#n= 200 k= 10 p= 0.5
#sigma: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] A [1, 3, 8, 10, 15, 16]
#sample size array: [100, 1000, 10000]
***** starting execution of DypChip with all profiles ******
[1.48578346e-27 1.48578346e-27 1.48578346e-27 7.16652671e-01
 0.00000000e+00 2.61082031e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 1.70560127e-02 0.00000000e+00
 5.20928472e-03]
for m= 100 err_vec= [np.float64(0.03334732859804013), np.float64(0.021082031204719076), np.float64(0.00705601267190001), np.float64(0.005209284721420736), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27)]
***** starting execution of DypChip with all profiles ******
[1.48578346e-27 1.48578346e-27 1.48578346e-27 7.16652671e-01
 0.00000000e+00 2.61082031e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 1.70560127e-02 0.00000000e+00
 5.20928472e-03]
for m= 1000 err_vec= [np.float64(0.011652671401959913), np.float64(0.009917968795280951), np.float64(0.0020560126719000107), np.float64(0.003790715278579263), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27)]
***** starting execution of DypChip with all profiles ******
[1.48578346e-27 1.48578346e-27 1.48578346e-27 7.16652671e-01
 0.00000000e+00 2.61082031e-01 0.00000000e+00 0.00000000e+00
 0.00000000e+00 0.00000000e+00 1.70560127e-02 0.00000000e+00
 5.20928472e-03]
for m= 10000 err_vec= [np.float64(0.0018473285980401544), np.float64(0.002182031204719048), np.float64(0.00014398732809998982), np.float64(0.00019071527857926398), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27), np.float64(1.4857834613322708e-27)]
