def PlotROC(graphMap, saveDir, saveName, saveFormats):
'''
Plot ROC curves (signal efficiency vs bkg efficiency)
'''
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
leg = plot.CreateLegend(0.25, 0.25, 0.60, 0.45) #0.50, 0.25, 0.85, 0.45
# For-loop: All graphs
for i, k in enumerate(graphMap["graph"], 0):
gr = graphMap["graph"][i]
gr_name = graphMap["name"][i]
plot.ApplyStyle(gr, i + 2)
gr.GetXaxis().SetTitle("Signal Efficiency")
gr.GetYaxis().SetTitle("Misidentification rate")
if i == 0:
gr.Draw("apl")
else:
gr.Draw("pl same")
leg.AddEntry(gr, gr_name, "l")
leg.Draw("same")
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
return
def PlotLossFunction(losses, nepochs, saveDir):
'''
Plot the loss function vs the number of the epoch
'''
def ApplyStyle(h, ymin, ymax, color):
h.SetLineColor(color)
h.SetMarkerColor(color)
h.SetMarkerStyle(8)
h.SetMarkerSize(0.6)
h.SetMaximum(ymax)
h.SetMinimum(ymin)
h.GetXaxis().SetTitle("# epoch")
h.GetYaxis().SetTitle("Loss")
ROOT.gStyle.SetOptStat(0)
canvas = ROOT.TCanvas()
canvas.cd()
leg=plot.CreateLegend(0.6, 0.75, 0.9, 0.85)
xarr = []
for i in range(1, nepochs+1):
xarr.append(i)
if (opt.lam > 0):
hLf = ROOT.TGraph(len(xarr), array('d', xarr), array('d', losses["L_f"]))
hLr = ROOT.TGraph(len(xarr), array('d', xarr), array('d', losses["L_r"]))
hLfr = ROOT.TGraph(len(xarr), array('d', xarr), array('d', losses["L_f - L_r"]))
ymax = max(hLf.GetHistogram().GetMaximum(), hLr.GetHistogram().GetMaximum(), hLfr.GetHistogram().GetMaximum())
ymin = min(hLf.GetHistogram().GetMinimum(), hLr.GetHistogram().GetMinimum(), hLfr.GetHistogram().GetMinimum())
ymax = ymax + 1
ymin = ymin - 1
ApplyStyle(hLf, ymin, ymax, ROOT.kRed)
ApplyStyle(hLr, ymin, ymax, ROOT.kBlue)
ApplyStyle(hLfr, ymin, ymax, ROOT.kGreen+1)
hLf.Draw("pa")
hLr.Draw("p same")
hLfr.Draw("p same")
leg.AddEntry(hLf, "L_{c}","pl")
leg.AddEntry(hLr,"L_{r}","pl")
leg.AddEntry(hLfr,"L_{c} - L_{r}","pl")
else:
h_loss = ROOT.TGraph(len(xarr), array('d', xarr), array('d', losses["loss"]))
ymax = h_loss.GetHistogram().GetMaximum()
ymin = h_loss.GetHistogram().GetMinimum()
ApplyStyle(h_loss, ymin, ymax, ROOT.kGreen +1)
h_loss.Draw("pa")
leg.AddEntry(h_loss, "L_{c}","pl")
leg.Draw()
saveName = "Loss_lam%s_opt%s" %(opt.lam, opt.opt)
plot.SavePlot(canvas, saveDir, saveName)
canvas.Close()
return
def PlotOutput(Y_train, Y_test, saveDir, saveName, isSB, saveFormats):
ROOT.gStyle.SetOptStat(0)
# Create canvas
canvas = plot.CreateCanvas()
canvas.cd()
canvas.SetLogy()
# Create histograms
h1 = ROOT.TH1F('train', '', 50, 0.0, 1.0)
h2 = ROOT.TH1F('test', '', 50, 0.0, 1.0)
# Fill histograms
for r in Y_train:
h1.Fill(r)
for r in Y_test:
h2.Fill(r)
if 0:
h1.Scale(1. / h1.Integral())
h2.Scale(1. / h2.Integral())
ymax = max(h1.GetMaximum(), h2.GetMaximum())
plot.ApplyStyle(h1, ROOT.kMagenta + 1)
plot.ApplyStyle(h2, ROOT.kGreen + 2)
for h in [h1, h2]:
h.SetMinimum(100)
h.SetMaximum(ymax * 1.1)
h.GetXaxis().SetTitle("Output")
h.GetYaxis().SetTitle("Entries")
h.Draw("HIST SAME")
# What is this for? Ask soti
graph = plot.CreateGraph([0.5, 0.5], [0, ymax * 1.1])
graph.Draw("same")
# Create legend
leg = plot.CreateLegend(0.6, 0.75, 0.9, 0.85)
if isSB:
leg.AddEntry(h1, "signal", "l")
leg.AddEntry(h2, "background", "l")
else:
leg.AddEntry(h1, "train", "l")
leg.AddEntry(h2, "test", "l")
leg.Draw()
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
return
def PlotTGraph(xVals, xErrs, yVals, yErrs, saveDir, saveName, jsonWr,
saveFormats):
# Create a TGraph object
graph = plot.GetGraph(xVals, yVals, xErrs, xErrs, yErrs, yErrs)
# Create a TCanvas object
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
#canvas.SetLogy()
# Create the TGraph with asymmetric errors
tgraph = ROOT.TGraphAsymmErrors(len(xVals), array.array("d", xVals),
array.array("d", yVals),
array.array("d", xErrs),
array.array("d", xErrs),
array.array("d", yErrs),
array.array("d", yErrs))
tgraph.SetName(saveName)
if "efficiency" in saveName.lower():
legName = "efficiency"
if saveName.lower().endswith("sig"):
plot.ApplyStyle(tgraph, ROOT.kBlue)
else:
plot.ApplyStyle(tgraph, ROOT.kRed)
elif "significance" in saveName.lower():
legName = "significance"
if saveName.lower().endswith("sig"):
plot.ApplyStyle(tgraph, ROOT.kGreen)
else:
plot.ApplyStyle(tgraph, ROOT.kGreen + 3)
else:
plot.ApplyStyle(tgraph, ROOT.kOrange)
# Draw the TGraph
tgraph.GetXaxis().SetLimits(-0.05, 1.0)
#tgraph.SetMaximum(1.1)
#tgraph.SetMinimum(0)
tgraph.Draw("AC")
# Create legend
leg = plot.CreateLegend(0.60, 0.70, 0.85, 0.80)
leg.AddEntry(tgraph, legName, "l")
leg.Draw()
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
# Write the Tgraph into the JSON file
jsonWr.addGraph(saveName, tgraph)
return
def PlotInputs(signal, bkg, var, saveDir, saveFormats):
ROOT.gStyle.SetOptStat(0)
# Create canvas
canvas = plot.CreateCanvas()
canvas.cd()
info = plot.GetHistoInfo(var)
# Create histograms
hsignal = ROOT.TH1F('signal', '', info["nbins"], info["xmin"],
info["xmax"])
hbkg = ROOT.TH1F('bkg', '', info["nbins"], info["xmin"], info["xmax"])
# Fill histograms
for r in signal:
hsignal.Fill(r)
for r in bkg:
hbkg.Fill(r)
if 1:
hsignal.Scale(1. / hsignal.Integral())
hbkg.Scale(1. / hbkg.Integral())
ymax = max(hsignal.GetMaximum(), hbkg.GetMaximum())
plot.ApplyStyle(hsignal, ROOT.kAzure - 3)
hsignal.SetFillColor(ROOT.kAzure - 3)
plot.ApplyStyle(hbkg, ROOT.kRed + 2)
for h in [hsignal, hbkg]:
h.SetMaximum(ymax * 1.2)
h.GetXaxis().SetTitle(info["xlabel"])
h.GetYaxis().SetTitle(info["ylabel"])
hsignal.Draw("HIST")
hbkg.Draw("HIST SAME")
# Create legend
leg = plot.CreateLegend(0.6, 0.75, 0.9, 0.85)
leg.AddEntry(hsignal, "Truth-matched", "f") #"pl"
leg.AddEntry(hbkg, "Unmatched", "l") #"pl"
leg.Draw()
plot.SavePlot(canvas, saveDir, var, saveFormats, True)
canvas.Close()
return
def PlotOvertrainingTest(Y_train_S, Y_test_S, Y_train_B, Y_test_B, saveDir,
saveName, saveFormats):
def ApplyStyle(histo):
if "_s" in histo.GetName():
color = ROOT.kBlue
else:
color = ROOT.kRed
plot.ApplyStyle(h, color)
histo.SetMarkerSize(0.5)
histo.GetXaxis().SetTitle("Output")
histo.GetYaxis().SetTitle("Entries")
histo.SetMinimum(10)
return
def GetLegendStyle(histoName):
if "test" in histoName:
legStyle = "f"
if "_s" in histoName:
legText = "signal (test)"
else:
legText = "background (test)"
elif "train" in histoName:
legStyle = "p"
if "_s" in histoName:
legText = "signal (train)"
else:
legText = "background (train)"
return legText, legStyle
def DrawStyle(histoName):
if "train" in histoName:
_style = "P"
else:
_style = "HIST"
return _style
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
canvas.SetLogy()
hList = []
DataList = [Y_train_S, Y_test_S, Y_train_B, Y_test_B]
ymax = 0
nbins = 500
# Create the histograms
htrain_s = ROOT.TH1F('train_s', '', nbins, 0.0, 1.0)
htest_s = ROOT.TH1F('test_s', '', nbins, 0.0, 1.0)
htrain_b = ROOT.TH1F('train_b', '', nbins, 0.0, 1.0)
htest_b = ROOT.TH1F('test_b', '', nbins, 0.0, 1.0)
# Append to list
hList.append(htrain_s)
hList.append(htest_s)
hList.append(htrain_b)
hList.append(htest_b)
for i in range(len(DataList)):
for r in DataList[i]:
hList[i].Fill(r)
# Clone the histograms
htrain_s1 = htrain_s.Clone("train_s")
htrain_b1 = htrain_b.Clone("train_b")
htest_s1 = htest_s.Clone("test_s")
htest_b1 = htest_b.Clone("test_b")
drawStyle = "HIST SAME"
leg = plot.CreateLegend(0.55, 0.68, 0.85, 0.88)
for h in hList:
h.Rebin(10)
# Legend
legText, legStyle = GetLegendStyle(h.GetName())
leg.AddEntry(h, legText, legStyle)
ApplyStyle(h)
ymax = max(htrain_s.GetMaximum(), htest_s.GetMaximum(),
htrain_b.GetMaximum(), htest_b.GetMaximum())
for h in hList:
h.SetMaximum(ymax * 2)
h.Draw(DrawStyle(h.GetName()) + " SAME")
#graph = plot.CreateGraph([0.5, 0.5], [0, ymax*2])
#graph.Draw("same")
#leg.Draw()
# Save & close canvas
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
return htrain_s1, htest_s1, htrain_b1, htest_b1
def PlotEfficiency(htest_s, htest_b, saveDir, saveName, saveFormats):
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
canvas.SetLeftMargin(0.145)
canvas.SetRightMargin(0.11)
# Calculate signal and background efficiency vs output
xvalue, eff_s, eff_b, error = CalcEfficiency(htest_s, htest_b)
graph_s = plot.GetGraph(xvalue, eff_s, error, error, error, error)
graph_b = plot.GetGraph(xvalue, eff_b, error, error, error, error)
plot.ApplyStyle(graph_s, ROOT.kBlue)
plot.ApplyStyle(graph_b, ROOT.kRed)
# Calculate significance vs output
h_signif0, h_signif1 = CalcSignificance(htest_s, htest_b)
plot.ApplyStyle(h_signif0, ROOT.kGreen)
plot.ApplyStyle(h_signif1, ROOT.kGreen + 3)
#=== Get maximum of significance
maxSignif0 = h_signif0.GetMaximum()
maxSignif1 = h_signif1.GetMaximum()
maxSignif = max(maxSignif0, maxSignif1)
# Normalize significance
h_signifScaled0 = h_signif0.Clone("signif0")
h_signifScaled0.Scale(1. / float(maxSignif))
h_signifScaled1 = h_signif1.Clone("signif1")
h_signifScaled1.Scale(1. / float(maxSignif))
#Significance: Get new maximum
ymax = max(h_signifScaled0.GetMaximum(), h_signifScaled1.GetMaximum())
for obj in [graph_s, graph_b, h_signifScaled0, h_signifScaled1]:
obj.GetXaxis().SetTitle("Output")
obj.GetYaxis().SetTitle("Efficiency")
obj.SetMaximum(ymax * 1.1)
obj.SetMinimum(0)
#Draw
h_signifScaled0.Draw("HIST")
h_signifScaled1.Draw("HIST SAME")
graph_s.Draw("PL SAME")
graph_b.Draw("PL SAME")
graph = plot.CreateGraph([0.5, 0.5], [0, ymax * 1.1])
graph.Draw("same")
#Legend
leg = plot.CreateLegend(0.50, 0.25, 0.85, 0.45)
leg.AddEntry(graph_s, "Signal Efficiency", "l")
leg.AddEntry(graph_b, "Bkg Efficiency", "l")
leg.AddEntry(h_signifScaled0, "S/#sqrt{S+B}", "l")
leg.AddEntry(h_signifScaled1, "2#times(#sqrt{S+B} - #sqrt{B})", "l")
leg.Draw()
# Define Right Axis (Significance)
signifColor = ROOT.kGreen + 2
rightAxis = ROOT.TGaxis(1, 0, 1, 1.1, 0, 1.1 * maxSignif, 510, "+L")
rightAxis.SetLineColor(signifColor)
rightAxis.SetLabelColor(signifColor)
rightAxis.SetTitleColor(signifColor)
rightAxis.SetTitleOffset(1.25)
rightAxis.SetLabelOffset(0.005)
rightAxis.SetLabelSize(0.04)
rightAxis.SetTitleSize(0.045)
rightAxis.SetTitle("Significance")
rightAxis.Draw()
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
return
def PlotAndWriteJSON(signal, bkg, saveDir, saveName, jsonWr, saveFormats,
**kwargs):
resultsDict = {}
resultsDict["signal"] = signal
resultsDict["background"] = bkg
normalizeToOne = False
if "normalizeToOne" in kwargs:
normalizeToOne = kwargs["normalizeToOne"]
# Create canvas
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
hList = []
gList = []
yMin = 100000
yMax = -1
xMin = 0.0
xMax = 1.0
nBins = 50
xTitle = "DNN output"
yTitle = "Entries"
log = True
if "log" in kwargs:
log = kwargs["log"]
if "xTitle" in kwargs:
xTitle = kwargs["xTitle"]
if "yTitle" in kwargs:
yTitle = kwargs["yTitle"]
elif "output" in saveName.lower():
xTitle = "Entries"
elif "efficiency" in saveName.lower():
xTitle = "Efficiency"
elif "significance" in saveName.lower():
xTitle = "Significance"
else:
pass
if "xMin" in kwargs:
xMin = kwargs['xMin']
if "xMax" in kwargs:
xMax = kwargs['xMax']
if "nBins" in kwargs:
xBins = kwargs['nBins']
# For-loop:
for i, key in enumerate(resultsDict.keys(), 0):
h = ROOT.TH1F(key, '', nBins, xMin, xMax)
for j, x in enumerate(resultsDict[key], 0):
h.Fill(x)
try:
yMin = min(x[0], yMin)
except:
pass
# Save maximum
yMax = max(h.GetMaximum(), yMax)
# Customise & append to list
plot.ApplyStyle(h, i + 1)
if normalizeToOne:
if "ymax" in kwargs:
yMax = kwargs["yMax"]
else:
yMax = 1.0
if "yMin" in kwargs:
yMin = kwargs["yMin"]
else:
yMin = 1e-4
if h.Integral() > 0.0:
h.Scale(1. / h.Integral())
hList.append(h)
if yMin <= 0.0:
yMin = 100
if log:
canvas.SetLogy()
# For-loop: All histograms
for i, h in enumerate(hList, 0):
h.SetMinimum(yMin * 0.85)
h.SetMaximum(yMax * 1.15)
h.GetXaxis().SetTitle(xTitle)
h.GetYaxis().SetTitle(yTitle)
if i == 0:
h.Draw("HIST")
else:
h.Draw("HIST SAME")
# Create legend
leg = plot.CreateLegend(0.6, 0.75, 0.9, 0.85)
for h in hList:
leg.AddEntry(h, h.GetName(), "l")
leg.Draw()
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
# Create TGraph
for h in hList:
gList.append(convertHistoToGaph(h))
# Write the Tgraph into the JSON file
for gr in gList:
gName = "%s_%s" % (saveName, gr.GetName())
jsonWr.addGraph(gName, gr)
return
def main():
ROOT.gStyle.SetOptStat(0)
# Apply tdr style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Definitions
filename = opts.filename
tfile = ROOT.TFile.Open(filename)
dName = ""
if ("TT" in filename):
dName = "TT"
elif ("QCD" in filename):
dName = "QCD"
#Signal and background branches
signal = uproot.open(filename)["treeS"]
background = uproot.open(filename)["treeB"]
# Input list
inputList = []
inputList.append("TrijetPtDR")
inputList.append("TrijetDijetPtDR")
inputList.append("TrijetBjetMass")
inputList.append("TrijetLdgJetBDisc")
inputList.append("TrijetSubldgJetBDisc")
inputList.append("TrijetBJetLdgJetMass")
inputList.append("TrijetBJetSubldgJetMass")
inputList.append("TrijetMass")
inputList.append("TrijetDijetMass")
inputList.append("TrijetBJetBDisc")
inputList.append("TrijetSoftDrop_n2")
inputList.append("TrijetLdgJetCvsL")
inputList.append("TrijetSubldgJetCvsL")
inputList.append("TrijetLdgJetPtD")
inputList.append("TrijetSubldgJetPtD")
inputList.append("TrijetLdgJetAxis2")
inputList.append("TrijetSubldgJetAxis2")
inputList.append("TrijetLdgJetMult")
inputList.append("TrijetSubldgJetMult")
nInputs = len(inputList)
#Signal and background dataframes
df_signal = signal.pandas.df(inputList)
df_background = background.pandas.df(inputList)
# Number of events to predict the output and plot the top-quark mass
if (opts.plotSignal):
nEvts = len(df_signal.index)
else:
nEvts = len(df_background.index)
# User few events for testing
if (opts.test):
nEvts = 1000
print "=== Number of events: ", nEvts
#Signal and background datasets
dset_signal = df_signal.values
dset_background = df_background.values
# Concat signal, background datasets
df_list = [df_signal, df_background]
df_all = pandas.concat(df_list)
dataset = df_all.values
# Target (top-quark mass) datasets
dset_target_all = pandas.concat([
signal.pandas.df(["TrijetMass"]),
background.pandas.df(["TrijetMass"])
]).values
dset_target_bkg = background.pandas.df(["TrijetMass"]).values
dset_target_signal = signal.pandas.df(["TrijetMass"]).values
# Signal and background inputs
X_signal = dset_signal[:nEvts, 0:nInputs]
X_background = dset_background[:nEvts, 0:nInputs]
if (opts.plotSignal):
X = dset_signal[:nEvts, 0:nInputs]
target = dset_target_signal[:nEvts, :]
else:
X = dset_background[:nEvts, 0:nInputs]
target = dset_target_bkg[:nEvts, :]
#Load models
lamValues = [0, 1, 5, 10,
20] #, 100, 500] # fixme! should be given as option
colors = [
ROOT.kBlack, ROOT.kBlue, ROOT.kMagenta, ROOT.kOrange, ROOT.kRed,
ROOT.kGreen, ROOT.kOrange + 7
]
canvas = plot.CreateCanvas()
canvas.cd()
ymaxFactor = 1.1
ymax = 0
histoList = []
if (opts.setLogY):
canvas.SetLogy()
ymaxFactor = 2
# load the models with different lambda
for lam in lamValues:
print "Lambda = ", lam
if (lam == 0):
# For labda = 0 load the simple classification (sequential) model
loaded_model = load_model(
'models_16Nov/Model_relu_relu_relu_sigmoid.h5')
else:
loaded_model = load_model(
'models_16Nov/modelANN_32_Adam_0p0008_500_tanh_relu_msle_lam%s.h5'
% (lam))
# Compile the model
loaded_model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['acc'])
Y = loaded_model.predict(X, verbose=1)
# Concatenate Y (predicted output) and target (top-quark mass)
# Ymass 0 column: output
# Ymass 1st column: top-quark mass
Ymass = numpy.concatenate((Y, target), axis=1)
# Get selected top candidates (pass the output working point)
Ymass_sel = Ymass[Ymass[:, 0] >=
opts.wp] # Select samples with y > WP (col 0)
massSel = Ymass_sel[:,
1] # Get the top-quark mass (col 1) for the selected samples.
#Plot resutls
nbins = 100
xmin = 0
xmax = 1000
# Change x-axis range when plotting the signal
if (opts.plotSignal):
nbins = 45
xmax = 450
width = float(xmax) / nbins
histo = ROOT.TH1F('histo_lam%s' % (lam), '', nbins, xmin, xmax)
mass_sel = []
print "selected entries:", len(massSel)
for mass in massSel:
histo.Fill(mass)
histoList.append(histo.Clone("histoClone_lam%s" % lam))
del loaded_model
# Create legend
leg = plot.CreateLegend(0.6, 0.67, 0.9, 0.85)
#Legend text
dText = dName
dText = dText.replace("TT", "t#bar{t}")
# Loop over the histograms in the histoList
for i in range(len(histoList)):
leg.AddEntry(histoList[i],
"%s (#lambda = %.0f)" % (dText, lamValues[i]), "f")
# Normalize histograms to unity
histoList[i].Scale(1. / (histoList[i].Integral()))
ymax = max(ymax, histoList[i].GetMaximum())
histoList[i].GetXaxis().SetTitle("m_{top} (GeV)")
histoList[i].GetYaxis().SetTitle("Arbitrary Units / %.0f GeV" %
(width))
plot.ApplyStyle(histoList[i], colors[i])
histoList[i].Draw("HIST same")
for i in range(len(histoList)):
histoList[i].SetMaximum(ymax * ymaxFactor)
leg.Draw("same")
if (opts.plotSignal):
sbText = "truth-matched"
else:
sbText = "unmatched"
# Additional text
tex1 = plot.Text(" %s top candidates" % sbText, 0.85, 0.5)
tex2 = plot.Text(" with output value > %s" % (opts.wp), 0.82, 0.45)
#Draw text
tex1.Draw()
tex2.Draw()
# Draw line to indicate the real value of the top-quark mass
graph = plot.CreateGraph([173., 173.], [0, ymax * ymaxFactor])
graph.Draw("same")
# CMS extra text and lumi text
#plot.CMSText("CMS Preliminary") #Fixme! cmsExtra doesn't work
#cmsText.Draw()
# Output directory
dirName = plot.getDirName(opts.saveDir)
# Save the plot
saveName = opts.saveName
if (opts.saveName == "TopMassANN"):
saveName = "TopMassANN_%s_%s" % (dName, sbText.replace("-", "_"))
# Save the histogram
plot.SavePlot(canvas, dirName, saveName)
def PlotAndWriteJSON(signal, bkg, saveDir, saveName, jsonWr, saveFormats):
resultsDict = {}
resultsDict["signal"] = signal
resultsDict["background"] = bkg
# Create canvas
ROOT.gStyle.SetOptStat(0)
canvas = plot.CreateCanvas()
canvas.cd()
hList = []
gList = []
yMin = 100000
yMax = -1
# For-loop:
for i, key in enumerate(resultsDict.keys(), 0):
h = ROOT.TH1F(key, '', 50, 0.0, 1.0)
for j, x in enumerate(resultsDict[key], 0):
h.Fill(x)
try:
yMin = min(x[0], yMin)
except:
pass
# Save maximum
yMax = max(h.GetMaximum(), yMax)
# Customise & append to list
plot.ApplyStyle(h, i + 1)
hList.append(h)
if yMin <= 0.0:
yMin = 100
canvas.SetLogy()
# For-loop: All histograms
for i, h in enumerate(hList, 0):
h.SetMinimum(yMin * 0.85)
h.SetMaximum(yMax * 1.15)
h.GetXaxis().SetTitle("DNN output")
if "output" in saveName.lower():
h.GetYaxis().SetTitle("Entries")
elif "efficiency" in saveName.lower():
h.GetYaxis().SetTitle("Efficiency")
elif "significance" in saveName.lower():
h.GetYaxis().SetTitle("Significance")
else:
pass
if i == 0:
h.Draw("HIST")
else:
h.Draw("HIST SAME")
# Create legend
leg = plot.CreateLegend(0.6, 0.75, 0.9, 0.85)
for h in hList:
leg.AddEntry(h, h.GetName(), "l")
leg.Draw()
plot.SavePlot(canvas, saveDir, saveName, saveFormats)
canvas.Close()
# Create TGraph
for h in hList:
gList.append(convertHistoToGaph(h))
# Write the Tgraph into the JSON file
for gr in gList:
gName = "%s_%s" % (saveName, gr.GetName())
jsonWr.addGraph(gName, gr)
return