import pandas as pd
from readSto import readStoFile
import numpy as np
from interpDFrame import interpDFrame
import matplotlib.pyplot as plt
# Plot simulated muscle activation vs. EMG signal
# Right Foot Strike (RFS) times
RFS1 = 0.863
RFS2 = 1.546
# ------------------------------------------------------
# Import CMC states file
cmc_results_dir = '../CMC/run/results'
cmc = readStoFile(cmc_results_dir + '/cmc_states.sto')
# Load emg activity
emg = pd.read_csv('emg_run.txt', sep='\t')
min_time = np.amax([np.amin(cmc['time']), np.amin(emg['time'])])
# recalc values as a function of the gait cycle percent
cmc_interp = interpDFrame(cmc, RFS1, RFS2, min_time)
emg_interp = interpDFrame(emg, RFS1, RFS2, min_time)
cmc_pct = cmc_interp['percentgaitcycle']
emg_pct = emg_interp['percentgaitcycle']
# ------------------------------------------------------
# Plot EMG (cyan) vs. CMC (blue) muscle activity
pelvis_trans = ['pelvis_tx', 'pelvis_ty', 'pelvis_tz']
pelvis_rot = ['pelvis_tilt', 'pelvis_list', 'pelvis_rotation']
lumbar = ['lumbar_extension', 'lumbar_bending', 'lumbar_rotation']
le = [
'hip_flexion_r', 'hip_adduction_r', 'hip_rotation_r', 'knee_angle_r',
'ankle_angle_r', 'hip_flexion_l', 'hip_adduction_l', 'hip_rotation_l',
'knee_angle_l', 'ankle_angle_l'
]
ue = [
'arm_flex_r', 'arm_add_r', 'arm_rot_r', 'elbow_flex_r', 'pro_sup_r',
'arm_flex_l', 'arm_add_l', 'arm_rot_l', 'elbow_flex_l', 'pro_sup_l'
]
# load ik/rra kinematic errors
rra_results_dir = '../RRA/run/results_rra_2'
rra_run_pErr = readStoFile(rra_results_dir + '/rra_run_2_pErr.sto')
#for n in rra_run_pErr.columns : print n
rra_run_pErr_pelvisTrans = rra_run_pErr[pelvis_trans].values
rra_run_pErr_pelvisRot = rra_run_pErr[pelvis_rot].values
rra_run_pErr_lumbarRot = rra_run_pErr[lumbar].values
rra_run_pErr_leRot = rra_run_pErr[le].values
rra_run_pErr_ueRot = rra_run_pErr[ue].values
# print to console
print
print
print 'runing ik/rra errors'
print 'max rms pelvis error'
print ' ', 100 * np.amax(matRMS(rra_run_pErr_pelvisTrans)), ' (trans, cm)'
print ' ', 100 * np.amax(matRMS(rra_run_pErr_pelvisRot)), ' (rot, deg)'
cmc_results_dir = '../CMC/walk/results' # Right Foot Strike (RFS) times RFS1 = 0.248 RFS2 = 1.3925 # --------------------------------------------------------- # Load simulation outputs from file, # normalize time to % gait cycle, # and calculate net muscle-generated joint moments #---------------------------------------------------------- # ID id_filename = id_results_dir + '/inverse_dynamics.sto' df = readStoFile(id_filename) #df.columns # put columns of interest into a new dataframe time = df['time'] # min time is the same for id and cmc min_time = np.amin(time) hipflex = df['hip_flexion_r_moment'] hipflex.name = 'hipflex' kneeext = -df['knee_angle_r_moment'] kneeext.name = 'kneeext' ankleext = -df['ankle_angle_r_moment'] ankleext.name = 'ankleext' # make dataframe from the id joint moments idJointMoments = pd.concat([time, hipflex, kneeext, ankleext], axis=1)