| 1 |
#!/usr/bin/env python
|
| 2 |
import sys
|
| 3 |
import os
|
| 4 |
import re
|
| 5 |
from array import *
|
| 6 |
from decimal import *
|
| 7 |
from optparse import OptionParser
|
| 8 |
from OSUT3Analysis.Configuration.configurationOptions import *
|
| 9 |
from OSUT3Analysis.Configuration.processingUtilities import *
|
| 10 |
|
| 11 |
parser = OptionParser()
|
| 12 |
parser = set_commandline_arguments(parser)
|
| 13 |
(arguments, args) = parser.parse_args()
|
| 14 |
|
| 15 |
if arguments.localConfig:
|
| 16 |
sys.path.append(os.getcwd())
|
| 17 |
exec("from " + arguments.localConfig.rstrip('.py') + " import *")
|
| 18 |
|
| 19 |
|
| 20 |
outputFileName = "stacked_histograms.root"
|
| 21 |
if arguments.outputFileName:
|
| 22 |
outputFileName = arguments.outputFileName
|
| 23 |
|
| 24 |
condor_dir = set_condor_output_dir(arguments)
|
| 25 |
|
| 26 |
|
| 27 |
|
| 28 |
|
| 29 |
if arguments.normalizeToData and arguments.normalizeToUnitArea:
|
| 30 |
print "Conflicting normalizations requsted, will normalize to unit area"
|
| 31 |
arguments.normalizeToData = False
|
| 32 |
if arguments.normalizeToData and arguments.noStack:
|
| 33 |
print "You have asked to scale non-stacked backgrounds to data. This is a very strange request. Will normalize to unit area instead"
|
| 34 |
arguments.normalizeToData = False
|
| 35 |
arguments.normalizeToUnitArea = True
|
| 36 |
|
| 37 |
from ROOT import TFile, gROOT, gStyle, gDirectory, TStyle, THStack, TH1F, TCanvas, TString, TLegend, TArrow, THStack, TIter, TKey, TPaveLabel, gPad
|
| 38 |
|
| 39 |
gROOT.SetBatch()
|
| 40 |
gStyle.SetOptStat(0)
|
| 41 |
gStyle.SetCanvasBorderMode(0)
|
| 42 |
gStyle.SetPadBorderMode(0)
|
| 43 |
gStyle.SetPadColor(0)
|
| 44 |
gStyle.SetCanvasColor(0)
|
| 45 |
gStyle.SetTextFont(42)
|
| 46 |
gROOT.ForceStyle()
|
| 47 |
outputFile = TFile(condor_dir + "/" + outputFileName, "RECREATE")
|
| 48 |
|
| 49 |
channels = []
|
| 50 |
processed_datasets = []
|
| 51 |
|
| 52 |
#### check which input datasets have valid output files
|
| 53 |
for sample in datasets:
|
| 54 |
fileName = condor_dir + "/" + sample + ".root"
|
| 55 |
if not os.path.exists(fileName):
|
| 56 |
continue
|
| 57 |
testFile = TFile(fileName)
|
| 58 |
if testFile.IsZombie() or not testFile.GetNkeys():
|
| 59 |
continue
|
| 60 |
processed_datasets.append(sample)
|
| 61 |
|
| 62 |
if len(processed_datasets) is 0:
|
| 63 |
sys.exit("No datasets have been processed")
|
| 64 |
|
| 65 |
#### open first input file and re-make its directory structure in the output file
|
| 66 |
testFile = TFile(condor_dir + "/" + processed_datasets[0] + ".root")
|
| 67 |
testFile.cd()
|
| 68 |
for key in testFile.GetListOfKeys():
|
| 69 |
if (key.GetClassName() != "TDirectoryFile"):
|
| 70 |
continue
|
| 71 |
outputFile.cd()
|
| 72 |
outputFile.mkdir(key.GetName())
|
| 73 |
rootDirectory = key.GetName()
|
| 74 |
|
| 75 |
testFile.cd(key.GetName())
|
| 76 |
for key2 in gDirectory.GetListOfKeys():
|
| 77 |
if (key2.GetClassName() != "TDirectoryFile"):
|
| 78 |
continue
|
| 79 |
outputFile.cd(key.GetName())
|
| 80 |
gDirectory.mkdir(key2.GetName())
|
| 81 |
channels.append(key2.GetName())
|
| 82 |
|
| 83 |
|
| 84 |
weight = intLumi / 10000.0
|
| 85 |
for dataset in processed_datasets:
|
| 86 |
dataset_file = "%s/%s.root" % (condor_dir,dataset)
|
| 87 |
if types[dataset] != "data":
|
| 88 |
os.system("mergeTFileServiceHistograms -i %s -o %s -w %g" % (dataset_file, dataset_file + "_tmp", weight))
|
| 89 |
else:
|
| 90 |
os.system("mergeTFileServiceHistograms -i %s -o %s -w %g" % (dataset_file, dataset_file + "_tmp", 1.0))
|
| 91 |
|
| 92 |
for channel in channels: # loop over final states, which each have their own directory
|
| 93 |
|
| 94 |
testFile.cd(rootDirectory+"/"+channel)
|
| 95 |
|
| 96 |
for key in gDirectory.GetListOfKeys(): # loop over histograms in the current directory
|
| 97 |
histogramName = key.GetName()
|
| 98 |
|
| 99 |
if re.match ('TH1', key.GetClassName()): # plot a 1-D histogram
|
| 100 |
|
| 101 |
numBgMCSamples = 0
|
| 102 |
numDataSamples = 0
|
| 103 |
numSignalSamples = 0
|
| 104 |
|
| 105 |
Stack = THStack("stack",histogramName)
|
| 106 |
|
| 107 |
if(intLumi < 1000.):
|
| 108 |
LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
|
| 109 |
else:
|
| 110 |
getcontext().prec = 2
|
| 111 |
LumiInFb = intLumi/1000.
|
| 112 |
LumiText = "L_{int} = " + str(LumiInFb) + " fb^{-1}"
|
| 113 |
|
| 114 |
LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
|
| 115 |
LumiLabel.SetBorderSize(0)
|
| 116 |
LumiLabel.SetFillColor(0)
|
| 117 |
LumiLabel.SetFillStyle(0)
|
| 118 |
|
| 119 |
BgMCLegend = TLegend(0.70,0.65,0.94,0.89, "Data & Bkgd. MC")
|
| 120 |
BgMCLegend.SetBorderSize(0)
|
| 121 |
BgMCLegend.SetFillColor(0)
|
| 122 |
BgMCLegend.SetFillStyle(0)
|
| 123 |
SignalMCLegend = TLegend(0.45,0.65,0.70,0.89,"Signal MC")
|
| 124 |
SignalMCLegend.SetBorderSize(0)
|
| 125 |
SignalMCLegend.SetFillColor(0)
|
| 126 |
SignalMCLegend.SetFillStyle(0)
|
| 127 |
|
| 128 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 129 |
Canvas = TCanvas(histogramName)
|
| 130 |
BgMCHistograms = []
|
| 131 |
SignalMCHistograms = []
|
| 132 |
DataHistograms = []
|
| 133 |
|
| 134 |
backgroundIntegral = 0
|
| 135 |
dataIntegral = 0
|
| 136 |
scaleFactor = 1
|
| 137 |
|
| 138 |
for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
|
| 139 |
dataset_file = "%s/%s.root_tmp" % (condor_dir,sample)
|
| 140 |
inputFile = TFile(dataset_file)
|
| 141 |
Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+histogramName).Clone()
|
| 142 |
Histogram.SetDirectory(0)
|
| 143 |
inputFile.Close()
|
| 144 |
xAxisLabel = Histogram.GetXaxis().GetTitle()
|
| 145 |
histoTitle = Histogram.GetTitle()
|
| 146 |
|
| 147 |
if( types[sample] == "bgMC"):
|
| 148 |
|
| 149 |
numBgMCSamples += 1
|
| 150 |
|
| 151 |
if(arguments.noStack):
|
| 152 |
Histogram.SetFillStyle(0)
|
| 153 |
Histogram.SetLineColor(colors[sample])
|
| 154 |
Histogram.SetLineWidth(2)
|
| 155 |
BgMCLegend.AddEntry(Histogram,labels[sample],"L")
|
| 156 |
else:
|
| 157 |
Histogram.SetFillStyle(1001)
|
| 158 |
Histogram.SetFillColor(colors[sample])
|
| 159 |
Histogram.SetLineColor(1)
|
| 160 |
Histogram.SetLineWidth(1)
|
| 161 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 162 |
Histogram.SetLineStyle(1)
|
| 163 |
|
| 164 |
backgroundIntegral += Histogram.Integral()
|
| 165 |
|
| 166 |
BgMCHistograms.append(Histogram)
|
| 167 |
|
| 168 |
elif( types[sample] == "signalMC"):
|
| 169 |
|
| 170 |
numSignalSamples += 1
|
| 171 |
|
| 172 |
Histogram.SetFillStyle(0)
|
| 173 |
Histogram.SetLineColor(colors[sample])
|
| 174 |
Histogram.SetLineStyle(1)
|
| 175 |
Histogram.SetLineWidth(2)
|
| 176 |
if(arguments.normalizeToUnitArea and Histogram.Integral() > 0):
|
| 177 |
Histogram.Scale(1./Histogram.Integral())
|
| 178 |
SignalMCLegend.AddEntry(Histogram,labels[sample],"L")
|
| 179 |
SignalMCHistograms.append(Histogram)
|
| 180 |
|
| 181 |
elif( types[sample] == "data"):
|
| 182 |
|
| 183 |
numDataSamples += 1
|
| 184 |
|
| 185 |
Histogram.SetFillStyle(0)
|
| 186 |
Histogram.SetLineColor(colors[sample])
|
| 187 |
Histogram.SetLineStyle(1)
|
| 188 |
Histogram.SetLineWidth(2)
|
| 189 |
if(arguments.normalizeToUnitArea and Histogram.Integral() > 0):
|
| 190 |
Histogram.Scale(1./Histogram.Integral())
|
| 191 |
|
| 192 |
dataIntegral += Histogram.Integral()
|
| 193 |
|
| 194 |
BgMCLegend.AddEntry(Histogram,labels[sample],"LEP")
|
| 195 |
DataHistograms.append(Histogram)
|
| 196 |
|
| 197 |
if dataIntegral > 0 and backgroundIntegral > 0:
|
| 198 |
scaleFactor = dataIntegral/backgroundIntegral
|
| 199 |
for bgMCHist in BgMCHistograms:
|
| 200 |
if arguments.normalizeToData:
|
| 201 |
bgMCHist.Scale(scaleFactor)
|
| 202 |
if arguments.normalizeToUnitArea and not arguments.noStack and backgroundIntegral > 0:
|
| 203 |
bgMCHist.Scale(1./backgroundIntegral)
|
| 204 |
elif arguments.normalizeToUnitArea and arguments.noStack and bgMCHist.Integral() > 0:
|
| 205 |
bgMCHist.Scale(1./bgMCHist.Integral())
|
| 206 |
if not arguments.noStack:
|
| 207 |
Stack.Add(bgMCHist)
|
| 208 |
|
| 209 |
|
| 210 |
|
| 211 |
finalMax = 0
|
| 212 |
if not arguments.noStack:
|
| 213 |
finalMax = Stack.GetMaximum()
|
| 214 |
else:
|
| 215 |
for bgMCHist in BgMCHistograms:
|
| 216 |
if(bgMCHist.GetMaximum() > finalMax):
|
| 217 |
finalMax = bgMCHist.GetMaximum()
|
| 218 |
for signalMCHist in SignalMCHistograms:
|
| 219 |
if(signalMCHist.GetMaximum() > finalMax):
|
| 220 |
finalMax = signalMCHist.GetMaximum()
|
| 221 |
for dataHist in DataHistograms:
|
| 222 |
if(dataHist.GetMaximum() > finalMax):
|
| 223 |
finalMax = dataHist.GetMaximum()
|
| 224 |
|
| 225 |
|
| 226 |
|
| 227 |
if len(DataHistograms) is 1:
|
| 228 |
dataIntegral += DataHistograms[0].Integral()
|
| 229 |
|
| 230 |
|
| 231 |
### Drawing histograms to canvas
|
| 232 |
|
| 233 |
|
| 234 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 235 |
|
| 236 |
makeRatioPlots = arguments.makeRatioPlots
|
| 237 |
if numBgMCSamples is 0 or numDataSamples is not 1:
|
| 238 |
makeRatioPlots = False
|
| 239 |
if makeRatioPlots:
|
| 240 |
Canvas.SetFillStyle(0)
|
| 241 |
Canvas.Divide(1,2)
|
| 242 |
Canvas.cd(1)
|
| 243 |
gPad.SetPad(0.01,0.25,0.99,0.99)
|
| 244 |
gPad.SetMargin(0.1,0.05,0.02,0.07)
|
| 245 |
gPad.SetFillStyle(0)
|
| 246 |
gPad.Update()
|
| 247 |
gPad.Draw()
|
| 248 |
Canvas.cd(2)
|
| 249 |
gPad.SetPad(0.01,0.01,0.99,0.25)
|
| 250 |
#gPad.SetMargin(l,r,b,t)
|
| 251 |
gPad.SetMargin(0.1,0.05,0.4,0.02)
|
| 252 |
gPad.SetFillStyle(0)
|
| 253 |
gPad.SetGridy(1)
|
| 254 |
gPad.Update()
|
| 255 |
gPad.Draw()
|
| 256 |
|
| 257 |
Canvas.cd(1)
|
| 258 |
|
| 259 |
if(numBgMCSamples is not 0):
|
| 260 |
if not arguments.noStack:
|
| 261 |
Stack.SetTitle(histoTitle)
|
| 262 |
Stack.Draw("HIST")
|
| 263 |
Stack.GetXaxis().SetTitle(xAxisLabel)
|
| 264 |
Stack.SetMaximum(1.1*finalMax)
|
| 265 |
if makeRatioPlots:
|
| 266 |
Stack.GetHistogram().GetXaxis().SetLabelSize(0)
|
| 267 |
else:
|
| 268 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 269 |
BgMCHistograms[0].Draw("HIST")
|
| 270 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 271 |
BgMCHistograms[0].SetMaximum(1.1*finalMax)
|
| 272 |
for bgMCHist in BgMCHistograms:
|
| 273 |
bgMCHist.Draw("HIST SAME")
|
| 274 |
for signalMCHist in SignalMCHistograms:
|
| 275 |
signalMCHist.Draw("HIST SAME")
|
| 276 |
for dataHist in DataHistograms:
|
| 277 |
dataHist.Draw("E SAME")
|
| 278 |
|
| 279 |
elif(numSignalSamples is not 0):
|
| 280 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 281 |
SignalMCHistograms[0].Draw("HIST")
|
| 282 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 283 |
SignalMCHistograms[0].SetMaximum(1.1*finalMax)
|
| 284 |
for signalMCHist in SignalMCHistograms:
|
| 285 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 286 |
signalMCHist.Draw("HIST SAME")
|
| 287 |
for dataHist in DataHistograms:
|
| 288 |
dataHist.Draw("E SAME")
|
| 289 |
|
| 290 |
elif(numDataSamples is not 0):
|
| 291 |
DataHistograms[0].SetTitle(histoTitle)
|
| 292 |
DataHistograms[0].Draw("E")
|
| 293 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 294 |
DataHistograms[0].SetMaximum(1.1*finalMax)
|
| 295 |
for dataHist in DataHistograms:
|
| 296 |
if(dataHist is not DataHistograms[0]):
|
| 297 |
dataHist.Draw("E SAME")
|
| 298 |
|
| 299 |
|
| 300 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 301 |
BgMCLegend.Draw()
|
| 302 |
if(numSignalSamples is not 0):
|
| 303 |
SignalMCLegend.Draw()
|
| 304 |
|
| 305 |
LumiLabel.Draw()
|
| 306 |
if arguments.normalizeToData and numBgMCSamples > 0 and numDataSamples > 0:
|
| 307 |
NormLabel = TPaveLabel(0.1,0.75,0.35,0.85,"MC scaled to data","NDC")
|
| 308 |
NormLabel.SetBorderSize(0)
|
| 309 |
NormLabel.SetFillColor(0)
|
| 310 |
NormLabel.SetFillStyle(0)
|
| 311 |
NormLabel.Draw()
|
| 312 |
elif arguments.normalizeToUnitArea:
|
| 313 |
NormLabel = TPaveLabel(0.1,0.75,0.35,0.85,"Scaled to unit area","NDC")
|
| 314 |
NormLabel.SetBorderSize(0)
|
| 315 |
NormLabel.SetFillColor(0)
|
| 316 |
NormLabel.SetFillStyle(0)
|
| 317 |
NormLabel.Draw()
|
| 318 |
|
| 319 |
|
| 320 |
if makeRatioPlots:
|
| 321 |
Canvas.cd(2)
|
| 322 |
BgSum = Stack.GetStack().Last()
|
| 323 |
Ratio = DataHistograms[0].Clone()
|
| 324 |
Ratio.Add(BgSum,-1)
|
| 325 |
Ratio.Divide(BgSum)
|
| 326 |
|
| 327 |
Ratio.SetTitle("")
|
| 328 |
Ratio.GetXaxis().SetTitle(xAxisLabel)
|
| 329 |
Ratio.GetYaxis().SetTitle("#frac{Data-MC}{MC}")
|
| 330 |
Ratio.GetYaxis().CenterTitle()
|
| 331 |
Ratio.GetYaxis().SetTitleSize(0.1)
|
| 332 |
Ratio.GetYaxis().SetTitleOffset(0.3)
|
| 333 |
Ratio.GetXaxis().SetTitleSize(0.15)
|
| 334 |
Ratio.GetYaxis().SetLabelSize(0.1)
|
| 335 |
Ratio.GetXaxis().SetLabelSize(0.15)
|
| 336 |
Ratio.GetYaxis().SetRangeUser(-1,1)
|
| 337 |
Ratio.GetYaxis().SetNdivisions(205)
|
| 338 |
Ratio.Draw()
|
| 339 |
|
| 340 |
Canvas.Write()
|
| 341 |
|
| 342 |
|
| 343 |
if re.match ('TH2', key.GetClassName()): # plot a 2-D histogram
|
| 344 |
|
| 345 |
numBgMCSamples = 0
|
| 346 |
numDataSamples = 0
|
| 347 |
numSignalSamples = 0
|
| 348 |
|
| 349 |
if(intLumi < 1000.):
|
| 350 |
LumiText = "L_{int} = " + str(intLumi) + " pb^{-1}"
|
| 351 |
else:
|
| 352 |
getcontext().prec = 2
|
| 353 |
LumiInFb = intLumi/1000.
|
| 354 |
LumiText = "L_{int} = " + str(LumiInFb) + " fb^{-1}"
|
| 355 |
|
| 356 |
LumiLabel = TPaveLabel(0.1,0.8,0.34,0.9,LumiText,"NDC")
|
| 357 |
LumiLabel.SetBorderSize(0)
|
| 358 |
LumiLabel.SetFillColor(0)
|
| 359 |
LumiLabel.SetFillStyle(0)
|
| 360 |
|
| 361 |
BgMCLegend = TLegend(0.76,0.65,0.99,0.9, "Data & Bkgd. MC")
|
| 362 |
BgMCLegend.SetBorderSize(0)
|
| 363 |
BgMCLegend.SetFillColor(0)
|
| 364 |
BgMCLegend.SetFillStyle(0)
|
| 365 |
SignalMCLegend = TLegend(0.76,0.135,0.99,0.377,"Signal MC")
|
| 366 |
SignalMCLegend.SetBorderSize(0)
|
| 367 |
SignalMCLegend.SetFillColor(0)
|
| 368 |
SignalMCLegend.SetFillStyle(0)
|
| 369 |
|
| 370 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 371 |
Canvas = TCanvas(histogramName)
|
| 372 |
Canvas.SetRightMargin(0.2413793);
|
| 373 |
BgMCHistograms = []
|
| 374 |
SignalMCHistograms = []
|
| 375 |
DataHistograms = []
|
| 376 |
|
| 377 |
for sample in processed_datasets: # loop over different samples as listed in configurationOptions.py
|
| 378 |
dataset_file = "%s/%s.root_tmp" % (condor_dir,sample)
|
| 379 |
inputFile = TFile(dataset_file)
|
| 380 |
Histogram = inputFile.Get(rootDirectory+"/"+channel+"/"+histogramName).Clone()
|
| 381 |
Histogram.SetDirectory(0)
|
| 382 |
inputFile.Close()
|
| 383 |
xAxisLabel = Histogram.GetXaxis().GetTitle()
|
| 384 |
yAxisLabel = Histogram.GetYaxis().GetTitle()
|
| 385 |
histoTitle = Histogram.GetTitle()
|
| 386 |
|
| 387 |
if( types[sample] == "bgMC"):
|
| 388 |
|
| 389 |
numBgMCSamples += 1
|
| 390 |
Histogram.SetMarkerColor(colors[sample])
|
| 391 |
Histogram.SetFillColor(colors[sample])
|
| 392 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 393 |
BgMCHistograms.append(Histogram)
|
| 394 |
|
| 395 |
elif( types[sample] == "signalMC"):
|
| 396 |
|
| 397 |
numSignalSamples += 1
|
| 398 |
Histogram.SetMarkerColor(colors[sample])
|
| 399 |
Histogram.SetFillColor(colors[sample])
|
| 400 |
SignalMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 401 |
SignalMCHistograms.append(Histogram)
|
| 402 |
|
| 403 |
elif( types[sample] == "data"):
|
| 404 |
|
| 405 |
numDataSamples += 1
|
| 406 |
Histogram.SetMarkerColor(colors[sample])
|
| 407 |
Histogram.SetFillColor(colors[sample])
|
| 408 |
BgMCLegend.AddEntry(Histogram,labels[sample],"F")
|
| 409 |
DataHistograms.append(Histogram)
|
| 410 |
|
| 411 |
|
| 412 |
outputFile.cd(rootDirectory+"/"+channel)
|
| 413 |
|
| 414 |
if(numBgMCSamples is not 0):
|
| 415 |
BgMCHistograms[0].SetTitle(histoTitle)
|
| 416 |
BgMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 417 |
BgMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 418 |
BgMCHistograms[0].Draw()
|
| 419 |
for signalMCHist in SignalMCHistograms:
|
| 420 |
signalMCHist.Draw("SAME")
|
| 421 |
for dataHist in DataHistograms:
|
| 422 |
dataHist.Draw("SAME")
|
| 423 |
|
| 424 |
elif(numSignalSamples is not 0):
|
| 425 |
SignalMCHistograms[0].SetTitle(histoTitle)
|
| 426 |
SignalMCHistograms[0].Draw()
|
| 427 |
SignalMCHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 428 |
SignalMCHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 429 |
for signalMCHist in SignalMCHistograms:
|
| 430 |
if(signalMCHist is not SignalMCHistograms[0]):
|
| 431 |
signalMCHist.Draw("SAME")
|
| 432 |
for dataHist in DataHistograms:
|
| 433 |
dataHist.Draw("SAME")
|
| 434 |
|
| 435 |
elif(numDataSamples is not 0):
|
| 436 |
DataHistograms[0].SetTitle(histoTitle)
|
| 437 |
DataHistograms[0].GetXaxis().SetTitle(xAxisLabel)
|
| 438 |
DataHistograms[0].GetYaxis().SetTitle(yAxisLabel)
|
| 439 |
DataHistograms[0].Draw()
|
| 440 |
for dataHist in DataHistograms:
|
| 441 |
if(dataHist is not DataHistograms[0]):
|
| 442 |
dataHist.Draw("SAME")
|
| 443 |
|
| 444 |
|
| 445 |
if(numBgMCSamples is not 0 or numDataSamples is not 0):
|
| 446 |
BgMCLegend.Draw()
|
| 447 |
if(numSignalSamples is not 0):
|
| 448 |
SignalMCLegend.Draw()
|
| 449 |
|
| 450 |
LumiLabel.Draw()
|
| 451 |
|
| 452 |
Canvas.Write()
|
| 453 |
|
| 454 |
|
| 455 |
|
| 456 |
for dataset in processed_datasets:
|
| 457 |
dataset_file = "%s/%s.root_tmp" % (condor_dir,dataset)
|
| 458 |
os.remove(dataset_file)
|
| 459 |
|
| 460 |
outputFile.Close()
|
