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
'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):
# print ib, h.GetBinContent(ib), h_inv.GetBinContent(ib)
if h.GetBinContent(ib) == 0:
h_inv.SetBinContent(ib, 0)
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")
counter += 1
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
m_out_mask = m_mask & (~accp)
ratios_to_plot = {
'pfmet': ([250, 350, 500, 700, 1000], 'E_{T}^{miss} [GeV]'),
'1': ([0, 2], 'yield'),
'jot12Mass': ([200, 600, 1000, 1500, 2500, 3500, 5000], 'm_{jj} [GeV]')
}
for k, v in ratios_to_plot.iteritems():
h_inc = s.draw(k, weight, vbins=v[0])
hs = {}
hs['h_t_in'] = s.draw(k, weight, mask=t_in_mask, vbins=v[0])
hs['h_t_out'] = s.draw(k, weight, mask=t_out_mask, vbins=v[0])
hs['h_m_in'] = s.draw(k, weight, mask=m_in_mask, vbins=v[0])
hs['h_m_out'] = s.draw(k, weight, mask=m_out_mask, vbins=v[0])
hs['h_e_in'] = s.draw(k, weight, mask=e_in_mask, vbins=v[0])
hs['h_e_out'] = s.draw(k, weight, mask=e_out_mask, vbins=v[0])
plot.Reset(False)
for name, h in hs.iteritems():
h.Divide(h_inc)
h.GetXaxis().SetTitle(v[1])
h.GetYaxis().SetTitle('Fraction of W events')
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)
plot.AddSqrtSLabel()
plot.SetCanvas(c)
root.gStyle.SetOptStat(0)
root.gStyle.SetNumberContours(999);
root.gStyle.SetPalette(root.kBird)
h.Draw('colz')
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)
fout = root.TFile.Open(getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/deep/flatten.root', 'UPDATE')
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')
plot.AddHistogram(hlo, '')
plot.AddHistogram(hqcd, 'k_{QCD}')
plot.AddHistogram(hewk, 'k_{QCD}#timesk_{EWK}')
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)
f.WriteTObject(t, 'events', 'overwrite')
'jetPt[0]+jetPt[1]' : ([0, 40, 80, 120, 160, 200, 300, 500],'Central jet 1 p_{T} + jet 2 p_{T} [GeV]'),
# 'barrelJet1Pt' : ([0, 40, 80, 120, 160, 200, 300, 500],'Barrel jet 1 p_{T} [GeV]'),
# 'barrelJet12Pt' : ([0, 40, 80, 120, 160, 200, 300, 500],'Barrel jet 1 + jet 2 p_{T} [GeV]'),
'barrelHT' : ([0, 80, 120, 160, 200, 240, 280, 320, 360, 400, 450, 500, 600, 1000],'Barrel H_{T} [GeV]'),
'barrelHTMiss' : (bins,'Barrel H_{T}^{miss} [GeV]'),
}
fout = root.TFile(getenv('CMSSW_BASE')+'/src/PandaAnalysis/data/vbf16/trig/param_nmu%i.root'%args.nmu, 'RECREATE')
for k,v in ratios_to_plot.iteritems():
plot.Clear()
plot.InitLegend(0.4, 0.8, 0.95, 0.9, 3)
# plot.AddCMSLabel()
plot.AddLumiLabel()
h_inc = s.draw(k, weight=None, vbins=v[0])
h_trig = s.draw(k, weight=None, mask=triggered, vbins=v[0])
hh_ratio = h_trig.Clone(); hh_ratio.Divide(h_inc)
h_ratio = root.TGraphAsymmErrors()
h_ratio.BayesDivide(h_trig, h_inc)
# for ib in xrange(1, h_ratio.GetNbinsX()+1):
# h_ratio.SetBinContent(ib, h_trig.GetBinContent(ib) / h_inc.GetBinContent(ib))
# h_ratio.SetBinError(ib, h_trig.GetBinError(ib) / h_inc.GetBinContent(ib))
# h_ratio.Divide(h_inc)
h_ratio.GetXaxis().SetTitle(v[1])
h_ratio.GetYaxis().SetTitle('Efficiency')
h_ratio.GetYaxis().SetTitleOffset(1.4)
if hh_ratio.GetMinimum() < 0.5:
h_ratio.SetMaximum(1.4)
h_ratio.SetMinimum(0)
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'],
cut=tAND(base_cut, 'fj1Tau32SD<0.57 && fj1MSD>110 && fj1MSD<210'))
h_tau = {
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')
plot.AddHistogram(h_inc, 'Inclusive', 10, root.kBlack, "e hist")
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)
def get_q(q, c, y):
hq = h.QuantilesX(q)
hq.SetLineColor(c)
hq.SetLineWidth(3)
intersection = None
return hq, hq.GetBinContent(hq.FindBin(250))
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)
draw('fj{0}[fjHiggsIdx]', 'Reco H {0}', 'hReco')
draw('fj{0}[fjVIdx]', 'Reco V {0}', 'vReco')
draw('genH{0}', 'Gen H {0}', 'hGen')
draw('genV{0}', 'Gen V {0}', 'vGen')
hbase = root.TH2D('h2', 'h', 20, 0, 2.5, 20, 0, 2 * 3.14159)
draw('fj{0}[fjHiggsIdx] - genH{0}', '|Reco H {0} - Gen H {0}|', 'hDiff')
draw('fj{0}[fjVIdx] - genV{0}', '|Reco V {0} - Gen V {0}|', 'vDiff')
hbase = root.TH2D('h3', 'h', 20, -1, 1, 20, -1, 1)
plot.Reset()
h = s.draw(['fjQ[fjHiggsIdx]', 'fjQ[fjVIdx]'], weight=weight, hbase=hbase)
h.GetXaxis().SetTitle('Reco H Q')
h.GetYaxis().SetTitle('Reco V Q')
h.Draw('colz')
plot.Draw(args.outdir + '/', args.prefix + 'charge')