text: def selection_3():
  
      # 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(0.0,1000.0,101,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([5.0,15.0,25.0,35.0,45.0,55.0,65.0,75.0,85.0,95.0,105.0,115.0,125.0,135.0,145.0,155.0,165.0,175.0,185.0,195.0,205.0,215.0,225.0,235.0,245.0,255.0,265.0,275.0,285.0,295.0,305.0,315.0,325.0,335.0,345.0,355.0,365.0,375.0,385.0,395.0,405.0,415.0,425.0,435.0,445.0,455.0,465.0,475.0,485.0,495.0,505.0,515.0,525.0,535.0,545.0,555.0,565.0,575.0,585.0,595.0,605.0,615.0,625.0,635.0,645.0,655.0,665.0,675.0,685.0,695.0,705.0,715.0,725.0,735.0,745.0,755.0,765.0,775.0,785.0,795.0,805.0,815.0,825.0,835.0,845.0,855.0,865.0,875.0,885.0,895.0,905.0,915.0,925.0,935.0,945.0,955.0,965.0,975.0,985.0,995.0])
  
      # Creating weights for histo: y4_PT_0
      y4_PT_0_weights = numpy.array([0.0,0.0,278.483678661,248.003225076,230.230800478,214.828853825,204.05734316,192.683953017,181.318802866,170.309812406,159.812581504,149.094270792,139.747478892,130.83052662,122.548213797,113.750021422,105.303948742,99.9161134109,92.4321198966,86.2500852541,81.0915297246,75.1141749047,70.3240990559,65.92294287,60.4532276101,57.6201500653,53.6734334856,49.718556913,47.2825590241,44.429001497,40.8712445802,38.2551188474,34.9593817036,33.1293272895,30.1815858441,27.7046639906,26.3781811402,24.6054426765,23.0292240425,20.9985578023,19.5983790157,18.3087441333,16.7980254425,16.1839139747,15.0334749717,13.6824281426,12.8267648841,11.6026339449,10.9844264807,10.0960112506,9.37135987861,8.9373842547,7.8033252375,7.61908939716,6.89443802515,6.40724244737,5.72353103988,5.76037500795,5.1339795508,4.60175201204,4.41342417525,4.1022724449,3.9057562152,3.41037184451,3.02552817802,2.90270548446,2.58746095766,2.68981326896,2.45235627474,2.1493941373,1.84643199985,1.70723292048,1.5434694624,1.58031623047,1.44521154755,1.26916610012,1.21184894979,1.08083826333,0.990768341382,0.953921573314,0.859757654918,0.847475265563,0.741029357811,0.614112667799,0.528136742307,0.560889513923,0.442161216815,0.429878827459,0.446255213267,0.376655713583,0.356185291323,0.397126135843,0.323432639707,0.290679948091,0.216986451956,0.18423376034,0.196516029696,0.208798299052,0.14329295582,0.110540264204])
  
      # Creating weights for histo: y4_PT_1
      y4_PT_1_weights = numpy.array([0.0,0.0,6809.77402574,4213.81458008,1126.72324386,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_2
      y4_PT_2_weights = numpy.array([0.0,0.0,676.722027941,1159.06193757,1963.34662375,2131.57168452,1623.41148399,1179.9650223,718.594311113,242.644868736,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_3
      y4_PT_3_weights = numpy.array([0.0,0.0,42.2970694239,63.4964674483,92.213682441,135.054201171,195.977033757,296.245969877,471.058868672,729.682968809,788.045776125,635.280335776,510.201681459,407.291225017,325.76952275,259.484673259,197.982356416,142.581483916,90.5464870584,30.0486962624,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_4
      y4_PT_4_weights = numpy.array([0.0,0.0,2.19078692842,2.98506255452,4.0549636867,4.73887174667,5.77210915065,6.89005844957,7.80296199351,9.48070965224,10.9399916911,13.1084903404,15.5084375672,18.5505461715,23.4575069366,29.101073182,38.4968464732,52.2696922747,74.6204611914,107.718225063,115.891155254,97.6439469307,82.3083442361,69.1591614023,57.2331182837,47.1859364141,37.8228870706,28.1123922948,17.6262629991,6.17861321168,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_5
      y4_PT_5_weights = numpy.array([0.0,0.0,0.310068427,0.448197678426,0.534178949103,0.626671430571,0.687447665841,0.79076762589,0.842944133697,0.917295727339,0.969717895919,1.0955650289,1.18772262753,1.30876137098,1.38968659085,1.58228703776,1.77709803153,1.9926477558,2.22739262871,2.54163276774,2.92228413606,3.39830749821,3.97776044753,4.77563343119,5.8403048174,7.24825712696,9.38842257677,12.3461502811,17.4087266984,24.527751855,26.7432759476,23.120363685,20.0608828082,17.3329279467,14.8839621036,12.3627743937,10.1404806264,7.75536257842,4.95440965817,1.65866593293,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_6
      y4_PT_6_weights = numpy.array([0.0,0.0,0.0981808374462,0.118238827402,0.133678646889,0.167774806626,0.183468540917,0.188650302284,0.211574510539,0.211561015068,0.212131423679,0.251724239127,0.245928883638,0.256511132983,0.263384326825,0.283452293433,0.290208426552,0.312325305689,0.345448691821,0.361877579447,0.364803297782,0.426518990355,0.456116359569,0.468685142373,0.531418993324,0.58072574946,0.619594607816,0.713192602941,0.837832082736,0.931545139253,1.04551319962,1.24595094687,1.46126070413,1.76933633571,2.06570289602,2.5535791992,3.24989056629,4.11628286623,5.72377050693,7.90164876483,8.54780096179,7.70818267838,6.98209630168,6.20982742057,5.61538087484,5.16264278176,4.72045714802,4.1658792252,3.72629171972,3.39264266877,3.09324412577,2.76172336072,2.42296102543,2.15534981685,1.92518304456,1.62254058999,1.38394864459,1.04612699368,0.700824552785,0.219601017411,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_7
      y4_PT_7_weights = numpy.array([0.0,0.0,0.0102342220016,0.0113397067885,0.0134730515733,0.0145110243352,0.0143335459546,0.0147432868234,0.0152250994716,0.0157848805668,0.0165397336536,0.0150923385351,0.0158466132707,0.0167966221682,0.0170578902263,0.0170553798468,0.018046992312,0.0188711679145,0.0188058016563,0.019370213752,0.0198651985583,0.0203132028071,0.0201259637023,0.0212324333626,0.0229354848562,0.0227619417784,0.0244882320937,0.0261886013788,0.0267964065782,0.0292738619437,0.0306435819818,0.0323223385341,0.0330841653629,0.0360394352097,0.0393034691589,0.0409883319264,0.0448849563574,0.0488146851081,0.0531850504162,0.0565898206811,0.0599634521823,0.0689039654188,0.0755636794609,0.0819048393396,0.0898479063075,0.100669695408,0.115327252143,0.127027003507,0.145527955409,0.164926357495,0.190419282011,0.2152896408,0.2521319532,0.300361707318,0.360588979847,0.442693476079,0.546366281035,0.68860337622,0.928402032038,1.24338215261,1.36981475358,1.27789756536,1.1767074796,1.08916941578,0.996949221823,0.922764365145,0.854908682227,0.785095825328,0.726622842855,0.660228963544,0.604713954082,0.554545759533,0.509643075454,0.453193064888,0.403064684371,0.35164850852,0.297659843306,0.227355806984,0.152754663172,0.0501422106544,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_8
      y4_PT_8_weights = numpy.array([0.0,0.0,0.00138718717612,0.00192856664779,0.00240512632334,0.00204094291875,0.00158320221463,0.00187019858253,0.00173013809438,0.00232465454232,0.00215444033889,0.00217935360991,0.00187268293029,0.00187037083856,0.00187238593713,0.00204007866866,0.00206465782238,0.00210072467144,0.00195477926387,0.00232261271437,0.00209845415875,0.00221232727523,0.00300105200382,0.0023242996355,0.0023570267965,0.00221267921212,0.00218056534199,0.00277743249078,0.0022098934163,0.00248973373407,0.00249327389251,0.00275010169545,0.00340467015986,0.00289588227182,0.00306068971482,0.00331884210725,0.00391162857006,0.00393635919029,0.00394082299745,0.00357427107243,0.00496126772744,0.00467037480932,0.00507763558644,0.00518937332212,0.0053332694769,0.00490356492685,0.0063190031316,0.00661569929602,0.00756405042197,0.0071500033512,0.007221586127,0.00816172093725,0.00844820647692,0.00824217341371,0.00978051035699,0.0101402967779,0.0125349867617,0.0130600305702,0.0133214335527,0.0151865372176,0.0167911763984,0.0186476509271,0.0212582207822,0.0221994217979,0.024309929424,0.026790030185,0.0320429481631,0.0355733355265,0.0370931386118,0.0444198261624,0.0510893718029,0.0589463257866,0.0659617348087,0.0772641361129,0.0940629006865,0.11195719877,0.138936398747,0.173419502091,0.226743732555,0.296103514576,0.329245129527,0.304697754074,0.293926703228,0.273925402037,0.254454530626,0.241117904389,0.228990188799,0.214565973618,0.198114186151,0.18725567082,0.178203764867,0.169454049451,0.153463343861,0.14687974804,0.137595608287,0.132936453879,0.123254892733,0.119417429294,0.113038164766,0.103927469017])
  
      # Creating weights for histo: y4_PT_9
      y4_PT_9_weights = numpy.array([0.0,0.0,1861440.13419,681871.978618,167532.480608,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_10
      y4_PT_10_weights = numpy.array([0.0,0.0,107706.690554,166262.3717,269770.513045,249558.177493,150055.319967,89662.7518992,47944.3223333,14401.5997751,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_11
      y4_PT_11_weights = numpy.array([0.0,0.0,6261.72143248,5597.17156921,5792.90631755,6875.56438448,8995.82948139,13388.2806091,22117.8536592,34984.7986737,36143.0464251,26946.6601014,20328.9336559,15501.8370399,11890.7534024,9100.12503337,6871.77587084,4883.30955172,2931.74473868,936.297718158,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_12
      y4_PT_12_weights = numpy.array([0.0,0.0,327.362002999,252.542672065,215.468477889,200.646725343,190.84911703,193.367152225,196.741754884,222.941831842,245.262714233,284.691247465,333.648242548,415.314227054,526.326755312,698.288492443,971.904570744,1408.91517268,2210.81812811,3285.9274406,3487.31254903,2923.55073707,2452.41285619,2048.64776691,1674.23564612,1394.49452377,1114.45447803,833.301067703,518.12579029,171.100168376,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_13
      y4_PT_13_weights = numpy.array([0.0,0.0,44.8420123372,33.5821162649,26.8188663111,23.6324223731,20.8491567782,19.5086737438,20.0626798671,19.4787751933,20.1352325503,20.8993841585,22.2205274973,23.8127542639,25.0524063027,27.837219316,29.7122474119,34.0371968411,39.7329829783,45.4705008654,55.8355825347,65.4844400875,81.3103430318,98.2196208947,127.476387222,169.44892759,226.612747314,321.461848235,472.654455087,678.034437571,740.854162517,646.576938885,561.013865351,483.356157904,414.224300976,349.956859935,283.322596963,216.189943905,137.73018827,46.9009586935,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_14
      y4_PT_14_weights = numpy.array([0.0,0.0,11.062155629,8.38280842671,6.42784089764,5.74886071633,4.97658842649,4.41923656203,4.43359896062,4.0149848656,4.00005304965,3.99473592271,4.01997111488,4.11082780999,4.15915903183,4.20131130588,4.33721737819,4.8862896065,5.02460031293,5.41220039909,5.85638056394,6.28639839575,7.22265518945,7.59621990725,8.75880086311,9.73205054791,11.415602216,12.8780699007,14.7987415762,17.3996592325,20.7235868825,24.8852313441,29.6015421749,36.3084129669,45.5754726845,59.3698389921,76.9427896243,105.967877521,148.684567283,209.452941472,230.743687278,208.199338382,186.049695746,169.691373894,152.174710937,138.383499509,124.252215108,112.337657086,101.725064155,91.4049901532,82.7841652714,75.0386656788,66.8473276857,59.9074767043,52.2071457587,44.9191807069,37.7582649791,29.7013710408,18.8115988216,6.32846217926,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_15
      y4_PT_15_weights = numpy.array([0.0,0.0,0.714433798809,0.580528094946,0.482894939474,0.402059669969,0.373093694457,0.321415235849,0.290961807813,0.271245144851,0.252850031701,0.262064958432,0.242303983848,0.210135046139,0.257435516502,0.248311103644,0.210206771885,0.296985470615,0.248347261928,0.260441380604,0.274189681669,0.277263962915,0.236233055316,0.325937502736,0.298425775574,0.277044886257,0.323102504247,0.356446113512,0.347316146931,0.351812299502,0.436905029641,0.449395589629,0.485841130592,0.511723371711,0.621361549958,0.591029594849,0.676273102667,0.734152470565,0.857845586301,0.914155141139,1.072384735,1.11066087769,1.34357564243,1.4336204031,1.63449857418,1.71510673185,2.10157261032,2.52397226007,2.81674328186,3.24511968318,3.85823773532,4.59900752626,5.56516395094,6.63794240947,8.1629558282,10.5743753869,13.3414731093,17.6245754021,23.7342854657,32.3277623777,35.6024975857,32.9840234066,30.9052293332,28.1614773367,26.4515804699,24.2293230894,22.2703240088,20.4078297326,18.9324063362,17.433657302,16.0246186235,14.8355310197,13.3557237269,11.9388743864,10.5732091051,9.55493748642,7.74899429292,6.10803922001,3.79952070052,1.49147720162,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y4_PT_16
      y4_PT_16_weights = numpy.array([0.0,0.0,0.0734932472288,0.0536253152834,0.0460443597745,0.0348501889178,0.0301608054768,0.0310572232429,0.0243748345088,0.0258184368726,0.0234747002671,0.0243737214917,0.0241917412792,0.0258207322296,0.0209431564775,0.0198659177312,0.0184193999559,0.0225699059887,0.0205754949819,0.0236504183145,0.0216667947152,0.0231070195152,0.024728373397,0.0227498026641,0.0236490472624,0.0328571297069,0.0297875519372,0.0303363386633,0.0297895353413,0.0354005508134,0.0326810573427,0.0343111844177,0.0361195636758,0.0371939216721,0.0411714248028,0.0436997065667,0.0429638829105,0.0451333033309,0.0476698268127,0.054348352723,0.0570589150984,0.0623013024062,0.0682577922071,0.0727636626301,0.0722183998161,0.0821564096224,0.08594486546,0.0891865950011,0.0960540257074,0.107254597375,0.12457761007,0.122404839044,0.143186099683,0.151671063811,0.169379164998,0.180922961853,0.201878222872,0.224078214372,0.242288290098,0.275518896719,0.299195850284,0.343770411356,0.372668801319,0.407874531786,0.4813841084,0.549090206172,0.598892519725,0.700748599304,0.812886799669,0.944717314065,1.08262628798,1.25944656888,1.47899129922,1.81717286301,2.16624581851,2.64560261836,3.40412874124,4.35030992305,5.74926836444,7.57580636084,8.31811940706,7.91943978252,7.33974276299,6.93563669895,6.54566094473,6.12767875469,5.75078191357,5.39464341803,5.07162584494,4.86630693592,4.47268017769,4.22764312062,3.98370367551,3.78489225749,3.51511695237,3.34217374504,3.17551850632,2.93788050927,2.78497314926,2.60727708956])
  
      # 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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_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=y4_PT_0_weights+y4_PT_1_weights+y4_PT_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=y4_PT_0_weights+y4_PT_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=y4_PT_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"p_{T} [ j_{2} ]   ( GeV ) ",\
                 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=(y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_16_weights).max()*1.1
      ymin=0 # linear scale
      #ymin=min([x for x in (y4_PT_0_weights+y4_PT_1_weights+y4_PT_2_weights+y4_PT_3_weights+y4_PT_4_weights+y4_PT_5_weights+y4_PT_6_weights+y4_PT_7_weights+y4_PT_8_weights+y4_PT_9_weights+y4_PT_10_weights+y4_PT_11_weights+y4_PT_12_weights+y4_PT_13_weights+y4_PT_14_weights+y4_PT_15_weights+y4_PT_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_3.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_3.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_3.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_3()
  