def plt_mtfluc(rom, tdir):
import numpy as np
import matplotlib.pyplot as plt
import sys
from myplot import plt_romcoef_in_t, plt_snapcoef_in_t, \
plt_snap_minmax, plt_mean_in_t, add_std_in_t, plt_sample_mean_var
sys.path.append('/Users/bigticket0501/Developer/PyMOR/postprocessing/')
from snapshot import Snapshot
import os
setup_old.style(1)
setup_old.text()
sub_dir = os.path.join(tdir, 'mtfluc')
checkdir(sub_dir)
rom_params = {'c': 'b', 'marker': 'o'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$N$'
ylabel = r'$\langle T_{fluc} \rangle_s$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(ylabel=ylabel, xlabel=xlabel)
ax.plot(rom.nbs, rom.mtfluc, **rom_params)
mtfluc_fom = np.loadtxt('../qoi/tmtfluc')
fom_params = {'c': 'k', 'marker': 'o', 'label': 'FOM'}
ax.plot(rom.nbs, mtfluc_fom * np.ones(len(rom.nbs)), **fom_params)
ax.legend(loc=0)
fname1 = 'mtfluc'
data = np.column_stack((rom.nbs, rom.mtfluc))
output = os.path.join(sub_dir, fname1)
if rom.info['method'] == 'l-rom':
output += '_' + rom.info['perc']
elif rom.info['method'] == 'l-rom-df':
output += '_' + rom.info['fwidth']
output += '.csv'
np.savetxt(output, data, delimiter=',', header='N, mtfluc', comments="")
output = os.path.join(sub_dir, 'mtfluc.png')
fig.savefig(output)
plt.close(fig)
def plt_tke_in_t(rom, nb, tdir):
import numpy as np
import matplotlib.pyplot as plt
import sys
from myplot import plt_romcoef_in_t, plt_snapcoef_in_t, \
plt_snap_minmax, plt_mean_in_t, add_std_in_t, plt_sample_mean_var
sys.path.append('/Users/bigticket0501/Developer/PyMOR/postprocessing/')
from snapshot import Snapshot
import os
setup_old.style(1)
setup_old.text()
sub_dir = os.path.join(tdir, 'tke')
checkdir(sub_dir)
K = rom.info['K']
T0 = rom.info['T0']
J0 = rom.info['J0']
print(f'Information K: {K}, N: {nb}, T0: {T0}, J0: {J0}')
rom_params = {'c': 'b', 'mfc': 'None'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$t$'
ylabel = 'tke' + r'$(t)$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(ylabel=ylabel, xlabel=xlabel)
ax.semilogy(rom.tke[str(nb)]['t'], rom.tke[str(nb)]['tke'], **rom_params)
ax.legend(loc='upper left',
bbox_to_anchor=(0.0, 1.11),
ncol=4,
borderaxespad=0,
frameon=False)
output = os.path.join(sub_dir, 'tke_N' + str(nb) + '.png')
fig.savefig(output)
plt.close(fig)
def plt_nu_1st_2nd(rom, tdir):
import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
sys.path.append('/Users/bigticket0501/Developer/PyMOR/postprocessing/')
from snapshot import Snapshot
import os
import math
setup_old.style(1)
setup_old.text()
sub_dir = os.path.join(tdir, 'nu')
checkdir(sub_dir)
# get the FOM data
filename = './fom/nus_mom.csv'
fom = pd.read_csv(filename).to_numpy()
rom_params = {'c': 'b', 'marker': 'o'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$N$'
ylabel = r'$\langle Nu \rangle_g$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(ylabel=ylabel,
xlabel=xlabel,
title='Predicted Mean Nu vs. Truth Mean Nu')
ax.plot(rom.nbs, rom.nus_ms, **rom_params)
fom_params = {'c': 'k', 'marker': 'o', 'label': 'FOM'}
ax.plot(rom.nbs, fom[0][0] * np.ones(len(rom.nbs)), '-o', **fom_params)
ax.legend(loc=0)
fname1 = 'mnu'
data = np.column_stack((rom.nbs, rom.nus_ms))
output = os.path.join(sub_dir, fname1)
if rom.info['method'] == 'l-rom':
output += '_' + rom.info['perc']
elif rom.info['method'] == 'l-rom-df':
output += '_' + rom.info['fwidth']
output += '.csv'
np.savetxt(output, data, delimiter=',', header='N, mnus', comments="")
output = os.path.join(sub_dir, 'mnus.png')
fig.savefig(output)
plt.close(fig)
rom_params = {'c': 'b', 'marker': 'o'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$N$'
ylabel = r'$Std(Nu)$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(ylabel=ylabel,
xlabel=xlabel,
title='Predicted Std(Nu) vs. Truth Std(Nu)')
ax.plot(rom.nbs, rom.nus_sds, **rom_params)
fom_params = {'c': 'k', 'marker': 'o', 'label': 'FOM'}
ax.plot(rom.nbs, fom[0][1] * np.ones(len(rom.nbs)), '-o', **fom_params)
ax.set_ylim([
fom[0][1] / 2,
fom[0][1] * 2,
])
ax.legend(loc=0)
fname1 = 'stdnu'
data = np.column_stack((rom.nbs, rom.nus_sds))
output = os.path.join(sub_dir, fname1)
if rom.info['method'] == 'l-rom':
output += '_' + rom.info['perc']
elif rom.info['method'] == 'l-rom-df':
output += '_' + rom.info['fwidth']
output += '.csv'
np.savetxt(output, data, delimiter=',', header='N, stdnu', comments="")
output = os.path.join(sub_dir, 'stdnu.png')
fig.savefig(output)
plt.close(fig)
rom_params = {'c': 'b', 'marker': 'o'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$N$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(xlabel=xlabel, title='Relative Error in Mean Nu')
ax.semilogy(rom.nbs, rom.mnuserr, **rom_params)
ylim_exp = math.ceil(math.log10(min(rom.mnuserr))) - 1
ax.set_ylim([10**ylim_exp, 1e0])
ax.legend(loc=0)
fname1 = 'mnu_err'
data = np.column_stack((rom.nbs, rom.mnuserr))
output = os.path.join(sub_dir, fname1)
if rom.info['method'] == 'l-rom':
output += '_' + rom.info['perc']
elif rom.info['method'] == 'l-rom-df':
output += '_' + rom.info['fwidth']
output += '.csv'
np.savetxt(output, data, delimiter=',', header='N, mnuserr', comments="")
output = os.path.join(sub_dir, 'mnuserr.png')
fig.savefig(output)
plt.close(fig)
rom_params = {'c': 'b', 'marker': 'o'}
rom_params['label'] = rom.info['method'].upper()
xlabel = r'$N$'
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
ax.set(xlabel=xlabel, title='Relative Error in Std(Nu)')
ax.semilogy(rom.nbs, rom.stdnuserr, **rom_params)
ax.legend(loc=0)
fname1 = 'stdnuerr'
data = np.column_stack((rom.nbs, rom.stdnuserr))
output = os.path.join(sub_dir, fname1)
if rom.info['method'] == 'l-rom':
output += '_' + rom.info['perc']
elif rom.info['method'] == 'l-rom-df':
output += '_' + rom.info['fwidth']
output += '.csv'
np.savetxt(output, data, delimiter=',', header='N, stdnuerr', comments="")
output = os.path.join(sub_dir, 'stdnuerr.png')
fig.savefig(output)
plt.close(fig)
def plt_coef_in_t(rom, nb, tdir):
import numpy as np
import matplotlib.pyplot as plt
import sys
from myplot import plt_romcoef_in_t, plt_snapcoef_in_t, \
plt_snap_minmax, plt_mean_in_t, add_mean_in_t, add_std_in_t, plt_sample_mean_var
sys.path.append('/Users/bigticket0501/Developer/PyMOR/postprocessing/')
from snapshot import Snapshot
import os
setup_old.style(1)
setup_old.text()
field = rom.field
sub_dir = os.path.join(tdir, 'rom' + field + '_' + str(nb))
checkdir(sub_dir)
# Create snapshot class
# Need to modify the ops_path so that it does not depends on the position
ops_path = '../ops/'
snap = Snapshot(ops_path, field)
snap.coef(rom.info['K'])
ds = (rom.info['Tf'] - rom.info['T0'] + 1) / rom.info['K']
snap.outputs['t'] = np.linspace(rom.info['T0'] - 1 + ds, rom.info['Tf'],
rom.info['K'])
umax, umin = snap.extrema()
snap.mean()
snap.var()
K = rom.info['K']
T0 = rom.info['T0']
J0 = rom.info['J0']
print(f'Information K: {K}, N: {nb}, T0: {T0}, J0: {J0}')
# Plot rom coefficient
if nb == 1:
fig, ax = plt.subplots(1, squeeze=True, tight_layout=True)
plt_romcoef_in_t(ax, 0, T0, rom)
plt_snapcoef_in_t(ax, 0, T0, snap)
plt_snap_minmax(ax, 0, T0, snap)
add_mean_in_t(ax, 0, T0, snap, rom)
add_std_in_t(ax, 0, T0, snap, rom)
ax.legend(loc='upper left',
bbox_to_anchor=(0.0, 1.11),
ncol=4,
borderaxespad=0,
frameon=False)
else:
N_list = [0, int(nb / 2) - 1, nb - 4]
# Number of coefficients you want
for N0 in N_list:
num_coef_show = 4
num_coef_show = min(num_coef_show, nb)
fig, axs = plt.subplots(num_coef_show,
sharex=True,
squeeze=True,
tight_layout=True)
for n in range(num_coef_show):
plt_romcoef_in_t(axs[n], n + N0, T0, rom)
plt_snapcoef_in_t(axs[n], n + N0, T0, snap)
plt_snap_minmax(axs[n], n + N0, T0, snap)
add_mean_in_t(axs[n], n + N0, T0, snap, rom)
add_std_in_t(axs[n], n + N0, T0, snap, rom)
if n == 0:
ax = axs[n]
ax.legend(loc='upper left',
bbox_to_anchor=(0.0, 1.51),
ncol=4,
borderaxespad=0,
frameon=False)
if rom.info['method'] == 'cp-rom':
print(rom.fnames['rom' + field])
s1 = 'rom' + field + '_N' + str(nb) + '_' + str(N0) + '.png'
else:
s1 = 'rom' + field + '_N' + str(nb) + '_' + str(N0) + '.png'
output = os.path.join(sub_dir, s1)
fig.savefig(output)
fig = plt_sample_mean_var(rom, snap, nb)
output = os.path.join(sub_dir,
field + 'a_' + field + 'v_N' + str(nb) + '.png')
fig.savefig(output)
def plt_rom_norm_w_N(rom, tdir):
import numpy as np
import matplotlib.pyplot as plt
import os
setup_old.style(1)
setup_old.text()
# Create subdirectory
sub_dir = os.path.join(tdir, 'rom_norm')
checkdir(sub_dir)
solver = rom.info['method'].upper()
# Create title
title = 'Norm of the predictied mean flow by ' + solver + ' at '
anc_lb = ''
for key, value in rom.info['parameters'].items():
if key == 'theta':
anc_lb += '\\' + str(key) + '^*_g=' + str(value)
else:
anc_lb += str(key) + '^*=' + str(value)
# Data transform
data = np.column_stack((rom.nbs, rom.rom_norm))
data = data[data[:, 0].argsort()]
# fix the h1 norm
for j in range(data.shape[0]):
if (rom.info['ifrom(1)']):
idx1 = 1
data[j,
idx1 + 2] = np.sqrt(data[j, idx1]**2 + data[j, idx1 + 1]**2)
if (rom.info['ifrom(2)']):
idx2 = 4
data[j,
idx2 + 2] = np.sqrt(data[j, idx2]**2 + data[j, idx2 + 1]**2)
if (rom.info['ifrom(1)'] and rom.info['ifrom(2)']):
idx3 = 7
data[j,
idx3 + 2] = np.sqrt(data[j, idx3]**2 + data[j, idx3 + 1]**2)
if (rom.info['ifrom(1)']):
fig, ax = plt.subplots(1, tight_layout=True)
ax.set(xlabel=r'$N$',
ylabel=r'$\|\tilde{u}\|_{*}$',
xlim=[0, max(rom.nbs)],
title=title + '$' + anc_lb + '$')
ax.plot(data[:, 0], data[:, idx1], 'b-o', mfc="None", label=r'$H^1_0$')
ax.plot(data[:, 0],
data[:, idx1 + 1],
'r-o',
mfc="None",
label=r'$L^2$')
ax.plot(data[:, 0],
data[:, idx1 + 2],
'k-o',
mfc="None",
label=r'$H^1$')
ax.legend(loc=0)
fname1 = 'rom_u_norm'
fname2 = 'N_list'
fname3 = 'rom_u_h10'
fname4 = 'rom_u_l2'
fname5 = 'rom_u_h1'
for key, value in rom.info['parameters'].items():
fname1 += '_' + str(key) + '_' + str(value)
fname2 += '_' + str(key) + '_' + str(value)
fname3 += '_' + str(key) + '_' + str(value)
fname4 += '_' + str(key) + '_' + str(value)
fname5 += '_' + str(key) + '_' + str(value)
fname2 += '.dat'
fname3 += '.dat'
fname4 += '.dat'
fname5 += '.dat'
output = os.path.join(sub_dir, fname1)
fig.savefig(output)
output = os.path.join(sub_dir, fname2)
np.savetxt(output, data[:, 0])
output = os.path.join(sub_dir, fname3)
np.savetxt(output, data[:, idx1])
output = os.path.join(sub_dir, fname4)
np.savetxt(output, data[:, idx1 + 1])
output = os.path.join(sub_dir, fname5)
np.savetxt(output, data[:, idx1 + 2])
if (rom.info['ifrom(2)']):
fig, ax = plt.subplots(1, tight_layout=True)
ax.set(ylabel=r'$\||T\|_{*}$', xlabel=r'$N$', title=title)
ax.plot(data[:, 0], data[:, idx2], 'b-o', mfc="None", label=r'$H^1_0$')
ax.plot(data[:, 0],
data[:, idx2 + 1],
'r-o',
mfc="None",
label=r'$L^2$')
ax.plot(data[:, 0],
data[:, idx2 + 2],
'k-o',
mfc="None",
label=r'$H^1$')
ax.legend(loc=0)
fname1 = 'rom_T_norm'
fname2 = 'N_list'
fname3 = 'rom_T_h10'
fname4 = 'rom_T_l2'
fname5 = 'rom_T_h1'
for key, value in rom.info['parameters'].items():
fname1 += '_' + str(key) + '_' + str(value)
fname2 += '_' + str(key) + '_' + str(value)
fname3 += '_' + str(key) + '_' + str(value)
fname4 += '_' + str(key) + '_' + str(value)
fname5 += '_' + str(key) + '_' + str(value)
fname2 += '.dat'
fname3 += '.dat'
fname4 += '.dat'
fname5 += '.dat'
output = os.path.join(sub_dir, fname1)
fig.savefig(output)
output = os.path.join(sub_dir, fname2)
np.savetxt(output, data[:, 0])
output = os.path.join(sub_dir, fname3)
np.savetxt(output, data[:, idx2])
output = os.path.join(sub_dir, fname4)
np.savetxt(output, data[:, idx2 + 1])
output = os.path.join(sub_dir, fname5)
np.savetxt(output, data[:, idx2 + 2])
if (rom.info['ifrom(1)'] and rom.info['ifrom(2)']):
fig, ax = plt.subplots(1, tight_layout=True)
ax.set(ylabel=r'$\||(u, T)\|_{*}$', xlabel=r'$N$', title=title)
ax.plot(data[:, 0], data[:, idx3], 'b-o', mfc="None", label=r'$H^1_0$')
ax.plot(data[:, 0],
data[:, idx3 + 1],
'r-o',
mfc="None",
label=r'$L^2$')
ax.plot(data[:, 0],
data[:, idx3 + 2],
'k-o',
mfc="None",
label=r'$H^1$')
ax.legend(loc=0)
fname1 = 'rom_norm'
fname2 = 'N_list'
fname3 = 'rom_h10'
fname4 = 'rom_l2'
fname5 = 'rom_h1'
for key, value in rom.info['parameters'].items():
fname1 += '_' + str(key) + '_' + str(value)
fname2 += '_' + str(key) + '_' + str(value)
fname3 += '_' + str(key) + '_' + str(value)
fname4 += '_' + str(key) + '_' + str(value)
fname5 += '_' + str(key) + '_' + str(value)
fname2 += '.dat'
fname3 += '.dat'
fname4 += '.dat'
fname5 += '.dat'
output = os.path.join(sub_dir, fname1)
fig.savefig(output)
output = os.path.join(sub_dir, fname2)
np.savetxt(output, data[:, 0])
output = os.path.join(sub_dir, fname3)
np.savetxt(output, data[:, idx3])
output = os.path.join(sub_dir, fname4)
np.savetxt(output, data[:, idx3 + 1])
output = os.path.join(sub_dir, fname5)
np.savetxt(output, data[:, idx3 + 2])
return