def draw(hist, data, back, sign, snorm=1, ratio=0, poisson=False, log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Some style
for i, s in enumerate(data):
hist[s].SetMarkerStyle(21)
hist[s].SetMarkerSize(1.25)
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
# Poisson error bars for data
if poisson:
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(
i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L,
U - N)
res_graph.SetPoint(i,
hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if samples[x]['plot']])
for i, s in enumerate(sign):
if 'sublabel' in samples[s]: n += 1
if 'subsublabel' in samples[s]: n += 1
#leg = TLegend(0.68, 0.9-0.05*n, 0.93, 0.9)
leg = TLegend(0.68 - 0.05, 0.9 - 0.05 * n, 0.93, 0.9) #DCMS
leg.SetTextSize(0.03) #DCMS
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("Signal x-sec=%.0f pb" % (1 * snorm))
if len(data) > 0:
leg.AddEntry(hist[data[0]], samples[data[0]]['label'], "ple1")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], samples[s]['label'], "f")
for i, s in enumerate(sign):
leg.AddEntry(hist[s], samples[s]['label'], "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[-1].GetXaxis().GetTitle(), 1000,
800 if ratio else 700)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
#c1.GetPad(bool(ratio)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if poisson: data_graph.Draw("SAME, PE")
elif len(data) > 0: hist['data_obs'].Draw("SAME, PE")
for i, s in enumerate(sign):
if samples[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
# Determine range
if 'data_obs' in hist:
bkg.SetMaximum((2.5 if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(
hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
else:
bkg.SetMaximum(bkg.GetMaximum() * (2.5 if log else 1.2))
bkg.SetMinimum(5.e-1 if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
leg.Draw()
#drawCMS(LUMI, "Preliminary")
#drawRegion(channel)
#drawAnalysis("LL")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(2)
errLine.SetFillStyle(0)
errLine.SetLineColor(2) #L#
errLine.SetLineStyle(2) #L#
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'data_obs' in hist:
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
if poisson: res_graph.Draw("SAME, PE0")
else: res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
else:
res = None
c1.Update()
# return list of objects created by the draw() function
return [
c1, bkg, leg, err if ratio else None, errLine if ratio else None,
res if ratio else None, data_graph if poisson else None,
res_graph if poisson else None
]
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
#if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
data_graph.Draw("SAME, PE")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
if len(sign) > 0:
if channel.startswith('X') and len(sign) > 0:
drawNorm(0.9 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
#res.Draw("SAME, PE0")
res_graph.Draw("SAME, PE0")
# if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
# drawRatio(hist['data_obs'], hist['BkgSum'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
eyh_c = array('f')
x_c = np.array(LC_dict['tpr'])
y_c = np.array(LC_dict['fpr'])
exl_c = np.array(LC_dict['tpr_e_l'])
eyl_c = np.array(LC_dict['fpr_e_l'])
exh_c = np.array(LC_dict['tpr_e_h'])
eyh_c = np.array(LC_dict['fpr_e_h'])
# somehow if you call one of the variables before the Draw() method the graph won't work properly
gr = GAE(n_bins, x, y, exl, exh, eyl, eyh)
gr_s = GAE(n_bins, x, y, exl, exh, eyl, eyh)
gr_LC = GAE(1, x_c, y_c, exl_c, exh_c, eyl_c, eyh_c)
gr_c = gr.Clone()
gr_sc = gr_s.Clone()
gr.SetTitle('ROC(zoomed in)')
#gr.SetMarkerColor(8)
#gr.SetMarkerStyle(21)
gr.SetFillColor(632 - 9)
gr_s.SetTitle('ROC')
gr_s.SetFillColor(632 - 9)
gr_sc.SetLineColor(4)
c1.cd(1)
gr.GetXaxis().SetRangeUser(0, 0.5)
gr.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.GetXaxis().SetRangeUser(0, 0.5)
gr_c.GetXaxis().SetRangeUser(0.00001, 0.01)
def plotDataOverMCEff(hist_mc_tight,
hist_mc_loose,
hist_data_tight,
hist_data_loose,
plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + (
g.GetErrorYlow(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt(
(g_data.GetErrorYhigh(i) / g_data_vals[i])**2 +
(g.GetErrorYhigh(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() /
xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(
g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
# maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2)
g.GetYaxis().SetRangeUser(0.001, 0.2)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.51, 1.49)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
def draw(hist, data, back, sign, snorm=1, ratio=0, poisson=True, log=False):
groupList = plt.cfg.getGroupPlot()
# If not present, create BkgSum
#if not 'BkgSum' in hist.keys():
# hist['BkgSum'] = hist['DATA'].Clone("BkgSum") if 'DATA' in hist else hist[back[0]].Clone("BkgSum")
# hist['BkgSum'].Reset("MICES")
# for i, s in enumerate(back): hist['BkgSum'].Add(hist[s].GetPtr())
hist['BkgSum'].SetMarkerStyle(0)
# Some style
for i, s in enumerate(data):
hist[s].SetMarkerStyle(20)
hist[s].SetMarkerSize(1.25)
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
# Poisson error bars for data
if poisson:
alpha = 1 - 0.6827
hist['DATA'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['DATA'].GetNbinsX())
data_graph.SetMarkerStyle(hist['DATA'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['DATA'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['DATA'].GetNbinsX()):
N = hist['DATA'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['DATA'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['DATA'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['DATA'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['DATA'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['DATA'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['DATA'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
#bkg = THStack("Bkg", ";"+hist['BkgSum'].GetXaxis().GetTitle()+";Events")
bkg = THStack(
"Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";" +
hist['BkgSum'].GetYaxis().GetTitle())
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len(
[x for x in data + back + ['BkgSum'] + sign if groupList[x]['plot']])
for i, s in enumerate(sign):
if 'sublabel' in groupList[s]: n += 1
if 'subsublabel' in groupList[s]: n += 1
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
#leg.SetNColumns(3)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
#leg.AddEntry( hist[data[0]] , groupList[data[0]]['label'], "pl")
leg.AddEntry(
hist[data[0]], '%s [%.1f]' %
(groupList[data[0]]['label'], hist[data[0]].Integral()), "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s] if s == 'BkgSum' else hist[s],
'%s [%.1f]' % (groupList[s]['label'], hist[s].Integral()),
"f")
for i, s in enumerate(sign):
if groupList[s]['plot']:
#leg.AddEntry( hist[s] if s=='BkgSum' else hist[s] , groupList[s]['label'], "fl")
leg.AddEntry(
hist[s] if s == 'BkgSum' else hist[s],
'%s [%.1f]' % (groupList[s]['label'], hist[s].Integral()),
"fl")
#print samples[s]
if 'sublabel' in groupList[s]:
leg.AddEntry(
hist[s] if s == 'BkgSum' else hist[s],
groupList[s]['sublabel'].replace("m_{#Chi}=1 GeV", ""), "")
if 'subsublabel' in groupList[s]:
leg.AddEntry(
hist[s] if s == 'BkgSum' else hist[s],
groupList[s]['subsublabel'].replace("m_{#Chi}=1 GeV",
""), "")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[-1].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if poisson: data_graph.Draw("SAME, PE")
elif len(data) > 0: hist['DATA'].Draw("SAME, PE")
for i, s in enumerate(sign):
if groupList[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075) #1.075
if 'DATA' in hist:
#bkg.SetMaximum((3.0 if log else 1.5)*max(bkg.GetMaximum(), hist['DATA'].GetBinContent(hist['DATA'].GetMaximumBin())+hist['DATA'].GetBinError(hist['DATA'].GetMaximumBin())))
bkg.SetMaximum((25.0 if log else 2.0) * max(
bkg.GetMaximum(),
hist['DATA'].GetBinContent(hist['DATA'].GetMaximumBin()) +
hist['DATA'].GetBinError(hist['DATA'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(
hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['DATA'].GetMinimum()), 5.e-1) if log else 0.)
else:
bkg.SetMaximum(bkg.GetMaximum() * (3.0 if log else 1.5)) #2.5 ; 1.2
bkg.SetMinimum(5.e-1 if log else 0.)
if not log:
#bkg.GetYaxis().SetNoExponent(1)
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
# bkg.GetYaxis().SetMoreLogLabels(True)
#set range on stack
bkg.SetMinimum(5.e-1 if log else 1.0)
leg.Draw()
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*1)
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
errLine.SetLineColor(1)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*1)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*1)
#err.GetYaxis().SetTitleOffset(err.GetYaxis().GetTitleOffset()*1)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'DATA' in hist:
res = hist['DATA'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
if poisson: res_graph.Draw("SAME, PE0")
else: res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['DATA'], hist['BkgSum'])
drawKolmogorov(hist['DATA'], hist['BkgSum'])
if len(sign) != 0: drawSB(hist['BkgSum'], hist[sign[0]])
#drawRelativeYield( hist['DATA'] , hist['BkgSum'] )
else:
res = None
c1.Update()
# return list of objects created by the draw() function
if not ratio:
return [
c1, bkg, leg, data_graph if poisson else None,
res_graph if poisson else None
]
else:
return [
c1, bkg, leg, err, errLine, res, data_graph if poisson else None,
res_graph if poisson else None
]
def dependencies(source, path, selection, plots, runRange, isMC, backgrounds,
cmsExtra):
hCanvas = TCanvas("hCanvas", "Distribution", 800, 800)
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0, 1, 1)
setTDRStyle()
plotPad.UseCurrentStyle()
plotPad.Draw()
plotPad.cd()
legend = TLegend(0.575, 0.16, 0.9, 0.4)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legendHist1 = TH1F()
legendHist2 = TH1F()
legendHist3 = TH1F()
legendHist1.SetMarkerColor(ROOT.kBlack)
legendHist2.SetMarkerColor(ROOT.kRed)
legendHist3.SetMarkerColor(ROOT.kBlue)
legend.AddEntry(legendHist1, "ee", "p")
legend.AddEntry(legendHist2, "#mu#mu", "p")
legend.AddEntry(legendHist3, "e#mu", "p")
latex = ROOT.TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = ROOT.TLatex()
latexCMS.SetTextFont(61)
#latexCMS.SetTextAlign(31)
latexCMS.SetTextSize(0.06)
latexCMS.SetNDC(True)
latexCMSExtra = ROOT.TLatex()
latexCMSExtra.SetTextFont(52)
#latexCMSExtra.SetTextAlign(31)
latexCMSExtra.SetTextSize(0.045)
latexCMSExtra.SetNDC(True)
latexSelection = ROOT.TLatex()
#~ latexSelection.SetTextFont(42)
latexSelection.SetTextSize(0.03)
latexSelection.SetNDC(True)
intlumi = ROOT.TLatex()
intlumi.SetTextAlign(12)
intlumi.SetTextSize(0.03)
intlumi.SetNDC(True)
if isMC:
if os.path.isfile("shelves/triggerEff_%s_%s_%s_MC.pkl" %
(selection.name, source, runRange.label)):
centralVals = pickle.load(
open(
"shelves/triggerEff_%s_%s_%s_MC.pkl" %
(selection.name, source, runRange.label), "rb"))
else:
centralVals = centralValues(source, path, selection, runRange,
isMC, backgrounds)
if os.path.isfile("shelves/triggerEff_%s_%s_%s.pkl" %
(selection.name, source, runRange.label)):
centralValsData = pickle.load(
open(
"shelves/triggerEff_%s_%s_%s.pkl" %
(selection.name, source, runRange.label), "rb"))
else:
centralValsData = centralValues(source, path, selection, runRange,
False, backgrounds)
else:
if os.path.isfile("shelves/triggerEff_%s_%s_%s.pkl" %
(selection.name, source, runRange.label)):
centralVals = pickle.load(
open(
"shelves/triggerEff_%s_%s_%s.pkl" %
(selection.name, source, runRange.label), "rb"))
else:
centralVals = centralValues(source, path, selection, runRange,
isMC, backgrounds)
for name in plots:
if isMC:
plotData = getPlot(name)
plotData.addRegion(selection)
#~ plot.cleanCuts()
plotData.cuts = plotData.cuts % runRange.runCut
plotData.cuts = plotData.cuts.replace("mll", "p4.M()")
plotData.cuts = plotData.cuts.replace("pt > 25", "p4.Pt() > 25")
plotData.cuts = plotData.cuts.replace("triggerSummary > 0 &&", "")
plotData.variable = plotData.variable.replace("mll", "p4.M()")
if plotData.variable == "pt":
plotData.variable = plotData.variable.replace("pt", "p4.Pt()")
name = name + "MC"
plot = getPlot(name)
plot.addRegion(selection)
#~ plot.cleanCuts()
plot.cuts = plot.cuts % runRange.runCut
plot.cuts = plot.cuts.replace("mll", "p4.M()")
plot.cuts = plot.cuts.replace("pt > 25", "p4.Pt() > 25")
plot.cuts = plot.cuts.replace("triggerSummary > 0 &&", "")
plot.variable = plot.variable.replace("mll", "p4.M()")
if plot.variable == "pt":
plot.variable = plot.variable.replace("pt", "p4.Pt()")
#~ if "Forward" in selection.name:
#~ plot.nBins = int(plot.nBins/2)
denominators, nominators = getHistograms(path, plot, runRange, isMC,
backgrounds, source)
effEE = TGraphAsymmErrors(nominators["EE"], denominators["EE"], "cp")
effMuMu = TGraphAsymmErrors(nominators["MuMu"], denominators["MuMu"],
"cp")
denominatorHistoOF = denominators["MuEG"].Clone()
nominatorHistoOF = nominators["MuEG"].Clone()
effOF = TGraphAsymmErrors(nominatorHistoOF, denominatorHistoOF, "cp")
if isMC:
denominatorsData, nominatorsData = getHistograms(
locations.triggerDataSetPath, plotData, runRange, False,
backgrounds, source)
effEEData = TGraphAsymmErrors(nominatorsData["EE"],
denominatorsData["EE"], "cp")
effMuMuData = TGraphAsymmErrors(nominatorsData["MuMu"],
denominatorsData["MuMu"], "cp")
denominatorHistoOFData = denominatorsData["MuEG"].Clone()
nominatorHistoOFData = nominatorsData["MuEG"].Clone()
effOFData = TGraphAsymmErrors(nominatorHistoOFData,
denominatorHistoOFData, "cp")
effEE.SetMarkerColor(ROOT.kBlack)
effMuMu.SetMarkerColor(ROOT.kRed)
effOF.SetMarkerColor(ROOT.kBlue)
effEE.SetLineColor(ROOT.kBlack)
effMuMu.SetLineColor(ROOT.kRed)
effOF.SetLineColor(ROOT.kBlue)
effEE.SetMarkerStyle(20)
effMuMu.SetMarkerStyle(21)
effOF.SetMarkerStyle(22)
plotPad.DrawFrame(plot.firstBin, 0.5, plot.lastBin, 1.2,
"; %s ; Efficiency" % (plot.xaxis))
selectionLabel = selection.latex
if plot.variable == "nJets":
selectionLabel = selectionLabel.replace(
"#notin (n_{jets}#geq 2 & p_{T}^{miss} > 100 GeV)",
"p_{T}^{miss} < 100 GeV")
if plot.variable == "met":
selectionLabel = selectionLabel.replace(
"#notin (n_{jets}#geq 2 & p_{T}^{miss} > 100 GeV)",
"n_{jets}=1")
legend.Clear()
legend.AddEntry(effEE, "ee", "p")
legend.AddEntry(effMuMu, "#mu#mu", "p")
legend.AddEntry(effOF, "e#mu", "p")
effEE.Draw("samep")
effMuMu.Draw("samep")
effOF.Draw("samep")
latex.DrawLatex(0.95, 0.96, "%s fb^{-1} (13 TeV)" % runRange.printval)
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
#~ yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
latexSelection.DrawLatex(0.20, 0.25, selectionLabel)
legend.Draw("same")
if isMC:
hCanvas.Print("fig/Triggereff_%s_%s_%s_%s_%s_MC.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
else:
hCanvas.Print("fig/Triggereff_%s_%s_%s_%s_%s.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
denominatorHistoSF = denominators["EE"].Clone()
denominatorHistoOF = denominators["MuEG"].Clone()
denominatorHistoSF.Add(denominators["MuMu"].Clone())
nominatorHistoSF = nominators["EE"].Clone()
nominatorHistoSFNoTrack = nominators["EE"].Clone()
nominatorHistoSF.Add(nominators["MuMu"].Clone())
nominatorHistoOF = nominators["MuEG"].Clone()
effSF = TGraphAsymmErrors(nominatorHistoSF, denominatorHistoSF, "cp")
effOF = TGraphAsymmErrors(nominatorHistoOF, denominatorHistoOF, "cp")
effSF.SetMarkerColor(ROOT.kBlack)
effOF.SetMarkerColor(ROOT.kBlue)
effSF.SetLineColor(ROOT.kBlack)
effOF.SetLineColor(ROOT.kBlue)
effSF.SetMarkerStyle(20)
effOF.SetMarkerStyle(22)
effSFNoFit = effSF.Clone()
effOFNoFit = effSF.Clone()
plotPad.DrawFrame(plot.firstBin, 0.5, plot.lastBin, 1.2,
"; %s ; Efficiency" % (plot.xaxis))
legend.Clear()
legend.AddEntry(effSF, "ee and #mu#mu", "p")
legend.AddEntry(effOF, "e#mu", "p")
effSF.Draw("samep")
effOF.Draw("samep")
latex.DrawLatex(0.95, 0.96, "%s fb^{-1} (13 TeV)" % runRange.printval)
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
yLabelPos = 0.81
else:
yLabelPos = 0.84
#~ yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtra))
latexSelection.DrawLatex(0.20, 0.25, selectionLabel)
legend.Draw("same")
if isMC:
hCanvas.Print("fig/Triggereff_SFvsOF_%s_%s_%s_%s_%s_MC.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
else:
hCanvas.Print("fig/Triggereff_SFvsOF_%s_%s_%s_%s_%s.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
if source == "SingleLepton":
plotPad.DrawFrame(plot.firstBin, .6, plot.lastBin, 1.4,
"; %s ; R_{T}" % (plot.xaxis))
else:
plotPad.DrawFrame(plot.firstBin, .6, plot.lastBin, 1.4,
"; %s ; R_{T}" % (plot.xaxis))
effSFvsOF = efficiencyRatioGeometricMean(effEE, effMuMu, effOF, source)
if isMC:
effSFvsOFData = efficiencyRatioGeometricMean(
effEEData, effMuMuData, effOFData, source)
effSFvsOF.SetLineColor(ROOT.kGreen - 2)
effSFvsOF.SetMarkerColor(ROOT.kGreen - 2)
effSFvsOF.SetMarkerStyle(21)
x = array("f", [plot.firstBin, plot.lastBin])
if isMC:
yData = array("f", [
float(centralValsData[runRange.label]["RT"]),
float(centralValsData[runRange.label]["RT"])
])
sfLineData = ROOT.TF1("sfLine",
str(centralValsData[runRange.label]["RT"]),
plot.firstBin, plot.lastBin)
y = array("f", [
float(centralVals[runRange.label]["RT"]),
float(centralVals[runRange.label]["RT"])
])
sfLine = ROOT.TF1("sfLine", str(centralVals[runRange.label]["RT"]),
plot.firstBin, plot.lastBin)
if isMC:
eyData = array("f", [
float(centralValsData[runRange.label]["RTErrSyst"]),
float(centralValsData[runRange.label]["RTErrSyst"])
])
eyData = array("f", [
float(centralVals[runRange.label]["RTErrSyst"]),
float(centralVals[runRange.label]["RTErrSyst"])
])
exData = array("f", [0., 0.])
ge = ROOT.TGraphErrors(2, x, yData, exData, eyData)
ge.SetFillColor(ROOT.kOrange - 9)
ge.SetFillStyle(1001)
ge.SetLineColor(ROOT.kWhite)
ge.Draw("SAME 3")
else:
ey = array("f", [
float(centralVals[runRange.label]["RTErrSyst"]),
float(centralVals[runRange.label]["RTErrSyst"])
])
ex = array("f", [0., 0.])
ge = ROOT.TGraphErrors(2, x, y, ex, ey)
ge.SetFillColor(ROOT.kOrange - 9)
ge.SetFillStyle(1001)
ge.SetLineColor(ROOT.kWhite)
ge.Draw("SAME 3")
effSFvsOF.Draw("samep")
if isMC:
effSFvsOFData.Draw("samep")
sfLine.SetLineColor(ROOT.kGreen - 2)
sfLine.SetLineWidth(3)
sfLine.SetLineStyle(2)
sfLine.Draw("SAME")
if isMC:
sfLineData.SetLineColor(ROOT.kBlack)
sfLineData.SetLineWidth(3)
sfLineData.SetLineStyle(2)
sfLineData.Draw("SAME")
latex.DrawLatex(0.95, 0.96, "%s fb^{-1} (13 TeV)" % runRange.printval)
latexCMS.DrawLatex(0.19, 0.88, "CMS")
if "Simulation" in cmsExtra:
#~ cmsExtra = "Preliminary"
cmsExtraLabel = cmsExtra.replace(
"#splitline{Private Work}{Simulation}", "Private Work")
#~ yLabelPos = 0.84
else:
#~ yLabelPos = 0.84
cmsExtraLabel = cmsExtra
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.19, yLabelPos, "%s" % (cmsExtraLabel))
legend.Clear()
if isMC:
legend.AddEntry(effSFvsOF, "R_{T} MC", "p")
legend.AddEntry(effSFvsOFData, "R_{T} Data", "p")
else:
legend.AddEntry(effSFvsOF, "R_{T}", "p")
if isMC:
legend.AddEntry(
sfLine,
"Mean R_{T} MC: %.3f" % (centralVals[runRange.label]["RT"]),
"l")
legend.AddEntry(
sfLineData, "Mean R_{T} Data: %.3f" %
(centralValsData[runRange.label]["RT"]), "l")
else:
legend.AddEntry(
sfLine,
"Mean R_{T}: %.3f" % (centralVals[runRange.label]["RT"]), "l")
legend.AddEntry(ge, "syst. uncert. Data", "f")
legend.Draw("same")
latexSelection.DrawLatex(0.20, 0.25, selectionLabel)
ROOT.gPad.RedrawAxis()
if isMC:
hCanvas.Print("fig/Triggereff_SFvsOF_Syst_%s_%s_%s_%s_%s_MC.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
else:
hCanvas.Print("fig/Triggereff_SFvsOF_Syst_%s_%s_%s_%s_%s.pdf" %
(source, selection.name, runRange.label,
plot.variablePlotName, plot.additionalName))
if unfoldedData:
N = origh14.GetBinContent(b + 1)
else:
N = 1. / pow(
h14.GetBinError(b + 1) / h14.GetBinContent(b + 1), 2)
print N
nevents += N
L = 0
if N > 0:
L = ROOT.Math.gamma_quantile(alpha / 2., N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2., N + 1, 1.)
h14G.SetPointEYlow(b, (N - L) / N * h14.GetBinContent(b + 1))
h14G.SetPointEYhigh(b, (U - N) / N * h14.GetBinContent(b + 1))
print "data events:", nevents
h14Gsys = h14G.Clone(histname + "sys")
new_hists += [h14Gsys]
h14Gsysstat = h14G.Clone(histname + "sysstat")
new_hists += [h14Gsysstat]
filename = "datacard_shapelimit13TeV_QCD_chi2016_backup.root"
print filename
fsys = TFile.Open(filename)
new_hists += [fsys]
uncertaintynames = ["jer", "jes", "pdf", "scale"]
#uncertaintynames=["jer","jes","scale"]
uncertainties = []
for u in uncertaintynames:
histname1 = 'QCD#chi' + str(massbins[massbin]).strip("()").replace(
',', "_").replace(' ', "") + "_rebin1_" + u + "Up"
print histname1
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf', mc_leg='MC', obs_leg='Observed', ratio_leg='Obs/MC'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Misidentification rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i)/g_data_vals[i])**2 + (g.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((g_data.GetErrorYhigh(i)/g_data_vals[i])**2 + (g.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
maxy = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), mc_leg, 'lep')
legend.AddEntry(g_data.GetName(), obs_leg, 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle(ratio_leg)
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
