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([5.64574326025,13.5759846026,13.309868826,13.5514166232,13.8093444067,13.2034209154,13.5391366335,13.7643124445,13.4736286885,13.2075169119,13.9690162726,14.0386122142,13.5637006129,13.8502843723,13.9894842554,13.2812088501,13.862568362,13.5391366335,13.6578645339,13.6087365751,13.5022886645,13.6824285132,13.5596046163,13.9362603001,13.7643124445,13.9976722486,13.702900496,13.6824285132,13.7069924926,13.0969730047,14.124588142,13.8502843723,13.6742405201,13.8584763654,13.0805970185,13.1542929566,13.4163127366,13.4777246851,13.7765924342,13.3671847779,13.7684044411,13.4490647091,13.4531607057,13.4613486988,13.3344328054,12.9905290941,13.404032747,13.5800765992,13.5186646507,13.7069924926,13.6988044995,13.4163127366,13.5145726542,13.8380043826,13.4081247435,13.600548582,13.5677926095,13.5637006129,13.7438404617,13.6046405785,13.5677926095,13.5555126198,13.1911369257,5.60889529118])
  
      # Creating weights for histo: y6_PHI_1
      y6_PHI_1_weights = numpy.array([0.814002298647,1.44595444342,1.48217093505,1.60419391627,1.84673426276,1.79860421962,1.77395040969,1.60432288895,1.83436970739,1.60396601112,1.64022736282,1.51970679324,1.50666773475,1.77386829975,1.61646474615,1.60398764008,1.60383824004,1.61599732029,1.6647670199,1.72544506404,1.62731086843,1.81041323115,1.56744391029,1.79825735519,1.53072194184,1.77363919299,1.74925294131,1.68907356486,1.59183697108,1.66422469376,1.43348054163,1.48278736041,1.54279891216,1.6405814369,1.83482992359,1.60423196721,1.84693933735,1.39741465164,1.61613670692,1.57938750201,1.59147007984,1.59131026586,1.46981358933,1.50703222278,1.70117536842,1.66444979516,1.54274363815,1.62819044613,1.7253220994,1.44612186759,1.54321587044,1.80990855542,1.48260311371,1.70045720685,1.44580183909,1.70123464779,1.73663284379,1.72524279321,1.67678871621,1.45757760611,1.68925660994,1.75002958119,1.68904512678,0.729216776085])
  
      # Creating weights for histo: y6_PHI_2
      y6_PHI_2_weights = numpy.array([3.04218718014,8.0526854434,7.40976064746,7.86183644923,8.37300531757,7.59956422202,6.84699019775,7.57997809969,7.42014459798,7.69110661743,7.47010160621,7.49045629791,7.61147291497,7.61029940047,7.30885906068,7.78129781833,7.25912931758,7.78079370295,8.04170647145,7.49936095901,7.45967633477,7.44951551729,7.4092400037,7.03868627105,7.59960141086,7.61074566655,7.79136772975,7.5096994565,7.40966560932,7.32913111051,7.67022714989,7.52959135374,7.62107589985,7.02794282842,7.56969745151,7.88083168222,7.50977383418,7.7814424416,7.49024556115,8.10242345069,7.93140437237,7.68038383527,7.53069462266,8.06220165432,7.84194455199,7.70025507205,7.61017130558,7.64952123026,7.50011713209,7.34956844407,7.78128955414,7.31894550048,7.17875597041,7.97169641433,7.2795914437,7.44004475941,7.62068335098,7.78145483788,7.94181311544,7.93126801329,7.42957403493,7.83145729914,7.96168848431,2.96177540451])
  
      # Creating weights for histo: y6_PHI_3
      y6_PHI_3_weights = numpy.array([3.81792546276,8.81720290887,8.89421238299,9.08605762443,9.27928412885,9.2629649129,9.12989241161,8.93259524391,9.04226752517,9.08084945046,9.49907557057,9.35159324628,9.3183291835,8.98224352326,9.14665444423,9.24629631867,9.55438702817,9.30569875223,8.94975946781,9.29524990398,8.82861457867,8.85647546565,9.39426614754,9.23496996216,9.03722591526,9.65812394112,9.51572791465,8.91071441316,9.02556236809,9.27876412396,8.96493711052,8.95998081392,9.15811080195,8.64651536638,9.3450972477,8.84390190992,9.52675364333,8.92705394181,8.92731800679,8.89353800165,9.06988872239,8.76159488598,8.99268424643,8.65855266707,9.25752923679,9.66960061153,8.9493654016,8.8767190935,9.28430542607,9.42809896567,9.07026247591,9.0919036169,9.14812102052,8.86638399632,9.44435318101,9.11404445009,9.08642731541,8.92253639933,8.98178851898,9.21367413692,9.37755692792,9.48296760661,9.14128376873,3.8774477412])
  
      # Creating weights for histo: y6_PHI_4
      y6_PHI_4_weights = numpy.array([1.12303341877,2.82831166778,2.83928158273,2.78285153537,2.83030900358,2.77897390612,2.77704511225,2.77901438995,2.77108236544,2.72862645374,2.84014777632,2.70201775621,2.7019680533,2.83409885106,2.77604824809,2.80854954718,2.79770990197,2.72076697945,2.8400832427,2.72575931688,2.74552143748,2.77118177127,2.74538956442,2.76208533566,2.92904024227,2.78876698425,2.8125257806,2.74035353653,2.7404361075,2.72963454115,2.66053345639,2.73066948416,2.66454816949,2.76320204799,2.88267583742,2.78671513557,2.78092915478,2.89645957884,2.81247968515,2.7927556434,2.72184360878,2.78680732646,2.77603381821,2.84317203854,2.72957882578,2.86970578086,2.70589378214,2.73649394463,2.81844924608,2.73058571069,2.72070284666,2.71287102964,2.79098437571,2.80165487066,2.80869384597,2.74635195721,2.7997048328,2.78968728988,2.76227412658,2.73254136018,2.83224260742,2.87173317891,2.7365216019,1.14370141513])
  
      # Creating weights for histo: y6_PHI_5
      y6_PHI_5_weights = numpy.array([0.368023593536,0.877999961924,0.881054318771,0.877493036312,0.900930443929,0.857359247307,0.896633379148,0.872693447009,0.883035609721,0.882787548253,0.895136608259,0.884551985467,0.903707131972,0.879980452675,0.881523234965,0.883025607242,0.88277474508,0.89764843067,0.883484120858,0.892346316845,0.880786652446,0.882775545279,0.895877591869,0.90444411459,0.883779794124,0.88679294076,0.889546022953,0.888574982336,0.876244326891,0.890073753721,0.880770248381,0.876222321438,0.87695770366,0.885281366203,0.909486564072,0.889550824143,0.893309755578,0.893129710965,0.873465238253,0.89434841295,0.862313274879,0.887571933788,0.89587719177,0.848249790051,0.890615888058,0.87497201162,0.883025207143,0.882008955323,0.887821595653,0.890579479036,0.858818808976,0.909713820385,0.877236572762,0.898883136621,0.911035347849,0.881751691575,0.89440442683,0.889307163766,0.892151068464,0.880507783344,0.900907638278,0.898121747955,0.87924186966,0.363751174862])
  
      # Creating weights for histo: y6_PHI_6
      y6_PHI_6_weights = numpy.array([0.127990703799,0.317530319867,0.328903452581,0.322682713166,0.308312273293,0.325200579137,0.301717557756,0.322865252701,0.32161756599,0.324431433912,0.32215698731,0.31210271773,0.332114788844,0.342752065306,0.31976717892,0.331543578054,0.299740812542,0.316130583522,0.331500492326,0.315788296903,0.325525171618,0.320096269911,0.327303182661,0.333244514629,0.319797268953,0.317785435362,0.318644150917,0.313484260102,0.316668205438,0.314372465889,0.320651186098,0.318386636217,0.324073552458,0.327539304447,0.315554274421,0.34101843956,0.316385299149,0.328405017685,0.314361669465,0.317210525797,0.31638749842,0.339273517555,0.312073127532,0.321243689834,0.318040350922,0.325207676786,0.326585520383,0.326644500846,0.321546389567,0.326397182769,0.321820898637,0.316098993986,0.321234392914,0.327482823157,0.306657521421,0.330946775742,0.311566395384,0.330934179914,0.330686561936,0.314646075257,0.325486484432,0.311822810448,0.30726212115,0.125119354917])
  
      # Creating weights for histo: y6_PHI_7
      y6_PHI_7_weights = numpy.array([0.0151813254109,0.0358189585933,0.038479915497,0.0360784678563,0.0356375845763,0.0357812613965,0.0359537339298,0.0359916406719,0.0349425773471,0.0366650731185,0.0351143625719,0.0364507166736,0.0356055870112,0.0355026877035,0.0372920535663,0.0358888168005,0.0353436512139,0.034464055543,0.0372740410535,0.0355036641845,0.0337053758835,0.0361750305146,0.0367083735562,0.0360838950792,0.0342875639311,0.0358281534404,0.0358904344901,0.0354431433153,0.0362294494251,0.0348088036371,0.0356348437239,0.035646263942,0.035455686696,0.0334843852349,0.0351789444288,0.0353492376912,0.0359904043547,0.0356958088292,0.0360537834217,0.0356122547422,0.0350905121276,0.034891205225,0.0358414008933,0.0347442804529,0.0357413639742,0.0356517330739,0.0353263930642,0.0364410733995,0.0350615362055,0.03638878766,0.0347150907946,0.0363460404218,0.0365740969375,0.0350899756916,0.0362083063059,0.0370938027719,0.0364709880845,0.0353670951404,0.0360858773776,0.0356714470944,0.036167767675,0.0343931538003,0.0372935958195,0.0152274295645])
  
      # Creating weights for histo: y6_PHI_8
      y6_PHI_8_weights = numpy.array([0.00277786728319,0.0059490030784,0.00606050242435,0.0060712267141,0.00663242147481,0.00675354164184,0.00598400367894,0.00648888737884,0.00606050390951,0.00607355988945,0.00661177339278,0.00643764217489,0.00637407168626,0.00566539232263,0.00660741149844,0.00641100298569,0.00586943749206,0.00618137308568,0.00645488480111,0.00714239182371,0.00649327006532,0.00728388977493,0.0064019985031,0.00597976356715,0.00691585252707,0.00628800410013,0.00711764620457,0.00600983643007,0.00623629552844,0.0063788316016,0.00649340372909,0.00632976808739,0.00666818841429,0.00638752865751,0.0068571488848,0.00532380416439,0.00713872943643,0.00625891886395,0.00686125087736,0.00623476582086,0.00632332697887,0.00719964892697,0.00643694860844,0.0062106781245,0.0064348174139,0.00637586278077,0.00615200715559,0.00607046631577,0.00624409555195,0.00658332528724,0.00597982445842,0.00567230719495,0.0070877203718,0.00602820331711,0.00635057359564,0.00646239521979,0.00700747607055,0.00654555933183,0.0072951472346,0.00581775119767,0.00654807666615,0.00625240349778,0.0068611706791,0.00229658860493])
  
      # Creating weights for histo: y6_PHI_9
      y6_PHI_9_weights = numpy.array([0.0,0.0,2.61134688903,0.0,2.60887615076,2.60925653061,5.2184653695,0.0,2.6054808087,5.21734999887,2.60925653061,0.0,2.60541734795,2.61268994912,2.60717771051,2.60521311974,2.60351044877,2.60717771051,0.0,2.59864474052,2.60248084629,5.22949215439,0.0,0.0,0.0,0.0,2.61354647684,2.60925653061,0.0,5.21015778131,0.0,5.20959240378,0.0,2.60507850604,0.0,2.60449851331,0.0,7.82170582166,5.20731935534,2.61354647684,0.0,7.81254439801,5.20588475794,5.20901548793,0.0,0.0,0.0,2.6065511799,0.0,0.0,5.20582322025,0.0,2.60449851331,0.0,0.0,0.0,0.0,2.60541734795,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y6_PHI_10
      y6_PHI_10_weights = numpy.array([4.21612524062,12.6406071654,3.15716326349,5.26409157452,6.31808729322,8.42328913012,7.37360671095,13.6911591704,11.5809283572,9.47379881013,7.3686546891,7.37499573961,2.10789101178,10.5297030003,6.31969948992,6.31996498293,7.37092484675,2.11002265134,6.31830661353,6.31817194316,5.27285669167,8.42595560343,11.5925061612,8.42630574638,8.42695216416,12.6368787201,10.5303840476,9.48713502438,4.21517870031,9.47152480475,5.26743139967,6.31905307215,2.10448616008,5.26442247885,7.36561883421,8.42963787608,6.3223928973,6.32141172747,1.05401457255,17.8994927683,7.3796591821,9.48470326229,6.314147223,7.36689243113,6.31819887724,12.6352126552,4.21526335026,12.6367132679,9.47776966215,10.53530144,7.37574219823,10.5303840476,9.47935492478,12.6413805581,2.10711069319,8.42705990045,7.37100180124,8.42329682557,8.4269906414,14.744331476,6.3207037461,4.21600596115,5.26573070529,5.26917441901])
  
      # Creating weights for histo: y6_PHI_11
      y6_PHI_11_weights = numpy.array([5.29416342104,14.2758079809,13.3615764331,12.6712275041,13.1280454902,13.591518553,14.2796427191,14.5115560019,14.7447257571,12.2072126597,12.4364631434,14.0553528035,14.9656420253,13.1304815864,13.3565351742,12.2060983571,14.2793353252,11.2876977803,11.0545779767,13.1283298295,11.2827410546,12.6698557591,13.5867654757,9.9051517168,12.2088879562,12.8988526429,12.4425111172,15.1991729682,11.5169982155,12.6695867895,11.515050107,10.3622233028,11.9786115455,13.3576110526,10.592895483,12.439575506,12.6680997718,13.822697933,15.8926688416,14.0520252652,14.0527207437,14.052932077,12.8956057954,10.8288125706,12.4386110579,13.1295209807,13.8190514736,14.9740492468,14.510130463,13.3583795372,11.9784309516,13.8200312914,11.7510478855,12.4368627554,15.6644942364,11.5169674761,13.3593247733,13.129951332,13.1300473926,12.6665512753,13.361011597,10.5966879545,11.9777085761,5.75881610765])
  
      # Creating weights for histo: y6_PHI_12
      y6_PHI_12_weights = numpy.array([3.65559834375,8.83363366244,8.69584437511,8.16816634186,8.85985579172,8.39020037494,8.61212284086,8.74926965524,8.80584959294,8.97197693841,8.91649728372,8.58363859127,8.61261142807,7.86442283051,8.55633156755,8.97152682264,8.50115583718,7.89186835139,8.52704711214,7.81001652882,8.16890884053,8.72150097432,8.224838611,8.22355366511,8.69400928772,8.22496171959,8.86180629342,8.33478227454,7.91862908075,7.69853785507,8.22581193827,8.41798059729,7.94712102462,9.00036885656,8.69458251208,9.082270692,8.83392219819,8.25166474179,7.83791985976,8.27923337126,8.88812075414,8.27986045563,7.91933310798,8.25237646332,8.22488477672,8.52937463388,8.03020008593,8.41809601159,8.77897729643,9.44265954248,8.1971430258,8.52946311818,8.50300246601,8.91652806087,8.14125172676,8.27982967848,7.64308897753,8.36292797551,8.83249875514,8.80527252144,8.44478364522,8.16822789615,8.58391173845,3.68224981423])
  
      # Creating weights for histo: y6_PHI_13
      y6_PHI_13_weights = numpy.array([1.57271028162,4.04273430888,4.10326043904,4.40600153235,4.32478867266,4.05325061932,4.26534997633,4.35519494889,4.28509126953,4.18412558692,4.25426324859,3.96272782109,4.66787161928,3.96221687283,4.18427122538,4.00271225335,3.90171501575,4.5168973306,4.16449230891,4.25501146617,4.33530316261,4.36505952722,3.9824624392,4.22414460831,4.2654755895,4.26400949567,4.24402971949,4.28461976501,4.35574109311,4.12363343906,4.10285447184,4.37497325853,4.30539630493,4.10313118491,4.22471988023,4.254759633,4.53675027995,4.36536597481,4.10282170318,4.50678638054,3.9521629645,4.0625120117,4.45574313458,4.37550665939,3.95235411498,4.16444861737,4.41623202731,4.06296955919,4.22453419119,4.14403556677,4.20354222772,4.34553365757,4.33499003992,4.21447967604,4.2648353871,3.87102717183,4.07336025646,4.07319095175,3.58904744776,4.38541189512,4.1434093214,4.20399067281,3.74070866451,1.78444250834])
  
      # Creating weights for histo: y6_PHI_14
      y6_PHI_14_weights = numpy.array([0.608255237563,1.52478884142,1.66069913111,1.62703300422,1.48533894594,1.54170885568,1.42586035197,1.39771944962,1.6691899173,1.51921706356,1.59273898467,1.63243241939,1.49395323308,1.52775055568,1.53621979653,1.55897898167,1.66065296254,1.47389414026,1.61835677425,1.64954287904,1.47980448769,1.56737820017,1.5616852298,1.48809828794,1.43432151332,1.53621517967,1.47405380659,1.56178526172,1.5588220085,1.48808135946,1.54176464272,1.48530355003,1.69743739212,1.48813022121,1.43423263881,1.55620155698,1.56997864532,1.58435169304,1.48828834859,1.64942899655,1.56163790701,1.58726608451,1.40616291301,1.4315206196,1.57866141584,1.5390514693,1.54473520596,1.4032985375,1.5080862042,1.55887818027,1.55596224985,1.63821003215,1.56735357693,1.58718375055,1.49089610375,1.52211067917,1.55879392262,1.47391953298,1.55599072047,1.52210952495,1.60424650267,1.47974485327,1.53901953603,0.560148349026])
  
      # Creating weights for histo: y6_PHI_15
      y6_PHI_15_weights = numpy.array([0.0716009357291,0.169302744169,0.15072455264,0.173688769302,0.134074647799,0.14922776736,0.167481269439,0.167596588545,0.173629928201,0.175171376003,0.17214684888,0.204112336161,0.158440771419,0.160070717183,0.167607576944,0.17215689204,0.152248277219,0.170447428081,0.179621677359,0.207121266848,0.163037821064,0.172208525697,0.184272605602,0.179635383318,0.179708166688,0.188891513888,0.158390437465,0.181073681962,0.18284612244,0.176629288347,0.140158203254,0.176770955978,0.183042023135,0.181116572162,0.161437886624,0.14485651158,0.184290683289,0.196470436764,0.194854551424,0.164476592326,0.166091650583,0.185948395436,0.15527445851,0.184222862422,0.155342870152,0.150777131536,0.190531384657,0.156966435556,0.181309400831,0.156895070043,0.179734278904,0.185843473955,0.178170027221,0.201061933132,0.167461655739,0.179864603672,0.155400529705,0.167502182843,0.155326210322,0.181207905839,0.175188508453,0.159961660283,0.179797964353,0.0716123494849])
  
      # Creating weights for histo: y6_PHI_16
      y6_PHI_16_weights = numpy.array([0.013179873801,0.0294304573743,0.0368313442185,0.0352153676656,0.032320659496,0.0328650978988,0.0325019682217,0.0328627562946,0.031418494822,0.0306920506042,0.0312365467768,0.0299701817579,0.0317783739827,0.0279830532223,0.0321442188425,0.0352074185352,0.0335858306049,0.0335839242001,0.0305197886359,0.0321405408292,0.0341278349717,0.0319592860221,0.0283530228489,0.030877083559,0.0310530427972,0.0334072678725,0.0343082116771,0.0306976696842,0.0337630145638,0.0353894050937,0.0321372826101,0.033044873798,0.0296146738486,0.0297877869592,0.0344876409572,0.0314204936586,0.0355640356255,0.0359289407625,0.0321425319631,0.0308732206822,0.0368331004217,0.030152665137,0.0312370435975,0.0296108186745,0.0324985829089,0.0297931056361,0.0324935530812,0.0308762709299,0.0344859386725,0.0307023605955,0.0325036820603,0.0321427360832,0.0348428946792,0.0395498505165,0.0301544367455,0.0299710483055,0.0314149169431,0.0328618358284,0.03322553165,0.0303337466345,0.0316002580037,0.0301548411344,0.0357442967913,0.0106542263458])
  
      # 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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=4, linestyle="dashdot",\
               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=3, linestyle="dashed",\
               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()
  