text: def selection_10():
  
      # 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(0.0,4000.0,401,endpoint=True)
  
      # Creating data sequence: middle of each bin
      xData = numpy.array([5.0,15.0,25.0,35.0,45.0,55.0,65.0,75.0,85.0,95.0,105.0,115.0,125.0,135.0,145.0,155.0,165.0,175.0,185.0,195.0,205.0,215.0,225.0,235.0,245.0,255.0,265.0,275.0,285.0,295.0,305.0,315.0,325.0,335.0,345.0,355.0,365.0,375.0,385.0,395.0,405.0,415.0,425.0,435.0,445.0,455.0,465.0,475.0,485.0,495.0,505.0,515.0,525.0,535.0,545.0,555.0,565.0,575.0,585.0,595.0,605.0,615.0,625.0,635.0,645.0,655.0,665.0,675.0,685.0,695.0,705.0,715.0,725.0,735.0,745.0,755.0,765.0,775.0,785.0,795.0,805.0,815.0,825.0,835.0,845.0,855.0,865.0,875.0,885.0,895.0,905.0,915.0,925.0,935.0,945.0,955.0,965.0,975.0,985.0,995.0,1005.0,1015.0,1025.0,1035.0,1045.0,1055.0,1065.0,1075.0,1085.0,1095.0,1105.0,1115.0,1125.0,1135.0,1145.0,1155.0,1165.0,1175.0,1185.0,1195.0,1205.0,1215.0,1225.0,1235.0,1245.0,1255.0,1265.0,1275.0,1285.0,1295.0,1305.0,1315.0,1325.0,1335.0,1345.0,1355.0,1365.0,1375.0,1385.0,1395.0,1405.0,1415.0,1425.0,1435.0,1445.0,1455.0,1465.0,1475.0,1485.0,1495.0,1505.0,1515.0,1525.0,1535.0,1545.0,1555.0,1565.0,1575.0,1585.0,1595.0,1605.0,1615.0,1625.0,1635.0,1645.0,1655.0,1665.0,1675.0,1685.0,1695.0,1705.0,1715.0,1725.0,1735.0,1745.0,1755.0,1765.0,1775.0,1785.0,1795.0,1805.0,1815.0,1825.0,1835.0,1845.0,1855.0,1865.0,1875.0,1885.0,1895.0,1905.0,1915.0,1925.0,1935.0,1945.0,1955.0,1965.0,1975.0,1985.0,1995.0,2005.0,2015.0,2025.0,2035.0,2045.0,2055.0,2065.0,2075.0,2085.0,2095.0,2105.0,2115.0,2125.0,2135.0,2145.0,2155.0,2165.0,2175.0,2185.0,2195.0,2205.0,2215.0,2225.0,2235.0,2245.0,2255.0,2265.0,2275.0,2285.0,2295.0,2305.0,2315.0,2325.0,2335.0,2345.0,2355.0,2365.0,2375.0,2385.0,2395.0,2405.0,2415.0,2425.0,2435.0,2445.0,2455.0,2465.0,2475.0,2485.0,2495.0,2505.0,2515.0,2525.0,2535.0,2545.0,2555.0,2565.0,2575.0,2585.0,2595.0,2605.0,2615.0,2625.0,2635.0,2645.0,2655.0,2665.0,2675.0,2685.0,2695.0,2705.0,2715.0,2725.0,2735.0,2745.0,2755.0,2765.0,2775.0,2785.0,2795.0,2805.0,2815.0,2825.0,2835.0,2845.0,2855.0,2865.0,2875.0,2885.0,2895.0,2905.0,2915.0,2925.0,2935.0,2945.0,2955.0,2965.0,2975.0,2985.0,2995.0,3005.0,3015.0,3025.0,3035.0,3045.0,3055.0,3065.0,3075.0,3085.0,3095.0,3105.0,3115.0,3125.0,3135.0,3145.0,3155.0,3165.0,3175.0,3185.0,3195.0,3205.0,3215.0,3225.0,3235.0,3245.0,3255.0,3265.0,3275.0,3285.0,3295.0,3305.0,3315.0,3325.0,3335.0,3345.0,3355.0,3365.0,3375.0,3385.0,3395.0,3405.0,3415.0,3425.0,3435.0,3445.0,3455.0,3465.0,3475.0,3485.0,3495.0,3505.0,3515.0,3525.0,3535.0,3545.0,3555.0,3565.0,3575.0,3585.0,3595.0,3605.0,3615.0,3625.0,3635.0,3645.0,3655.0,3665.0,3675.0,3685.0,3695.0,3705.0,3715.0,3725.0,3735.0,3745.0,3755.0,3765.0,3775.0,3785.0,3795.0,3805.0,3815.0,3825.0,3835.0,3845.0,3855.0,3865.0,3875.0,3885.0,3895.0,3905.0,3915.0,3925.0,3935.0,3945.0,3955.0,3965.0,3975.0,3985.0,3995.0])
  
      # Creating weights for histo: y11_M_0
      y11_M_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,134.695387362,131.931889673,133.774188132,136.435385907,139.812983083,144.009379573,144.828178889,148.10347615,151.788173068,157.724568104,158.850467163,156.905768789,153.732871442,173.077355266,164.889262113,166.629260658,157.62226819,172.872655437,170.211557662,171.849156293,163.558663225,164.172762712,170.62095732,156.393969217,158.031667847,161.921064595,157.826968019,158.133967762,152.095172812,158.543367419,153.22107187,152.197572726,157.62226819,152.606972384,154.039871185,155.268170158,142.985880429,151.378773411,144.521179145,148.308175979,146.363477605,150.252874352,151.788173068,141.041182056,149.229375208,143.395280087,146.772877262,137.868284709,136.230686078,134.900087191,140.631782398,134.695387362,132.955388817,130.601290786,132.341289331,130.601290786,135.207086934,126.507194209,125.893094723,120.980198831,112.996705507,120.058999602,122.310797719,120.570799174,115.964903025,116.579002512,122.310797719,122.310797719,107.674409958,105.217912012,107.162610386,114.22490448,105.934411413,100.202716206,102.045014666,102.454414323,102.556814238,99.1791870623,104.399112697,99.3838868911,105.729711584,97.6439083461,93.0380621977,92.7310024545,94.7780507427,93.4474718554,94.8804006571,91.5027834816,93.2427620265,91.8098332248,94.9827505715,85.1569487882,84.4404793874,82.1887412704,83.4169602433,85.0545988738,87.3063469908,85.3616486171,79.7322833246,79.1181738381,80.6534525542,76.4570160635,77.0711355499,76.2523162347,77.2758353788,75.9452564914,74.0005681177,73.3864586312,73.7958682888,73.3864586312,71.7488200007,74.3076278609,69.087672226,67.0406239379,67.2453337667,65.8124049649,67.4500335955,69.1900221404,68.7806124828,57.828941641,65.7100450505,61.820668303,58.1359913843,59.9783298436,57.828941641,62.0253781318,61.308908731,54.2466146367,55.7818933529,58.9548106995,52.9160357494,53.7348550647,52.7113359206,52.4042761774,53.7348550647,52.5066260918,53.1207455782,49.4360686595,48.8219491731,50.7666375468,47.9007799434,45.6490418264,46.5702110561,45.8537416552,44.1137531103,47.6960801146,46.7749108849,43.1925838806,43.0902339662,43.0902339662,44.7278725967,41.8620049933,42.3737745653,45.0349223399,38.5867377322,42.9878840518,41.4526053356,36.8467591872,39.8149667051,40.2243763627,39.4055570474,36.7444092728,38.6890976466,32.9573724397,34.3903012414,35.4138303855,34.9020708134,37.1538089304,30.1938747506,34.799710899,35.9255899575,34.6973609846,30.8079842371,32.1385631244,30.0915148362,33.2644321829,30.296224665,29.272705521,28.8632958633,32.8550225253,28.2491763769,26.4068379175,27.6350668904,29.0679956921,28.2491763769,27.7374168048,26.4068379175,27.3280071472,25.8950783455,26.8162475751,23.8480400573,25.5880286022,23.9503899717,26.0997881743,21.8010017691,22.9268708276,22.51746117,24.1550998005,25.3833187734,24.6668593726,22.3127613411,23.3362804852,20.9821824538,22.3127613411,19.3445438233,19.7539534809,20.4704228818,19.7539534809,21.3915921115,20.777472625,18.7304343368,19.3445438233,17.8092651072,18.8327842512,19.4469037377,17.0928057063,17.6045652783,18.2186747648,17.7069151927,19.1398439945,16.6833960487,19.3445438233,18.2186747648,15.762226819,17.8092651072,16.8880958775,17.3998554495,17.6045652783,15.4551670758,14.022238274,16.4786862198,16.6833960487,14.3292980172,14.7386976749,14.1245881884,15.8645767334,14.6363477605,13.510478702,13.2034189588,13.8175384452,11.9751999858,12.1798998147,12.7940093011,12.5893094723,11.9751999858,12.9987191299,12.7940093011,11.9751999858,13.1010690443,11.6681402426,14.6363477605,11.770490157,12.3845996435,10.2352114409,12.2822497291,11.258730585,12.2822497291,10.0305016121,9.41639312562,10.3375613553,9.41639312562,9.00698446799,10.0305016121,9.41639312562,7.26699892304,10.0305016121,9.62109795444,10.0305016121,8.29052006712,10.2352114409,7.88111140949,7.88111140949,7.57405566626,7.9834643239,8.69992872476,8.59757681035,7.16464700863,7.77875949508,7.06229509422,6.65288643658,7.26699892304,6.75523835099,6.55053452217,6.8575912654,5.83407012131,7.57405566626,6.65288643658,6.8575912654,5.62936529249,6.44818260776,6.24347777895,5.83407012131,6.44818260776,7.06229509422,6.03877395013,5.32230954926,6.44818260776,6.8575912654,6.24347777895,5.11760472045,5.42466146367,5.32230954926,5.7317172069,6.14112586454,5.7317172069,4.50349223399,6.65288643658,5.32230954926,4.19643649077,6.03877395013,3.88937974754,4.6058441484,4.6058441484,4.91290089163,4.50349223399,4.70819706281,3.68467591872,2.96821051786,3.58232400431,5.42466146367,5.11760472045,4.91290089163,3.88937974754,3.68467591872,5.62936529249,2.96821051786,4.09408357636,3.88937974754,4.70819706281,4.50349223399,4.19643649077,3.4799710899,4.29878840518,3.07056343227,4.09408357636,3.58232400431,3.3776191755,3.17291534668,3.17291534668,3.3776191755,2.14939420259,3.3776191755,2.66115477463,3.78702783313,3.479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 
      # Creating weights for histo: y11_M_1
      y11_M_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.304262344601,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.303102055981,0.0,0.0,0.0,0.304383383345,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.303284616073,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_2
      y11_M_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.00387745632,0.250384061101,0.50113298103,0.250748919929,0.501597318638,0.753699893745,0.752535175939,0.250924324881,0.752759338922,0.501922406615,1.0037061834,0.503266351506,0.752932471254,0.251018225412,0.752746839512,0.753332762296,0.0,0.753359620534,0.250852324144,0.502318255717,0.251143322819,0.25046763154,0.0,1.00403839914,0.502516076968,0.752548605058,1.00291644791,0.251018225412,0.502347593177,0.752784441045,0.501741320112,0.754272180809,0.251741745014,0.753598555548,0.0,0.250917507021,0.251018225412,0.0,0.250748919929,0.0,0.25046763154,0.502564731698,0.0,0.250774331954,0.0,0.0,0.250889099269,0.502072916046,0.75254777865,0.0,0.250889099269,0.0,0.502248734202,0.250657085416,0.250384061101,0.251148591165,0.0,0.250384061101,0.0,0.25067898521,0.0,0.0,0.0,0.250657085416,0.0,0.250852324144,0.0,0.0,0.0,0.0,0.250460400476,0.0,0.0,0.0,0.0,0.0,0.501829332491,0.250811107079,0.251462006139,0.0,0.0,0.251366349492,0.0,0.502210926068,0.251757240151,0.502946015372,0.251421408879,0.250924324881,0.0,0.0,0.0,0.251421408879,0.250811107079,0.251741745014,0.0,0.0,0.0,0.0,0.0,0.0,0.250657085416,0.0,0.0,0.0,0.0,0.250852324144,0.0,0.0,0.0,0.0,0.0,0.251421408879,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_3
      y11_M_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.26055976087,6.18831153631,5.08671631375,4.5380632354,4.67859895186,5.49826147513,3.16364158028,3.16265437403,3.16613194009,3.30206881919,3.3010440341,3.43801179216,3.98635611465,2.88597966525,1.92497601959,3.02432971519,2.33707775404,3.29927681303,3.29942103452,3.98865654898,1.3765240388,1.78624104046,1.92493133124,3.71295584307,2.06281824739,3.1630159434,2.06178939972,1.37576230558,2.74926277132,2.20020952912,1.37631278479,0.96256245652,1.51189926661,1.3760446547,1.23712076573,2.33814824312,1.92526039999,1.78779294493,1.64847701727,0.961555140511,1.23914799173,1.37562011538,1.09993261285,1.37416165018,0.963308853511,0.963120756188,0.823884455033,0.96264705969,0.824105256102,1.09997526991,0.962271068173,0.824211390931,1.23770679248,0.825499634152,0.412675573478,1.92530915092,0.550430354303,0.824643547578,0.54935783393,0.825551736704,0.550299437754,0.961873951254,0.412642971296,0.0,0.412883272736,0.687667258103,0.275216852965,0.412582438897,0.275041451195,0.550323102266,0.137557847397,0.687464129245,0.412358997152,0.413019369071,0.275440904096,0.412468178914,0.137907533727,0.137721569257,0.275036271409,0.687691125744,0.275164750412,0.962271271302,0.274861682155,0.549886578349,0.412208884926,0.0,0.137560792765,0.0,0.137721569257,0.2750094584,0.137855431174,0.412800700855,0.274699686891,0.550034253029,0.0,0.137557847397,0.137399508451,0.275350308625,0.137201559379,0.41256761049,0.0,0.275422520934,0.0,0.274759406775,0.2748429943,0.0,0.275344824146,0.0,0.137399508451,0.0,0.0,0.0,0.0,0.0,0.137719436404,0.137448767199,0.137719436404,0.137700951677,0.137360710839,0.13749487745,0.137399508451,0.137475478644,0.0,0.0,0.0,0.137201559379,0.0,0.2752772838,0.0,0.137399508451,0.137309319238,0.0,0.137427641798,0.13711858124,0.0,0.0,0.0,0.0,0.137592683996,0.0,0.137448767199,0.137700951677,0.0,0.0,0.0,0.0,0.137309319238,0.0,0.137907533727,0.0,0.0,0.0,0.0,0.0,0.13734811685,0.0,0.0,0.0,0.137201559379,0.137700951677,0.0,0.137623254889,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.137592683996,0.137399508451,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.136871982806,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13774320248,0.0,0.0,0.0,0.0,0.0,0.0,0.137427641798,0.137427641798,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13734811685,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.137855431174,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.137399508451,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_4
      y11_M_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.71280025409,4.56447250654,4.24333939078,4.9593651599,3.69992659199,3.60177661402,3.42861951842,3.33106276727,3.1826378187,2.88683308301,3.00999278206,3.05938392486,2.26952045651,2.5412724502,2.78797363784,2.46699083292,2.41772795538,2.17149573758,2.34385117533,1.85061414197,2.0227130492,1.65317583187,1.62822523306,1.33189040111,1.70207295302,1.2582911951,1.35643816685,1.25827816816,1.03628307367,1.23354702319,1.28250527075,0.813994973241,1.01145873598,0.98694113262,1.08556298645,1.15982756849,1.03618988095,0.691317671814,0.888149527742,0.789403917983,1.03623597627,0.789438589686,0.641648754563,0.789506029153,0.715434746862,0.567451010745,0.764563747545,0.690721739402,0.567388681846,0.616528204288,0.542488487276,0.616966310309,0.443999808861,0.419677308986,0.468866534216,0.271379924231,0.617032847911,0.517897331811,0.246650883614,0.370037051003,0.370078135969,0.320653824572,0.419462765301,0.246551177417,0.345506921743,0.369803768566,0.320591295259,0.32059269816,0.246789971252,0.246789069387,0.296037216469,0.222087385344,0.320586986348,0.320929995704,0.345267125835,0.19735353478,0.246688060497,0.24680379985,0.271235726023,0.221943688173,0.172769193407,0.0739970587935,0.296007555129,0.0986705044381,0.222166949887,0.197209035951,0.123371937963,0.074103889724,0.123300289791,0.172720292277,0.0987001757996,0.0494186296468,0.0740954522751,0.123288164716,0.0740530646157,0.197408147722,0.172758371026,0.0493544368937,0.0493449272273,0.0,0.0987648796096,0.0,0.148161704163,0.0987009273538,0.148071617861,0.0492850934888,0.123420939299,0.0493020385318,0.0493487451229,0.0493558698571,0.0986543410115,0.0740470321404,0.0493086822714,0.0493917240049,0.074091003074,0.0246945893706,0.123378251018,0.0986120936423,0.0493544368937,0.0493483943976,0.0493757108891,0.0492109802197,0.0740068991438,0.0986514149603,0.0,0.0246986177013,0.0246184218532,0.0246986177013,0.0247223668154,0.0247143201747,0.0493677544349,0.0494076669756,0.0739895332304,0.0246534442809,0.0,0.0247143201747,0.0493473923252,0.0493581245198,0.0,0.0246550475967,0.0246933468009,0.0246572621765,0.0,0.0246591661139,0.0740620832667,0.0,0.0,0.0,0.024597719039,0.0247223668154,0.0,0.0,0.0246054850995,0.0,0.0,0.0,0.0246613907145,0.0,0.0,0.0,0.0246534442809,0.0246965534323,0.0,0.0,0.0,0.0,0.0,0.0,0.0247043194929,0.0246578834613,0.0,0.0,0.0,0.0,0.0246550475967,0.0,0.0246550475967,0.0,0.0,0.0,0.0,0.0246054850995,0.0,0.0,0.0,0.0,0.0,0.0247143201747,0.0,0.0,0.0493020385318,0.0,0.0246054850995,0.0,0.0,0.0,0.0,0.0,0.0,0.0247143201747,0.0,0.0,0.0,0.0494189202478,0.0247109632324,0.0,0.0,0.0,0.0,0.0,0.0,0.0246550475967,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0247002410585,0.0,0.0,0.0,0.0,0.0,0.0246965534323,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0492541394747,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0246933468009,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0493558698571,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0246613907145,0.0,0.0,0.0,0.0246698582256,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0246441951534,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_5
      y11_M_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.56285442336,1.55028589755,1.56286442617,1.38644793652,1.5061745231,1.18472634811,1.23520951001,1.12160964215,1.14684972268,1.17847259376,0.894743800081,0.964142368275,0.825687227825,0.86330618097,0.844306150942,0.819307838232,0.882229689531,0.712009738184,0.7752442772,0.69331689433,0.73739145845,0.636353214462,0.705746181085,0.617632662836,0.598587420125,0.611374907364,0.497780941154,0.586183040358,0.453646860338,0.604821368918,0.560879542035,0.390805631657,0.441150954895,0.34663914175,0.390702002586,0.47261418119,0.378148380952,0.277315994713,0.371714976206,0.277318495414,0.277346903384,0.365458120987,0.270988219599,0.245728133455,0.258434497937,0.264607129529,0.277328998361,0.308789723954,0.233105292403,0.176480507604,0.207955437182,0.220578178205,0.208003650707,0.201602454996,0.226900951916,0.201741393973,0.113462329283,0.189033228985,0.126081269241,0.151200315819,0.157512986696,0.176415789449,0.14494656147,0.107185968593,0.138674101874,0.176454700364,0.08191686992,0.119785703162,0.113510742865,0.138628689135,0.151296942926,0.0756889728247,0.132420147469,0.0945184950201,0.0756784398699,0.100821583209,0.107150458631,0.0882219986809,0.0692887173782,0.0756696874146,0.0566882626064,0.0756302263447,0.0945458226863,0.0882000425216,0.0693246374548,0.0503832038652,0.0567098286563,0.0378045552106,0.0378338034155,0.0441272388961,0.0504503927135,0.0756152821525,0.0882194679709,0.0126033755912,0.0441811140095,0.0314945550831,0.0189064537768,0.0378383646951,0.0819046064798,0.0504236852213,0.0315195721011,0.050434428235,0.0315395977188,0.0441431433577,0.0126068965789,0.0504035295671,0.0314975859334,0.0441178662668,0.0567077080614,0.0315397777693,0.0315100194213,0.0504028893875,0.0063031261991,0.0188864581675,0.0126022252685,0.0252235358909,0.0251956380648,0.0567144099414,0.0188997018827,0.0189178569757,0.018909794714,0.0189000619837,0.0441136950967,0.00630811559877,0.0189049833643,0.0315231631085,0.0125846403354,0.012596423641,0.025267888333,0.0126253817645,0.0315054381361,0.0252155236433,0.0252103321869,0.0,0.00629427371574,0.0126069065817,0.00630466463067,0.0126159091072,0.00629036661969,0.0126133984029,0.0378002339984,0.0189130656316,0.00628875816848,0.0125933627823,0.0189062737263,0.0,0.0,0.00630551186835,0.00631124147566,0.0126090271766,0.0,0.0188858379935,0.018894270359,0.0,0.012604986043,0.0,0.0126032755631,0.0252083716369,0.00630466463067,0.0126033755912,0.0,0.0,0.0,0.00629895402869,0.0,0.0063020588997,0.00631124147566,0.00628875816848,0.0,0.0,0.0,0.00630764146576,0.00630811559877,0.00630896083588,0.00630466463067,0.0,0.0,0.0,0.00630466463067,0.00630896083588,0.0,0.0,0.00630121666343,0.00630044644736,0.0,0.0,0.0,0.0126058362815,0.0,0.0,0.00630369435849,0.0,0.00630369435849,0.0,0.0,0.0,0.0,0.0063020588997,0.0126006248195,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00629895402869,0.0,0.00630215492663,0.0,0.0,0.0,0.00628875816848,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00629111783043,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00630215492663,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00629895402869,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_6
      y11_M_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.529712706438,0.55107352483,0.479513983446,0.408146136588,0.522469052679,0.400615315932,0.508071969051,0.400918978055,0.422476739455,0.343797008582,0.393493127022,0.436372718097,0.350724253907,0.350707258825,0.307551497671,0.243786025761,0.293378849094,0.436389713179,0.257405509419,0.286304146442,0.293106677857,0.164675444572,0.22182725541,0.200466786916,0.272189731013,0.157395651272,0.200371689436,0.279326415727,0.207562233564,0.19315307843,0.229226514138,0.164652151313,0.17177314057,0.135984322496,0.129002567946,0.18592169612,0.13600566632,0.150463257437,0.128892174893,0.0786433663959,0.179053208791,0.12904098183,0.0787717542417,0.107365579475,0.0716953770745,0.121569469011,0.0858701250442,0.0715608410079,0.129029735084,0.0715461452607,0.0715224521173,0.0930893358767,0.0787851003795,0.114549750447,0.0930328272297,0.0574517740309,0.0573600505747,0.0500488163728,0.0786639854289,0.0858565789643,0.0572982684539,0.0716524145075,0.085940179771,0.0644535727803,0.10024551495,0.0859106883055,0.0643609245918,0.0357270609457,0.0572577051925,0.0214863395681,0.0502100947005,0.0428579223457,0.0357806954244,0.0285695322629,0.0143431642316,0.0358370041292,0.0501543108438,0.0428017386047,0.0358066129241,0.0142771433374,0.0429885845333,0.0214957518443,0.0571973226666,0.0358270570078,0.0214350219189,0.0358545490519,0.0429565438056,0.0214848375028,0.00715459453186,0.00715788108076,0.0358148105518,0.0142713925017,0.0286122698951,0.00715788108076,0.0285956996903,0.0286126697794,0.0214543038389,0.00711937222484,0.014259763367,0.0286290650347,0.0287018939588,0.042903859052,0.0143391953802,0.00717057740657,0.0214830530192,0.0,0.00716125760363,0.0357214125803,0.0215432331037,0.0143360987763,0.0142564368297,0.0215244785311,0.0143318350102,0.0142995293591,0.0142933186565,0.00717821769555,0.0213867758808,0.00719431803627,0.0215544448591,0.00719431803627,0.0,0.00711172443803,0.0,0.0143078294572,0.00717821769555,0.0,0.00716125760363,0.0,0.0142904769788,0.00715788108076,0.00717821769555,0.00708918346117,0.0143365036591,0.014352199117,0.0,0.0,0.00717821769555,0.0,0.00716125760363,0.0,0.0,0.0,0.00716098518247,0.00708918346117,0.00715788108076,0.00717829517313,0.0,0.0,0.0,0.00718190912729,0.0,0.0,0.0,0.00719431803627,0.0071135114209,0.00716125760363,0.0143360987763,0.0,0.0,0.0143046478779,0.0,0.0,0.00715788108076,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0071227387506,0.0,0.00709894063754,0.0,0.0,0.0,0.00716125760363,0.0143365036591,0.0,0.00716098518247,0.0,0.0,0.00711172443803,0.0,0.00708918346117,0.0,0.0,0.0,0.00711172443803,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00716098518247,0.0,0.0,0.0,0.0,0.00709894063754,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_7
      y11_M_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0442730880009,0.0485697699825,0.0459082832629,0.0447813355514,0.0313146037662,0.0324030156494,0.0415421362075,0.0361721297795,0.0340191839772,0.0269924617349,0.0328972130244,0.0345604981601,0.0356339378578,0.0280847502512,0.0286281494341,0.0253785460452,0.0259272886953,0.0237637607325,0.0253836380552,0.0242600954944,0.0259163712584,0.0124217801019,0.0151212787805,0.0177766781384,0.0178216051725,0.0199853426831,0.00972072134056,0.0178215318308,0.0210531141145,0.0140422755904,0.0199798001455,0.0129601291844,0.0172833922961,0.0156033747738,0.00863638620846,0.0102617840662,0.0113373391482,0.0172864936025,0.00702527007653,0.00647477455967,0.0108015884577,0.0102609437798,0.00864491375348,0.00863626047982,0.0107975232316,0.00701948236812,0.00809662163105,0.013998721094,0.00863705571347,0.00863968344205,0.00593854819574,0.0102611156089,0.0081020070078,0.00859851255108,0.00972219865208,0.00594183495194,0.0086435024495,0.00809742629435,0.00863810973857,0.00485923068413,0.00648062618013,0.00594162226099,0.00917692296927,0.00428093970815,0.00701740260687,0.00648040720275,0.0048585580359,0.00324191508249,0.00373837138206,0.00702116398869,0.00648330839113,0.0037773388786,0.00377347062744,0.00432149033782,0.00270162873122,0.00431884165447,0.00270020066342,0.0048606336062,0.00323982065289,0.00432094027502,0.0054011536032,0.00540136315093,0.00270254655029,0.00270142651766,0.0032404629167,0.00270148623876,0.00216397953818,0.00216068754328,0.00269928389208,0.00162094706386,0.00324001972324,0.00269975537448,0.00107965278799,0.00432001931273,0.00216016996038,0.000541008243213,0.000539843681683,0.00378161784332,0.00215954760361,0.0026980569901,0.00324041891167,0.004320086368,0.00270448067588,0.00216137066889,0.00161762678002,0.000540234383432,0.00270152186188,0.00107970936588,0.00161715320214,0.00162114194325,0.00162163752364,0.00162219387287,0.00108002787843,0.000540449903276,0.00269585254794,0.000538148754839,0.00378177186091,0.000540234383432,0.00162058664176,0.00162198642062,0.00378397106437,0.0,0.0,0.0,0.00107883031314,0.000540215419362,0.000541008243213,0.000540449903276,0.00107942019001,0.00161923820209,0.0010779732629,0.000541879647463,0.0,0.00107962554678,0.000539781655554,0.00215776016144,0.00161978721715,0.00107810737345,0.0,0.000540730068596,0.00107922111966,0.0,0.00053956885983,0.000541879647463,0.0,0.000539944683691,0.0,0.000540644154025,0.000538891496781,0.0,0.0,0.00108021332818,0.00161868813929,0.0,0.0,0.000540449903276,0.000541383228882,0.000539959142484,0.0,0.0,0.0,0.00108051088596,0.00108171473769,0.0,0.000541008243213,0.0,0.0,0.0,0.0,0.000541879647463,0.0,0.0,0.0,0.0,0.000539850806306,0.0,0.0,0.0,0.000539834880678,0.0,0.0,0.00053947456335,0.0,0.000540215419362,0.000541879647463,0.0,0.0,0.0,0.000540234383432,0.0,0.0,0.0,0.0,0.000540355082927,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0016169164132,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000540730068596,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00108078958444,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000538148754839,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00053947456335,0.0,0.0,0.0,0.0,0.0,0.0,0.00053956885983,0.0,0.00053956885983,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00053947456335,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_8
      y11_M_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00496500325862,0.002107077151,0.00567435360519,0.00284112724814,0.00283551154243,0.00213086928652,0.00283993862508,0.00354711477887,0.000710731629023,0.00282840059197,0.00353989060853,0.00212918138238,0.00284239753799,0.00355304750022,0.00213139863583,0.000711222743424,0.000709212255446,0.0,0.00423452348522,0.0028336369183,0.0042230641492,0.00212816128988,0.00212869063919,0.00355433523708,0.0,0.00137949729121,0.00354593023915,0.00212915613991,0.00426070882901,0.00283908112374,0.00142146325805,0.000708746754721,0.00283816014504,0.0,0.00354163382337,0.00141534530155,0.000709935006571,0.0,0.00284129540749,0.000710731629023,0.000710202279714,0.0,0.00142040493064,0.00141842413968,0.000709935006571,0.000704988226143,0.00209596601249,0.00141894903444,0.00212835951746,0.000709935006571,0.00213037000544,0.00213139863583,0.000708746754721,0.00139786489556,0.000709212255446,0.000709935006571,0.0,0.0,0.000711222743424,0.0,0.0,0.0,0.00142066700681,0.00141868176129,0.000710731629023,0.000710731629023,0.00141994388447,0.000710202279714,0.00142146325805,0.000711222743424,0.0,0.0,0.0,0.0,0.0,0.0,0.000711222743424,0.000708746754721,0.000709935006571,0.000709935006571,0.000711222743424,0.0,0.00142142502314,0.0,0.000711222743424,0.00141706178907,0.0,0.0,0.0,0.00212789401674,0.000710731629023,0.0,0.0,0.0,0.0,0.0,0.0,0.00141571985517,0.0,0.000710731629023,0.0,0.000711222743424,0.000708746754721,0.000710202279714,0.0,0.0,0.000711222743424,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000711222743424,0.000709212255446,0.0,0.0,0.0,0.0,0.0,0.000711222743424,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000699318323379,0.000709935006571,0.000710731629023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000709212255446,0.0,0.0,0.000708746754721,0.000704062421672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000710731629023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000708746754721,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000709212255446,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_9
      y11_M_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_10
      y11_M_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,26.3028457174,0.0,0.0,0.0,0.0,0.0,26.365709718,0.0,0.0,26.32395418,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_11
      y11_M_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,17.2950573872,23.0080271552,5.76422753303,28.7993591672,17.2794272081,5.75465632991,17.2689078189,17.278706702,11.5137932471,5.76306319516,17.2701855164,17.2456594886,23.025040706,17.2978049172,11.5197110039,0.0,17.2636625345,23.0681557914,0.0,5.76613062982,5.76469346031,17.2676493349,11.529509887,17.266198716,0.0,11.5196053297,0.0,0.0,5.75900434409,17.2638642762,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.75900434409,0.0,0.0,11.5135434717,0.0,5.76163275037,0.0,5.76682904041,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.76880610916,0.0,0.0,0.0,0.0,0.0,5.75465632991,5.74956283208,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.76070665985,0.0,0.0,5.76378177991,0.0,0.0,5.74258256893,5.75321339635,5.74881158438,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.76682904041,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.76549178107,0.0,0.0,0.0,5.75321339635,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.75050429339,0.0,0.0,0.0,5.76422753303,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.75050429339,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_12
      y11_M_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,8.99310552193,12.4598896493,12.4596107595,15.9246042224,6.92527561244,12.4687468028,9.69163969502,13.8473661117,8.99820247237,6.23023910139,8.99706767963,6.92090089026,8.30677421755,6.92154426004,9.69187050032,5.54050149541,5.53673071377,4.84326271709,6.23061031325,6.23347229901,3.46144290793,5.53869352054,4.15497533777,4.14902344601,4.1547156818,7.61805696335,3.45965032008,2.76850576345,2.07621006536,4.84323001967,0.692323490889,2.07944133961,2.07678323186,2.76820379317,2.76930396512,1.38411343685,1.3821410132,0.690095450363,0.691467107046,4.84701907339,2.0794759604,2.07777473298,2.77113502052,2.0776699089,0.692183180498,0.693433087383,1.38444618116,3.45671716935,0.692176833353,1.38723988702,2.7714110252,0.690963278303,0.691467107046,1.38391725234,2.77327093127,3.4656137521,1.38363451585,0.692323490889,1.38719180258,1.38336331962,3.46555508908,0.690585719295,0.0,1.38523188088,2.075556117,0.0,0.692323490889,0.0,0.691555293905,0.0,0.0,0.693048315709,0.691012805274,1.38490009826,0.690095450363,0.69311159483,0.0,0.0,0.0,0.0,0.693159871606,0.0,0.692947819234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.38723411689,0.693048315709,0.690585719295,1.38232565744,0.694079823076,0.0,0.690585719295,0.692077875579,0.0,0.693267676916,0.0,0.691467107046,0.0,0.690953757584,0.0,0.0,0.692183180498,0.0,0.0,0.0,1.38227853469,0.0,0.0,0.691012805274,0.0,0.692323490889,0.691555293905,0.693154197642,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.692384269619,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.693433087383,0.0,0.0,0.0,0.0,0.691740418992,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.690953757584,0.692077875579,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.690095450363,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.692646522144,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.693159871606,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.692323490889,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_13
      y11_M_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.04231313839,3.02510416379,2.5224810981,2.77353166992,2.51933287648,2.77399593779,3.27266514055,2.26623406408,1.76404188753,3.02307412979,2.52085767779,1.50963371837,2.01775820653,3.02604028559,2.52048747728,1.76509331769,3.27710147792,1.76433015842,0.756763598065,2.52316384496,1.25909399293,1.51169000019,0.75673522614,1.0086058528,1.51306429376,1.00728511693,0.251834213492,0.503637779236,2.2685053353,0.503967014944,1.51324681475,0.504085812898,1.25914011627,0.756943388071,0.0,0.755747974181,0.504120708848,0.504066695986,0.251834213492,0.756190849311,0.0,1.26058071214,0.252304701939,0.754283102864,0.75566225152,0.0,0.503967014944,0.0,0.252304701939,0.0,0.504598024764,0.503984918084,0.0,0.504625182917,0.252501181313,0.0,0.503769018355,0.251834213492,0.0,0.755885737325,0.75558365977,0.252583717822,0.0,0.503118133016,0.252437913437,0.0,0.252132801452,0.252383900575,0.504688602514,0.503913760689,0.757377463352,0.252501181313,1.25882059074,0.504085812898,0.252304701939,0.0,0.252383900575,0.0,0.504085812898,0.503512912422,0.0,0.0,0.504657954766,0.252501181313,0.0,0.0,0.0,0.251333077297,0.252437913437,0.252437913437,0.0,0.0,0.0,0.0,0.0,0.252492381465,0.252492381465,0.0,0.0,0.0,0.0,0.252074236944,0.0,0.0,0.0,1.00881386301,0.0,0.251125522252,0.0,0.0,0.0,0.0,0.0,0.252492381465,0.0,0.0,0.0,0.252583717822,0.250588124613,0.0,0.0,0.25201415522,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.251481916113,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.252492381465,0.0,0.0,0.251073936934,0.0,0.0,0.0,0.252492381465,0.251834213492,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.250588124613,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.504556756509,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.252132801452,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.252501181313,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.252437913437,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_14
      y11_M_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.27309359862,1.13065156405,1.34435405222,1.34410493015,0.989718689807,1.34354031759,1.34396161282,1.06075695345,0.636215534291,0.778148840437,0.778406234512,1.27321671686,0.778386035425,0.778539548483,0.636753214738,0.495519085545,0.353968696368,0.565401769066,1.06082236002,0.707890646278,0.635774617088,0.424221023173,0.847977467356,0.212487657388,0.424602112606,0.707512250057,0.212292784296,0.212346552341,0.636303736969,0.707497148835,0.212314137616,0.353509215242,0.282951401299,0.35383009216,0.777559988972,0.353741600923,0.424532954781,0.353553845605,0.353720055231,0.0707603434505,0.283078366986,0.212439371953,0.141685819243,0.636857480499,0.282826647893,0.424620580342,0.0706770943581,0.141474594509,0.0705476374506,0.282920044622,0.141428809913,0.2120903125,0.141401973984,0.212141579705,0.212085695566,0.141336086487,0.141412458272,0.212255656451,0.212401186061,0.212259984827,0.141453145003,0.141174012864,0.141541732425,0.0707530333049,0.141512684215,0.141493062245,0.141361479625,0.141388988857,0.0709056999247,0.141235860544,0.0706869245802,0.0705356911337,0.141247787623,0.141674854024,0.0706283953223,0.0709056999247,0.0707530333049,0.0707744539552,0.212350592158,0.0,0.0708003761163,0.0,0.0706770943581,0.0706465071699,0.212260177199,0.212205735852,0.0707603434505,0.0,0.0707197529051,0.0707535238541,0.0708003761163,0.141411688783,0.0709056999247,0.0707197529051,0.0,0.0706360036449,0.0,0.211851386162,0.0706869245802,0.0707603434505,0.0705869871949,0.0,0.0707757332307,0.0,0.0,0.0,0.0707530333049,0.0,0.0,0.0,0.0,0.0706283953223,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0707327380322,0.0705894014667,0.0,0.0,0.0,0.0706465071699,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0706869245802,0.0,0.0,0.0707001309354,0.141558180253,0.0,0.0707757332307,0.0708003761163,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0706465071699,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0705869871949,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0708603385475,0.0707542260128,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0707535238541,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0705356911337,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_15
      y11_M_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.151836314967,0.0762429419298,0.0380237431123,0.0764321631447,0.0377124027092,0.0760645054968,0.0383333993165,0.0379704692443,0.0380407328372,0.0,0.0,0.151701165399,0.152270660979,0.0768147717505,0.0760719514285,0.0,0.0378163502785,0.0380237431123,0.0760458020257,0.0,0.0,0.0386352550209,0.0381239972631,0.0761295096619,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0764458140193,0.0,0.0378621782149,0.0384073858752,0.114035683878,0.0,0.0,0.0,0.0770906257889,0.0380651684938,0.0,0.0,0.0380407328372,0.0,0.0,0.0380482083162,0.0379704692443,0.0764122777796,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.114888243049,0.0,0.0,0.0,0.0,0.113667996682,0.0,0.0,0.0,0.0,0.0,0.0,0.0381124147028,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0381611087319,0.0,0.0,0.0,0.0,0.0379318508608,0.0,0.0382882805165,0.0,0.0,0.0761677734772,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0378163502785,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0378163502785,0.0,0.0,0.0378163502785,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0381124147028,0.0,0.0,0.0,0.037778913789,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0380237431123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0383333993165,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # Creating weights for histo: y11_M_16
      y11_M_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0135494270694,0.0,0.0,0.00451005094307,0.00451531945356,0.0045242559022,0.00450009043612,0.0,0.00451565342746,0.0135454328571,0.0,0.00902607010563,0.00903530395467,0.00902899887389,0.0135321316486,0.00451334544643,0.00450009043612,0.0,0.0135587533147,0.0,0.0,0.0,0.0,0.00451601916257,0.0,0.0090176909218,0.0,0.00451887574626,0.00451715101648,0.0,0.00903175824904,0.0,0.0,0.0045242559022,0.0,0.0,0.00451565342746,0.00451005094307,0.0,0.0,0.0,0.00452921065046,0.00451367845787,0.0,0.0,0.00451258413993,0.00902823756738,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00449996724114,0.0,0.0,0.0,0.0,0.00902933092287,0.0,0.00903478615075,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00450507502067,0.0,0.0,0.0,0.0,0.0,0.00450009043612,0.0,0.0,0.0,0.00452921065046,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00451835794234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00450009043612,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00450386424497,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00451887574626,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00451917410911,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00450859281494,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
  
      # 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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_13_weights+y11_M_14_weights+y11_M_15_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_13_weights+y11_M_14_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_13_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_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=y11_M_0_weights+y11_M_1_weights+y11_M_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=y11_M_0_weights+y11_M_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=y11_M_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"M [ a_{1} , a_{2} ]   ( GeV ) ",\
                 fontsize=16,color="black")
      plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 1000.0\ \mathrm{fb}^{-1})$ ",\
                 fontsize=16,color="black")
  
      # Boundary of y-axis
      ymax=(y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_13_weights+y11_M_14_weights+y11_M_15_weights+y11_M_16_weights).max()*1.1
      #ymin=0 # linear scale
      ymin=min([x for x in (y11_M_0_weights+y11_M_1_weights+y11_M_2_weights+y11_M_3_weights+y11_M_4_weights+y11_M_5_weights+y11_M_6_weights+y11_M_7_weights+y11_M_8_weights+y11_M_9_weights+y11_M_10_weights+y11_M_11_weights+y11_M_12_weights+y11_M_13_weights+y11_M_14_weights+y11_M_15_weights+y11_M_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_10.png')
      plt.savefig('../../PDF/MadAnalysis5job_0/selection_10.png')
      plt.savefig('../../DVI/MadAnalysis5job_0/selection_10.eps')
  
  # Running!
  if __name__ == '__main__':
      selection_10()
  