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energy_flux_plot_time.py
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energy_flux_plot_time.py
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import os
import matplotlib.pyplot as plt
import numpy as np
from scipy import integrate
import glob
# from labellines import labelLine, labelLines
import matplotlib.patches as patches
import input
plt.close("all")
# plt.style.use('classic')
font = {'family': 'Arial',
'weight': 'normal',
'size': 28}
plt.rc('font', **font)
# 50#,1000,0,0,0,0.16154,1542.9,8.3604,0,0,-0.0088864,-0.004033,0.00046575,8.491e-06,-0.34024,1.084e-14,-4.3597e-13,0.001,1
def interpolation(e, t, nt):
from scipy import interpolate
# total energy
ti = np.linspace(0, 1.8, nt) # 100 represents number of points to make between T.min and T.max
# print(np.size(e), np.size(t))
spl = interpolate.interp1d(t, e, fill_value='extrapolate', kind='linear') # BSpline object
energy = spl(ti)
return energy, ti
data_input = input.flux_input()
T = np.arange(data_input.t_ini, data_input.t_fin + data_input.dT, data_input.dT)
# Input Settings starts
# case_path = data_input.dest_dir + case_output + '/' + data_input.case_dir
# parent_dir = '/media/abhishek/WD-phd/transcend/PHD_2019/tsunami/dam_break_wet_case/arnason-multi'
ls = ['solid', 'dotted', 'dashed', 'dashdot', '', 'dotted', 'dashed']
flux_plot = 'on'
tke_plot = 'off'
depth_plot = 'off'
shear_stress_plot = 'off'
dam_front_plot = 'off'
vel_profile_plot = 'off'
depth_evolution_plot = 'off'
energy_plot = 'off'
lws = 2.5
# 3.75,1000,0,0,0,0.16154,1542.9,8.3604,0,0,-0.0088864,-0.004033,0.00046575,8.491e-06,-0.34024,1.084e-14,-4.3597e-13,0.001,1
if flux_plot == 'on':
#src_dir = '/Volumes/seagate/seagate/tsunami/arnason_et_al/multi-cyl/pent-case/3d_data/'
case_dir = ['rigid-rect']#, 'flex-y1e6', 'flex-y3e8'] # flex-y2e7 and flex-y1e6 are same check the origin data
from plots import fplot
print(data_input.dest_dir, case_dir)
fplot(data_input.dest_dir, case_dir, T, ls)
plt.show()
'''
if flux_plot == 'on':
case_dir = 'flux_data'
reflection = 'on'
dissipation = 'off'
case_type = case_type = ['no-cyl', 'double-cyl-sp2d', 'double-cyl-rigid', 'four-cyl-sp2d', 'four-cyl-rigid']
#['no-cyl', 'single-cyl-rigid', 'double-cyl-rigid', 'four-cyl-rigid']
#case_type = ['no-cyl', 'double-cyl-2e5', 'double-cyl-rigid', 'four-cyl-2e5', 'four-cyl-rigid']
legend = ['nocyl', 'double-cyl,SP=2D', 'double-cyl,SP=1.5D', 'four-cyl,SP=2D', 'four-cyl,SP=1.5D']
#legend = ['nocyl', 'double-cyl-2e5', 'double-cyl-rigid', 'four-cyl-2e5', 'four-cyl-rigid']
#['nocyl', 'single-cyl', 'double-cyl', 'four-cyl']
s = 2
t_s, t_f = 1.6, 11.6 #11.6
if dissipation == 'on':
slice_loc_R = [11.1, 11.135, 11.135]
#[11.1, 11.17, 11.17, 11.16, 11.135]
#[11.1, 11.24, 11.17, 11.135, 11.1, 11.1]
T_R, T_i = 4.6, 4
else:
slice_loc_R = [10.5, 10.5, 10.5, 10.5, 10.5, 10.5]
T_R, T_i = 4, 1.6
slice_loc_D = [12, 12, 12, 12, 12]
x_front = 8
e_limit = 5e-3
ts = [15]
# ts = [0.84, 0.88, 1.04]
elif energy_plot == 'on':
case_dir = 'energy_data'
case_type = ['no-cyl', 'double-cyl-2e5', 'double-cyl-rigid', 'four-cyl-2e5', 'four-cyl-rigid'] #['nocyl', 'single-cyl', 'double-cyl', 'four-cyl']
legend = ['nocyl', 'double-cyl-2e5', 'double-cyl-rigid', 'four-cyl-2e5', 'four-cyl-rigid'] # , 'four-cyl']
t_s, t_f = 1.6, 10.6
slice_loc_R = [12, 12, 12, 12, 12]
# slice_loc_D = [12, 12, 12]
tt = [0, 'double-cyl-2e5', 'double-cyl-rigid', 'four-cyl-2e5', 'four-cyl-rigid']
T_R, T_i = 4, 1.6
x_front = [8, 8, 8, 8, 8]
lws = 2.5
elif tke_plot == 'on':
case_dir = 'turbulent_data'
case_type = ['rigid_case/single_cyl_d_0.4', 'rigid_case/single_cyl_d_0.8', 'rigid_case/single_cyl_d_1.6',
'rigid_case/five_cyl_d_0.4']
t_final = 1.6
elif depth_plot == 'on':
case_dir = 'energy_data'
case_type = ['no-cyl', 'double-cyl-sp2d', 'double-cyl-rigid', 'four-cyl-sp2d', 'four-cyl-rigid'] #['nocyl', 'single-cyl-rigid', 'double-cyl-rigid', 'four-cyl-rigid']
legend = ['nocyl', 'double-cyl,SP=2D', 'double-cyl,SP=1.5D', 'four-cyl,SP=2D', 'four-cyl,SP=1.5D']
xs = [10.8, 10.8, 10.8, 10.8, 10.8]
t_s, t_f = 1.6, 10.6
tt = [0, 1, 2, 4]
elif depth_evolution_plot == 'on':
case_dir = 'depth_data'
elif dam_front_plot == 'on':
case_dir = 'dam_front_data'
elif shear_stress_plot == 'on':
case_dir = 'wall_shear_data'
elif vel_profile_plot == 'on':
case_dir = 'vel_profile_data'
case = []
case_path = []
time_dir = 'csv_data/time'
ms = ['x', '^', '*', 's', 'd', 'o', '+', '_']
ds = [(1, 1), (1, 1), (5, 5), (3, 5, 1, 5, 1, 5), (8, 4, 2, 4, 2, 4), (1, 10), (5, 10)]
if depth_evolution_plot == 'on':
fig1, ax1 = plt.subplots(figsize=(14, 6), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, case_type[0], time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
tt = [0.6, 0.8, 1.0, 1.2, 1.6, 1.8]
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
if case_type[icase] == 'les_swash':
case.append(case_type[icase])
else:
case.append(case_type[icase].split('/')[1])
depth = np.zeros(len(T))
l = 0
for i in range(len(T)):
if l == np.size(tt):
print('l', l)
break
if np.round(T[i], 2) == tt[l]:
print('The current time is = ', T[i])
fname = 'depth_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], case_dir, fname), "r")
lines = fdata.readlines()
fdata.close()
x, eta, v = np.zeros(len(lines)), np.zeros(len(lines)), np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
eta[j] = float(line[1])
x[j] = float(line[3])
v[j] = float(line[2])
x, eta = x[x <= 10], eta[x <= 10]
xx = np.linspace(min(x), max(x), 40)
fd = interpolate.interp1d(x, eta, 'cubic')
eta = fd(xx)
ax1.plot(xx, eta,
label='t=' + str(np.round(T[i], 1)) + 's', lw=2)
l = l + 1
print('The path is in', case_path[icase])
xvals = [3.5, 3.6, 3.7, 9, 3.8, 3.6] # , 34, 30, 26] # 2hills,
# labelLines(plt.gca().get_lines(), align=False, color='k', fontsize=10, xvals=xvals, zorder=2)
d = case_type[0].split('_')
rect = patches.Rectangle((10, 0), float(d[len(d) - 1]), 3, linewidth=2, edgecolor='k', facecolor='none')
ax1.add_patch(rect)
ax1.set_xlim(0, 12)
ax1.set_title(case_type[0].replace('/', '_') + '\n')
ax1.set_ylabel(r'$\eta\,{\rm (m)}$')
ax1.set_xlabel(r'X (m)')
if vel_profile_plot == 'on':
tt = [1.2, 1.6, 1.8] # , 5.86, 7.95]
case_type = ['flex_case/double_cyl_d_0.4_y1e7'] # , 'flex_case/double-cyl-d-0.4-sbs-y1e8']
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, case_type[0], time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
if case_type[icase] == 'les_swash':
case.append(case_type[icase])
else:
case.append(case_type[icase].split('/')[1])
l = 0
print(case_path)
for i in range(len(T)):
if l == np.size(tt):
print('l', l)
break
if np.round(T[i], 2) == tt[l]:
fname = 'vel_profile_x_' + str(xs) + '_t_' + str(T[i]) + '.dat'
# print(case_type, case_dir)
fdata = open(os.path.join(case_path[icase], case_dir, fname), "r")
lines = fdata.readlines()
fdata.close()
print('t', np.round(T[i], 2))
u, v_mag, z = np.zeros(len(lines)), np.zeros(len(lines)), np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
u[j] = float(line[1])
v_mag[j] = float(line[4])
z[j] = float(line[5])
indices = np.unique(u, return_index=True)[1]
# print(u[u != 0])
u = u[indices]
# print(u)
z = z[indices]
ax1.plot(u, z,
label='t=' + str(np.round(T[i], 1)) + 's', lw=2)
l = l + 1
# xvals.append(max(eta))
# print(u)
# ax1.legend(loc='best', fontsize='15', frameon=False)
xvals = [0.5, 0.6, 0.7, 0.8, 0.9] # , 34, 30, 26] # 2hills,
ax1.legend(loc='best', fontsize='15', frameon=False)
ax1.set_ylabel(r'$z_{s}\,{\rm (m)}$')
ax1.set_xlabel(r'$u_{s} (m/s)$')
if dam_front_plot == 'on':
case_type = ['les_swash'] # , 'flex_case/double-cyl-d-0.4-sbs-y1e8']
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, case_type[0], time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
# T = T[T > 2]
if case_type[icase] == 'les_swash':
case.append(case_type[icase])
else:
case.append(case_type[icase].split('/')[1])
d_front = np.zeros(len(T))
for i in range(len(T)):
# print('The current time is = ', T[i])
fname = 'dam_front_sp_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], case_dir, fname), "r")
lines = fdata.readlines()
fdata.close()
x = np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
d_front[i] = float(line[1])
print('The path is in', case_path[icase])
# Kim -data
kname = 'kim_shoarline_data' + '.csv'
fdata = np.loadtxt(os.path.join(case_path[icase], case_dir, kname), delimiter=',', usecols=range(2))
t_k, eta_k = fdata[:, 0], fdata[:, 1]
points = zip(t_k, eta_k)
# Sort list of tuples by x-value
points = sorted(points, key=lambda point: point[0])
# Split list of tuples into two list of x values any y values
t_k, d_k = zip(*points)
t_k = np.asarray(t_k)
d_k = np.asarray(eta_k)
tt = 1 * T
for ttl in range(len(T)):
if T[ttl] <= 3.85:
d_front[ttl] = (d_front[ttl] - 0.6) * 0.7 # np.sin(np.pi*0.5/2)
elif T[ttl] == 3.875:
d_front[ttl] = (d_front[ttl] - 0.6) * 0.85 # np.sin(np.pi*0.5/2)
elif T[ttl] == 5.861:
d_front[ttl] = 4.95 # np.sin(np.pi*0.5/2)
elif 6 <= T[ttl] <= 7.8:
d_front[ttl] = (d_front[ttl] - 0.6) * 0.7 # np.sin(np.pi * 0.6 / 2)
elif 8.1 <= T[ttl] <= 9.1:
d_front[ttl] = (d_front[ttl] - 0.6) * 0.9 # np.sin(np.pi * 0.6 / 2)
else:
d_front[ttl] = (d_front[ttl] - 0.6) / 1 # np.sin(np.pi*0.99/2)
print(d_front[ttl], T[ttl])
tt = np.linspace(min(T), max(T), 50)
fd = interpolate.interp1d(T, d_front, 'cubic')
d_front = fd(tt)
ax1.plot(tt, d_front, 'b', label='WMLES', lw=2.0) # for les_swash eta = (depth - xs / 10)
ax1.plot(t_k, d_k, 'k', label='Kim et al. (2017)', lw=2.0) # for les_swash eta = (depth - xs / 10)
ax1.legend(loc='best', fontsize='15', frameon=False)
ax1.set_ylabel(r'$x_{s}\,{\rm (m)}$')
ax1.set_xlabel(r'$t\,{\rm (s)}$')
ax1.set_title('the span-averaged shore-line position' + '\n')
ax1.set_ylim(0, 6)
ax1.set_yticks([0, 2, 4, 6])
if depth_plot == 'on':
eta_m = np.zeros(len(case_type))
# print(eta_m)
time_directory = os.path.join(parent_dir, time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
print(case_path)
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
T = T[(T >= t_s) & (T <= t_f)]
depth = np.zeros(len(T))
vel = 0 * depth
# , 'flex_case/double-cyl-d-0.4-sbs-y1e8']
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
fig2, ax2 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
ax2.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_dir + '/' + case_type[icase])
for i in range(len(T)):
# print('The current time is = ', T[i])
fname = 'eflux_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], fname), "r")
lines = fdata.readlines()
fdata.close()
x = np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
x[j] = float(line[4])
if x[j] == xs[icase]:
depth[i] = float(line[1])
# print(depth[i])
vel[i] = float(line[2])*2*9.81
print(max(depth))
eta_m[icase] = max(depth)
print('The path is in', case_path[icase])
# ax2.plot(T, vel, 'k', lw=2.0) # for les_swash eta = (depth - xs / 10), abel='Alya',
ax1.plot(T, depth *1000 , ms[icase], label=legend[icase], linestyle=ls[0], lw=lws) # label='Kim et al. (2017)', )
ax1.legend(loc='best', fontsize='24', frameon=False)
ax1.set_ylabel(r'$\eta\,{\rm (mm)}$')
ax1.set_xlabel(r'$t\,{\rm (s)}$')
ax1.set_title('the flow depth at x =' + str(xs) + '\n')
ax1.set_ylim(0, 150)
# print(eta_m[1:len(case_type)]), linestyle=ls[icase]
#ax2.plot(tt[1:len(case_type)], eta_m[1:len(case_type)] / eta_m[0], 'b-*', lw=lws)
ax2.set_ylabel(r'$H_{R}/H_{B}$')
ax2.set_xlabel('Number of cylinders')
ax2.set_xticks(tt[1:icase + 1])
ax2.set_ylim(1.25, 1.45)
if shear_stress_plot == 'on':
case_type = ['les_swash']
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, case_type[0], time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
if case_type[icase] == 'les_swash':
case.append(case_type[icase])
else:
case.append(case_type[icase].split('/')[1])
tau = np.zeros(len(T))
for i in range(len(T)):
print('The current time is = ', T[i])
fname = 'shear_sp_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], case_dir, fname), "r")
lines = fdata.readlines()
fdata.close()
x = np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
x[j] = float(line[2])
if x[j] == xs:
tau[i] = float(line[1])
print('The path is in', case_path[icase])
print(max(tau))
print(T[np.where(tau == max(tau))])
ax1.plot(T, tau, 'b', label='x=' + str(xs), lw=2.0)
ax1.legend(loc='best', fontsize='15', frameon=False)
ax1.set_ylabel(r'$\langle \tau \rangle \,{\rm (Pa)}$')
ax1.set_xlabel(r'$t\,{\rm (s)}$')
ax1.set_title('the span-averaged wall shear stress x =' + str(xs) + '\n')
ax1.set_ylim(0, 70)
ax1.set_yticks([0, 10, 20, 30, 40, 50, 60, 70])
if tke_plot == 'on':
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
fig2, ax2 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
ax2.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, case_type[0], time_dir)
t_index = len(glob.glob1(time_directory, "*.csv"))
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
if case_type[icase] == 'no_cyl':
case.append(case_type[icase])
else:
case.append(case_type[icase].split('/')[1])
for i in range(len(T)):
if T[i] == t_final:
print('The kinetic energy plots are at t = ', T[i])
fname = 'turbulent_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], case_dir, fname), "r")
lines = fdata.readlines()
fdata.close()
tke, epsi, x = np.zeros(len(lines)), np.zeros(len(lines)), np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
tke[j] = float(line[1])
# epsi[j] = float(line[2])
x[j] = float(line[2])
print('The path is in', case_path[icase])
# tke
ax1.plot(x[(x >= 6) & (x <= 15)], tke[(x >= 6) & (x <= 15)], label=case[icase], lw=2.0,
dashes=ds[icase])
ax1.legend(loc='best', fontsize='15', frameon=False)
ax1.set_ylabel(r'$k/u_{b}^{2}$')
ax1.set_xlabel(r'$X\,{\rm (m)}$')
ax1.set_title('turbulent kinetic energy at t =' + str(t_final) + '\n')
ax2.plot(x[x <= 15], epsi[x <= 15], label=case[icase], lw=2.0,
dashes=ds[icase])
ax2.legend(loc='best', fontsize='15', frameon=False)
ax2.set_ylabel(r'$\epsilon $')
ax2.set_xlabel(r'$X\,{\rm (m)}$')
ax2.set_title('turbulent dissipation at t =' + str(t_final) + '\n')
break
if energy_plot == 'on':
# Figure set up
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
fig2, ax2 = plt.subplots(figsize=(11, 7), dpi=90)
fig3, ax3 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
ax2.spines[axis].set_linewidth(2)
ax3.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, time_dir)
# print(case_path, time_directory)
case.append(case_type[icase])
t_index = len(glob.glob1(time_directory, "*.csv"))
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
T = T[(T >= t_s) & (T <= t_f)]
# print(t_s,T)
if icase == 0:
pf8, tf8, kf8 = np.zeros((len(case_type), len(T))), np.zeros((len(case_type), len(T))), np.zeros(
(len(case_type), len(T)))
pf10, tf10, kf10 = np.zeros((len(case_type), len(T))), np.zeros((len(case_type), len(T))), np.zeros(
(len(case_type), len(T)))
Epi8, Eki8, Epi10, Eki10 = np.zeros(len(case_type)), np.zeros(len(case_type)), \
np.zeros(len(case_type)), np.zeros(len(case_type))
Eti8, Eti10 = np.zeros(len(case_type)), np.zeros(len(case_type))
# print(len(T))
for i in range(len(T)):
# print(icase)
fname = 'eflux_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], fname), "r")
lines = fdata.readlines()
fdata.close()
x = np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
x[j] = float(line[4])
if x[j] == x_front[icase]:
pf8[icase, i], kf8[icase, i], tf8[icase, i] = float(line[1]), float(line[2]), float(line[3])
if x[j] == slice_loc_R[icase]:
pf10[icase, i], kf10[icase, i], tf10[icase, i] = float(line[1]), float(line[2]), float(line[3])
print('The path is in', case_path[icase])
T10 = T[T >= T_R]
T8 = T[T >= T_i][0:len(T10)]
# print(max(tf10[icase, :][T >= T_i]), max(tf8[icase, :][T >= T_i])) # , T10)
Eti8[icase] = integrate.trapz(tf8[icase, :][T >= T_i][0:len(T10)], T8) # / (max(T8) - min(T8))
Epi8[icase] = integrate.trapz(pf8[icase, :][T >= T_i][0:len(T10)], T8) # / (max(T8) - min(T8))
Eki8[icase] = integrate.trapz(kf8[icase, :][T >= T_i][0:len(T10)], T8) # / (max(T8) - min(T8))
Epi10[icase] = integrate.trapz(pf10[icase, :][T >= T_R], T10) # / (max(T10) - min(T10))
Eki10[icase] = integrate.trapz(kf10[icase, :][T >= T_R], T10) # / (max(T10) - min(T10))
Eti10[icase] = integrate.trapz(tf10[icase, :][T >= T_R], T10) # / (max(T10) - min(T10))
print(kf8[0, len(T) - 1], kf10[1:icase + 1, len(T) - 1],
abs(3 * kf8[0, len(T) - 1] - kf10[1:icase + 1, len(T) - 1]) / (3 * kf8[0, len(T) - 1]))
# print(abs(integrate.trapz(tf8[0, :],T)-integrate.trapz(tf10[icase, :],T))/integrate.trapz(tf8[0, :],T))
# T8) - integrate.cumtrapz(kf10[icase, :][T >= T_R][0:len(T10)],
# T10))))
#if icase == 3:
# Epi10[icase] = Epi10[icase] * 0.9
# print(2.5*Eki8[1:icase+1] - Eki10[1:icase+1]) #(4*Eki8 - Eki10)/4*Eti8,
# print(len(Epi10[1:icase+1]),tt)
if icase > 0:
ax1.plot(tt[1:icase + 1], 1 * abs(Epi8[0] - Epi10[1:icase + 1]) / Eti8[0],
'b*-') # , ms[1:icase+1])#, label=legend[1:icase+1], lw=lws,
ax2.plot(tt[1:icase + 1], abs(3 * Eki8[icase] - Eki10[1:icase + 1]) / 3 * Eti8[0],
'b*-') # , ms[1:icase+1])#, label=legend[1:icase+1], lw=lws,
ax3.plot(tt[1:icase + 1],
100 * (abs(2 * Epi8[0] - Epi10[1:icase + 1]) + abs(3 * Eki8[icase] - Eki10[1:icase + 1])) / 3 * Eti8[0]
, 'b*-') # 3 * kf8[0, len(T)-1] - kf10[1:icase + 1,len(T)-1])/ (3 * kf8[ 0, len(T)-1])
# label=legend[1:icase+1], lw=lws)
ax1.legend(loc='best', fontsize='24', frameon=False)
ax1.set_ylabel(r'Potential Energy Reduction')
ax1.set_xlabel(r'Number of cylinders')
ax1.set_title('Potential Energy Reduction' + '\n')
ymin, ymax = ax1.get_ylim()
xmin, xmax = min(tt[1:icase + 1]), max(tt[1:icase + 1])
x_ticks_labels = ['1 cyl', '2 cyl', '4 cyl']
N = 4
ax1.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))
#ax1.set_xticks(np.round(np.linspace(xmin, xmax, 4), 1))
ax1.set_xticklabels(x_ticks_labels, rotation='horizontal', fontsize=18)
ax2.legend(loc='best', fontsize='24', frameon=False)
ax2.set_ylabel(r'Kinetic Energy Reduction')
ax2.set_xlabel(r'Number of cylinders')
ax2.set_title('Kinetic Energy Reduction' + '\n')
ymin, ymax = ax2.get_ylim()
# print(ymax,ymin, 2.5*abs(Eki8[0] - Eki10[1:icase+1]) / Eti8[0])
N = 4
ax2.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))
xmin, xmax = min(tt[1:icase + 1]), max(tt[1:icase + 1])
ax2.set_xticks(tt[1:icase + 1])
ax3.legend(loc='best', fontsize='24', frameon=False)
ax3.set_ylabel(r'Total Energy Reduction(%)')
ax3.set_xlabel(r'Number of cylinders')
ax3.set_title('Total Energy Reduction' + '\n')
ymin, ymax = ax3.get_ylim()
N = 4
ax3.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))
#ax3.set_xticks(tt[1:icase + 1])
if flux_plot == 'on':
# Figure set up
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
fig2, ax2 = plt.subplots(figsize=(11, 7), dpi=90)
fig3, ax3 = plt.subplots(figsize=(11, 7), dpi=90)
fig4, ax4 = plt.subplots(figsize=(11, 7), dpi=90)
fig5, ax5 = plt.subplots(figsize=(11, 7), dpi=90)
fig6, ax6 = plt.subplots(figsize=(11, 7), dpi=90)
fig7, ax7 = plt.subplots(figsize=(11, 7), dpi=90)
fig8, ax8 = plt.subplots(figsize=(11, 7), dpi=90)
fig9, ax9 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
ax2.spines[axis].set_linewidth(2)
ax3.spines[axis].set_linewidth(2)
ax4.spines[axis].set_linewidth(2)
ax5.spines[axis].set_linewidth(2)
ax6.spines[axis].set_linewidth(2)
ax7.spines[axis].set_linewidth(2)
ax8.spines[axis].set_linewidth(2)
ax9.spines[axis].set_linewidth(2)
for icase in range(len(case_type)):
case_path.append(parent_dir + '/' + case_dir + '/' + case_type[icase])
time_directory = os.path.join(parent_dir, time_dir)
print(case_path, time_directory)
# if case_type[icase] == 'no_cyl':
case.append(case_type[icase])
#
t_index = len(glob.glob1(time_directory, "*.csv"))
# print(time_directory, parent_dir)
T = np.zeros(t_index)
for i in range(t_index):
T[i] = t_input(i, time_directory, 'time')
T = T[(T >= t_s) & (T <= t_f)]
T = np.setdiff1d(T, np.asarray(ts))
if icase == 0:
pf8, tf8, kf8 = np.zeros((len(case_type), len(T))), np.zeros((len(case_type), len(T))), np.zeros(
(len(case_type), len(T)))
pf10, tf10, kf10 = np.zeros((len(case_type), len(T))), np.zeros((len(case_type), len(T))), np.zeros(
(len(case_type), len(T)))
pf15, tf15, kf15 = np.zeros((len(case_type), len(T))), np.zeros((len(case_type), len(T))), np.zeros(
(len(case_type), len(T)))
Ept8, Ekt8, Et8 = 0 * tf8, 0 * kf8, 0 * pf8
Ept10, Ekt10, Et10 = 0 * tf10, 0 * kf10, 0 * pf10
Ept15, Ekt15, Et15 = 0 * tf15, 0 * kf15, 0 * pf15
Epi8, Eki8, Epi10, Eki10 = np.zeros(len(case_type)), np.zeros(len(case_type)), \
np.zeros(len(case_type)), np.zeros(len(case_type))
Eti8, Eti10 = np.zeros(len(case_type)), np.zeros(len(case_type))
for i in range(len(T)):
# print(T[i])
fname = 'eflux_' + str(T[i]) + '.dat'
fdata = open(os.path.join(case_path[icase], fname), "r")
lines = fdata.readlines()
fdata.close()
x = np.zeros(len(lines))
for j in range(len(lines)):
line = lines[j]
line = line.strip()
line = line.split()
x[j] = float(line[4])
if x[j] == x_front:
pf8[icase, i], kf8[icase, i], tf8[icase, i] = float(line[1]), float(line[2]), float(line[3])
if x[j] == slice_loc_R[icase]:
pf10[icase, i], kf10[icase, i], tf10[icase, i] = float(line[1]), float(line[2]), float(line[3])
if x[j] == slice_loc_D[icase]:
pf15[icase, i], kf15[icase, i], tf15[icase, i] = float(line[1]), float(line[2]), float(line[3])
print('The path is in', case_path[icase])
if reflection == 'on':
# print(T)
T10 = T[T >= T_R]
T8 = T[T >= T_i][0:len(T10)]
# print(len(T10) - 1)
if icase == 0:
pf1, tf1, kf1 = np.zeros((len(case_type), len(T10) - 1)), np.zeros(
(len(case_type), len(T10) - 1)), np.zeros(
(len(case_type), len(T10) - 1))
pf2, tf2, kf2 = np.zeros((len(case_type), len(T10) - 1)), np.zeros(
(len(case_type), len(T10) - 1)), np.zeros(
(len(case_type), len(T10) - 1))
pf1[icase, :] = integrate.cumtrapz(pf8[icase, :][T >= T_i][0:len(T10)],
T8)
kf1[icase, :] = integrate.cumtrapz(kf8[icase, :][T >= T_i][0:len(T10)],
T8)
tf1[icase, :] = integrate.cumtrapz(tf8[icase, :][T >= T_i][0:len(T10)],
T8)
pf2[icase, :] = integrate.cumtrapz(pf10[icase, :][T >= T_R],
T10) # [tf10[icase, :] >= e_limit], T10) # , initial=0)
tf2[icase, :] = integrate.cumtrapz(tf10[icase, :][T >= T_R],
T10) # [tf10[icase, :] >= e_limit], T10) # , initial=0)
kf2[icase, :] = integrate.cumtrapz(kf10[icase, :][T >= T_R],
T10) # [tf10[icase, :] >= e_limit], T10) # , initial=0)
ax7.plot(T10[1:len(T10)], pf2[icase, :] / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax8.plot(T10[1:len(T10)], kf2[icase, :] / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
if icase > 0:
ax9.plot(T10[1:len(T10)], tf2[icase, :] / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax7.legend(loc='best', fontsize='24', frameon=False)
ax7.set_ylabel(r'$\phi^{p}_{I}$')
ax7.set_xlabel(r'Time [s]')
ax7.set_title('Cumulative potential Energy Flux New \n')
ax7.set_xlim(4, 12)
ax7.set_ylim(0.3, 0.7)
# ax7.set_yticks(np.arange(0,0.18,0.03))
ax8.legend(loc='best', fontsize='24', frameon=False)
ax8.set_ylabel(r'$\phi^{k}_{I}$')
ax8.set_xlabel(r'Time [s]')
ax8.set_title('Cumulative kinetic Energy Flux New \n')
ax8.set_xlim(4, 12)
ax8.set_ylim(0.3, 0.7)
# ax8.set_yticks(np.arange(0,0.18,0.03))
ax9.legend(loc='best', fontsize='24', frameon=False)
ax9.set_ylabel(r'$\phi^{t}_{I}$')
ax9.set_xlabel(r'Time [s]')
ax9.set_title('Cumulative total Energy Flux New \n')
ax9.set_xlim(4, 12)
ax9.set_ylim(0.8, 1)
if icase > 0:
ax1.plot(T10[1:len(T10)], (pf2[icase, :] - pf2[0, :]) / tf2[0, :], ms[icase], label=legend[icase],
lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax2.plot(T10[1:len(T10)], (kf2[0, :] - kf2[icase, :]) / tf2[0, :], ms[icase], label=legend[icase],
lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax3.plot(T10[1:len(T10)], abs(tf2[0, :] - tf2[icase, :]) / tf2[0, :], ms[icase], label=legend[icase],
lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax1.legend(loc='best', fontsize='24', frameon=False)
ax1.set_ylabel(r'$\phi^{p}_{r}$')
ax1.set_xlabel(r'Time [s]')
ax1.set_title('Cumulative reflected potential Energy Flux New \n')
ax1.set_xlim(4, 12)
ax1.set_yticks(np.arange(0, 0.26, 0.05))
ax2.legend(loc='best', fontsize='24', frameon=False)
ax2.set_ylabel(r'$\phi^{k}_{r}$')
ax2.set_xlabel(r'Time [s]')
ax2.set_title('Cumulative reflected kinetic Energy Flux New \n')
ax2.set_xlim(4, 12)
ax2.set_yticks(np.arange(0, 0.26, 0.05))
ax3.legend(loc='best', fontsize='24', frameon=False)
ax3.set_ylabel(r'$\phi^{t}_{r}$')
ax3.set_xlabel(r'Time [s]')
ax3.set_title('Cumulative reflected total Energy Flux New \n')
ax3.set_xlim(4, 12)
ax3.set_yticks(np.arange(0, 0.26, 0.05))
elif dissipation == 'on':
T15 = T[T >= T_R] # T[tf10[icase, :] >= e_limit]
T10 = T[T >= T_i][0:len(T15)] # T[tf8[icase, :] >= e_limit]
if icase == 0:
pf1, tf1, kf1 = np.zeros((len(case_type), len(T15) - 1)), np.zeros(
(len(case_type), len(T15) - 1)), np.zeros(
(len(case_type), len(T15) - 1))
pf2, tf2, kf2 = np.zeros((len(case_type), len(T15) - 1)), np.zeros(
(len(case_type), len(T15) - 1)), np.zeros(
(len(case_type), len(T15) - 1))
pf1[icase, :] = integrate.cumtrapz(pf10[icase, :][T >= T_i][0:len(T15)],
T10) # [tf8[icase, :] >= e_limit][0:len(T10)], T8[0:len(T10)])
# initial=0)
kf1[icase, :] = integrate.cumtrapz(kf10[icase, :][T >= T_i][0:len(T15)],
T10) # [tf8[icase, :] >= e_limit][0:len(T10)], T8[0:len(T10)])
# initial=0)
tf1[icase, :] = integrate.cumtrapz(tf10[icase, :][T >= T_i][0:len(T15)],
T10) # [tf8[icase, :] >= e_limit][0:len(T10)], T8[0:len(T10)])
# initial=0)
pf2[icase, :] = integrate.cumtrapz(pf15[icase, :][T >= T_R],
T15) # [tf10[icase, :] >= e_limit], T10) # , initial=0)
tf2[icase, :] = integrate.cumtrapz(tf15[icase, :][T >= T_R],
T15) # [tf10[icase, :] >= e_limit], T10) # , initial=0)
kf2[icase, :] = integrate.cumtrapz(kf15[icase, :][T >= T_R], T15)
ax7.plot(T15[1:len(T15)], pf2[icase, :] / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax8.plot(T15[1:len(T15)], kf2[icase, :] / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
if icase > 0:
ax9.plot(T15[1:len(T15)], (pf2[icase, :] + kf2[icase, :]) / tf2[0, :], ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax7.legend(loc='best', fontsize='24', frameon=False)
ax7.set_ylabel(r'$\phi^{p}_{tr}$')
ax7.set_xlabel(r'Time [s]')
ax7.set_title('Cumulative transmitted potential Energy Flux New \n')
ax7.set_xlim(4, 12)
# ax7.set_ylim(0, 0.15)
# ax7.set_yticks(np.arange(0,0.18,0.03))
ax8.legend(loc='best', fontsize='24', frameon=False)
ax8.set_ylabel(r'$\phi^{k}_{tr}$')
ax8.set_xlabel(r'Time [s]')
ax8.set_title('Cumulative transmitted kinetic Energy Flux New \n')
ax8.set_xlim(4, 12)
# ax8.set_ylim(0, 0.15)
# ax8.set_yticks(np.arange(0,0.18,0.03))
ax9.legend(loc='best', fontsize='24', frameon=False)
ax9.set_ylabel(r'$\phi^{t}_{tr}$')
ax9.set_xlabel(r'Time [s]')
ax9.set_title('Cumulative transmitted total Energy Flux New \n')
ax9.set_xlim(4, 12)
# ax9.set_ylim(0, 0.15)
# ax9.set_yticks(np.arange(0,0.18,0.03))
if icase >=3:
print(kf2[icase, :] , kf1[icase, :], abs(kf2[icase, :] - kf1[icase, :])/ tf1[0,:])
if icase > 0:
ax1.plot(T15[1:len(T15)], abs(pf2[icase, :] - pf1[icase, :]) / tf1[0, :], ms[icase],
label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax2.plot(T15[1:len(T15)], abs(kf2[icase, :] - kf1[icase, :]) / tf1[0, :], ms[icase],
label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax3.plot(T15[1:len(T15)],
(abs(pf2[icase, :] - pf1[icase, :]) + abs(kf2[icase, :] - kf1[icase, :])) / tf1[0, :],
ms[icase], label=legend[icase],
lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax1.legend(loc='best', fontsize='24', frameon=False)
ax1.set_ylabel(r'$\phi^{p}_{d}$')
ax1.set_xlabel(r'Time [s]')
ax1.set_title('Cumulative dissipated potential Energy Flux New \n')
ax1.set_xlim(4, 12)
ax1.set_ylim(0, 0.15)
ax1.set_yticks(np.arange(0, 0.18, 0.03))
ax2.legend(loc='best', fontsize='24', frameon=False)
ax2.set_ylabel(r'$\phi^{k}_{d}$')
ax2.set_xlabel(r'Time [s]')
ax2.set_title('Cumulative dissipated kinetic Energy Flux New \n')
ax2.set_xlim(4, 12)
ax2.set_ylim(0, 0.2)
ax2.set_yticks(np.arange(0, 0.21, 0.03))
ax3.legend(loc='best', fontsize='24', frameon=False)
ax3.set_ylabel(r'$\phi^{t}_{d}$')
ax3.set_xlabel(r'Time [s]')
ax3.set_title('Cumulative dissipated total Energy Flux New \n')
ax3.set_xlim(4, 12)
ax3.set_ylim(0, 0.30)
ax3.set_yticks(np.arange(0, 0.31, 0.06))
if icase > 0:
if reflection == 'on':
# print(T10, T8)
ax4.plot(T10[1:len(T10)][T10[1:len(T10)] >= 4.5],
abs(1.4 * kf1[icase, :][T10[1:len(T10)] >= 4.5] - kf2[icase, :][T10[1:len(T10)] >= 4.5]) /
tf1[icase, :][T10[1:len(T10)] >= 4.5], ms[icase],
# - (Ekt8[0, :] - Ekt10[0, :])) / Eki8[icase]
label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ymin, ymax = ax4.get_ylim()
N = 4
ax4.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))
ax5.plot(T10[1:len(T10)][T10[1:len(T10)] >= 4.5],
abs(tf1[icase, :][T10[1:len(T10)] >= 4.5] - tf2[icase, :]
[T10[1:len(T10)] >= 4.5]) / tf1[icase, :][T10[1:len(T10)] >= 4.5]
# - (Et8[0, :] - Et10[0, :])/ Eti8[icase]
, ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax6.plot(T10[1:len(T10)][T10[1:len(T10)] >= 4.5],
(1.2 * pf1[icase, :][T10[1:len(T10)] >= 4.5] - pf1[0, :][T10[1:len(T10)] >= 4.5]) /
tf1[icase, :][T10[1:len(T10)] >= 4.5], # / Epi8[icase]
ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ymin, ymax = ax5.get_ylim()
N = 4
ax5.set_yticks(np.round(np.linspace(ymin, ymax, N), 2))
ax4.legend(loc='best', fontsize='24', frameon=False)
ax4.set_ylabel(r'$\phi_{r}^{k}$')
ax4.set_xlabel(r'Time [s]')
ax4.set_title('Reflected kinetic Energy Flux \n')
ax4.set_xlim(4.0, 12.2)
ax5.legend(loc='best', fontsize='24', frameon=False)
ax5.set_ylabel(r'$\phi_{r}^{t}$')
ax5.set_xlabel(r'Time [s]')
ax5.set_title('Reflected total energy flux \n')
ax5.set_xlim(4.0, 12.2)
ax6.legend(loc='best', fontsize='24', frameon=False)
ax6.set_ylabel(r'$\phi_{r}^{p}$')
ax6.set_xlabel(r'Time [s]')
ax6.set_title('Reflected potential Energy Flux \n')
ax6.set_xlim(4.0, 12.2)
elif dissipation == 'on':
ax4.plot(T15[1:len(T15)][T15[1:len(T15)] >= 4.5],
1.5 * abs(kf1[icase, :][T15[1:len(T15)] >= 4.5] - kf2[icase, :][T15[1:len(T15)] >= 4.5]) /
tf1[icase, :][T15[1:len(T15)] >= 4.5], ms[icase],
# - (Ekt8[0, :] - EkT15[0, :])) / Eki8[icase]
label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax5.plot(T15[1:len(T15)][T15[1:len(T15)] >= 4.5],
abs(tf1[icase, :][T15[1:len(T15)] >= 4.5] - tf2[icase, :]
[T15[1:len(T15)] >= 4.5]) / tf1[icase, :][T15[1:len(T15)] >= 4.5]
# - (Et8[0, :] - ET15[0, :])/ Eti8[icase]
, ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax6.plot(T15[1:len(T15)][T15[1:len(T15)] >= 4.5],
abs(1.2 * pf1[icase, :][T15[1:len(T15)] >= 4.5] - pf2[icase, :][T15[1:len(T15)] >= 4.5]) /
tf1[icase, :][T15[1:len(T15)] >= 4.5], # / Epi8[icase]
ms[icase], label=legend[icase], lw=lws,
linestyle=ls[icase], dashes=ds[icase])
ax4.legend(loc='best', fontsize='24', frameon=False)
ax4.set_ylabel(r'$\phi_{d}^{k}$')
ax4.set_xlabel(r'Time [s]')
ax4.set_title('Dissipated kinetic Energy Flux Coefficient \n')
ax4.set_xlim(4.0, 12.2)
ax5.legend(loc='best', fontsize='24', frameon=False)
ax5.set_ylabel(r'$\phi_{d}^{t}$')
ax5.set_xlabel(r'Time [s]')
ax5.set_title('Dissipated Total Energy Flux \n')
ax5.set_xlim(4.0, 12.2)
ax6.legend(loc='best', fontsize='24', frameon=False)
ax6.set_ylabel(r'$\phi_{d}^{p}$')
ax6.set_xlabel(r'Time [s]')
ax6.set_title('Dissipated potential Energy Flux \n')
ax6.set_xlim(4.0, 12.2)
# print(case)
data_directory = 'interpolate_linear'
slices = ['8', '11.1', '11', '10.4', '11.5', '15', '17']
ls = ['solid', 'dotted', 'dashed', 'dashdot', 'dashdot', 'dotted', 'dashed']
ms = ['x', '^', '*', 's', 'd', 'o', '+']
ds = [(), (1, 1), (5, 5), (3, 5, 1, 5, 1, 5), (8, 4, 2, 4, 2, 4), (1, 10), (5, 10)]
t_final = 10
slice = slices[2]
dissislice = ['13', '12', '15'] # , '10.8', '10.8', '10.8']
refslice = ['10']
case = ['yang_case_0.7M']#, 'flex_case_double_cyl_d_0.4_sbs-y3e8', 'flex_case_double_cyl_d_0.4_sbs-y3e9',
#'flex_case_single_cyl_d_0.4_y3e8',
#'flex_case_single_cyl_d_0.4_y3e9'] # 'rigid_case_single_cyl_d_1.6',
# 'rigid_case_double_cyl_d_0.4_sbs'] # , 'flex_case_double_cyl_d_0.4_sbs-y3e8'] # ,
# 'flex_case_double_cyl_d_0.4_sbs-y3e8'] # ,]
cc = ['rd0.14'] #'rd0.4m4', 'rd0.4s', 'rd0.8s', 'rd1.6s', 'rd0.4m2'] # , 'fd0.4s']
E_Dnormt = np.zeros(len(case) - 1)
section = ['Transect I', 'Transect I', 'Transect III']
alt = 'interpolate'
cumulative = 'on'
dissipation = 'off'
reflection = 'on'
energy_flux_plot = 'on'
total_energy = 'off'
nt = 45
E_R = np.zeros((len(case), nt))
# fig, ax = plt.subplots(figsize=(11, 7), dpi=90)
interpolate = 'no'
for icase in range(len(case)):
if icase == 0:
if energy_flux_plot == 'on':
fig1, ax1 = plt.subplots(figsize=(11, 7), dpi=90)
fig2, ax2 = plt.subplots(figsize=(11, 7), dpi=90)
fig3, ax3 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(2)
ax2.spines[axis].set_linewidth(2)
ax3.spines[axis].set_linewidth(2)
if cumulative == 'on' and total_energy == 'off':
fig4, ax4 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax4.spines[axis].set_linewidth(2)
fig6, ax6 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax6.spines[axis].set_linewidth(2)
fig8, ax8 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax8.spines[axis].set_linewidth(2)
if reflection == 'on':
fig5, ax5 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax5.spines[axis].set_linewidth(2)
if dissipation == 'on':
fig7, ax7 = plt.subplots(figsize=(11, 7), dpi=90)
for axis in ['top', 'bottom', 'left', 'right']:
ax7.spines[axis].set_linewidth(2)