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FigureS123.py
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FigureS123.py
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import os
import numpy as np
import matplotlib.pyplot as plt
from utilities import getJointIndices
# %% Settings
# cases = ['174', '145']
# cases_no_mtp = ['106', '119', '115']
# case_4exp = '145'
# cases = ['31', '32', '28', '27', '4']
cases = ['31', '32', '28', '66', '40']
case_4exp = '40'
# color_mtp=['#377eb8','#ff7f00']
# linestyle_mtp=['solid','dashdot']
# color_no_mtp=['black', '#984ea3','#4daf4a']
# linestyle_no_mtp=['solid','dashed','dashdot']
colors=['black', '#984ea3','#4daf4a','#377eb8','#ff7f00']
linestyles=['solid','dashed','dashdot','solid','dashdot']
linewidth_s = 3
fontsize_tick = 14
fontsize_label = 15
fontsize_title = 17
# %% Fixed settings
pathMain = os.getcwd()
# Load results
pathTrajectories = os.path.join(pathMain, 'Results')
optimaltrajectories = np.load(os.path.join(pathTrajectories,
'optimalTrajectories.npy'),
allow_pickle=True).item()
# Load experimental data
pathData = os.path.join(pathMain, 'OpenSimModel', 'new_model')
experimentalData = np.load(os.path.join(pathData, 'experimentalData.npy'),
allow_pickle=True).item()
subject = 'new_model'
swing = 65
# %% Visualize results
plt.close('all')
# %% Joint coordinates
joints_title = ['pelvis tilt', 'pelvis list', 'pelvis rotation', 'pelvis tx',
'pelvis ty', 'pelvis tz', 'hip flexion', 'hip adduction',
'hip rotation', 'hip flexion', 'hip adduction', 'hip rotation',
'knee angle', 'knee angle', 'ankle angle', 'ankle angle',
'subtalar angle', 'subtalar angle', 'mtp angle', 'mtp angle',
'lumbar extension', 'lumbar bending', 'lumbar rotation',
'arm flexion', 'arm adduction', 'arm rotation', 'arm flexion',
'arm adduction', 'arm rotation', 'elbow flexion', 'elbow flexion']
jointsToPlot = ['pelvis_tilt', 'pelvis_list', 'pelvis_rotation',
'pelvis_tx', 'pelvis_ty', 'pelvis_tz',
'hip_flexion_r', 'hip_adduction_r', 'hip_rotation_r',
'knee_angle_r', 'ankle_angle_r',
'subtalar_angle_r', 'mtp_angle_r',
'lumbar_extension', 'lumbar_bending', 'lumbar_rotation',
'arm_flex_r', 'arm_add_r', 'arm_rot_r', 'elbow_flex_r']
kinematic_ylim_ub = [10, 10, 20, 2, 1, 0.5, 50, 20, 20, 20, 30, 20, 60, 20, 20, 20, 20, 0, 40, 120]
kinematic_ylim_lb = [-10, -10, -20, 0, 0.8, -0.5, -50, -20, -20, -80, -30, -20, 0, -20, -20, -20, -20, -30, -10, -10]
NJointsToPlot = len(jointsToPlot)
fig, axs = plt.subplots(4, 6)
count = 0
for i, ax in enumerate(axs.flat):
plotExperimentalData = True
if i < NJointsToPlot:
for c, case in enumerate(cases):
c_joints = optimaltrajectories[case]['joints']
if not jointsToPlot[i] in c_joints:
continue
c_joint_idx = c_joints.index(jointsToPlot[i])
# Simulated data
ax.plot(optimaltrajectories[case]['GC_percent'],
optimaltrajectories[case]['coordinate_values'][c_joint_idx:c_joint_idx+1, :].T, linewidth=linewidth_s, color=colors[c], linestyle=linestyles[c])
if plotExperimentalData and not jointsToPlot[i] == 'pelvis_tz':
# Mean measured data
ax.plot(experimentalData[subject]["kinematics"]["positions"]["GC_percent"],
experimentalData[subject]["kinematics"]["positions"]["mean"][jointsToPlot[i]], linewidth=linewidth_s, color='grey')
# Mean +/- 2 standard deviation
ax.fill_between(experimentalData[subject]["kinematics"]["positions"]["GC_percent"],
experimentalData[subject]["kinematics"]["positions"]["mean"][jointsToPlot[i]] + 2*experimentalData[subject]["kinematics"]["positions"]["std"][jointsToPlot[i]],
experimentalData[subject]["kinematics"]["positions"]["mean"][jointsToPlot[i]] - 2*experimentalData[subject]["kinematics"]["positions"]["std"][jointsToPlot[i]],
facecolor='grey', alpha=0.4)
plotExperimentalData = False
ax.vlines(swing, kinematic_ylim_lb[i], kinematic_ylim_ub[i], color='k')
ax.set_title(joints_title[c_joint_idx])
ax.set_ylim((kinematic_ylim_lb[i],kinematic_ylim_ub[i]))
if i == 4:
ax.set_yticks([kinematic_ylim_lb[i],0.9,kinematic_ylim_ub[i]])
else:
ax.set_yticks([kinematic_ylim_lb[i],0,kinematic_ylim_ub[i]])
plt.setp(ax.get_yticklabels(), fontsize=fontsize_tick)
ax.set_xticks([0,50,100])
if i > 13:
ax.set_xticklabels(['0','50','100'], fontsize=fontsize_tick)
else:
ax.set_xticklabels([])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.setp(axs[2, 2:6], xlabel='Gait cycle (%)')
plt.setp(axs[-1, :], xlabel='Gait cycle (%)')
plt.setp(axs[:, 0], ylabel='Joint angle (deg)')
plt.setp(axs[0, 3], ylabel='Joint position (m)')
fig.align_ylabels()
for ax in axs.flat:
ax.xaxis.get_label().set_fontsize(fontsize_label)
ax.yaxis.get_label().set_fontsize(fontsize_label)
ax.title.set_fontsize(fontsize_title)
for ax in (axs[3,2:6].flat):
ax.set_visible(False)
fig.set_size_inches(16,12)
fig.tight_layout()
# %% Muscle activations
muscles = optimaltrajectories[cases[0]]['muscles']
musclesToPlot = ['glut_med1_r', 'glut_med2_r', 'glut_med3_r', 'glut_min1_r',
'glut_min2_r', 'glut_min3_r', 'semimem_r', 'semiten_r',
'bifemlh_r', 'bifemsh_r', 'sar_r', 'add_long_r', 'add_brev_r',
'add_mag1_r', 'add_mag2_r', 'add_mag3_r', 'tfl_r', 'pect_r',
'grac_r', 'glut_max1_r', 'glut_max2_r', 'glut_max3_r',
'iliacus_r', 'psoas_r', 'quad_fem_r', 'gem_r', 'peri_r',
'rect_fem_r', 'vas_med_r', 'vas_int_r', 'vas_lat_r',
'med_gas_r', 'lat_gas_r', 'soleus_r', 'tib_post_r',
'flex_dig_r', 'flex_hal_r', 'tib_ant_r', 'per_brev_r',
'per_long_r', 'per_tert_r', 'ext_dig_r', 'ext_hal_r',
'ercspn_r', 'intobl_r', 'extobl_r',]
musclesToPlot_title = ['gluteus med 1', 'gluteus med 2', 'gluteus med 3', 'gluteus min 1',
'gluteus min 2', 'gluteus min 3', 'semimem', 'semiten',
'bifemlh', 'bifemsh', 'sartorius', 'adductor long', 'adductor brev',
'adductor mag 1', 'adductor mag 2', 'adductor mag 3', 'tfl', 'pectineus',
'gracilis', 'gluteus max 1', 'gluteus max 2', 'gluteus max 3',
'iliacus', 'psoas', 'quadratus fem', 'gemellus', 'piriformis',
'rectus fem', 'vastus med', 'vastus int', 'vastus lat',
'gastroc med', 'gastroc lat', 'soleus', 'tibialis post',
'flex dig', 'flex hal', 'tibialis ant', 'peroneus brev',
'peroneus long', 'peroneus tert', 'ext dig', 'ext hal',
'ercspn', 'intobl', 'extobl',]
NMusclesToPlot = len(musclesToPlot)
idxMusclesToPlot = getJointIndices(muscles, musclesToPlot)
mappingEMG = {'glut_med1_r': 'GluMed_r',
'glut_med2_r': 'GluMed_r',
'glut_med3_r': 'GluMed_r',
'semimem_r': 'HamM_r',
'semiten_r': 'HamM_r',
'bifemlh_r': 'HamL_r',
'bifemsh_r': 'HamL_r',
'add_long_r': 'AddL_r',
'tfl_r': 'TFL_r',
'rect_fem_r': 'RF_r',
'vas_med_r': 'VM_r',
'vas_int_r': 'VL_r',
'vas_lat_r': 'VL_r',
'med_gas_r': 'GM_r',
'lat_gas_r': 'GL_r',
'soleus_r': 'Sol_r',
'tib_ant_r': 'TA_r',
'per_brev_r': 'PerB_l',
'per_long_r': 'PerL_l',
'glut_med1_l': 'GluMed_l',
'glut_med2_l': 'GluMed_l',
'glut_med3_l': 'GluMed_l',
'semimem_l': 'HamM_l',
'semiten_l': 'HamM_l',
'bifemlh_l': 'HamL_l',
'bifemsh_l': 'HamL_l',
'add_long_l': 'AddL_l',
'tfl_l': 'TFL_l',
'rect_fem_l': 'RF_l',
'vas_med_l': 'VM_l',
'vas_int_l': 'VL_l',
'vas_lat_l': 'VL_l',
'med_gas_l': 'GM_l',
'lat_gas_l': 'GL_l',
'soleus_l': 'Sol_l',
'tib_ant_l': 'TA_l',
'per_brev_l': 'PerB_l',
'per_long_l': 'PerL_l'}
fig, axs = plt.subplots(8, 6)
for i, ax in enumerate(axs.flat):
if i < NMusclesToPlot:
for c, case in enumerate(cases):
ax.plot(optimaltrajectories[case]['GC_percent'],
optimaltrajectories[case]['muscle_activations'][idxMusclesToPlot[i]:idxMusclesToPlot[i]+1, :].T, c=colors[c], linestyle=linestyles[c], linewidth=linewidth_s, label='case_' + case + '_no_mtp')
if musclesToPlot[i] in mappingEMG and case == case_4exp:
# Normalize EMG such that peak mean EMG = peak activation
exp_mean = experimentalData[subject]["EMG"]["mean"][mappingEMG[musclesToPlot[i]]]
exp_mean_peak = np.max(exp_mean)
sim = optimaltrajectories[case]['muscle_activations'][idxMusclesToPlot[i], :].T
sim_peak = np.max(sim)
scaling_emg = sim_peak / exp_mean_peak
ax.fill_between(experimentalData[subject]["EMG"]["GC_percent"],
experimentalData[subject]["EMG"]["mean"][mappingEMG[musclesToPlot[i]]] * scaling_emg + 2*experimentalData[subject]["EMG"]["std"][mappingEMG[musclesToPlot[i]]] * scaling_emg,
experimentalData[subject]["EMG"]["mean"][mappingEMG[musclesToPlot[i]]] * scaling_emg - 2*experimentalData[subject]["EMG"]["std"][mappingEMG[musclesToPlot[i]]] * scaling_emg,
facecolor='grey', alpha=0.4)
ax.plot(experimentalData[subject]["EMG"]["GC_percent"],
experimentalData[subject]["EMG"]["mean"][mappingEMG[musclesToPlot[i]]] * scaling_emg, color='grey', linewidth=linewidth_s)
ax.vlines(swing, 0, 1, color='k')
ax.set_title(musclesToPlot_title[i])
ax.set_ylim((0,1))
ax.set_yticks([0, 1])
plt.setp(ax.get_yticklabels(), fontsize=fontsize_tick)
ax.set_xticks([0,50,100])
if i > 41:
ax.set_xticklabels(['0','50','100'], fontsize=fontsize_tick)
else:
ax.set_xticklabels([])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
handles, labels = ax.get_legend_handles_labels()
plt.legend(handles, labels, loc='upper right')
plt.setp(axs[-1, :], xlabel='Gait cycle (%)')
plt.setp(axs[:, 0], ylabel='Act (-)')
fig.align_ylabels()
for ax in axs.flat:
ax.xaxis.get_label().set_fontsize(fontsize_label)
ax.yaxis.get_label().set_fontsize(fontsize_label)
ax.title.set_fontsize(fontsize_title)
for ax in (axs[7,4:6].flat):
ax.set_visible(False)
fig.set_size_inches(16,12)
fig.tight_layout()
# %% Kinetics
jointsToPlot = ['hip_flexion_r', 'hip_adduction_r', 'hip_rotation_r',
'knee_angle_r', 'ankle_angle_r',
'subtalar_angle_r', 'mtp_angle_r',
'lumbar_extension', 'lumbar_bending', 'lumbar_rotation',
'arm_flex_r', 'arm_add_r', 'arm_rot_r', 'elbow_flex_r']
kinetic_ylim_ub = [60, 20, 20, 70, 30, 30, 10, 20, 20, 20, 5, 5, 5, 5]
kinetic_ylim_lb = [-60, -100, -20, -50, -110, -30, -20, -20, -20, -20, -5, -5, -5, -5]
NJointsToPlot = len(jointsToPlot)
fig, axs = plt.subplots(4, 6)
count = 0
for i, ax in enumerate(axs.flat):
plotExperimentalData = True
if i < NJointsToPlot:
for c, case in enumerate(cases):
c_joints = optimaltrajectories[case]['joints']
if not jointsToPlot[i] in c_joints:
continue
c_joint_idx = c_joints.index(jointsToPlot[i])
ax.plot(optimaltrajectories[case]['GC_percent'],
optimaltrajectories[case]['joint_torques'][c_joint_idx:c_joint_idx+1, :].T, c=colors[c], linestyle=linestyles[c], linewidth=linewidth_s, label='case_' + case + '_no_mtp')
if plotExperimentalData and not jointsToPlot[i] == 'mtp_angle_r':
ax.plot(experimentalData[subject]["kinetics"]["GC_percent"],
experimentalData[subject]["kinetics"]["mean"][jointsToPlot[i]], linewidth=linewidth_s, color='grey')
ax.fill_between(experimentalData[subject]["kinetics"]["GC_percent"],
experimentalData[subject]["kinetics"]["mean"][jointsToPlot[i]] + 2*experimentalData[subject]["kinetics"]["std"][jointsToPlot[i]],
experimentalData[subject]["kinetics"]["mean"][jointsToPlot[i]] - 2*experimentalData[subject]["kinetics"]["std"][jointsToPlot[i]],
facecolor='grey', alpha=0.4)
plotExperimentalData = False
ax.vlines(swing, kinetic_ylim_lb[i], kinetic_ylim_ub[i], color='k')
ax.set_title(joints_title[c_joint_idx])
ax.set_ylim((kinetic_ylim_lb[i],kinetic_ylim_ub[i]))
ax.set_yticks([kinetic_ylim_lb[i],0,kinetic_ylim_ub[i]])
plt.setp(ax.get_yticklabels(), fontsize=fontsize_tick)
ax.set_xticks([0,50,100])
if i > 7:
ax.set_xticklabels(['0','50','100'], fontsize=fontsize_tick)
else:
ax.set_xticklabels([])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.setp(axs[1, 2:6], xlabel='Gait cycle (%)')
plt.setp(axs[2, :], xlabel='Gait cycle (%)')
plt.setp(axs[:, 0], ylabel='Joint torque (Nm)')
fig.align_ylabels()
for ax in axs.flat:
ax.xaxis.get_label().set_fontsize(fontsize_label)
ax.yaxis.get_label().set_fontsize(fontsize_label)
ax.title.set_fontsize(fontsize_title)
for ax in (axs[2,2:6].flat):
ax.set_visible(False)
for ax in (axs[3,:].flat):
ax.set_visible(False)
fig.set_size_inches(16,12)
fig.tight_layout()
# # %% Ground reaction forces
# GRF_labels = optimaltrajectories[cases[0]]['GRF_labels']
# GRFToPlot = ['GRF_x_r', 'GRF_y_r', 'GRF_z_r', 'GRF_x_l','GRF_y_l', 'GRF_z_l']
# NGRFToPlot = len(GRFToPlot)
# idxGRFToPlot = getJointIndices(GRF_labels, GRFToPlot)
# fig, axs = plt.subplots(2, 3, sharex=True)
# fig.suptitle('Ground reaction forces')
# for i, ax in enumerate(axs.flat):
# color_mtp=iter(plt.cm.rainbow(np.linspace(0,1,len(cases))))
# color_no_mtp=iter(plt.cm.rainbow(np.linspace(0,1,len(cases_no_mtp))))
# if i < NJointsToPlot:
# for case in cases:
# ax.plot(optimaltrajectories[case]['GC_percent'],
# optimaltrajectories[case]['GRF'][idxGRFToPlot[i]:idxGRFToPlot[i]+1, :].T, c=next(color_mtp), label='case_' + case + '_mtp')
# ax.fill_between(experimentalData_mtp[subject]["GRF"]["GC_percent"],
# experimentalData_mtp[subject]["GRF"]["mean"][GRFToPlot[i]] + 2*experimentalData_mtp[subject]["GRF"]["std"][GRFToPlot[i]],
# experimentalData_mtp[subject]["GRF"]["mean"][GRFToPlot[i]] - 2*experimentalData_mtp[subject]["GRF"]["std"][GRFToPlot[i]],
# facecolor='blue', alpha=0.5)
# for case in cases_no_mtp:
# ax.plot(optimaltrajectories_no_mtp[case]['GC_percent'],
# optimaltrajectories_no_mtp[case]['GRF'][idxGRFToPlot[i]:idxGRFToPlot[i]+1, :].T, c=next(color_no_mtp), linestyle='dashed', label='case_' + case + '_no_mtp')
# ax.fill_between(experimentalData_no_mtp[subject]["GRF"]["GC_percent"],
# experimentalData_no_mtp[subject]["GRF"]["mean"][GRFToPlot[i]] + 2*experimentalData_no_mtp[subject]["GRF"]["std"][GRFToPlot[i]],
# experimentalData_no_mtp[subject]["GRF"]["mean"][GRFToPlot[i]] - 2*experimentalData_no_mtp[subject]["GRF"]["std"][GRFToPlot[i]],
# facecolor='blue', alpha=0.5)
# ax.set_title(GRF_labels[idxGRFToPlot[i]])
# # ax.set_ylim((kinetic_ylim_lb[i],kinetic_ylim_ub[i]))
# handles, labels = ax.get_legend_handles_labels()
# plt.legend(handles, labels, loc='upper right')
# plt.setp(axs[-1, :], xlabel='Gait cycle (%)')
# plt.setp(axs[:, 0], ylabel='(N)')
# fig.align_ylabels()
# # %% Metabolic cost and cost function value
# fig, (ax1, ax2) = plt.subplots(1, 2)
# color_mtp=plt.cm.rainbow(np.linspace(0,1,len(cases)))
# for count, case in enumerate(cases):
# print(optimaltrajectories[case]["COT"])
# ax1.scatter(count, optimaltrajectories[case]["COT"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax2.scatter(count, optimaltrajectories[case]["objective"], s=80, c=color_mtp[count, :].reshape(1,-1))
# color_no_mtp=plt.cm.rainbow(np.linspace(0,1,len(cases_no_mtp)))
# for count, case in enumerate(cases_no_mtp):
# print(optimaltrajectories_no_mtp[case]["COT"])
# ax1.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["COT"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax2.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax1.set_title("Cost of Transport")
# ax1.set_ylabel("(J/Kg/m)")
# ax2.set_title("Optimal cost value")
# ax2.set_ylabel("()")
# x_locations = np.linspace(0, len(cases)+len(cases_no_mtp)-1, len(cases)+len(cases_no_mtp))
# ax1.set_xticks(x_locations)
# xticklabels = ["Case_" + case + "_mtp" for case in cases] + ["Case_" + case + "_no_mtp" for case in cases_no_mtp]
# ax1.set_xticklabels(xticklabels)
# ax2.set_xticks(x_locations)
# ax2.set_xticklabels(xticklabels)
# # %% Cost terms
# fig, ((ax11, ax12, ax13), (ax21, ax22, ax23), (ax31, ax32, ax33)) = plt.subplots(3, 3)
# color_mtp=plt.cm.rainbow(np.linspace(0,1,len(cases)))
# for count, case in enumerate(cases):
# ax11.scatter(count, optimaltrajectories[case]["objective_terms"]["metabolicEnergyRateTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax12.scatter(count, optimaltrajectories[case]["objective_terms"]["activationTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax13.scatter(count, optimaltrajectories[case]["objective_terms"]["armExcitationTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax21.scatter(count, optimaltrajectories[case]["objective_terms"]["jointAccelerationTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax22.scatter(count, optimaltrajectories[case]["objective_terms"]["passiveJointTorqueTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax23.scatter(count, optimaltrajectories[case]["objective_terms"]["activationDtTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax31.scatter(count, optimaltrajectories[case]["objective_terms"]["forceDtTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax32.scatter(count, optimaltrajectories[case]["objective_terms"]["armAccelerationTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# ax33.scatter(count, optimaltrajectories[case]["objective_terms"]["mtpExcitationTerm"], s=80, c=color_mtp[count, :].reshape(1,-1))
# color_no_mtp=plt.cm.rainbow(np.linspace(0,1,len(cases_no_mtp)))
# for count, case in enumerate(cases_no_mtp):
# ax11.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["metabolicEnergyRateTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax12.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["activationTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax13.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["armExcitationTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax21.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["jointAccelerationTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax22.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["passiveJointTorqueTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax23.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["activationDtTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax31.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["forceDtTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax32.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["armAccelerationTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax33.scatter(count+len(cases), optimaltrajectories_no_mtp[case]["objective_terms"]["mtpExcitationTerm"], s=80, c=color_no_mtp[count, :].reshape(1,-1), marker="^")
# ax11.set_title("metabolicEnergyRateTerm")
# ax11.set_ylabel("(J/Kg/m)")
# ax12.set_title("activationTerm")
# ax12.set_ylabel("()")
# ax13.set_title("armExcitationTerm")
# ax13.set_ylabel("()")
# ax21.set_title("jointAccelerationTerm")
# ax21.set_ylabel("()")
# ax22.set_title("passiveJointTorqueTerm")
# ax22.set_ylabel("()")
# ax23.set_title("activationDtTerm")
# ax23.set_ylabel("()")
# ax31.set_title("forceDtTerm")
# ax31.set_ylabel("()")
# ax32.set_title("armAccelerationTerm")
# ax32.set_ylabel("()")
# ax33.set_title("mtpExcitationTerm")
# ax33.set_ylabel("()")
# x_locations = np.linspace(0, len(cases)+len(cases_no_mtp)-1, len(cases)+len(cases_no_mtp))
# ax11.set_xticks(x_locations)
# xticklabels = ["Case_" + case + "_mtp" for case in cases] + ["Case_" + case + "_no_mtp" for case in cases_no_mtp]
# ax11.set_xticklabels(xticklabels)
# ax12.set_xticks(x_locations)
# ax12.set_xticklabels(xticklabels)
# ax13.set_xticks(x_locations)
# ax13.set_xticklabels(xticklabels)
# ax21.set_xticks(x_locations)
# ax21.set_xticklabels(xticklabels)
# ax22.set_xticks(x_locations)
# ax22.set_xticklabels(xticklabels)
# ax23.set_xticks(x_locations)
# ax23.set_xticklabels(xticklabels)
# ax31.set_xticks(x_locations)
# ax31.set_xticklabels(xticklabels)
# ax32.set_xticks(x_locations)
# ax32.set_xticklabels(xticklabels)
# ax33.set_xticks(x_locations)
# ax33.set_xticklabels(xticklabels)
# # %% Comparison contribution to COT
# no_mtp_COT_perMuscle_GC = optimaltrajectories_no_mtp[cases_no_mtp[0]]['COT_perMuscle_GC']
# mtp_COT_perMuscle_GC = optimaltrajectories[cases[0]]['COT_perMuscle_GC']
# sum_mtp_COT_perMuscle_GC = np.sum(mtp_COT_perMuscle_GC)
# sum_no_mtp_COT_perMuscle_GC = np.sum(no_mtp_COT_perMuscle_GC)
# ratio_noMtp_over_mtp = no_mtp_COT_perMuscle_GC / mtp_COT_perMuscle_GC * 100
# idx_sort = np.argsort(ratio_noMtp_over_mtp)
# m_sort = []
# r_sort = []
# mtp_sort = []
# no_mtp_sort = []
# mtp_sort_norm = []
# no_mtp_sort_norm = []
# for idx_s in idx_sort:
# m_sort.append(muscles[idx_s])
# r_sort.append(ratio_noMtp_over_mtp[idx_s])
# mtp_sort.append(mtp_COT_perMuscle_GC[idx_s])
# no_mtp_sort.append(no_mtp_COT_perMuscle_GC[idx_s])
# mtp_sort_norm.append(mtp_COT_perMuscle_GC[idx_s] / sum_mtp_COT_perMuscle_GC * 100)
# no_mtp_sort_norm.append(no_mtp_COT_perMuscle_GC[idx_s] / sum_no_mtp_COT_perMuscle_GC * 100)