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
def compute_momentum(self):
if self.info['init'] == 'zero':
self.info['J0'] = mypostpro.find_nearest(self.outputs['t'][0, :],
501)
self.coef_mean(self.info['J0'])
self.coef_variance(self.info['J0'])
elif self.info['init'] == 'ic':
self.info['J0'] = 0
print(self.info['J0'])
self.outputs['t'] += int(self.info['T0']) - 1
self.coef_mean(self.info['J0'])
self.coef_variance(self.info['J0'])
return
fs.append(filenames.pop())
files_dict = setup.create_dict(fs, '^.*_([0-9]*)nb_.*$')
dict_final = sorted(files_dict.items(), key=operator.itemgetter(0))
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])
'^.*_(.*)rom_.*_' + str(int(anchor - 90)))
color_ctr = 0
tpath = root + '/'
for element in filenames:
z = re.match(r"^.*_(\d+)nb_.*", element)
if z.groups()[0] == N:
fname = element
# Create ROM class with field specified
rom = ROM(fname, field)
rom.get_coef()
K = rom.info['K']
nb = rom.info['nb']
if rom.info['init'] == 'zero':
T0 = mypostpro.find_nearest(rom.outputs['t'][0, :], 501)
elif rom.info['init'] == 'ic':
T0 = 0
rom.outputs['t'] += 500
rom.coef_mean(T0)
rom.coef_variance(T0)
print(rom.outputs['Ta'])
print(rom.outputs['Tv'])
# Create snapshot class
snap = Snapshot(ops_path, field)
fomu = snap.coef(rom.info['K'])
snap.outputs['t'] = np.linspace(500, 1000, rom.info['K'])
umax, umin = snap.extrema()
ul_bounds = [umax, umin]
uas = snap.mean()
romu.append(info[2])
t_grid.append(info[1])
K_match = re.match(r"^.*(\d\d\d)s_.*", fname)
nb_match = re.match(r"^.*_(\d+)nb_.*", fname)
K = int(K_match.groups()[0])
nb = int(nb_match.groups()[0])
T = int(len(romu) / nb)
t_grid = np.reshape(np.array(t_grid).astype(np.float64), (nb, T), order='F')
sp1 = fname.split('_')
print(sp1)
for element in sp1:
if re.match(r"zero", element):
T0 = mypostpro.find_nearest(t_grid[0, :], 501)
elif re.match(r"ic", element):
T0 = 0
t_grid += 500
print(f'Information K: {K}, N: {nb}, T:{T}, T0: {T0}')
romu = np.reshape(np.array(romu).astype(np.float64), (nb, T), order='F')
fomu = np.loadtxt(ops_path + 'uk')
fomu = np.reshape(np.array(fomu).astype(np.float64), (int(len(fomu) / K), K),
order='F')
umax = np.loadtxt(ops_path + 'umax')
umin = np.loadtxt(ops_path + 'umin')
ul_bounds = [umax, umin]
m_his.append(m)
sd_his.append(sd)
merr_all = np.array(merr_his)
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)
