text: def selection_5():
  
      # 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(-3.2,3.2,65,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([-3.15,-3.05,-2.95,-2.85,-2.75,-2.65,-2.55,-2.45,-2.35,-2.25,-2.15,-2.05,-1.95,-1.85,-1.75,-1.65,-1.55,-1.45,-1.35,-1.25,-1.15,-1.05,-0.95,-0.85,-0.75,-0.65,-0.55,-0.45,-0.35,-0.25,-0.15,-0.05,0.05,0.15,0.25,0.35,0.45,0.55,0.65,0.75,0.85,0.95,1.05,1.15,1.25,1.35,1.45,1.55,1.65,1.75,1.85,1.95,2.05,2.15,2.25,2.35,2.45,2.55,2.65,2.75,2.85,2.95,3.05,3.15])
  
      # Creating weights for histo: y6_PHI_0
      y6_PHI_0_weights = numpy.array([5.02343976068,12.0161378595,11.9874778837,12.065265818,12.5360854207,12.200369704,12.3886975451,12.4746734725,12.5115214414,11.9137859459,12.3272855969,12.3272855969,12.1348657593,12.290441628,12.3436615831,11.7909620495,12.4828614656,12.1839937178,12.4746734725,12.0407018388,12.0447938353,12.4623934829,12.290441628,12.2740656418,12.5934013723,12.2699696453,11.9096899493,12.1799017213,12.7121292721,12.1962777075,12.4746734725,12.4214495174,12.3641335658,12.2372176729,11.9915738802,12.0611698215,11.5985382119,12.2658776487,12.0079498664,12.3272855969,12.1962777075,12.4091695278,12.0243258526,12.2167456902,12.1348657593,11.6558581635,12.0693578146,12.0038538699,12.425545514,12.6466253274,12.0325138457,12.1799017213,11.9424419217,12.3518495762,11.9055979528,11.9956658768,12.0980177904,12.1307697628,12.2249336833,12.1062057835,12.3723215589,12.2085576971,12.4050735313,5.06028772958])
  
      # Creating weights for histo: y6_PHI_1
      y6_PHI_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121313846429,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0242945760233,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121753353338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y6_PHI_2
      y6_PHI_2_weights = numpy.array([0.0,0.0200698970219,0.0100299570107,0.050250317505,0.0100407292302,0.0100299570107,0.0,0.0100584804597,0.0,0.040135757044,0.0100367004945,0.0,0.0301512400403,0.0401515579521,0.0100184162323,0.0200696201754,0.0201006435066,0.0502126746512,0.0200560918874,0.0100262836301,0.0300849497764,0.0200748224096,0.0100696700149,0.0200856359495,0.0,0.0100459438605,0.0100184162323,0.0200698970219,0.0100187054749,0.0100584804597,0.0100299570107,0.0201156386676,0.0200646452033,0.0201072671613,0.0,0.0301018415422,0.0501547848177,0.0,0.0200629841246,0.0201027343171,0.0100584804597,0.0,0.0100584804597,0.0300858299002,0.0401631689751,0.0100184162323,0.0100367004945,0.0100568565693,0.0200707936738,0.0100153626572,0.0200777065712,0.0301217331666,0.0301422074082,0.0201043829998,0.0200795287993,0.0100329196809,0.0200975857995,0.0200759008711,0.0200666575052,0.0401428269587,0.020107903495,0.0401912668243,0.0100609844739,0.0200794007062])
  
      # Creating weights for histo: y6_PHI_3
      y6_PHI_3_weights = numpy.array([0.0549440416889,0.0935183345912,0.153995362745,0.0769853108248,0.109991475969,0.143027054172,0.104518696899,0.110012723248,0.0715479574283,0.0549629326731,0.109987291514,0.120959012652,0.0934920903421,0.0770336148684,0.0880438086128,0.0990475834852,0.104409819828,0.0880017203125,0.120954746946,0.0990077296023,0.110041648798,0.0879709666027,0.0714714997244,0.0935123219769,0.115485989827,0.0824892092368,0.093479252598,0.0990497772769,0.137450151247,0.0880624964682,0.0989472784528,0.109985341477,0.0714863687572,0.0990485991295,0.0660416620958,0.0990091921301,0.0604928722052,0.0825019251036,0.126526287269,0.0824969687593,0.131964128175,0.11551950609,0.104475999212,0.0935537196391,0.0825551042398,0.0825231723826,0.0714964439487,0.0935443350856,0.093597311093,0.126469858071,0.10455375694,0.0770151301419,0.0879693415718,0.082474340204,0.0605171257914,0.066060918712,0.104566919691,0.0935349099064,0.104515284334,0.0879881513044,0.132039082725,0.098982257243,0.0769703605405,0.0220198227906])
  
      # Creating weights for histo: y6_PHI_4
      y6_PHI_4_weights = numpy.array([0.0424562815274,0.0710534217181,0.0789511946729,0.0868402696399,0.0592151396105,0.0700732346465,0.0858520259068,0.0641556367841,0.0759659009493,0.0809211085447,0.0878053855436,0.0720413047052,0.0690738879519,0.0552592787097,0.0671243762728,0.080933013164,0.0641175179526,0.0700669416323,0.0779440718971,0.0671241758584,0.0641427300925,0.0799406408928,0.0730335166447,0.0838819917863,0.0710565481838,0.0690643081404,0.0611976395108,0.0789593315002,0.0670952760923,0.0611837708297,0.0582225669249,0.0730298290186,0.089808848797,0.0799389173284,0.0720471568076,0.0641552359552,0.0641640141088,0.0710599552297,0.0592134160461,0.065131214323,0.0681014368787,0.0700757598688,0.0690710420665,0.0621693690919,0.0641304647272,0.064143291253,0.0769548260916,0.0690874760527,0.0838884451321,0.0690776958268,0.0542757246751,0.0759864634735,0.0631553281001,0.0789636604527,0.0631594566381,0.0424174011209,0.0601948456874,0.0838692053434,0.0740030817496,0.0799435268611,0.0670990839672,0.0710421183422,0.0542734800331,0.0384915422657])
  
      # Creating weights for histo: y6_PHI_5
      y6_PHI_5_weights = numpy.array([0.0113435023242,0.0252043028552,0.0292400990639,0.0274718870069,0.027724897987,0.0229364466272,0.0229405357744,0.022180814641,0.0242036221227,0.0279813779403,0.0262142381841,0.0254575739083,0.0300019207866,0.0259634998415,0.0254601266244,0.0226881449683,0.0229319613689,0.0269715146316,0.0302448089268,0.0302527591572,0.0236944312734,0.0226874607763,0.027224469596,0.0262169509451,0.0274832501947,0.0239477863501,0.0272249657352,0.0247006135494,0.022939247413,0.0282325844141,0.0231947190834,0.0267160269567,0.020926426733,0.0269755717698,0.0279797894947,0.0272226770931,0.0299961631713,0.0277288270893,0.0257060796245,0.0259654123781,0.0292372102535,0.0229375549381,0.0279719552968,0.0282346569956,0.0272231452245,0.0254600946154,0.0221843836423,0.0254575418993,0.025717822919,0.0279796574576,0.0267212844316,0.0274775005817,0.0277325161242,0.0247045506539,0.0294903532706,0.0259636998977,0.0247071833925,0.0287365097862,0.0269834899912,0.023444805243,0.0221826231484,0.0247065552163,0.0279783130805,0.0123502487584])
  
      # Creating weights for histo: y6_PHI_6
      y6_PHI_6_weights = numpy.array([0.00571757925756,0.0105904049979,0.00915828447233,0.0108715636269,0.0137389137781,0.0117407920608,0.00859453662836,0.0108793313788,0.00973922933927,0.007445108288,0.00916582029137,0.0100192602946,0.0120212308981,0.0123076679994,0.0103007788195,0.0100211597449,0.0111644788534,0.0106000622029,0.0085747353591,0.00916970616675,0.0100179506737,0.0123069382106,0.00916260822098,0.0100241788711,0.0108762022844,0.00916293212724,0.0103058073642,0.0117353236434,0.00916671603213,0.01030616726,0.00944219830395,0.00629296673288,0.0105949636785,0.00802297304635,0.0100343159373,0.00830536731775,0.00859358290438,0.0108733431119,0.00801019174543,0.0103152746242,0.0111803942472,0.00916548638801,0.00887921123979,0.0117495495262,0.0114657916495,0.0100217195829,0.00743271887365,0.0105912147635,0.0100186504711,0.0105955834991,0.00715724459952,0.00886493337199,0.00916060080196,0.0117285356079,0.0108807409708,0.0117340939993,0.00973306312385,0.00686133923842,0.00887623510112,0.00944565630316,0.0106075400388,0.0111732063275,0.00715995781428,0.0031470322076])
  
      # Creating weights for histo: y6_PHI_7
      y6_PHI_7_weights = numpy.array([0.000280979454763,0.000863907889816,0.000863612427511,0.00114464192227,0.000993270087716,0.000929009760615,0.00095023652694,0.000388868625842,0.000907272955107,0.00092920254453,0.000928648500322,0.00101537653543,0.00095055084854,0.000820783804419,0.000864021045592,0.00103706975501,0.000799756108441,0.000950122532972,0.00116481172983,0.000950552944017,0.00110183341576,0.000994039546994,0.00082109435416,0.000561688928226,0.000842381051137,0.000840869373786,0.000927389956634,0.000582840676462,0.000950212219402,0.00114458031524,0.000842251969733,0.00116640806446,0.000885049160665,0.000819519393394,0.00079920332152,0.000950376504826,0.000927117125484,0.000885384437039,0.000950260834477,0.000691150032656,0.000775827433639,0.00088569959683,0.00101496749825,0.000754445182896,0.000713010890423,0.000669327312577,0.00064803600555,0.000885631703365,0.00101402369526,0.00071272171455,0.000907182849581,0.00101520009623,0.000819343373298,0.000927315776736,0.000799112796899,0.000755842447184,0.000864067146093,0.000755876813012,0.000907006410389,0.000691333177375,0.000885625836028,0.000970702634991,0.000756251903455,0.000345306082767])
  
      # Creating weights for histo: y6_PHI_8
      y6_PHI_8_weights = numpy.array([8.49823031485e-05,0.000113542473047,0.000112805007044,0.000170265763738,0.000170391382117,5.67472710316e-05,8.51343815685e-05,0.000283052507117,8.52559463045e-05,8.48038686265e-05,5.65917626062e-05,5.67948159546e-05,8.52862818065e-05,5.64019986725e-05,2.83684905078e-05,0.000140349449897,8.52544169081e-05,0.000141818279168,8.51765364831e-05,0.000142012557048,2.84292654515e-05,2.8397400553e-05,0.000112247935565,8.51290064086e-05,2.84080914789e-05,0.000113513162092,0.000113531782122,0.000112515505681,0.000141537954183,2.84292654515e-05,0.000113672991434,0.000113605801935,8.53270855068e-05,8.52084013815e-05,2.83684905078e-05,0.000113577916436,8.52068719852e-05,5.68781754791e-05,0.000113664037783,5.67472710316e-05,8.49761113206e-05,8.34978977924e-05,0.000113622892567,2.83684905078e-05,0.000113426550745,8.51632916138e-05,0.000142041467093,0.000227331698603,5.68161978063e-05,5.66216971985e-05,2.84080914789e-05,0.00011364406654,2.84292654515e-05,2.84080914789e-05,0.000170382324527,5.64216432487e-05,2.84292654515e-05,0.000227036807232,0.000113624421964,8.51476264378e-05,5.67658910608e-05,5.68463105807e-05,5.67472710316e-05,0.0])
  
      # Creating weights for histo: y6_PHI_9
      y6_PHI_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y6_PHI_10
      y6_PHI_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05462838872,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0521138287,0.0,0.0,0.0,1.0529581672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y6_PHI_11
      y6_PHI_11_weights = numpy.array([0.229982513283,0.230597153025,0.0,0.230619671243,0.459685969748,0.460296958926,0.691102924228,0.921150340701,0.230360173764,0.459685969748,0.0,0.230645225193,0.230673161616,0.230764041453,0.460504848955,0.0,0.460825330071,0.460787287348,0.230597153025,0.0,0.0,0.460485635458,0.460770763741,0.46088066494,0.460540201788,0.460697752456,0.230128535854,0.460725035621,0.0,0.0,0.0,0.690320550664,0.229952463375,0.230673161616,0.230186253197,0.0,0.0,0.230838551391,0.0,0.230186253197,0.230020171736,0.690931924112,0.460485635458,0.230752244366,0.0,0.921574574696,0.0,0.69009344714,0.230838551391,0.0,0.0,0.921845869262,0.460602069245,0.230645225193,0.46092946722,0.230645225193,0.230498318801,0.461073568441,0.920850994431,0.0,0.0,0.230597153025,0.0,0.460791130047])
  
      # Creating weights for histo: y6_PHI_12
      y6_PHI_12_weights = numpy.array([0.138528995559,0.138405937866,0.166013598564,0.138429095331,0.221524350745,0.166124308173,0.138344312851,0.138380549283,0.110713340817,0.221578166847,0.360250607901,0.221648985607,0.13850683825,0.193746971215,0.221587014384,0.304737355329,0.359986720507,0.166161621697,0.193828560889,0.166087533195,0.276819453839,0.166111729284,0.19393453899,0.304752973155,0.249306307912,0.249225141381,0.193820213431,0.193855026564,0.193736315704,0.110572588051,0.110783813369,0.138480026368,0.166131347735,0.0276405119856,0.138333811209,0.166029331792,0.166144695975,0.221615557306,0.332560626646,0.332046815578,0.2770746091,0.110802431664,0.193851526017,0.166042179953,0.304621491068,0.221447761852,0.08307913751,0.221541930415,0.221618403905,0.138503991651,0.249263685867,0.166026562128,0.138354660622,0.193765627977,0.0829020329089,0.277033102613,0.193894455803,0.193902149313,0.24925483833,0.166218592139,0.110771811494,0.221372788597,0.193984200598,0.0553163756499])
  
      # Creating weights for histo: y6_PHI_13
      y6_PHI_13_weights = numpy.array([0.0100733687265,0.0806907880253,0.0705854667452,0.0201922357046,0.0403002963059,0.0502138415758,0.0503766205678,0.0706141724581,0.0302955846049,0.0806580161755,0.0402604603018,0.0504171484221,0.0504566385012,0.050451188664,0.0503722509878,0.0605176146134,0.0806588051274,0.0302708297205,0.151254953068,0.0604995051319,0.0504721444413,0.0604795567855,0.0302647062397,0.0201802193596,0.0605511329332,0.0503073505874,0.0705667746531,0.0806493983927,0.0302297799441,0.0604398360897,0.040253899863,0.0302709328911,0.0503760197506,0.0403349070207,0.0604481564983,0.0805928366074,0.050376001544,0.0504212752477,0.0806811992248,0.0806985561675,0.100809244586,0.0302593838485,0.0605042873945,0.0403123672706,0.05043628961,0.0604376755752,0.0504686669839,0.0807043215855,0.0704819926637,0.0403280492077,0.0201467920728,0.0806918804203,0.060527373342,0.070621212337,0.0301753119159,0.0604966831115,0.100899427861,0.0605590770723,0.0302714062623,0.0302647123085,0.0705519059434,0.0806749482979,0.070546201214,0.0403423717198])
  
      # Creating weights for histo: y6_PHI_14
      y6_PHI_14_weights = numpy.array([0.0113195183002,0.0226208266425,0.019801788206,0.0197924158297,0.00283012138697,0.0226303452218,0.0226410526616,0.0254644656432,0.025476642807,0.0169802835571,0.0113226039512,0.0197976445076,0.00849104977562,0.0367940026905,0.0254643309826,0.0283154417217,0.0282865204767,0.0226227888395,0.00849254643177,0.0226316148787,0.0282938190801,0.0254680245299,0.0198195287755,0.0197909268684,0.0226290063109,0.0169692914064,0.0198039774023,0.0169700493531,0.03960967846,0.0198160352953,0.0254534273231,0.0424594041188,0.0254551317413,0.0282854354972,0.0197963363762,0.0254647003374,0.039608062533,0.0311326526218,0.0226450501571,0.0282947847888,0.016970861164,0.0226572927275,0.0226289409043,0.0367736189262,0.0141406035775,0.0198102718225,0.022628817786,0.0169859469964,0.0197862137481,0.0198017381892,0.019799472044,0.0226237814804,0.00849365449597,0.0509203591786,0.0113196183337,0.0141485870261,0.0480935258183,0.0254513804823,0.0169675985305,0.0226294833941,0.0339694816446,0.0254373796295,0.0282671947598,0.0113218498519])
  
      # Creating weights for histo: y6_PHI_15
      y6_PHI_15_weights = numpy.array([0.00152449660526,0.00152495990767,0.00150849612533,0.0,0.0,0.00455604860715,0.00304418526603,0.0,0.0015216293306,0.00152094974159,0.00303822379309,0.0,0.0015333359899,0.00609026747182,0.00456713004457,0.00303278589915,0.00608591219271,0.0,0.0015216293306,0.0015333359899,0.0015333359899,0.00152767589991,0.0,0.00306990800537,0.00150849612533,0.00152094974159,0.00152192834976,0.0015172740515,0.00151115656855,0.0,0.0030817056705,0.0,0.00152094974159,0.0,0.0,0.00456877287712,0.0015216293306,0.00304418526603,0.00456359618174,0.00150849612533,0.00305715773373,0.00152094974159,0.00150849612533,0.00153629545228,0.0,0.00153565722957,0.0045827605917,0.0,0.0,0.0,0.0,0.00304074122714,0.00153629545228,0.0,0.0015216293306,0.0,0.0,0.0,0.0,0.0,0.00152192834976,0.00152325207096,0.00457591624399,0.00151881878685])
  
      # Creating weights for histo: y6_PHI_16
      y6_PHI_16_weights = numpy.array([0.0,0.000180503369873,0.0,0.000180766968647,0.000361169010643,0.000180003621709,0.000180503369873,0.0,0.000361173245471,0.000361059174613,0.000361320001499,0.000180657132616,0.000180766968647,0.0,0.000541980560476,0.000361100175444,0.0,0.000361596381753,0.000361281503066,0.0,0.0,0.000361596381753,0.000180734321975,0.000180755034132,0.000361794571688,0.00018034371687,0.000180547142592,0.0,0.000360958039228,0.0,0.0,0.000180766968647,0.0,0.000180503369873,0.0,0.000361189414813,0.0,0.000360002315619,0.0,0.0,0.000361702252444,0.0,0.0,0.0,0.0,0.0,0.000180003621709,0.000180626141377,0.0,0.0,0.000360644353993,0.0,0.000180640770782,0.0,0.000361124814442,0.0,0.000180402041997,0.000180657132616,0.000180734321975,0.000360688396201,0.0,0.000180003621709,0.0,0.0])
  
      # 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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_13_weights+y6_PHI_14_weights+y6_PHI_15_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_13_weights+y6_PHI_14_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_13_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_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=y6_PHI_0_weights+y6_PHI_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=y6_PHI_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"\phi [ j_{2} ] ",\
                 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=(y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_13_weights+y6_PHI_14_weights+y6_PHI_15_weights+y6_PHI_16_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y6_PHI_0_weights+y6_PHI_1_weights+y6_PHI_2_weights+y6_PHI_3_weights+y6_PHI_4_weights+y6_PHI_5_weights+y6_PHI_6_weights+y6_PHI_7_weights+y6_PHI_8_weights+y6_PHI_9_weights+y6_PHI_10_weights+y6_PHI_11_weights+y6_PHI_12_weights+y6_PHI_13_weights+y6_PHI_14_weights+y6_PHI_15_weights+y6_PHI_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_5.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_5.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_5.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_5()
  