def get_nu_1st_2nd(self):
import pandas as pd
filename = './fom/nus_mom.csv'
fom = pd.read_csv(filename).to_numpy()
# For now, grep proj and rom relerr at the same time
if self.info['init'] == 'zero':
self.info['J0'] = mypostpro.find_nearest(self.outputs['t'][0, :],
501)
elif self.info['init'] == 'ic':
self.info['J0'] = 0
files_dict = self.cdict('nu')
nbs = []
ms = []
sds = []
merr = []
verr = []
sderr = []
for nb, fnames in files_dict.items():
for fname in fnames:
nuss = mypostpro.read_nuss(fname)
nuss[:, 2] = nuss[:, 2] / 40
avgidx1 = mypostpro.find_nearest(nuss[:, 1],
int(self.info['J0']))
rom_mean = mypostpro.cmean(nuss[avgidx1:-1, :], 2)
rom_var = mypostpro.cvar(nuss[avgidx1:-1, :], rom_mean, 2)
rom_sd = mypostpro.csd(nuss[avgidx1:-1, :], rom_mean, 2)
[mean_err, var_err,
sd_err] = mypostpro.cnuss_err(fom[0][0], fom[0][1], fom[0][1],
rom_mean, rom_var, rom_sd)
merr.append(mean_err)
verr.append(var_err)
sderr.append(sd_err)
nbs.append(int(nb))
ms.append(rom_mean)
sds.append(rom_sd)
self.nbs, self.nus_ms, self.nus_sds, self.mnuserr, self.stdnuserr = [
list(tuple)
for tuple in zip(*sorted(zip(nbs, ms, sds, merr, sderr)))
]
return
color_ctr = 0
i = 0
tpath = './nu/'
fig, ax = plt.subplots(1, tight_layout=True)
# get the FOM data
filename = '../../../../fom_nuss/nuss_fom_'+str(int(anchor))
data = mypostpro.read_nuss(filename)
data[:, 2] = data[:, 2]/40
idx1 = mypostpro.find_nearest(data[:, 0], 0)
idx2 = mypostpro.find_nearest(data[:, 0], 1000)
avgidx1 = mypostpro.find_nearest(data[:, 0], 501)
nuss_fom = data[avgidx1:idx2, :]
fom_mean = mypostpro.cmean(nuss_fom[:idx2], 2)
fom_var = mypostpro.cvar(nuss_fom[:idx2], fom_mean, 2)
fom_sd = mypostpro.csd(nuss_fom[:idx2], fom_mean, 2)
print('FOM data at deg '+str(int(anchor-90)), fom_mean, fom_var, fom_sd)
ax.plot(nuss_fom[:, 0], nuss_fom[:, 2], 'k-', mfc="None", label=r'FOM')
for nb, fnames in dict_final:
for fname in fnames:
forleg = fname.split('_')
deg = int(forleg[-2])
match_rom = re.match('^.*_(.*)rom_.*$', fname)
assert match_rom is not None
m_all = np.array(m_his)
sderr_all = np.array(sderr_his)
sd_all = np.array(sd_his)
fom_m_list = []
fom_sd_list = []
for i, test in enumerate(P_test):
filename = '../../../fom_nuss/nuss_fom_'+str(test)
data = mypostpro.read_nuss(filename)
data[:, 2] = data[:, 2]/40
idx1 = mypostpro.find_nearest(data[:, 0], 0)
idx2 = mypostpro.find_nearest(data[:, 0], 1000)
nuss_fom = data[idx1:idx2, :]
avgidx1 = mypostpro.find_nearest(data[:, 0], 501)
fom_mean = mypostpro.cmean(nuss_fom[avgidx1:idx2], 2)
fom_var = mypostpro.cvar(nuss_fom[avgidx1:idx2], fom_mean, 2)
fom_sd = mypostpro.csd(nuss_fom[avgidx1:idx2], fom_mean, 2)
fom_m_list.append(fom_mean)
fom_sd_list.append(fom_sd)
fig1, ax1 = plt.subplots(1, tight_layout=True)
fig2, ax2 = plt.subplots(1, tight_layout=True)
fig3, ax3 = plt.subplots(1, tight_layout=True)
fig4, ax4 = plt.subplots(1, tight_layout=True)
for i, train, in enumerate(P_train):
qoi_params = {'c': colors[i], 'marker': 'o', 'mfc': 'None',
'linestyle': '--', 'label': r'\textit{it} = '+str(i+1)}
ax1.semilogy(angle, merr_all[i, :], **qoi_params)