#n= 500 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(1023.3), np.float64(89.2), np.float64(46.35), np.float64(7.8), np.float64(5.45), np.float64(4.05), np.float64(3.2), np.float64(3.15), np.float64(3.0), np.float64(2.9), np.float64(3.3), np.float64(2.7), np.float64(2.7), np.float64(2.8), np.float64(2.5), np.float64(2.3), np.float64(2.7), np.float64(2.7), np.float64(2.7), np.float64(2.7)] 

#variance of distances:
 Lv= [np.float64(122.67175714075346), np.float64(22.60663619382592), np.float64(26.728308962596195), np.float64(2.6095976701399777), np.float64(2.7335873865673292), np.float64(1.5402921800749363), np.float64(1.1445523142259597), np.float64(0.6344288770224761), np.float64(0.7745966692414834), np.float64(0.9433981132056604), np.float64(1.2688577540449522), np.float64(1.004987562112089), np.float64(1.1874342087037917), np.float64(0.6), np.float64(1.02469507659596), np.float64(0.9), np.float64(1.1), np.float64(0.45825756949558405), np.float64(0.9), np.float64(1.004987562112089)] 

#beta =  0.6000000000000001 

#mean of distances:
 Ld= [np.float64(981.05), np.float64(89.35), np.float64(40.4), np.float64(5.8), np.float64(3.15), np.float64(1.2), np.float64(0.85), np.float64(0.8), np.float64(1.0), np.float64(0.4), np.float64(0.1), np.float64(0.0), np.float64(0.4), np.float64(0.1), np.float64(0.2), np.float64(0.0), np.float64(0.0), np.float64(0.2), np.float64(0.0), np.float64(0.1)] 

#variance of distances:
 Lv= [np.float64(182.8344127892777), np.float64(28.036627828610204), np.float64(15.54155719353759), np.float64(3.5014282800023193), np.float64(2.074246851269154), np.float64(1.0770329614269007), np.float64(0.6726812023536854), np.float64(0.6000000000000001), np.float64(0.6324555320336759), np.float64(0.66332495807108), np.float64(0.30000000000000004), np.float64(0.0), np.float64(0.9165151389911681), np.float64(0.30000000000000004), np.float64(0.4000000000000001), np.float64(0.0), np.float64(0.0), np.float64(0.4000000000000001), np.float64(0.0), np.float64(0.30000000000000004)] 

#beta =  0.8 

#mean of distances:
 Ld= [np.float64(1069.95), np.float64(107.25), np.float64(38.15), np.float64(2.8), np.float64(1.45), np.float64(0.2), 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), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(230.9276347689899), np.float64(36.72073664838438), np.float64(24.838528539347898), np.float64(2.282542442102666), np.float64(2.2186707732333795), np.float64(0.45825756949558405), 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), np.float64(0.0)] 

#beta =  1.0 

#mean of distances:
 Ld= [np.float64(1035.35), np.float64(103.3), np.float64(40.8), np.float64(4.15), np.float64(2.25), np.float64(0.4), 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), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(134.78261200911638), np.float64(40.47357162396222), np.float64(21.69469981354893), np.float64(3.9246018906380806), np.float64(2.2830900113661747), np.float64(0.8888194417315589), 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), np.float64(0.0)] 

#beta =  1.2000000000000002 

#mean of distances:
 Ld= [np.float64(991.25), np.float64(94.25), np.float64(40.2), np.float64(9.0), np.float64(1.35), 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), np.float64(0.0)] 

#variance of distances:
 Lv= [np.float64(218.01413371614237), np.float64(59.48371625915785), np.float64(24.896987769607794), np.float64(5.004997502496879), np.float64(1.659066002303706), np.float64(0.45000000000000007), 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), np.float64(0.0)] 

