#n= 1500 k= 12 p= 0.5
#sigma: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
#sample size array: [20, 40, 60, 80, 100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300, 320, 340, 360, 380, 400]
#beta =  0.4 

#mean of distances:
 Ld= [np.float64(10225.4), np.float64(901.6), np.float64(417.25), np.float64(49.7), np.float64(28.75), np.float64(7.6), np.float64(4.2), np.float64(3.7), np.float64(2.9), np.float64(3.05), np.float64(3.3), np.float64(3.0), np.float64(3.0), np.float64(2.6), np.float64(3.0), np.float64(2.7), np.float64(2.3), np.float64(2.7), np.float64(2.2), np.float64(2.2)] 

#variance of distances:
 Lv= [np.float64(1429.1917785937617), np.float64(110.02154334492859), np.float64(82.74939576818673), np.float64(17.501714201757494), np.float64(12.42628262997426), np.float64(4.635730794599704), np.float64(1.4), np.float64(1.1224972160321824), np.float64(0.8306623862918074), np.float64(1.15), np.float64(0.9), np.float64(1.0954451150103321), np.float64(0.8944271909999159), np.float64(0.9165151389911681), np.float64(0.8944271909999159), np.float64(0.6403124237432849), np.float64(0.9), np.float64(0.9), np.float64(1.077032961426901), np.float64(0.6)] 

#beta =  0.6000000000000001 

#mean of distances:
 Ld= [np.float64(9518.0), np.float64(943.5), np.float64(358.9), np.float64(49.0), np.float64(18.9), np.float64(3.0), np.float64(1.7), np.float64(0.9), np.float64(0.45), np.float64(0.5), np.float64(0.6), np.float64(0.3), np.float64(0.0), np.float64(0.2), np.float64(0.2), np.float64(0.0), np.float64(0.0), np.float64(0.1), np.float64(0.1), np.float64(0.3)] 

#variance of distances:
 Lv= [np.float64(1019.3381185848001), np.float64(265.8549416505174), np.float64(101.94331758384166), np.float64(29.171904291629644), np.float64(10.487611739571596), np.float64(1.1832159566199232), np.float64(1.7204650534085253), np.float64(0.9433981132056604), np.float64(0.47169905660283024), np.float64(0.806225774829855), np.float64(0.8), np.float64(0.45825756949558394), np.float64(0.0), np.float64(0.4000000000000001), np.float64(0.6000000000000001), np.float64(0.0), np.float64(0.0), np.float64(0.30000000000000004), np.float64(0.30000000000000004), np.float64(0.6403124237432849)] 

#beta =  0.8 

#mean of distances:
 Ld= [np.float64(9576.55), np.float64(965.45), np.float64(357.8), np.float64(48.45), np.float64(15.4), np.float64(2.3), np.float64(0.85), np.float64(0.5), np.float64(0.05), np.float64(0.1), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(873.3797412924117), np.float64(246.114043687068), np.float64(128.77406571200586), np.float64(21.652309345656413), np.float64(10.338762014864256), np.float64(2.749545416973504), np.float64(0.8674675786448736), np.float64(0.5477225575051661), np.float64(0.15000000000000002), np.float64(0.20000000000000004), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#beta =  1.0 

#mean of distances:
 Ld= [np.float64(9337.75), np.float64(878.3), np.float64(335.1), np.float64(51.25), np.float64(19.4), np.float64(3.6), np.float64(1.1), np.float64(0.25), np.float64(0.05), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(762.6104592123032), np.float64(220.97411613127906), np.float64(86.91282989294503), np.float64(28.06888847104566), np.float64(13.143059004660977), np.float64(4.624932431938872), np.float64(1.0677078252031311), np.float64(0.4609772228646444), np.float64(0.15000000000000002), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#beta =  1.2000000000000002 

#mean of distances:
 Ld= [np.float64(9554.15), np.float64(891.65), np.float64(403.65), np.float64(32.55), np.float64(24.6), np.float64(3.25), np.float64(2.35), np.float64(0.1), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(1031.9851512982152), np.float64(178.22823148985123), np.float64(80.96698401200331), np.float64(21.337115550139387), np.float64(16.306133815224257), np.float64(2.666927070618917), np.float64(2.1569654610122995), np.float64(0.20000000000000004), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

