def auto_min( histogram, nbins=20 ):
"""Find the first nonzero entry in the histogam and return a minimum
"""
nbinx=histogram.GetNbinsX()
i = 1
first = 1
last = nbinx
if ( histogram.Integral() == 0. ):
return histogram.GetXaxis().GetXmin()
content = histogram.GetBinContent(i)
while ( i<nbinx and content == 0 ):
content=histogram.GetBinContent(i)
first=i
i+=1
xmin=histogram.GetXaxis().GetBinCenter(first)
xwid=histogram.GetXaxis().GetBinWidth(nbinx/2)
xmin-=nbins*xwid
xxmin = histogram.GetXaxis().GetXmin()
return TMath.Max(xmin,xxmin)
def runCheck(filename):
## Input
f = ROOT.TFile.Open(filename)
if f is None:
print("File %s could not be opened" % (filename))
if not (ROOT.gDirectory.cd("Method_BDT/BDTD")):
print "No directory for BDT method found\n"
quit()
## Get histograms and set styles
sig = f.Get("Method_BDT/BDTD/MVA_BDTD_S")
sigOv = f.Get("Method_BDT/BDTD/MVA_BDTD_Train_S")
bkg = f.Get("Method_BDT/BDTD/MVA_BDTD_B")
bkgOv = f.Get("Method_BDT/BDTD/MVA_BDTD_Train_B")
TMVAGlob.SetSignalAndBackgroundStyle(sig, bkg)
TMVAGlob.NormalizeHists(sig, bkg)
TMVAGlob.NormalizeHists(sigOv, bkgOv)
setTrainHistStyle(sigOv, sig.GetLineColor())
setTrainHistStyle(bkgOv, bkg.GetLineColor())
## Frame
c = ROOT.TCanvas("canvas", "Overtraining check", 800, 600)
ROOT.gStyle.SetOptStat(0)
xmin = TMath.Max(
TMath.Min(sig.GetMean() - 10 * sig.GetRMS(),
bkg.GetMean() - 10 * bkg.GetRMS()),
sig.GetXaxis().GetXmin())
xmax = TMath.Min(
TMath.Max(sig.GetMean() + 10 * sig.GetRMS(),
bkg.GetMean() + 10 * bkg.GetRMS()),
sig.GetXaxis().GetXmax())
ymin = 0
ymax = TMath.Max(sig.GetMaximum(), bkg.GetMaximum()) * 1.3
frame = ROOT.TH2F("ovCheck", "Overtraining check", 500, xmin, xmax, 500,
ymin, ymax)
frame.GetXaxis().SetTitle("BDTD response")
frame.GetYaxis().SetTitle("(1/N) dN^{ }/^{ }dx")
frame.Draw()
## Legends
legend = ROOT.TLegend(c.GetLeftMargin(), 1 - c.GetTopMargin() - 0.12,
c.GetLeftMargin() + 0.35, 1 - c.GetTopMargin())
legend.SetFillStyle(1)
lSigEntry = "Signal"
lBkgEntry = "Background"
legend.AddEntry(sig, lSigEntry + " (test sample)", "F")
legend.AddEntry(bkg, lBkgEntry + " (test sample)", "F")
legend.SetBorderSize(1)
legend.SetMargin(0.2)
legend.Draw("same")
legend2 = ROOT.TLegend(1 - c.GetRightMargin() - 0.35,
1 - c.GetTopMargin() - 0.12, 1 - c.GetRightMargin(),
1 - c.GetTopMargin())
legend2.SetFillStyle(1)
legend2.SetBorderSize(1)
legend2.AddEntry(sigOv, "Signal (training sample)", "P")
legend2.AddEntry(bkgOv, "Background (training sample)", "P")
legend2.SetMargin(0.1)
legend2.Draw("same")
sig.Draw("samehist")
sigOv.Draw("e1 same")
bkg.Draw("samehist")
bkgOv.Draw("e1 same")
## Tests
kolS = kolB = -1
if math.isnan(sig.ComputeIntegral(True)) or math.isnan(
sigOv.ComputeIntegral(True)):
kolS = -1
else:
kolS = sig.KolmogorovTest(sigOv)
if math.isnan(bkg.ComputeIntegral(True)) or math.isnan(
bkgOv.ComputeIntegral(True)):
kolB = -1
else:
kolB = bkg.KolmogorovTest(bkgOv)
print(
"Kolmogorov-Smirnov test values for signal (background): %1.3f (%1.3f)"
% (kolS, kolB))
chi2S = sig.Chi2Test(sigOv, "WW")
chi2B = bkg.Chi2Test(bkgOv, "WW")
print("Chi2 test values for signal (background): %1.3f (%1.3f)" %
(chi2S, chi2B))
tHeader = ROOT.TText(0.165, 0.74, "Sig\t\t\t\t\t\t\t\t\tBkg")
tHeader.SetNDC()
tHeader.SetTextSize(0.032)
tHeader.AppendPad()
tTableText = "#splitline{KS:\t%1.3f \t%1.3f}{#chi^{2}: \t\t%1.3f \t%1.3f}" % (
kolS, kolB, chi2S, chi2B)
tTable = ROOT.TLatex(0.12, 0.69, tTableText)
tTable.SetNDC()
tTable.SetTextSize(0.032)
tTable.AppendPad()
## Print to EPS
c.Print("plots/overtrain_BDTD.eps")
# c.Print("plots/overtrain_BDTD.pdf")
## Save to temp text file
return (kolS, kolB, chi2S, chi2B)
ientry = tree_l.LoadTree( jentry )
if ientry < 0:
break
# copy next entry into memory and verify
nb = tree_l.GetEntry( jentry )
if nb <= 0:
continue
nJet[0] = tree_l.nJet
mindr_lep1_jet[0] = tree_l.mindr_lep1_jet
mindr_lep2_jet[0] = tree_l.mindr_lep2_jet
avg_dr_jet[0] = tree_l.avg_dr_jet
lep1_abs_eta[0] = TMath.Abs(tree_l.lep1_eta)
lep2_abs_eta[0] = TMath.Abs(tree_l.lep2_eta)
max_lep_eta[0] = TMath.Max(TMath.Abs(tree_l.lep1_eta), TMath.Abs(tree_l.lep2_eta))
lep1_conePt[0] = tree_l.lep1_conePt
lep2_conePt[0] = tree_l.lep2_conePt
mindr_tau_jet[0] = tree_l.mindr_tau_jet
ptmiss[0] = TMath.Min(tree_l.ptmiss, 500)
mT_lep1[0] = tree_l.mT_lep1
mT_lep2[0] = tree_l.mT_lep2
htmiss[0] = tree_l.htmiss
dr_leps[0] = tree_l.dr_leps
tau_pt[0] = tree_l.tau_pt
tau_abs_eta[0] = TMath.Abs(tree_l.tau_eta)
dr_lep1_tau[0] = tree_l.dr_lep1_tau
dr_lep2_tau[0] = tree_l.dr_lep2_tau
mTauTauVis1[0] = tree_l.mTauTauVis1
mTauTauVis2[0] = tree_l.mTauTauVis2
'''
h1.Draw("axis") # draw axis only
h2.GetZaxis().SetTitleSize(0.05)
h2.GetZaxis().SetLabelSize(0.05)
h2.Draw("same, colz")
HeaderLabel.SetLabel(title + ": " + plot["label2"])
HeaderLabel.Draw()
outname = outputDir + plot["name"] + "_2.pdf"
c.SaveAs(outname)
fout.cd()
c.Write()
print "Wrote plot: ", outname
else:
h1.SetLineColor(plot["color1"])
h2.SetLineColor(plot["color2"])
h1.SetMaximum(1.1*TMath.Max(h1.GetMaximum(), h2.GetMaximum()))
h1.SetMinimum(0)
# print "Title is ", h1.GetTitle()
h1.SetTitle(title + h1.GetTitle())
h1.SetLineWidth(2)
h2.SetLineWidth(2)
h1.Draw("hist")
h2.Draw("hist same")
leg = TLegend(0.45, 0.7, 0.95, 0.90)
leg.AddEntry(h1, plot["label1"], "l")
leg.AddEntry(h2, plot["label2"], "l")
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.Draw("l")
h.Rebin(rebin)
h.SetDirectory(0)
h.SetLineColor(color[i])
h.SetLineWidth(2)
hist.append(h)
i += 1
#----- Drawing -----------------------
can = TCanvas('can_Signal_' + var, 'can_Signal_' + var, 900, 600)
can.SetRightMargin(0.2)
hist[0].GetYaxis().SetNdivisions(505)
hist[0].GetXaxis().SetTitle(xtitle)
hist[0].GetXaxis().SetRangeUser(xmin, xmax)
hist[0].SetMaximum(1.2 *
TMath.Max(hist[0].GetBinContent(hist[0].GetMaximumBin()),
hist[4].GetBinContent(hist[4].GetMaximumBin())))
hist[0].Draw('hist')
hist[1].Draw('same hist')
hist[2].Draw('same hist')
hist[3].Draw('same hist')
hist[4].Draw('same hist')
#gPad.RedrawAxis()
leg = TLegend(0.81, 0.65, 0.96, 0.9)
leg.AddEntry(hist[0], '1.0 TeV', 'L')
leg.AddEntry(hist[1], '1.5 TeV', 'L')
leg.AddEntry(hist[2], '2.0 TeV', 'L')
leg.AddEntry(hist[3], '2.5 TeV', 'L')
leg.AddEntry(hist[4], '3.0 TeV', 'L')
leg.SetFillColor(0)
leg.SetTextFont(42)
histQCD.Add(hist[1])
hsQCD = THStack('QCD', 'QCD')
hsQCD.Add(hist[0])
hsQCD.Add(hist[1])
#----- Drawing -----------------------
can = TCanvas('can_' + var, 'can_' + var, 900, 600)
if logy:
gPad.SetLogy()
hAux = hist[3].Clone('aux')
hAux.Reset()
hAux.GetXaxis().SetRangeUser(xmin, xmax)
hAux.GetXaxis().SetTitle(xtitle)
hAux.SetMaximum(1.2 * TMath.Max(hist[3].GetBinContent(
hist[3].GetMaximumBin()), histQCD.GetBinContent(histQCD.GetMaximumBin())))
hAux.SetMinimum(0.01)
hAux.Draw()
hsQCD.Draw('same hist')
hist[3].Draw('same E')
hist[2].Draw('same hist')
gPad.RedrawAxis()
#----- keep the GUI alive ------------
if __name__ == '__main__':
rep = ''
while not rep in ['q', 'Q']:
rep = raw_input('enter "q" to quit: ')
if 1 < len(rep):
rep = rep[0]
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 __init__(self,
base, geom,
volume="EMSS", geomvolume="same",
stat="radlen",
xmin=+0., xmax=-1.,
ymin=+0., ymax=-1.,
canvas=0,
legend=False
):
self.name = volume
self.base = base
self.geom = geom
baseFile = get_geom_file(base) # get the files
compFile = get_geom_file(geom)
hname = "h_"+stat+"_"+volume+"_eta" # retrive the histograms from the files
print "Get histo: " + str(hname)
self.histo_base = stat_histo( stat, volume, file=baseFile )
if ( geomvolume == "same" ):
self.histo_comp = stat_histo( stat, volume, compFile )
else:
self.histo_comp = stat_histo( stat, geomvolume, compFile )
self.histo_diff = self.histo_base.Clone( hname + "_diff" ) # difference the histograms
self.histo_diff.Add( self.histo_comp, -1.0 )
self.histo_diff.Divide(self.histo_base) # diff will be (old-new)/old * 100%
if ( canvas == 0 ):
canvas = TCanvas("temp"+base+geom+volume+geomvolume,"Differential 1D: baseline="+base+" compare="+geom,500,400);
self.canvas = canvas
# Detect and apply optional x-axis range
if ( xmax > xmin ):
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# ... or zoom in on an appropriate scale
else:
# auto_range( self.histo_base )
# auto_range( self.histo_comp )
xmin = auto_min( self.histo_base )
xmax = auto_max( self.histo_base )
self.histo_base.GetXaxis().SetRangeUser(xmin,xmax)
self.histo_comp.GetXaxis().SetRangeUser(xmin,xmax)
# xmin = TMath.Min( self.histo_base.GetXaxis().GetXmin(),self.histo_comp.GetXaxis().GetXmin() )
# xmax = TMath.Max( self.histo_base.GetXaxis().GetXmax(),self.histo_comp.GetXaxis().GetXmax() )
# print "xmin="+str(xmin)
# print "xmax="+str(xmax)
# Detect and apply optional y-axis range
if ( ymax > ymin ):
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
else:
ymin = self.histo_base.GetMinimum()
ymax = TMath.Max( self.histo_base.GetMaximum(),
self.histo_comp.GetMaximum())
ymax *= 1.05
# Current range in y-axis extends from 0 to ymax. We want
# to expand this to go from -0.2*ymax to ymax.
ymin = -0.2 * ymax
self.histo_base.GetYaxis().SetRangeUser(ymin,ymax)
# Draw the baseline and comparison histograms
self.histo_base.SetLineWidth(2)
self.histo_base.SetFillStyle(1001)
self.histo_base.SetFillColor(22)
self.histo_base.Draw()
self.histo_comp.SetLineColor(2)
self.histo_comp.SetLineStyle(2)
self.histo_comp.SetFillStyle(3345)
self.histo_comp.SetFillColor(2)
self.histo_comp.Draw("same")
# Rescale difference histogram so that it is in percent
self.histo_diff.Scale(100.0)
# This is the maximum of the histogram.
yfull_max = self.histo_diff.GetMaximum()
yfull_min = self.histo_diff.GetMinimum()
yfull = TMath.Max( yfull_max, TMath.Abs( yfull_min ) )
self.max_differential = yfull
yfull *= 1.3
if ( yfull == 0. ):
yfull = 1.0
# We need to rescale the histogram so that it fits w/in 10% of ymax
self.histo_diff.Scale( 0.10 * ymax / yfull )
# Next we shift the histogram down by 0.1 * ymax
nbinx=self.histo_diff.GetNbinsX();
i=1
while ( i<=nbinx ):
self.histo_diff[i] -= 0.1 * ymax
i+=1
# Reset the line color and draw on the same plot
self.histo_diff.SetLineColor(4)
self.histo_diff.Draw("same")
self.line = TLine(xmin, -0.1 * ymax, xmax, -0.1*ymax )
self.line.SetLineStyle(3)
self.line.Draw()
# And superimpose an axis on the new plot
xa = xmax
self.axis = TGaxis( xa, -0.2*ymax, xa, 0.0, -yfull, +yfull, 50510, "-+L" )
self.axis.SetLabelSize(0.03)
self.axis.SetLabelOffset(-0.02)
self.axis.SetLineColor(4)
self.axis.SetTextColor(4)
self.axis.SetLabelColor(4)
self.axis.SetNdivisions(4)
# self.axis.SetTitle("(base-comp)/base [%]")
self.axis.SetTitleSize(0.0175)
self.axis.SetTitleOffset(-0.5)
self.axis.Draw();
# Add the legend if requested
if ( legend ):
self.legend = TLegend( 0.78, 0.80, 0.98, 0.98 )
self.legend.AddEntry( self.histo_base, base )
self.legend.AddEntry( self.histo_comp, geom )
self.legend.AddEntry( self.histo_diff, "#frac{"+base+"-"+geom+"}{"+base+"} [%]" )
self.legend.Draw()
##### signal only
leg_sig = TLegend(0.6, 0.7, 0.85, 0.85)
leg_sig.SetLineColor(0)
leg_sig.SetFillColor(0)
leg_sig.AddEntry(hist_allCutsSig_1000, "M=1000", "l")
leg_sig.AddEntry(hist_allCutsSig_5000, "M=5000", "l")
leg_sig.AddEntry(hist_allCutsSig_8000, "M=8000", "l")
can_allCuts_sig.cd()
hist_allCutsSig_1000.GetXaxis().SetRangeUser(xmin, xmax)
hist_allCutsSig_1000.GetXaxis().SetTitle(xtitle)
hist_allCutsSig_1000.GetYaxis().SetTitle("arb. units")
max_1000_5000 = TMath.Max(
hist_allCutsSig_1000.GetBinContent(hist_allCutsSig_1000.GetMaximumBin()),
hist_allCutsSig_5000.GetBinContent(hist_allCutsSig_5000.GetMaximumBin()))
hist_allCutsSig_1000.SetMaximum(1.2 * TMath.Max(
hist_allCutsSig_8000.GetBinContent(hist_allCutsSig_8000.GetMaximumBin()),
max_1000_5000))
hist_allCutsSig_1000.DrawNormalized("hist")
hist_allCutsSig_5000.DrawNormalized("hist same")
hist_allCutsSig_8000.DrawNormalized("hist same")
leg_sig.Draw()
can_allCuts_sig.Write()
can_allCuts_sig.SaveAs(outputDir + var + '_signal_allCuts.png')
can_allCuts_sig.SaveAs(outputDir + var + '_signal_allCuts.svg')
can_allCuts_sig.Close()
can_allOtherCuts_sig.cd()
hist_allOtherCutsSig_1000.GetXaxis().SetRangeUser(xmin, xmax)
