g_list_mp1, N_mp[0])
pf_mean_mp2, pf_cov_mp2 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp2, N_mp[1])
pf_mean_mp3, pf_cov_mp3 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp3, N_mp[2])
print('-----------------------------------------------------')
print('cov = 0.1 | SUS cov =', round(pf_cov_sus1, 3), '| MP cov =',
round(pf_cov_mp1, 3))
print('cov = 0.2 | SUS cov =', round(pf_cov_sus2, 3), '| MP cov =',
round(pf_cov_mp2, 3))
print('cov = 0.3 | SUS cov =', round(pf_cov_sus3, 3), '| MP cov =',
round(pf_cov_mp3, 3))
print('-----------------------------------------------------')
# number of LSF calls
ncall_sus1 = uutil.get_mean_ncall_from_SUS(g_list_sus1, N_sus[0], p0)
ncall_sus2 = uutil.get_mean_ncall_from_SUS(g_list_sus2, N_sus[1], p0)
ncall_sus3 = uutil.get_mean_ncall_from_SUS(g_list_sus3, N_sus[2], p0)
ncall_mp1 = uutil.get_mean_ncall_from_MP(g_list_mp1, N_mp[0], Nb)
ncall_mp2 = uutil.get_mean_ncall_from_MP(g_list_mp2, N_mp[1], Nb)
ncall_mp3 = uutil.get_mean_ncall_from_MP(g_list_mp3, N_mp[2], Nb)
print('cov = 0.1 | SUS ncall =', round(ncall_sus1, 0), '| MP ncall =',
round(ncall_mp1, 0))
print('cov = 0.2 | SUS ncall =', round(ncall_sus2, 0), '| MP ncall =',
round(ncall_mp2, 0))
print('cov = 0.3 | SUS ncall =', round(ncall_sus3, 0), '| MP ncall =',
round(ncall_mp3, 0))
print('-----------------------------------------------------')
print('Thank you for the music!')
g_list_list_sus = \
np.load(direction + 'sus_' + example_name_list[i] + '_N' + repr(N) + '_Nsim100_cs_g_list.npy')
# pf_temp, cov_temp = \
# uutil.get_mean_and_cov_pf_from_SUS(g_list_list_sus, N, p0)
pf_array = \
uutil.get_pf_array_from_SUS(g_list_list_sus, N, p0)
pf_rel_line_sus[i] = np.mean(pf_array) / pf_ref_list[i]
pf_rel_lb_line_sus[i] = np.percentile(pf_array, 5) / pf_ref_list[i]
pf_rel_ub_line_sus[i] = np.percentile(pf_array, 95) / pf_ref_list[i]
cov_line_sus[i] = np.std(pf_array) / np.mean(pf_array)
ncall_array_sus[i] = uutil.get_mean_ncall_from_SUS(g_list_list_sus,
N_sus_list[i], 0.1)
# -- load mp data ----------------------------------------------------------
for i in range(0, len(example_list)):
example = example_list[i]
direction = 'python/data/example' + repr(
example_list[i]) + '/summary_data/'
N = N_mp_list[i]
g_list_list_mp = \
np.load(direction + 'mp_' + example_name_list[i] + '_N' + repr(N) + '_Nsim100_b' + repr(Nb_list[i]) + '_cs_sss2_g_list.npy')
pf_temp, cov_temp = \
uutil.get_mean_and_cov_pf_from_MP(g_list_list_mp, N)
pf_array = \
g_list_sus = np.load(direction + 'sus_waarts_N' + repr(N_sus[0]) +
'_Nsim100_cs_g_list.npy')
g_list_mp1 = np.load(direction + 'mp_waarts_N' + repr(N_mp[0]) +
'_Nsim100_b5_cs_sss2_g_list.npy')
g_list_mp2 = np.load(direction + 'mp_waarts_N' + repr(N_mp[1]) +
'_Nsim100_b10_cs_sss2_g_list.npy')
g_list_mp3 = np.load(direction + 'mp_waarts_N' + repr(N_mp[2]) +
'_Nsim100_b20_cs_sss2_g_list.npy')
# ---------------------------------------------------------------------------
# POST-PROCESSING
# ---------------------------------------------------------------------------
print('Ncall,SUS =', uutil.get_mean_ncall_from_SUS(g_list_sus, N_sus[0], 0.1))
print('Ncall,MP1 =', uutil.get_mean_ncall_from_MP(g_list_mp1, N_mp[0], 5))
print('Ncall,MP2 =', uutil.get_mean_ncall_from_MP(g_list_mp2, N_mp[1], 10))
print('Ncall,MP3 =', uutil.get_mean_ncall_from_MP(g_list_mp3, N_mp[2], 20))
pf_mean_sus, pf_cov_sus = uutil.get_mean_and_cov_pf_from_SUS(
g_list_sus, N_sus[0], 0.1)
pf_mean_mp1, pf_cov_mp1 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp1, N_mp[0])
pf_mean_mp2, pf_cov_mp2 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp2, N_mp[1])
pf_mean_mp3, pf_cov_mp3 = uutil.get_mean_and_cov_pf_from_MP(
g_list_mp3, N_mp[2])
print('SUS: pf =', pf_mean_sus, '| cov =', pf_cov_sus)
print('MP1: pf =', pf_mean_mp1, '| cov =', pf_cov_mp1)
# ---------------------------------------------------------------------------
# -- load sus data ----------------------------------------------------------
direction = 'python/data/example' + repr(example) + '/nsamples_study_sus/'
nsamples_list_sus = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000,\
2000, 3000, 4000, 5000]
ncall_points_sus = np.zeros(len(nsamples_list_sus))
cov_points_sus = np.zeros(len(nsamples_list_sus))
for i in range(0, len(nsamples_list_sus)):
N = nsamples_list_sus[i]
g_list_list_sus = \
np.load(direction + 'sus_' + example_name + '_N' + repr(N) + '_Nsim100_cs_g_list.npy')
ncall_points_sus[i] = \
uutil.get_mean_ncall_from_SUS(g_list_list_sus, N, p0)
pf_temp, cov_points_sus[i] = \
uutil.get_mean_and_cov_pf_from_SUS(g_list_list_sus, N, p0)
# -- load mp data ----------------------------------------------------------
direction = 'python/data/example' + repr(example) + '/nsamples_study_mp/'
nsamples_list_mp = [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, \
30, 40, 50, 60, 70, 80, 90, 100]
if example == 4:
nsamples_list_mp = [100, 200, 300, 400, 500]
ncall_points_mp = np.zeros(len(nsamples_list_mp))
cov_points_mp = np.zeros(len(nsamples_list_mp))
for i in range(0, len(nsamples_list_mp)):
