#sigma: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
#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(1241.95), np.float64(112.75), np.float64(42.5), np.float64(9.8), np.float64(5.65), np.float64(5.4), np.float64(4.25), np.float64(4.45), np.float64(3.3), np.float64(3.7), np.float64(3.6), np.float64(3.6), np.float64(2.6), np.float64(3.4), np.float64(3.7), np.float64(3.1), np.float64(2.8), np.float64(3.1), np.float64(3.0), np.float64(3.2)] 

#variance of distances:
 Lv= [np.float64(185.78865008390582), np.float64(53.22792969860842), np.float64(20.22498454881981), np.float64(7.1386273190299), np.float64(1.7471405209656148), np.float64(2.2891046284519194), np.float64(0.8139410298049853), np.float64(1.1500000000000001), np.float64(1.2688577540449522), np.float64(1.676305461424021), np.float64(1.3564659966250538), np.float64(1.2), np.float64(1.2806248474865698), np.float64(0.66332495807108), np.float64(1.1), np.float64(0.7), np.float64(0.7483314773547882), np.float64(0.8306623862918076), np.float64(0.8944271909999159), np.float64(1.0770329614269007)] 

#beta =  0.6000000000000001 

#mean of distances:
 Ld= [np.float64(948.2), np.float64(104.0), np.float64(57.3), np.float64(3.7), np.float64(5.85), np.float64(1.9), np.float64(0.6), np.float64(0.6), np.float64(0.2), np.float64(0.4), np.float64(0.7), np.float64(0.1), np.float64(0.0), np.float64(0.2), np.float64(0.3), np.float64(0.1), np.float64(0.2), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(253.07560135263927), np.float64(44.01306624174235), np.float64(40.77204924945519), np.float64(2.794637722496424), np.float64(3.162672920173694), np.float64(1.9467922333931784), np.float64(0.58309518948453), np.float64(0.66332495807108), np.float64(0.4000000000000001), np.float64(0.4898979485566356), np.float64(0.7810249675906654), np.float64(0.30000000000000004), np.float64(0.0), np.float64(0.4000000000000001), np.float64(0.45825756949558394), np.float64(0.30000000000000004), np.float64(0.4000000000000001), np.float64(0.0), np.float64(0.0), np.float64(0.0)] 

#beta =  0.8 

#mean of distances:
 Ld= [np.float64(989.35), np.float64(94.8), np.float64(33.65), np.float64(6.05), np.float64(1.65), np.float64(0.45), np.float64(0.15), np.float64(0.05), 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(233.09451409245995), np.float64(39.45136246062992), np.float64(24.147515400140033), np.float64(6.381418337642502), np.float64(1.0259142264341596), np.float64(0.5220153254455275), np.float64(0.22912878474779197), np.float64(0.15000000000000002), 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 =  1.0 

#mean of distances:
 Ld= [np.float64(1072.05), np.float64(106.15), np.float64(41.0), np.float64(3.35), np.float64(1.35), np.float64(0.5), 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), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(244.1927978053407), np.float64(51.72187641607756), np.float64(17.504285189632853), np.float64(3.6813720268399934), np.float64(1.517399090549352), np.float64(0.6708203932499369), 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), np.float64(0.0)] 

#beta =  1.2000000000000002 

#mean of distances:
 Ld= [np.float64(937.3), np.float64(90.55), np.float64(33.55), np.float64(5.15), np.float64(2.3), np.float64(0.6), np.float64(0.15), 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), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(186.43781805202508), np.float64(32.44183256229525), np.float64(16.49007277121602), np.float64(3.6609425015970953), np.float64(2.882707061079915), np.float64(0.9949874371066199), np.float64(0.22912878474779197), 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), np.float64(0.0)] 

