.format('Eta'), fmt.format('Phi')],
weight=weight,
hbase=hbase)
h.GetXaxis().SetTitle(label_fmt.format('#eta'))
h.GetYaxis().SetTitle(label_fmt.format('#phi'))
h.Draw('colz')
plot.Draw(args.outdir + '/', args.prefix + fname)