def extract_MET(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "MissingET.MET"
var_qcd = "MissingET.MET"
var_wjet = "MissingET.MET"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
MET_tt = Hist(MET_NBINS, MET_NLO, MET_NHI, title='MET_tt', legendstyle='L')
MET_qcd = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_qcd',
legendstyle='L')
MET_wjet = Hist(MET_NBINS,
MET_NLO,
MET_NHI,
title='MET_wjet',
legendstyle='L')
# FILLING THE TREE
fill_MET_tree(tt_n_entries, t_tt, leaf_tt, MET_tt)
fill_MET_tree(qcd_n_entries, t_qcd, leaf_qcd, MET_qcd)
fill_MET_tree(wjet_n_entries, t_wjet, leaf_wjet, MET_wjet)
#set line colors
MET_tt.SetLineColor('blue')
MET_qcd.SetLineColor('green')
MET_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
MET_qcd.SetStats(0)
MET_qcd.Draw('HIST')
MET_tt.Draw('HIST SAME')
MET_wjet.Draw('HIST SAME')
#make legend
l1 = Legend([MET_tt, MET_qcd, MET_wjet], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/MET_plots/MET.pdf")
#make the plots wait on screen
wait(True)
def plotResults(variable, data, templates, results):
resCan = Canvas()
leg = Legend(nTemplates + 2)
data.Draw('PE')
leg.AddEntry(data, style='LEP')
nBins = len(inputTemplates[variable]['data'][whichBinFromFile])
h_tSumAfter = Hist(nBins, 0, nBins, title='after_' + variable)
if useT1:
plotTemplateAfter(templates[tNames['t1']], results[tNames['t1']][0],
resCan, leg, h_tSumAfter)
pass
if useT2:
plotTemplateAfter(templates[tNames['t2']], results[tNames['t2']][0],
resCan, leg, h_tSumAfter)
pass
if useT3:
plotTemplateAfter(templates[tNames['t3']], results[tNames['t3']][0],
resCan, leg, h_tSumAfter)
pass
if useT4:
plotTemplateAfter(templates[tNames['t4']], results[tNames['t4']][0],
resCan, leg, h_tSumAfter)
pass
leg.Draw()
h_tSumAfter.SetLineColor(2)
h_tSumAfter.SetLineStyle(7)
h_tSumAfter.SetMarkerSize(0)
h_tSumAfter.Draw('SAME HIST')
resCan.Update()
return resCan, h_tSumAfter
def makeDiscr(discr_dict,
outfile,
xtitle="discriminator",
nbins=30,
x_min=0,
x_max=1):
c = ROOT.TCanvas("c", "c", 800, 500)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetMargin(0.15, 0.1, 0.2, 0.1)
#ROOT.gPad.SetLogy(1)
#ROOT.gPad.SetGrid(1,1)
ROOT.gStyle.SetGridColor(17)
l = TLegend(0.17, 0.75, 0.88, 0.88)
l.SetTextSize(0.055)
l.SetBorderSize(0)
l.SetFillStyle(0)
l.SetNColumns(2)
colors = [2, 4, 8, ROOT.kCyan + 2]
counter = 0
for leg, discr in discr_dict.iteritems():
a = Hist(nbins, x_min, x_max)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.05)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.2)
if counter == 0: a.draw("hist")
else: a.draw("same hist")
l.AddEntry(a, leg, "l")
counter += 1
l.Draw("same")
c.SaveAs(outfile)
res = ROOT.TMath.KolmogorovProb(dn * sqrt(n))
return dn
if __name__ == '__main__':
"""
this is an example of drawing a quantile-quantile plot with
confidential level (CL) band by Zhiyi Liu, [email protected]
"""
ROOT.gROOT.SetStyle("Plain")
ROOT.gStyle.SetOptStat(0)
can = Canvas(name="can", title="can", width=600, height=450)
rand = ROOT.TRandom3()
h1 = Hist(100, -5, 5, name="h1", title="Histogram 1")
h1.Sumw2()
h1.SetLineColor(ROOT.kRed)
h2 = Hist(100, -5, 5, name="h2", title="Histogram 2")
h2.SetLineColor(ROOT.kBlue)
for ievt in xrange(10000):
#some test histograms:
#1. let 2 histograms screwed
#h1.Fill(rand.Gaus(0.5, 0.8))
#h2.Fill(rand.Gaus(0, 1))
#2. long tail and short tail
h1.Fill(rand.Gaus(0, 0.8))
h2.Fill(rand.Gaus(0, 1))
#hs = ROOT.THStack("hs", "2 example distributions")
#hs.Add(h1)
def extract_Electron(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "Electron.PT"
var_qcd = "Electron.PT"
var_wjet = "Electron.PT"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
numElectrons_tt = Hist(NBINS,NLO,NHI, title = 'numElectrons_tt', legendstyle = 'L')
numElectrons_qcd = Hist(NBINS,NLO,NHI, title = 'numElectrons_qcd', legendstyle = 'L')
numElectrons_wjet = Hist(NBINS,NLO,NHI, title = 'numElectrons_wjet', legendstyle = 'L')
# interesting values to plot
max_ept_per_event_tt = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event tt', legendstyle = 'L')
min_ept_per_event_tt = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event tt', legendstyle = 'L')
max_ept_per_event_qcd = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event qcd', legendstyle = 'L')
min_ept_per_event_qcd = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event qcd', legendstyle = 'L')
max_ept_per_event_wjet = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Max ElectronPT/Event wjet', legendstyle = 'L')
min_ept_per_event_wjet = Hist(PT_NBINS,PT_NLO,PT_NHI, title = 'Min ElectronPT/Event wjet', legendstyle = 'L')
# FILLING THE TREE
fill_Electron_tree(tt_n_entries, t_tt, leaf_tt, numElectrons_tt, min_ept_per_event_tt, max_ept_per_event_tt)
fill_Electron_tree(qcd_n_entries, t_qcd, leaf_qcd, numElectrons_qcd, min_ept_per_event_qcd, max_ept_per_event_qcd)
fill_Electron_tree(wjet_n_entries, t_wjet, leaf_wjet, numElectrons_wjet, min_ept_per_event_wjet, max_ept_per_event_wjet)
#set line colors
numElectrons_tt.SetLineColor('blue')
numElectrons_qcd.SetLineColor('green')
numElectrons_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
numElectrons_wjet.SetStats(0)
numElectrons_wjet.Draw('HIST')
numElectrons_tt.Draw('HIST SAME')
numElectrons_qcd.Draw('HIST SAME')
#make legend
l1 = Legend([numElectrons_tt, numElectrons_qcd, numElectrons_wjet], textfont = 42, textsize = .03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/ElectronPT_plots/numElectrons.pdf");
################ MIN MAX STUFF
# TT
#set line colors
max_ept_per_event_tt.SetLineColor('blue')
min_ept_per_event_tt.SetLineColor('green')
#begin drawing stuff
c2 = Canvas()
min_ept_per_event_tt.SetStats(0)
min_ept_per_event_tt.Draw('HIST')
max_ept_per_event_tt.Draw('HIST SAME')
#make legend
l2 = Legend([min_ept_per_event_tt, max_ept_per_event_tt], textfont = 42, textsize = .03)
l2.Draw()
#save as pdf
c2.SaveAs("../plots/ElectronPT_plots/e_maxminpt_tt.pdf")
# QCD
#set line colors
max_ept_per_event_qcd.SetLineColor('blue')
min_ept_per_event_qcd.SetLineColor('green')
#begin drawing stuff
c3 = Canvas()
max_ept_per_event_qcd.SetStats(0)
max_ept_per_event_qcd.Draw('HIST')
min_ept_per_event_qcd.Draw('HIST SAME')
#make legend
l3 = Legend([min_ept_per_event_qcd, max_ept_per_event_qcd], textfont = 42, textsize = .03)
l3.Draw()
#save as pdf
c3.SaveAs("../plots/ElectronPT_plots/e_maxminpt_qcd.pdf")
#WJET
#set line colors
max_ept_per_event_wjet.SetLineColor('blue')
min_ept_per_event_wjet.SetLineColor('green')
#begin drawing stuff
c4 = Canvas()
min_ept_per_event_wjet.SetStats(0)
min_ept_per_event_wjet.Draw('HIST')
max_ept_per_event_wjet.Draw('HIST SAME')
#make legend
l4 = Legend([min_ept_per_event_wjet, max_ept_per_event_wjet], textfont = 42, textsize = .03)
l4.Draw()
#save as pdf
c4.SaveAs("../plots/ElectronPT_plots/e_maxminpt_wjet.pdf")
#make the plots wait on screen
wait(True)
# create histograms
h1 = Hist(100, 40, 160)
h2 = Hist(100, 40, 160)
# fill the histograms with our distributions
map(h1.Fill, x1)
map(h2.Fill, x2)
# normalize the histograms
h1 /= h1.Integral()
h2 /= h2.Integral()
# set visual attributes
h1.SetFillStyle("solid")
h1.SetFillColor("green")
h1.SetLineColor("green")
h2.SetFillStyle("solid")
h2.SetFillColor("red")
h2.SetLineColor("red")
# plot with ROOT
h1.GetXaxis().SetTitle('Smarts')
h1.GetYaxis().SetTitle('Probability')
h1.SetTitle("Histogram of IQ: #mu=100, #sigma=15")
h1.Draw("hist")
h2.Draw("same")
# plot with matplotlib
plt.figure()
rplt.hist(h1, alpha=0.75)
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# create a histogram with 100 bins from 40 to 160
h = Hist(100, 40, 160)
# fill the histogram with our distribution
map(h.Fill, x)
# normalize the histogram
h /= h.Integral()
# set visual attributes
h.SetFillStyle('solid')
h.SetFillColor('green')
h.SetLineColor('green')
# the histogram of the data
plt.figure(figsize=(16, 12), dpi=200)
axes = plt.axes()
axes.minorticks_on()
rplt.hist(h, label=r'$\epsilon$(Something complicated)', alpha=0.7)
plt.xlabel('Discovery', CMS.x_axis_title)
plt.ylabel('Probability of a discovery', CMS.y_axis_title)
#plt.title(r'combined, CMS Preliminary, $\mathcal{L}$ = 5.1 fb$^{-1}$ at $\sqrt{s}$ = 7 TeV',
# fontsize=30,
# verticalalignment='bottom')
#plt.title(r'e+jets, CMS Preliminary, $\mathcal{L}$ = 5.1 fb$^{-1}$ at $\sqrt{s}$ = 7 TeV',
#fontsize=30,
# verticalalignment='bottom')
plt.title(
print "==================================== TRAIN ===================================="
response = RooUnfoldResponse(40, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = Hist(40, -10.0, 10.0)
hMeas = Hist(40, -10.0, 10.0)
hTrue.SetLineColor('red')
hMeas.SetLineColor('blue')
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
xt = gRandom.Gaus(0.0, 2.0)
x = smear(xt)
hTrue.Fill(xt)
if x != None: hMeas.Fill(x)
print "==================================== UNFOLD ==================================="
unfold = RooUnfoldBayes(response, hMeas, 4)
# OR
# unfold= RooUnfoldSvd (response, hMeas, 20); # OR
# unfold= RooUnfoldTUnfold (response, hMeas);
hReco = unfold.Hreco()
# h_data.Scale(absolute_eta_initialValues['data'][whichBinFromFile][0] / h_data.Integral() )
# h_data.FillRandom( dataFillingHistogram, int(absolute_eta_initialValues['data'][whichBinFromFile][0]) )
# h_data.FillRandom( dataFillingHistogram, int(absolute_eta_initialValues['data'][whichBinFromFile][0]) )
pass
# for bin in range (0,nBins+1):
# # h_data.SetBinContent( bin, t1Scale * h_t1.GetBinContent( bin ) + t2Scale*h_t2.GetBinContent( bin ) + t3Scale*h_t3.GetBinContent( bin ) )
# h_data.SetBinError(bin, sqrt(h_data.GetBinContent(bin)))
# h_t1.SetBinError( bin, sqrt(h_t1.GetBinContent(bin)))
# h_t2.SetBinError( bin, sqrt(h_t2.GetBinContent(bin)))
# h_t3.SetBinError( bin, sqrt(h_t3.GetBinContent(bin)))
# h_t4.SetBinError( bin, sqrt(h_t4.GetBinContent(bin)))
# pass
# Make pretty
h_t1.SetLineColor(4)
h_t2.SetLineColor(8)
h_t3.SetLineColor(6)
h_t4.SetLineColor(7)
h_data.SetLineColor(1)
h_data.SetMarkerColor(1)
ymax = getMax([h_data, h_t1, h_t2, h_t3])
ymax = ymax * 1.1
h_data.GetYaxis().SetRangeUser(0, ymax)
h_t1.GetYaxis().SetRangeUser(0, ymax)
h_t2.GetYaxis().SetRangeUser(0, ymax)
h_t3.GetYaxis().SetRangeUser(0, ymax)
h_t4.GetYaxis().SetRangeUser(0, ymax)
c = Canvas()
def makeDiscr(train_discr_dict, discr_dict, outfile, xtitle="discriminator"):
c = ROOT.TCanvas("c", "c", 800, 500)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetMargin(0.15, 0.1, 0.2, 0.1)
#ROOT.gPad.SetLogy(1)
#ROOT.gPad.SetGrid(1,1)
ROOT.gStyle.SetGridColor(17)
l = TLegend(0.17, 0.75, 0.88, 0.88)
l.SetTextSize(0.055)
l.SetBorderSize(0)
l.SetFillStyle(0)
l.SetNColumns(2)
colors = [2, 1, 4, ROOT.kCyan + 2]
counter = 0
for leg, discr in train_discr_dict.iteritems():
a = Hist(30, 0, 1)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.06)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.9)
if counter == 0: a.draw("hist")
else: a.draw("same hist")
l.AddEntry(a, leg, "l")
counter += 1
counter = 0
for leg, discr in discr_dict.iteritems():
a = Hist(30, 0, 1)
#fill_hist_with_ndarray(a, discr)
a.fill_array(discr)
a.SetLineColor(colors[counter])
a.SetMarkerColor(colors[counter])
a.SetMarkerStyle(34)
a.SetMarkerSize(1.8)
a.SetLineWidth(2)
a.GetXaxis().SetTitle(xtitle)
a.GetXaxis().SetLabelSize(0.05)
a.GetXaxis().SetTitleSize(0.06)
a.GetXaxis().SetTitleOffset(1.45)
a.GetYaxis().SetTitle("a.u.")
a.GetYaxis().SetTickSize(0)
a.GetYaxis().SetLabelSize(0)
a.GetYaxis().SetTitleSize(0.06)
a.GetYaxis().SetTitleOffset(0.9)
a.Scale(1. / a.Integral())
#a.GetYaxis().SetRangeUser(0.00001,100)
a.GetYaxis().SetRangeUser(0, 0.4)
a.draw("same p X0")
l.AddEntry(a, leg, "p")
counter += 1
# counter = 0
# for leg,discr in train_discr_dict.iteritems():
# d = Hist(30, 0, 1)
# d.fill_array(discr)
# d.SetLineColor(colors[counter])
# d.SetLineWidth(2)
# l.AddEntry(d,leg,"l")
#
# b = Hist(30, 0, 1)
# d.fill_array(discr_dict[leg.split(" ")[0] + " test"])
# b.SetLineColor(colors[counter])
# b.SetMarkerColor(colors[counter])
# b.SetMarkerStyle(34)
# b.SetMarkerSize(1.8)
# b.SetLineWidth(2)
# l.AddEntry(b,leg,"p")
# counter += 1
l.Draw("same")
c.SaveAs(outfile)
# create histograms
h1 = Hist(100, 40, 160)
h2 = Hist(100, 40, 160)
# fill the histograms with our distributions
map(h1.Fill, x1)
map(h2.Fill, x2)
# normalize the histograms
h1 /= h1.Integral()
h2 /= h2.Integral()
# set visual attributes
h1.SetFillStyle('solid')
h1.SetFillColor('green')
h1.SetLineColor('green')
h2.SetFillStyle('solid')
h2.SetFillColor('red')
h2.SetLineColor('red')
stack = HistStack()
stack.Add(h1)
stack.Add(h2)
# plot with ROOT
h1.SetTitle('Histogram of IQ: #mu=100, #sigma=15')
stack.Draw()
h1.GetXaxis().SetTitle('Smarts')
h1.GetYaxis().SetTitle('Probability')
def extract_JetBTag(f_tt, f_qcd, f_wjet):
# get trees from files
t_tt = f_tt.Get("Delphes")
t_qcd = f_qcd.Get("Delphes")
t_wjet = f_wjet.Get("Delphes")
# get number of entries
tt_n_entries = t_tt.GetEntries()
qcd_n_entries = t_qcd.GetEntries()
wjet_n_entries = t_wjet.GetEntries()
# define leaves
var_tt = "Jet.BTag"
var_qcd = "Jet.BTag"
var_wjet = "Jet.BTag"
leaf_tt = t_tt.GetLeaf(var_tt)
leaf_qcd = t_qcd.GetLeaf(var_qcd)
leaf_wjet = t_wjet.GetLeaf(var_wjet)
# create the histograms
loose_tt = Hist(NBINS, NLO, NHI, title='loose_tt', legendstyle='L')
medium_tt = Hist(NBINS, NLO, NHI, title='medium_tt', legendstyle='L')
tight_tt = Hist(NBINS, NLO, NHI, title='tight_tt', legendstyle='L')
loose_qcd = Hist(NBINS, NLO, NHI, title='loose_qcd', legendstyle='L')
medium_qcd = Hist(NBINS, NLO, NHI, title='medium_qcd', legendstyle='L')
tight_qcd = Hist(NBINS, NLO, NHI, title='tight_qcd', legendstyle='L')
loose_wjet = Hist(NBINS, NLO, NHI, title='loose_wjet', legendstyle='L')
medium_wjet = Hist(NBINS, NLO, NHI, title='medium_wjet', legendstyle='L')
tight_wjet = Hist(NBINS, NLO, NHI, title='tight_wjet', legendstyle='L')
# FILLING THE TREE
fill_JetBTag_tree(tt_n_entries, t_tt, leaf_tt, loose_tt, medium_tt,
tight_tt)
fill_JetBTag_tree(qcd_n_entries, t_qcd, leaf_qcd, loose_qcd, medium_qcd,
tight_qcd)
fill_JetBTag_tree(wjet_n_entries, t_wjet, leaf_wjet, loose_wjet,
medium_wjet, tight_wjet)
#set line colors
loose_tt.SetLineColor('blue')
medium_tt.SetLineColor('green')
tight_tt.SetLineColor('red')
loose_qcd.SetLineColor('blue')
medium_qcd.SetLineColor('green')
tight_qcd.SetLineColor('red')
loose_wjet.SetLineColor('blue')
medium_wjet.SetLineColor('green')
tight_wjet.SetLineColor('red')
#begin drawing stuff
c1 = Canvas()
tight_tt.SetStats(0)
tight_tt.Draw('HIST')
medium_tt.Draw('HIST SAME')
loose_tt.Draw('HIST SAME')
#make legend
l1 = Legend([tight_tt, medium_tt, loose_tt], textfont=42, textsize=.03)
l1.Draw()
#save as pdf
c1.SaveAs("../plots/JetBTag_plots/btag_tt.pdf")
c2 = Canvas()
tight_qcd.SetStats(0)
tight_qcd.Draw('HIST')
medium_qcd.Draw('HIST SAME')
loose_qcd.Draw('HIST SAME')
#make legend
l2 = Legend([tight_qcd, medium_qcd, loose_qcd], textfont=42, textsize=.03)
l2.Draw()
#save as pdf
c2.SaveAs("../plots/JetBTag_plots/btag_qcd.pdf")
c3 = Canvas()
tight_wjet.SetStats(0)
tight_wjet.Draw('HIST')
medium_wjet.Draw('HIST SAME')
loose_wjet.Draw('HIST SAME')
#make legend
l3 = Legend([tight_wjet, medium_wjet, loose_wjet],
textfont=42,
textsize=.03)
l3.Draw()
#save as pdf
c3.SaveAs("../plots/JetBTag_plots/btag_wjet.pdf")
wait(True)
