def writeTitleTexts(lumi):
# Texts for
# 1) "CMS Preliminary"
# 2) "sqrt(s) = 7"
# 3) integrated luminosity
tex = TLatex()
tex.SetNDC()
tex.SetTextFont(43)
tex.SetTextSize(27)
tex.SetLineWidth(2)
tex.DrawLatex(0.62, 0.96, "CMS Preliminary")
tex2 = TLatex()
tex2.SetNDC()
tex2.SetTextFont(43)
tex2.SetTextSize(27)
tex2.SetLineWidth(2)
tex2.DrawLatex(0.2, 0.96, "#sqrt{s} = 7 TeV")
mystring = '%.1f' % float(lumi)
tex = TLatex()
tex.SetNDC()
tex.SetTextFont(43)
tex.SetTextSize(27)
tex.SetLineWidth(2)
tex.DrawLatex(0.43, 0.96, mystring + " fb^{-1}")
return 0
data_hist.SetLineColor(1)
data_hist.SetLineWidth(2)
leg = TLegend(0.77, 0.7, 0.97, 0.99)
leg.AddEntry(data_hist, "Data", "lep")
leg.AddEntry(Diboson_hist, "Diboson")
leg.AddEntry(Triboson_hist, "Triboson")
leg.AddEntry(Z_jets_hist, "Z+jets")
leg.AddEntry(Top_hist, "Top")
leg.AddEntry(W_jets_hist, "W+jets")
leg.AddEntry(sig_hist, "signal", "lep")
tex1 = TLatex(0.54, 0.75, "#int Ldt = 36.5 fb^{-1}")
tex1.SetNDC()
tex1.SetTextFont(42)
tex1.SetLineWidth(2)
tex2 = TLatex(0.54, 0.85, "#sqrt{s} = 13 TeV")
tex2.SetNDC()
tex2.SetTextFont(42)
tex2.SetLineWidth(2)
tex3 = TLatex(0.54, 0.93, "ATLAS Internal")
tex3.SetNDC()
tex3.SetTextFont(42)
tex3.SetLineWidth(2)
cc = TCanvas(var, var, 800, 750)
# cc.SetLogy()
pad1 = TPad("p1", "p1", 0, 0.25, 1, 1, 0, 0)
pad1.SetMargin(0.15, 0.03, 0, 0.01)
pad2 = TPad("p2", "p2", 0, 0, 1, 0.25, 0, 0)
pad2.SetMargin(0.15, 0.03, 0.3, 0.01)
def cal_fraction(year, outputdir, eORb, pt_down, pt_up, fit_type, fdata, ftrue,
ffake):
sieie_cut = 0
if eORb == "barrel":
sieie_cut = 0.01015
if eORb == "endcap":
sieie_cut = 0.0272
lumi = 0
if year == "2016":
lumi = 35.92
if year == "2017":
lumi = 41.50
if year == "2018":
lumi = 59.74
histname = "hist_" + eORb + "_" + str(pt_down) + "to" + str(pt_up)
hdata = fdata.Get(histname)
htrue = ftrue.Get(histname)
hfake = ffake.Get(histname)
c1 = TCanvas(histname, histname, 1000, 700)
htrue.Scale(lumi)
if fit_type == 'closureTEST':
hdata.Scale(lumi)
hfake.Scale(lumi)
for i in range(hdata.GetNbinsX() + 2):
if hdata.GetBinContent(i) < 0:
hdata.SetBinContent(i, 0)
if htrue.GetBinContent(i) < 0:
htrue.SetBinContent(i, 0)
if hfake.GetBinContent(i) < 0:
hfake.SetBinContent(i, 0)
mc = TObjArray(2)
mc.Add(htrue)
mc.Add(hfake)
fit = TFractionFitter(hdata, mc)
fit.Constrain(1, 0.0, 1.0)
status = fit.Fit()
val0, err0, val1, err1 = ROOT.Double(0), ROOT.Double(0), ROOT.Double(
0), ROOT.Double(0)
n_data_total = hdata.Integral()
fit.GetResult(0, val0, err0)
fit.GetResult(1, val1, err1)
# get fake rate
nbin = hdata.FindFixBin(sieie_cut) - 1
nbins = hdata.GetNbinsX()
fake_err_in_window, fake_err_total, data_err_in_window, data_err_total = ROOT.Double(
0), ROOT.Double(0), ROOT.Double(0), ROOT.Double(0)
n_hfake_in_window = hfake.IntegralAndError(1, nbin, fake_err_in_window)
n_hfake_total = hfake.IntegralAndError(1, nbins, fake_err_total)
sector1 = n_hfake_in_window / n_hfake_total
n_hdata_in_window = hdata.IntegralAndError(1, nbin, data_err_in_window)
n_hdata_total = hdata.IntegralAndError(1, nbins, data_err_total)
sector2 = n_hdata_in_window / n_hdata_total
fakerate = val1 * sector1 / sector2
err_sector1 = sqrt(
(sector1)**2 * ((fake_err_in_window / n_hfake_in_window)**2 +
(fake_err_total / n_hfake_total)**2))
err_sector2 = sqrt(
(sector2)**2 * ((data_err_in_window / n_hdata_in_window)**2 +
(data_err_total / n_hdata_total)**2))
tmp = val1 * sector1
tmp_err = sqrt(
(val1 * sector1)**2 * ((err_sector1 / sector1)**2 + (err1 / val1)**2))
fakerate_err = sqrt(
(tmp / sector2)**2 * ((tmp_err / tmp)**2 + (err_sector2 / sector2)**2))
result = fit.GetPlot()
hdata.Draw("p e")
hdata.GetYaxis().SetTitle("Events/bin")
hdata.GetYaxis().SetTitleOffset(1.45)
hdata.GetXaxis().SetTitle("#sigma_{i#etai#eta}")
hdata.SetStats(0)
hdata.SetLineColor(1)
hdata.SetMarkerStyle(20)
result.Draw("HIST same")
result.SetLineColor(8)
result.SetLineWidth(2)
result.SetStats(0)
htrue.SetLineColor(2)
htrue.SetLineWidth(2)
htrue.SetStats(0)
htrue.DrawNormalized("HIST e same", val0 * n_data_total)
hfake.SetLineColor(4)
hfake.SetLineWidth(2)
hfake.SetStats(0)
hfake.DrawNormalized("HIST e same", val1 * n_data_total)
maximum = 1.3 * hdata.GetMaximum()
hdata.SetMaximum(maximum)
leg = TLegend(0.53, 0.48, 0.88, 0.88, "brNDC")
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetFillColor(10)
leg.SetBorderSize(0)
leg.SetHeader(
str(pt_down) + "GeV<p_{T}^{#gamma}<" + str(pt_up) + "GeV", "c")
leg.AddEntry(hdata, "Data", "LPE")
leg.AddEntry(result, "Template prediction", "LPE")
leg.AddEntry(htrue, "True template from W#gamma", "LPE")
leg.AddEntry(hfake, "Fake template from Data", "LPE")
leg.Draw("same")
statusStr = "fit status: " + str(int(status))
chi2 = fit.GetChisquare()
ndf = fit.GetNDF()
chi2ToNDF = chi2 / ndf
chiInt = (100 * chi2ToNDF)
strChi = "#chi^{2}/ndf = " + str(round(
chiInt / 100, 3)) #+ '.' + str(round(chiInt % 100,3))
FRInt = (10000 * fakerate)
FRErrInt = (10000 * fakerate_err)
strFR = "fake rate = (" + str(round(FRInt / 100, 3)) + '#pm' + str(
round(FRErrInt / 100, 3)) + '%)'
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.03)
latex.SetLineWidth(1)
latex.DrawLatex(0.75, 0.90, eORb)
latex.DrawLatex(0.55, 0.44, statusStr)
latex.DrawLatex(0.55, 0.38, strChi)
latex.DrawLatex(0.55, 0.34, strFR)
PNG_name = outputdir + '/' + str(year) + '_' + histname + '.png'
c1.SaveAs(PNG_name)
return fakerate, fakerate_err
stacked_mc[histogram_name_index].GetXaxis().SetLabelSize(0.035)
stacked_mc[histogram_name_index].GetXaxis().SetLabelFont(42)
stacked_mc[histogram_name_index].GetXaxis().SetTitleSize(0.035)
#stacked_mc[histogram_name_index].SetTitle(histogram_descriptions[histogram_name_index])
stacked_mc[histogram_name_index].GetYaxis().SetTitleSize(0.06)
stacked_mc[histogram_name_index].GetYaxis().SetLabelSize(0.05)
stacked_mc[histogram_name_index].GetYaxis().SetTitleOffset(0.81)
stacked_mc[histogram_name_index].GetYaxis().SetTitleFont(42)
stacked_mc[histogram_name_index].GetYaxis().SetLabelFont(42)
#cmslabel.Draw()
text3 = TLatex(0.1969, 0.906825, "CMS")
text3.SetNDC()
text3.SetTextAlign(13)
text3.SetTextFont(61)
text3.SetTextSize(0.07475)
text3.SetLineWidth(2)
text3.Draw()
text2 = TLatex(0.1969, 0.817125, "Preliminary")
text2.SetNDC()
text2.SetTextAlign(13)
text2.SetTextFont(52)
text2.SetTextSize(0.05681)
text2.SetLineWidth(2)
text2.Draw()
text4 = TLatex(0.1969, 0.727425, "t#bar{t} sample")
text4.SetNDC()
text4.SetTextAlign(12)
text4.SetTextFont(42)
text4.SetTextSize(0.05681)
text4.SetLineWidth(2)
text4.Draw()
def train_and_apply():
np.random.seed(1)
ROOT.gROOT.SetBatch()
#Extract data from root file
tree = uproot.open("out_all.root")["outA/Tevts"]
branch_mc = [
"MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta",
"MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi",
"MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta",
"MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi",
"MC_pis_pt", "MC_q2"
]
branch_rec = [
"B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt",
"Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P",
"mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt",
"q2"
]
nvariable = len(branch_mc)
x_train = tree.array(branch_mc[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_train = np.vstack(
(x_train, tree.array(branch_mc[i], entrystop=options.maxevents)))
x_test = tree.array(branch_rec[0], entrystop=options.maxevents)
for i in range(1, nvariable):
x_test = np.vstack(
(x_test, tree.array(branch_rec[i], entrystop=options.maxevents)))
x_train = x_train.T
x_test = x_test.T
x_test = array2D_float(x_test)
#Different type of reconstruction variables
#BN normalization
gamma = 0
beta = 0.2
ar = np.array(x_train)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_train = np.vstack((x_train, a))
x_train = x_train.T
ar = np.array(x_test)
a = K.constant(ar[:, 0])
mean = K.mean(a)
var = K.var(a)
x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
for i in range(1, nvariable):
a = K.constant(ar[:, i])
mean = K.mean(a)
var = K.var(a)
a = K.eval(K.batch_normalization(a, mean, var, gamma, beta))
x_test = np.vstack((x_test, a))
x_test = x_test.T
#Add noise, remain to be improved
noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape)
x_test_noisy = x_test + noise
x_train = np.clip(x_train, -1., 1.)
x_test = np.clip(x_test, -1., 1.)
x_train_noisy = np.clip(x_train_noisy, -1., 1.)
x_test_noisy = np.clip(x_test_noisy, -1., 1.)
# Network parameters
input_shape = (x_train.shape[1], )
batch_size = 128
latent_dim = 2
# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Shape info needed to build Decoder Model
shape = K.int_shape(x)
# Generate the latent vector
latent = Dense(latent_dim, name='latent_vector')(x)
# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()
# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim, ), name='decoder_input')
x = Dense(shape[1])(latent_inputs)
x = Reshape((shape[1], ))(x)
outputs = Activation('tanh', name='decoder_output')(x)
# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()
autoencoder.compile(loss='mse', optimizer='adam')
# Train the autoencoder
autoencoder.fit(x_train_noisy,
x_train,
validation_data=(x_test_noisy, x_test),
epochs=options.epochs,
batch_size=batch_size)
# Predict the Autoencoder output from corrupted test imformation
x_decoded = autoencoder.predict(x_test_noisy)
# Draw Comparision Plots
c = TCanvas("c", "c", 700, 700)
fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00)
fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29)
fPads1.SetBottomMargin(0.007)
fPads1.SetLeftMargin(0.10)
fPads1.SetRightMargin(0.03)
fPads2.SetLeftMargin(0.10)
fPads2.SetRightMargin(0.03)
fPads2.SetBottomMargin(0.25)
fPads1.Draw()
fPads2.Draw()
fPads1.cd()
nbin = 50
min = -1.
max = 1.
variable = "P^{B}"
lbin = (max - min) / nbin
lbin = str(float((max - min) / nbin))
xtitle = branch_rec[options.branch - 1]
ytitle = "Events/" + lbin + "GeV"
h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_rec.Sumw2()
h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max)
h_pre.Sumw2()
for i in range(x_test_noisy.shape[0]):
h_rec.Fill(x_test_noisy[i][options.branch - 1])
h_pre.Fill(x_decoded[i][options.branch - 1])
h_rec = UnderOverFlow1D(h_rec)
h_pre = UnderOverFlow1D(h_pre)
maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum())
h_rec.SetLineColor(2)
h_rec.SetFillStyle(0)
h_rec.SetLineWidth(2)
h_rec.SetLineStyle(1)
h_pre.SetLineColor(3)
h_pre.SetFillStyle(0)
h_pre.SetLineWidth(2)
h_pre.SetLineStyle(1)
h_rec.SetStats(0)
h_pre.SetStats(0)
h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1)
h_rec.Draw("HIST")
h_pre.Draw("same HIST")
h_rec.GetYaxis().SetTitleSize(0.06)
h_rec.GetYaxis().SetTitleOffset(0.78)
theLeg = TLegend(0.5, 0.45, 0.95, 0.82, "", "NDC")
theLeg.SetName("theLegend")
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.05)
theLeg.AddEntry(h_rec, "Reconstruction", "L")
theLeg.AddEntry(h_pre, "Prediction", "L")
theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
fPads1.cd()
theLeg.Draw()
title = TLatex(
0.91, 0.93, "AE prediction compare with reconstruction, epochs=" +
str(options.epochs))
title.SetNDC()
title.SetTextSize(0.05)
title.SetTextFont(42)
title.SetTextAlign(31)
title.SetLineWidth(2)
title.Draw()
fPads2.cd()
h_Ratio = h_pre.Clone("h_Ratio")
h_Ratio.Divide(h_rec)
h_Ratio.SetLineColor(1)
h_Ratio.SetLineWidth(2)
h_Ratio.SetMarkerStyle(8)
h_Ratio.SetMarkerSize(0.7)
h_Ratio.GetYaxis().SetRangeUser(0, 2)
h_Ratio.GetYaxis().SetNdivisions(504, 0)
h_Ratio.GetYaxis().SetTitle("Pre/Rec")
h_Ratio.GetYaxis().SetTitleOffset(0.35)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetTitleSize(0.13)
h_Ratio.GetYaxis().SetLabelSize(0.11)
h_Ratio.GetXaxis().SetLabelSize(0.1)
h_Ratio.GetXaxis().SetTitleOffset(0.8)
h_Ratio.GetXaxis().SetTitleSize(0.14)
h_Ratio.SetStats(0)
axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L")
axis1.SetLineColor(1)
axis1.SetLineWidth(1)
for i in range(1, h_Ratio.GetNbinsX() + 1, 1):
D = h_rec.GetBinContent(i)
eD = h_rec.GetBinError(i)
if D == 0: eD = 0.92
B = h_pre.GetBinContent(i)
eB = h_pre.GetBinError(i)
if B < 0.1 and eB >= B:
eB = 0.92
Err = 0.
if B != 0.:
Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) /
(B * B * B * B))
h_Ratio.SetBinContent(i, D / B)
h_Ratio.SetBinError(i, Err)
if B == 0.:
Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) /
(eB * eB * eB * eB))
h_Ratio.SetBinContent(i, D / 0.92)
h_Ratio.SetBinError(i, Err)
if D == 0 and B == 0:
h_Ratio.SetBinContent(i, -1)
h_Ratio.SetBinError(i, 0)
h_Ratio.Draw("e0")
axis1.Draw()
c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
def plotUpperLimits(labels, values):
# see CMS plot guidelines: https://ghm.web.cern.ch/ghm/plots/
N = len(labels)
yellow = TGraph(2 * N) # yellow band
green = TGraph(2 * N) # green band
median = TGraph(N) # median line
up2s = []
for i in range(N):
# file_name = "higgsCombine"+labels[i]+"AsymptoticLimits.mH125.root"
file_name = "higgsCombine.AsymptoticLimits.mH125." + labels[i] + ".root"
limit = getLimits(file_name)
# print'limit = ',limit
# print'values[i] = ',values[i]
up2s.append(limit[4])
yellow.SetPoint(i, values[i], limit[4]) # + 2 sigma
green.SetPoint(i, values[i], limit[3]) # + 1 sigma
median.SetPoint(i, values[i], limit[2]) # median
green.SetPoint(2 * N - 1 - i, values[i], limit[1]) # - 1 sigma
yellow.SetPoint(2 * N - 1 - i, values[i], limit[0]) # - 2 sigma
W = 800
H = 600
T = 0.08 * H
B = 0.12 * H
L = 0.12 * W
R = 0.04 * W
c = TCanvas("c", "c", 100, 100, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.SetTickx(0)
c.SetTicky(0)
c.SetGrid()
# c.SetLogy()
# gPad.SetLogy()
c.cd()
# ROOT.gPad.SetLogy()
# c.SetLogy()
frame = c.DrawFrame(1.4, 0.001, 4.1, 10)
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitleSize(0.05)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(0.9)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().CenterTitle(True)
# frame.GetYaxis().SetTitle("95% upper limit on #sigma / #sigma_{SM}")
if (args.atlas_compare):
frame.GetYaxis().SetTitle(
"95% CL limits on #sigma(gg#rightarrow X)#times B(X#rightarrow HH) [pb]"
)
elif (args.CMS_compare):
frame.GetYaxis().SetTitle(
"95% CL limit on #sigma(gg#rightarrow X#rightarrow HH) (fb)")
# frame.GetYaxis().SetTitle("95% upper limit on #sigma #times BR / (#sigma #times BR)_{SM}")
# frame.GetXaxis().SetTitle("background systematic uncertainty [%]")
#if(args.SM_Radion): frame.GetXaxis.SetTitle("Standard Model")
#else: frame.GetXaxis().SetTitle("Radion Mass (GeV)")
# frame.SetMinimum(0)
# frame.SetMinimum(1) # need Minimum > 0 for log scale
frame.SetMinimum(1.000001) # need Minimum > 0 for log scale
# frame.SetMaximum(max(up2s)*1.05)
# frame.SetMaximum(max(up2s)*2)
# frame.SetMaximum(1000.)
if (args.atlas_compare):
frame.SetMaximum(7 * 1e2) # ATLAS
elif (args.CMS_compare):
frame.SetMaximum(8 * 1e4) # CMS HH
#frame.GetXaxis().SetLimits(min(values),max(values))
# frame.SetLogy()
frame.GetXaxis().SetLimits(min(values) - 10, max(values) + 10)
yellow.SetFillColor(ROOT.kOrange)
yellow.SetLineColor(ROOT.kOrange)
yellow.SetFillStyle(1001)
yellow.Draw('F')
green.SetFillColor(ROOT.kGreen + 1)
green.SetLineColor(ROOT.kGreen + 1)
green.SetFillStyle(1001)
green.Draw('Fsame')
median.SetLineColor(1)
median.SetLineWidth(2)
median.SetLineStyle(2)
median.Draw('Lsame')
CMS_lumi.CMS_lumi(c, 4, 11)
ROOT.gPad.SetTicks(1, 1)
frame.Draw('sameaxis')
# yboost = 0.075
yboost = -0.2
x1 = 0.15
x2 = x1 + 0.24
y2 = 0.76 + yboost
y1 = 0.60 + yboost
legend = TLegend(x1, y1, x2, y2)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.041)
legend.SetTextFont(42)
legend.AddEntry(median, "AsymptoticLimits CL_{s} expected", 'L')
legend.AddEntry(green, "#pm 1 std. deviation", 'f')
# legend.AddEntry(green, "AsymptoticLimits CL_{s} #pm 1 std. deviation",'f')
legend.AddEntry(yellow, "#pm 2 std. deviation", 'f')
# legend.AddEntry("","STAT Only","")
# legend.AddEntry(green, "AsymptoticLimits CL_{s} #pm 2 std. deviation",'f')
legend.Draw()
label = TLatex()
label.SetNDC()
label.SetTextAngle(0)
label.SetTextColor(kBlack)
label.SetTextFont(42)
label.SetTextSize(0.045)
label.SetLineWidth(2)
label.DrawLatex(0.7, 0.7 + yboost, "STAT only")
print " "
# c.SaveAs("UpperLimit.png")
outFile = ''
if (args.CMS_compare):
outFile += "CMS_Compare_"
if (args.atlas_compare):
outFile += "atlas_Compare_"
if args.SM_Radion: outFile += "SM_"
c.SaveAs(outFile + "UpperLimit.pdf")
c.SaveAs(outFile + "UpperLimit.C")
c.Close()
def plot(ana, histos, h_legend, minY, maxY, minX, maxX, whatX, whatY, c1=0):
global stack, leg, dum
# print what
# gROOT.SetBatch(True) # suppress root window popping up #! better: sys.argv.append('b')
gROOT.ProcessLine(
"gErrorIgnoreLevel = kWarning;"
) # suppress standard output of the form: "Info in <TCanvas::Print>: ... has been created" and
# "Info in <TCanvas::SaveAs>: ... has been created"
#prepare the layout
if c1 == 0:
c1 = TCanvas('c1', '', 800, 600)
c1.SetLogy()
dum = histo.Clone()
# dum.Reset()
dum.SetMinimum(minY)
dum.SetMaximum(maxY)
dum.GetXaxis().SetRangeUser(minX, maxX)
# last = dum.FindLastBinAbove(0,1)
# last = last + 0.5 * last
# dum.GetXaxis().SetRange(0,last+10)
dum.GetXaxis().SetTitleSize(0.038)
dum.GetYaxis().SetTitleSize(0.04)
dum.GetXaxis().SetLabelSize(0.03)
dum.GetYaxis().SetLabelSize(0.03)
dum.GetXaxis().SetTitleOffset(1.2)
dum.GetYaxis().SetTitleOffset(1.1)
dum.SetXTitle(whatX)
dum.SetYTitle(whatY)
dum.SetTitle('')
dum.SetStats(0)
dum.Draw()
tex = TLatex(
0.89, 0.93,
"#scale[0.8]{CMSSW_7_2_3} #scale[0.6]{ #sqrt{s} = 13 TeV, #int L dt = 4 fb^{-1}, bx = 25 ns, PU = 20}"
)
tex.SetNDC()
tex.SetTextAlign(31)
tex.SetTextFont(42)
tex.SetTextSize(0.047)
tex.SetLineWidth(2)
tex.Draw()
#CMS_lumi_v2( c1, 14, 11 )
# tex1 = TLatex(0.15,0.89,"CMSSW_7_2_3 ")
# tex1.SetNDC()
# tex1.SetTextAlign(13)
# tex1.SetTextFont(61)
# tex1.SetTextSize(0.045)
# tex1.SetLineWidth(2)
# tex1.Draw()
# a legend
# leg = TLegend(0.63,0.525,0.87,0.875) #for 33 TeV
# leg = TLegend(0.65,0.65,0.9,0.9)
leg = TLegend(0.6545226, 0.534965, 0.8944724, 0.8846154) #fits best
leg.SetBorderSize(1)
leg.SetTextFont(62)
leg.SetTextSize(0.035)
leg.SetLineColor(0)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(1001)
if ana == 'TTbar':
title = ' t#bar{t}'
leg.AddEntry(histo, title, 'l')
elif ana == 'WJets':
leg.AddEntry(histo, 'W+jets', 'l')
elif ana == 'DYJets':
leg.AddEntry(histo, 'DY+jets', 'l')
elif ana == 'QCD':
leg.AddEntry(histo, 'QCD', 'l')
elif ana == 'SingleTop':
leg.AddEntry(histo, 't / #bar{t}', 'l')
elif ana == 'TTH':
leg.AddEntry(histo, 'ttH', 'l')
elif ana == 'TTW':
leg.AddEntry(histo, 'ttW', 'l')
elif ana == 'TTZ':
leg.AddEntry(histo, 'ttZ', 'l')
elif ana == 'T1tttt_1500_100':
leg.AddEntry(histo, 'T1t^{4}(1500,100)', 'l')
elif ana == 'T1tttt_1200_800':
leg.AddEntry(histo, 'T1t^{4}(1200,800)', 'l')
elif ana == 'T5tttt_1000_280':
leg.AddEntry(histo, 'T5t^{4}(1000,280)', 'l')
elif ana == 'T5tttt_1300_280':
leg.AddEntry(histo, 'T5t^{4}(1300,280)', 'l')
elif ana == 'T5tttt_1000_285':
leg.AddEntry(histo, 'T5t^{4}(1000,285)', 'l')
elif ana == 'T5tttt_1300_285':
leg.AddEntry(histo, 'T5t^{4}(1300,285)', 'l')
elif ana == 'T1ttbbWW_1300_290':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1300,290)', 'l')
elif ana == 'T1ttbbWW_1300_295':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1300,295)', 'l')
elif ana == 'T1ttbbWW_1000_715':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1000,715)', 'l')
elif ana == 'T1ttbbWW_1000_720':
leg.AddEntry(histo, 'T1t^{2}b^{2}W^{2}(1000,720)', 'l')
elif ana == 'SqGl_1300_100':
leg.AddEntry(histo, 'SqGl(1300,100)', 'l')
# bgrd stack
if histos != 0 and h_legend != 0:
stack = THStack('stack', '')
N = len(histos)
for i in range(N):
h = histos[i][0]
stack.Add(h)
for i in range(N - 1, -1, -1):
h = histos[i][0]
t = h_legend[h]
leg.AddEntry(h, t, 'f')
# draw bgrds + signals
# stack.Draw('samehist')
stack.Draw('same')
#histo.Draw('samehist')
histo.Draw('same')
leg.Draw("same")
gPad.RedrawAxis()
c1.SaveAs(dir_png_noCuts + what + ".png")
c1.SaveAs(dir_pdf_noCuts + what + ".pdf")
c1.SaveAs(dir_root_noCuts + what + ".root")
def doULPlot(suffix):
xbins = array('d', [])
xbins_env = array('d', [])
ybins_exp = array('d', [])
ybins_obs = array('d', [])
ybins_1s = array('d', [])
ybins_2s = array('d', [])
ybins_th = array('d', [])
for i in range(len(mass)):
curFile = "higgsCombine_lim_%s%03d%s.Asymptotic.mH%03d.root" % (
options.signalmodel, mass[i], suffix, mass[i])
print "curFile: %s" % curFile
if options.lvqqBR:
sf = table_signalscale[mass[i]] * xsDict_lvj[mass[i]]
#*0.1057*0.577; # BR(W->munu)*BR(H->bb)
else:
sf = table_signalscale[mass[i]] * xsDict[mass[i]]
curAsymLimits = getAsymLimits(curFile)
#print mass[i]
#print curAsymLimits
#raw_input("zixu")
xbins.append(mass[i] / 1000.)
#GeV->TeV
xbins_env.append(mass[i] / 1000.)
ybins_exp.append(curAsymLimits[3] * sf)
ybins_obs.append(curAsymLimits[0] * sf)
ybins_2s.append(curAsymLimits[1] * sf)
ybins_1s.append(curAsymLimits[2] * sf)
#ybins_th.append(sf);#/20.); #*0.25);
if options.lvqqBR:
ybins_th.append(xsDict_lvj[mass[i]])
#/20.); #*0.25);
else:
ybins_th.append(xsDict[mass[i]])
#/20.); #*0.25);
for i in range(len(mass) - 1, -1, -1):
curFile = "higgsCombine_lim_%s%03d%s.Asymptotic.mH%03d.root" % (
options.signalmodel, mass[i], suffix, mass[i])
print "curFile: %s" % curFile
if options.lvqqBR:
sf = table_signalscale[mass[i]] * xsDict_lvj[mass[i]]
#*0.1057*0.577; # BR(W->munu)*BR(H->bb)
else:
sf = table_signalscale[mass[i]] * xsDict[mass[i]]
curAsymLimits = getAsymLimits(curFile)
xbins_env.append(mass[i] / 1000.)
ybins_2s.append(curAsymLimits[5] * sf)
ybins_1s.append(curAsymLimits[4] * sf)
nPoints = len(mass)
curGraph_exp = ROOT.TGraphAsymmErrors(nPoints, xbins, ybins_exp)
curGraph_obs = ROOT.TGraphAsymmErrors(nPoints, xbins, ybins_obs)
curGraph_th = ROOT.TGraph(nPoints, xbins, ybins_th)
curGraph_1s = ROOT.TGraphAsymmErrors(nPoints * 2, xbins_env, ybins_1s)
curGraph_2s = ROOT.TGraphAsymmErrors(nPoints * 2, xbins_env, ybins_2s)
curGraph_obs.SetMarkerStyle(20)
curGraph_obs.SetLineWidth(3)
curGraph_obs.SetLineStyle(1)
curGraph_obs.SetMarkerSize(1.6)
curGraph_exp.SetMarkerSize(1.3)
curGraph_exp.SetMarkerColor(ROOT.kBlack)
curGraph_exp.SetLineStyle(2)
curGraph_exp.SetLineWidth(3)
curGraph_exp.SetMarkerSize(2)
curGraph_exp.SetMarkerStyle(24)
curGraph_exp.SetMarkerColor(ROOT.kBlack)
#curGraph_th.SetLineStyle(ROOT.kDashed);
curGraph_th.SetFillStyle(3344)
curGraph_th.SetLineWidth(2)
curGraph_th.SetMarkerSize(2)
curGraph_th.SetLineColor(ROOT.kRed)
curGraph_1s.SetFillColor(ROOT.kGreen)
curGraph_1s.SetFillStyle(1001)
curGraph_1s.SetLineStyle(ROOT.kDashed)
curGraph_1s.SetLineWidth(3)
curGraph_2s.SetFillColor(ROOT.kYellow)
curGraph_2s.SetFillStyle(1001)
curGraph_2s.SetLineStyle(ROOT.kDashed)
curGraph_2s.SetLineWidth(3)
oneLine = ROOT.TF1("oneLine", "1", 799, 1001)
oneLine.SetLineColor(ROOT.kRed)
oneLine.SetLineWidth(3)
setStyle()
can_SM = ROOT.TCanvas("can_SM", "can_SM", 630, 600)
hrl_SM = can_SM.DrawFrame(mass[0] / 1000. - 0.01, 1e-4,
mass[nPoints - 1] / 1000. + 0.01, 1e2)
if options.signalmodel == "BulkGravWW":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow G_{Bulk} #rightarrow WW #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow G_{Bulk} #rightarrow WW) (pb)")
elif options.signalmodel == "WprimeWZ":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ) (pb)")
elif options.signalmodel == "WprimeWZ-HVT-A":
if options.lvqqBR:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ #rightarrow l#nuqq') (pb)"
)
else:
hrl_SM.GetYaxis().SetTitle(
"#sigma_{95%} (pp #rightarrow W' #rightarrow WZ) (pb)")
hrl_SM.GetYaxis().SetTitleOffset(1.35)
hrl_SM.GetYaxis().SetTitleSize(0.045)
hrl_SM.GetYaxis().SetTitleFont(42)
if options.signalmodel == "BulkGravWW":
hrl_SM.GetXaxis().SetTitle("M_{G_{Bulk}} (TeV)")
elif options.signalmodel == "WprimeWZ":
hrl_SM.GetXaxis().SetTitle("M_{W'} (TeV)")
elif options.signalmodel == "WprimeWZ-HVT-A":
hrl_SM.GetXaxis().SetTitle("M_{W'} (TeV)")
hrl_SM.GetXaxis().SetTitleSize(0.045)
hrl_SM.GetXaxis().SetTitleFont(42)
hrl_SM.GetXaxis().SetNdivisions(510)
hrl_SM.GetYaxis().SetNdivisions(505)
can_SM.SetGridx(1)
can_SM.SetGridy(1)
curGraph_2s.Draw("F")
curGraph_1s.Draw("Fsame")
if options.keepblind == 0:
curGraph_obs.Draw("PLsame")
curGraph_exp.Draw("Lsame")
curGraph_th.Draw("Csame")
leg2 = ROOT.TLegend(0.32, 0.67, 0.9, 0.92)
leg2.SetFillColor(0)
leg2.SetShadowColor(0)
leg2.SetTextFont(42)
leg2.SetTextSize(0.035)
leg2.AddEntry(curGraph_1s, "Asympt. CL_{S} Expected #pm 1 s.d.", "LF")
leg2.AddEntry(curGraph_2s, "Asympt. CL_{S} Expected #pm 2 s.d.", "LF")
if options.signalmodel == "BulkGravWW":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(G_{Bulk}#rightarrow WW#rightarrow l#nuqq'), #tilde{k}=0.5",
"L")
else:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(G_{Bulk}#rightarrow WW), #tilde{k}=0.5",
"L")
elif options.signalmodel == "WprimeWZ":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT B}#rightarrow WZ#rightarrow l#nuqq')",
"L")
else:
leg2.AddEntry(curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT B}#rightarrow WZ)",
"L")
elif options.signalmodel == "WprimeWZ-HVT-A":
if options.lvqqBR:
leg2.AddEntry(
curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT A}#rightarrow WZ#rightarrow l#nuqq')",
"L")
else:
leg2.AddEntry(curGraph_th,
"#sigma_{TH} #times BR(W'_{HVT A}#rightarrow WZ)",
"L")
leg2.AddEntry(curGraph_obs, "Asympt. CL_{S} Observed", "LP")
#ROOT.gPad.SetLogx();
#hrl_SM.GetXaxis().SetMoreLogLabels()
#hrl_SM.GetXaxis().SetNoExponent()
ROOT.gPad.SetLogy()
can_SM.Update()
can_SM.RedrawAxis()
can_SM.RedrawAxis("g")
can_SM.Update()
leg2.Draw()
line = TLine(1.1, 1e-4, 1.1, 1e0)
line.SetLineWidth(2)
line.SetLineColor(kBlack)
line.SetLineStyle(9)
line.Draw()
banner = TLatex(0.95, 0.96, "%s fb^{-1} (13 TeV)" % (Lumi))
banner.SetNDC()
banner.SetTextSize(0.038)
banner.SetTextFont(42)
banner.SetTextAlign(31)
banner.SetLineWidth(2)
banner.Draw()
CMStext = TLatex(0.15, 0.96, "CMS")
CMStext.SetNDC()
CMStext.SetTextSize(0.041)
CMStext.SetTextFont(61)
CMStext.SetTextAlign(11)
CMStext.SetLineWidth(2)
CMStext.Draw()
if suffix == "_el_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow e#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
elif suffix == "_mu_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow #mu#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
elif suffix == "_em_HP":
Extratext = TLatex(0.241, 0.96, "Preliminary W#rightarrow l#nu")
Extratext.SetNDC()
Extratext.SetTextSize(0.032)
Extratext.SetTextFont(52)
Extratext.SetTextAlign(11)
Extratext.SetLineWidth(2)
Extratext.Draw()
#CMS_lumi.lumi_13TeV = "%s fb^{-1}"%(Lumi)
#CMS_lumi.writeExtraText = 1
#CMS_lumi.extraText = "Preliminary"
#iPos = 11
#if(iPos == 0):
# CMS_lumi.relPosX = 0.15
#CMS_lumi.CMS_lumi(can_SM, 4, 11)
os.system("mkdir -p %s/LimitResult/" % (os.getcwd()))
os.system("mkdir -p %s/LimitResult/Limit_%s_sys%s/" %
(os.getcwd(), options.signalmodel, options.Sys))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.png" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.pdf" %
(options.signalmodel, options.Sys, suffix))
#can_SM.SaveAs("./LimitResult/Limit_sys%s/Lim%s.root"%(options.Sys, suffix));
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s.C" %
(options.signalmodel, options.Sys, suffix))
ROOT.gPad.SetLogx()
hrl_SM.GetXaxis().SetMoreLogLabels()
hrl_SM.GetXaxis().SetNoExponent()
can_SM.Update()
can_SM.RedrawAxis()
can_SM.RedrawAxis("g")
leg2.Draw()
can_SM.Update()
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.png" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.pdf" %
(options.signalmodel, options.Sys, suffix))
can_SM.SaveAs("./LimitResult/Limit_%s_sys%s/Lim%s_Logx.C" %
(options.signalmodel, options.Sys, suffix))
def plotUpperLimits(masses, limit, ALP_xs, name):
N = len(masses)
print str(N) + ' mass points'
yellow = TGraph(2 * N) # yellow band
green = TGraph(2 * N) # green band
median = TGraph(N) # median line
print 'limit = ', limit
for i in range(N):
print 'ALP mass: ' + str(masses[i]) + ' GeV'
x_mass = masses[i]
yellow.SetPoint(i, x_mass, limit[x_mass][4] * ALP_xs) # + 2 sigma
green.SetPoint(i, x_mass, limit[x_mass][3] * ALP_xs) # + 1 sigma
median.SetPoint(i, x_mass, limit[x_mass][2] * ALP_xs) # median
green.SetPoint(2 * N - 1 - i, x_mass,
limit[x_mass][1] * ALP_xs) # - 1 sigma
yellow.SetPoint(2 * N - 1 - i, x_mass,
limit[x_mass][0] * ALP_xs) # - 2 sigma
W = 800
H = 600
T = 0.08 * H
B = 0.12 * H
L = 0.12 * W
R = 0.04 * W
c = TCanvas("c", "c", 100, 100, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.SetTickx(0)
c.SetTicky(0)
c.SetGrid()
c.cd()
frame = c.DrawFrame(1.4, 0.001, 4.1, 10)
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetTitleSize(0.04)
frame.GetXaxis().SetTitleSize(0.05)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetTitleOffset(1.3)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().CenterTitle(True)
frame.GetXaxis().SetTitle("m_{a} [GeV]")
if (args.pb):
frame.GetYaxis().SetTitle(
"95% CL limits on #sigma(gg#rightarrow H)#times B(X#rightarrow HH) [pb]"
)
elif (args.fb):
if name == 'coeff':
frame.GetYaxis().SetTitle(
"95% CL limits on |C_{Zh}^{eff}| [#frac{#Lambda}{1 TeV}]")
frame.SetMinimum(0.01) # need Minimum > 0 for log scale
frame.SetMaximum(1.) # CMS HH
elif name == 'Br':
#frame.GetYaxis().SetTitle("95% CL limits on #sigma(gg#rightarrow H#rightarrow Za#rightarrow 2l + 2#gamma)(fb)")
frame.GetYaxis().SetTitle(
"95% CL limits on Br(pp#rightarrow H#rightarrow Za#rightarrow 2l + 2#gamma)"
)
frame.SetMinimum(0.000001) # need Minimum > 0 for log scale
frame.SetMaximum(1.) # CMS HH
else:
frame.GetYaxis().SetTitle(
"95% CL limits on #sigma(pp#rightarrow H#rightarrow Za#rightarrow 2l + 2#gamma)(fb)"
)
frame.SetMinimum(0.1) # need Minimum > 0 for log scale
frame.SetMaximum(8 * 1e4) # CMS HH
frame.GetXaxis().SetLimits(0, 32)
yellow.SetFillColor(ROOT.kOrange)
yellow.SetLineColor(ROOT.kOrange)
yellow.SetFillStyle(1001)
yellow.Draw('F')
green.SetFillColor(ROOT.kGreen + 1)
green.SetLineColor(ROOT.kGreen + 1)
green.SetFillStyle(1001)
green.Draw('Fsame')
median.SetLineColor(1)
median.SetLineWidth(2)
median.SetLineStyle(2)
median.Draw('Lsame')
CMS_lumi.CMS_lumi(c, 4, 11)
ROOT.gPad.SetTicks(1, 1)
frame.Draw('sameaxis')
# yboost = 0.075
yboost = -0.2
x1 = 0.15
x2 = x1 + 0.24
y2 = 0.88 + yboost
y1 = 0.80 + yboost
legend = TLegend(x1, y1, x2, y2)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.SetTextSize(0.035)
legend.SetTextFont(42)
legend.AddEntry(median, "AsymptoticLimits CL_{s} expected", 'L')
legend.AddEntry(green, "#pm 1 std. deviation", 'f')
# legend.AddEntry(green, "AsymptoticLimits CL_{s} #pm 1 std. deviation",'f')
legend.AddEntry(yellow, "#pm 2 std. deviation", 'f')
# legend.AddEntry("","STAT Only","")
# legend.AddEntry(green, "AsymptoticLimits CL_{s} #pm 2 std. deviation",'f')
legend.Draw()
label = TLatex()
label.SetNDC()
label.SetTextAngle(0)
label.SetTextColor(kBlack)
label.SetTextFont(42)
label.SetTextSize(0.045)
label.SetLineWidth(2)
label.DrawLatex(0.7, 0.7 + yboost, "STAT only")
print " "
# c.SaveAs("UpperLimit.png")
outFile = "ALP_"
if args.SM_Radion: outFile += "SM_"
c.SaveAs(outFile + name + "_UpperLimit.pdf")
c.SaveAs(outFile + name + "_UpperLimit.C")
c.Close()
