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
fws = []
m_list = []
sd_list = []
for fw, fnames in dict_final:
# get the FOM data
for fname in fnames:
nuss = mypostpro.read_nuss(fname)
nuss[:, 2] = nuss[:, 2] / 40
avgidx1 = mypostpro.find_nearest(nuss[:, 1], int(sys.argv[3]))
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_mean, fom_var, fom_sd, rom_mean,
rom_var, rom_sd)
merr_list.append(mean_err)
verr_list.append(var_err)
sderr_list.append(sd_err)
fws.append(float(fw))
m_list.append(rom_mean)
sd_list.append(rom_sd)
data = np.column_stack((fws, merr_list, sderr_list, m_list, sd_list))
# sd_list, fom_means, fom_stds))
data = data[data[:, 0].argsort()]
print(data)
anchor = setup.find_anchor()
solver = checker.rom_checker(fname, '^.*_(.*)rom_.*$')