text: def selection_14():
  
      # Library import
      import numpy
      import matplotlib
      import matplotlib.pyplot   as plt
      import matplotlib.gridspec as gridspec
  
      # Library version
      matplotlib_version = matplotlib.__version__
      numpy_version      = numpy.__version__
  
      # Histo binning
      xBinning = numpy.linspace(0.0,8000.0,81,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([50.0,150.0,250.0,350.0,450.0,550.0,650.0,750.0,850.0,950.0,1050.0,1150.0,1250.0,1350.0,1450.0,1550.0,1650.0,1750.0,1850.0,1950.0,2050.0,2150.0,2250.0,2350.0,2450.0,2550.0,2650.0,2750.0,2850.0,2950.0,3050.0,3150.0,3250.0,3350.0,3450.0,3550.0,3650.0,3750.0,3850.0,3950.0,4050.0,4150.0,4250.0,4350.0,4450.0,4550.0,4650.0,4750.0,4850.0,4950.0,5050.0,5150.0,5250.0,5350.0,5450.0,5550.0,5650.0,5750.0,5850.0,5950.0,6050.0,6150.0,6250.0,6350.0,6450.0,6550.0,6650.0,6750.0,6850.0,6950.0,7050.0,7150.0,7250.0,7350.0,7450.0,7550.0,7650.0,7750.0,7850.0,7950.0])
  
      # Creating weights for histo: y15_TET_0
      y15_TET_0_weights = numpy.array([0.0736934995206,2.29268691842,7.32840998566,13.4040329995,20.2779993581,28.8919522887,38.5621763525,47.9990406078,58.4757920096,68.5185837676,77.5951763185,83.8509311845,88.5182073541,91.7402447099,93.865082966,91.6952047468,89.7382463529,85.0504902001,79.7158945781,74.6596987277,67.9536242313,62.1399890024,56.674393488,50.5087185481,45.8046024087,39.7699313613,35.5612148153,30.8366426927,26.3454303786,23.2543969154,19.5124039864,16.6792983115,14.9720637126,12.4296377991,11.1768508273,9.23216042327,7.79922959926,6.57509860389,5.49835548756,4.8473960218,4.01220070723,3.02552831698,2.60383746306,2.20671138898,1.77273854513,1.51071716017,1.14634345921,1.00714477345,0.863851691047,0.597736309445,0.540419156484,0.519948773284,0.454443227044,0.282491808162,0.319338577923,0.204704192002,0.208798308642,0.159669268961,0.118728422561,0.0777876161606,0.0900698460807,0.0532230763204,0.0491289996804,0.0327526731203,0.0204704192002,0.0122822539201,0.0163763345601,0.0204704192002,0.0204704192002,0.00409408464003,0.00818816928007,0.0122822539201,0.00409408464003,0.00409408464003,0.00818816928007,0.00818816928007,0.00409408464003,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_1
      y15_TET_1_weights = numpy.array([20.6425046828,174.067972206,8.67379156258,0.644012966344,0.13363400525,0.0242793972542,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_2
      y15_TET_2_weights = numpy.array([0.0,361.945747306,501.821958999,77.0081720671,7.18860758924,1.76726181188,0.642536117015,0.271112803828,0.120410642978,0.0401787927712,0.0401686319645,0.030115961939,0.0100547815229,0.0,0.0100697975343,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_3
      y15_TET_3_weights = numpy.array([0.0,0.0,233.37611362,478.330781638,292.039305059,95.6006276118,33.5742042856,10.4832010847,2.6840622947,0.913159921042,0.423583225505,0.209037903548,0.13204239369,0.027502016098,0.0440114093306,0.0164798545288,0.0055036524364,0.0,0.00549437360274,0.0,0.00548468851541,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_4
      y15_TET_4_weights = numpy.array([0.0,0.0,0.0,0.0,52.5550954064,123.463659636,92.2050510567,41.8309948152,21.0865758863,10.2128773245,4.78224091681,2.06846270387,0.636495634077,0.232896647387,0.130265192574,0.0681059972445,0.0355223230975,0.0147998376152,0.0167825378171,0.00690893547847,0.00592297253984,0.00296350324915,0.0,0.00197350595122,0.000983912690473,0.00197240005979,0.000987948653084,0.00197005439953,0.0,0.0,0.0,0.0,0.0,0.000986798269461,0.0,0.0,0.000986294425485,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_5
      y15_TET_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,15.8964481204,36.5072829626,27.1879962435,12.9960721756,7.3872471329,4.682042827,3.06048342501,1.79954924704,0.961179811745,0.453219825185,0.147970597738,0.0594940017278,0.0345450960178,0.0214360282614,0.0113420404476,0.00629947762222,0.00479123347904,0.00327843942529,0.00126060209691,0.0015101714136,0.00151107003294,0.000756465443576,0.0,0.000504254282045,0.000252008672106,0.0,0.0,0.000251948977535,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_6
      y15_TET_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.87635647674,9.42478340276,8.41848375816,6.38888516787,4.34524528483,2.48812787134,1.63546572077,1.16085383132,0.880336173033,0.601415179479,0.398270682348,0.286292322364,0.181196455069,0.113635804337,0.0767141920669,0.0369226119349,0.0177405725886,0.00544819752727,0.00286093289089,0.00229327519443,0.00171949954898,0.00143246477552,0.0,0.00171569982281,0.00028630211908,0.00028617066315,0.0,0.0,0.0,0.000286426277456,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_7
      y15_TET_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.363140022156,0.952220164261,0.912734433348,0.742526030842,0.507857543515,0.287508406595,0.177949689475,0.118320779257,0.0863455429812,0.0697078339285,0.0573985183616,0.0533990678611,0.0472842235362,0.0395898996841,0.0282221263119,0.0204943926817,0.0144272658345,0.00969355876816,0.00658641169143,0.00321617811884,0.0016847266144,0.000691013701827,0.000430621948481,0.000324146846589,8.62941626652e-05,8.65063055359e-05,6.4771174614e-05,4.32887124728e-05,2.15591260989e-05,4.31042713875e-05,0.0,0.0,0.0,0.0,0.0,0.0,2.15824232584e-05,0.0,0.0,0.0,2.15560122673e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_8
      y15_TET_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0494279524796,0.137507075693,0.135777298427,0.114142905471,0.0860474271826,0.0658032178503,0.0490994964645,0.0362352414569,0.0271619224785,0.0193554267793,0.0138326344609,0.012046416162,0.00924520373251,0.00586854135408,0.00671652901067,0.00541971878745,0.00592632484267,0.00379881404339,0.00357028035345,0.0031504987753,0.00221170679662,0.00177797348566,0.00124947486731,0.00096232325784,0.000738546076696,0.00039352395665,0.000423826169898,0.000312586234663,0.000170295637714,0.000139957472209,0.000198578673624,0.000255596784556,0.000111831343174,2.84112671099e-05,2.84112671099e-05,2.83530396013e-05,2.84324434494e-05,5.68330332885e-05,8.52758421552e-05,0.0,0.0,0.0,0.0,0.0,0.0,2.7975860185e-05,0.0,0.0,2.83530396013e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_9
      y15_TET_9_weights = numpy.array([28.6912833119,185.048432578,15.6396625719,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_10
      y15_TET_10_weights = numpy.array([0.0,346.499958649,509.76349705,115.857704726,11.5911262467,5.27181305314,1.05535412138,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_11
      y15_TET_11_weights = numpy.array([0.0,0.0,313.712122135,657.580102662,461.385893694,128.529882633,53.2062870296,18.4309944188,5.0693337254,2.76518536585,0.460099467516,0.230000589819,0.46099397174,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_12
      y15_TET_12_weights = numpy.array([0.0,0.0,0.0,0.0,176.340233724,417.663675006,297.618574075,94.3464319418,41.0115584969,20.433222175,10.4398741268,5.81468000787,1.79998902992,0.581395289828,0.221465270462,0.166148773318,0.0553268074806,0.0,0.0277291744575,0.0,0.0277334871022,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_13
      y15_TET_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,99.0071910057,208.109086982,130.592170897,46.5585202006,20.1544989161,10.3947164633,7.01709763822,4.47651870727,2.5506365585,1.50218259028,0.473675420712,0.231934146627,0.0806283239387,0.0504346936939,0.0302539250363,0.0,0.0301944555358,0.0100662445663,0.0100940010454,0.0100796616138,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_14
      y15_TET_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24.6193613572,54.3369424621,43.6029127295,30.6403504687,18.0331807234,7.82545294115,4.32002048247,2.86034341103,2.29734067755,1.51646554204,1.11761619987,0.63657397041,0.582841493783,0.452651456828,0.21495745393,0.101816462222,0.050918599868,0.0198105703624,0.0169821773433,0.00565840194835,0.005660202534,0.00565697071361,0.00282510656554,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_15
      y15_TET_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.30872598033,5.6668113094,4.93224960339,3.6708186901,2.36884537865,1.05733371505,0.495146739254,0.2771558735,0.199633646932,0.111046787712,0.117330865334,0.106532887779,0.117372233681,0.0897761312576,0.0914046096549,0.0532331459738,0.0471962267971,0.0396365837809,0.0228508186074,0.0121986405478,0.00918601459359,0.00302926073263,0.00153785205848,0.0,0.0,0.00151229726716,0.0,0.0,0.00152413705118,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y15_TET_16
      y15_TET_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.334215852215,0.807987820875,0.789616610021,0.62474787502,0.432972799087,0.3154337905,0.222992924613,0.15637026878,0.0967856483961,0.0776379879547,0.0519971662647,0.037385406386,0.0250985201213,0.0155337441786,0.014442905875,0.0104721024026,0.0110120277528,0.0119160999103,0.00758211629326,0.00595749152457,0.00487583073874,0.00397545577686,0.00306928892699,0.00180615091034,0.00162512581572,0.00162595806985,0.00108439940643,0.00108426345747,0.0,0.000361187895393,0.000180268632997,0.00018061878823,0.0,0.000180832801937,0.0,0.000180407508906,0.0,0.000180409396017,0.000361433720388,0.0,0.0,0.0,0.0,0.0,0.0,0.000180678559558,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating a new Canvas
      fig   = plt.figure(figsize=(12,6),dpi=80)
      frame = gridspec.GridSpec(1,1,right=0.7)
      pad   = fig.add_subplot(frame[0])
  
      # Creating a new Stack
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights+y15_TET_14_weights+y15_TET_15_weights+y15_TET_16_weights,\
               label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights+y15_TET_14_weights+y15_TET_15_weights,\
               label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights+y15_TET_14_weights,\
               label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights,\
               label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights,\
               label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights,\
               label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights,\
               label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights,\
               label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights,\
               label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights,\
               label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights,\
               label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights,\
               label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights,\
               label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights,\
               label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights,\
               label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights+y15_TET_1_weights,\
               label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
      pad.hist(x=xData, bins=xBinning, weights=y15_TET_0_weights,\
               label="$signal$", histtype="step", rwidth=1.0,\
               color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\
               bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical")
  
  
      # Axis
      plt.rc('text',usetex=False)
      plt.xlabel(r"TET",\
                 fontsize=16,color="black")
      plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\
                 fontsize=16,color="black")
  
      # Boundary of y-axis
      ymax=(y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights+y15_TET_14_weights+y15_TET_15_weights+y15_TET_16_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y15_TET_0_weights+y15_TET_1_weights+y15_TET_2_weights+y15_TET_3_weights+y15_TET_4_weights+y15_TET_5_weights+y15_TET_6_weights+y15_TET_7_weights+y15_TET_8_weights+y15_TET_9_weights+y15_TET_10_weights+y15_TET_11_weights+y15_TET_12_weights+y15_TET_13_weights+y15_TET_14_weights+y15_TET_15_weights+y15_TET_16_weights) if x])/100. # log scale
      plt.gca().set_ylim(ymin,ymax)
  
      # Log/Linear scale for X-axis
      plt.gca().set_xscale("linear")
      #plt.gca().set_xscale("log",nonposx="clip")
  
      # Log/Linear scale for Y-axis
      plt.gca().set_yscale("linear")
      #plt.gca().set_yscale("log",nonposy="clip")
  
      # Legend
      plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.)
  
      # Saving the image
      plt.savefig('../../HTML/MadAnalysis5job_0/selection_14.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_14.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_14.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_14()
  