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frequency analysis added #6

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101 changes: 58 additions & 43 deletions analyze_file.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,59 +11,74 @@
from collections import deque

folder = 'exo_data/'
filename = "20220211_1459_cmaes_LEFT.csv"
filename = "20220303_1149_comment_update_func_LEFT.csv"
filename2 = "20220303_1152_everything_LEFT.csv"

df = pd.read_csv(folder + '/' + filename, usecols=np.arange(23))
df = pd.read_csv(folder + '/' + filename, usecols=np.arange(22))
df2 = pd.read_csv(folder + '/' + filename2, usecols=np.arange(22))

plt.figure(1)

plt.plot(df.loop_time, df.ankle_angle, 'g-', label='ankle-angle')
plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
plt.plot(df.loop_time, df.gait_phase, 'k-', label='gait_phase')
plt.plot(df.loop_time, 3*df.did_toe_off, 'b-', label = 'toe_off')
plt.plot(df.loop_time, df.commanded_torque, 'm-', label = 'commanded torque')
plt.plot(df.loop_time, -df.commanded_position/1000, 'y-',label='commanded position' )
maxs_frequency =1/np.diff(df.loop_time)
our_frequency = 1/np.diff(df2.loop_time)

print('maxs freq',np.mean(maxs_frequency))
print('our freq', np.mean(our_frequency))
#frequency
plt.figure(0)
plt.plot(maxs_frequency, label = 'no update func')
plt.plot(our_frequency, label = 'w/ update func')
plt.legend()
plt.show()

plt.figure(2)
# plt.plot(df.loop_time, 0.001*df.motor_current, 'm.-',label='motor_current')
plt.plot(df.loop_time, df.ankle_angle, 'g-', label='ankle-angle')
plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
plt.plot(df.loop_time, df.accel_z,'b-', label='acc z')
plt.plot(df.loop_time, df.gyro_z/100, label='gyro z')
plt.legend()

plt.figure(3)
myfilt = filters.Butterworth(N=2, Wn=3, fs = 175)
heelstrike = []
gyro_history = deque([0, 0, 0], maxlen=3)
filtered_gyro = []
# plt.figure(1)

# plt.plot(df.loop_time, df.ankle_angle, 'g-', label='ankle-angle')
# plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
# plt.plot(df.loop_time, df.gait_phase, 'k-', label='gait_phase')
# plt.plot(df.loop_time, 3*df.did_toe_off, 'b-', label = 'toe_off')
# plt.plot(df.loop_time, df.commanded_torque, 'm-', label = 'commanded torque')
# plt.plot(df.loop_time, -df.commanded_position/1000, 'y-',label='commanded position' )

# plt.legend()

# check heel strike logic is correct
for gyro_z in df.gyro_z:
# fitered = -myfilt.filter(gyro_z) # the sign from Max's device was different from ours (neurobionics)
fitered = myfilt.filter(gyro_z)
# plt.figure(2)
# # plt.plot(df.loop_time, 0.001*df.motor_current, 'm.-',label='motor_current')
# plt.plot(df.loop_time, df.ankle_angle, 'g-', label='ankle-angle')
# plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
# plt.plot(df.loop_time, df.accel_z,'b-', label='acc z')
# plt.plot(df.loop_time, df.gyro_z/100, label='gyro z')
# plt.legend()

# plt.figure(3)
# myfilt = filters.Butterworth(N=2, Wn=3, fs = 175)
# heelstrike = []
# gyro_history = deque([0, 0, 0], maxlen=3)
# filtered_gyro = []

# # check heel strike logic is correct
# for gyro_z in df.gyro_z:
# # fitered = -myfilt.filter(gyro_z) # the sign from Max's device was different from ours (neurobionics)
# fitered = myfilt.filter(gyro_z)

filtered_gyro.append(fitered)
gyro_history.appendleft(fitered)

if (gyro_history[1] > 100 and
gyro_history[1] > gyro_history[0] and
gyro_history[1] > gyro_history[2]):
heelstrike.append(1)
# flag = False
else:
heelstrike.append(0)
# filtered_gyro.append(fitered)
# gyro_history.appendleft(fitered)

# if (gyro_history[1] > 100 and
# gyro_history[1] > gyro_history[0] and
# gyro_history[1] > gyro_history[2]):
# heelstrike.append(1)
# # flag = False
# else:
# heelstrike.append(0)

plt.plot(df.loop_time, np.array(heelstrike)*6,'g-', label='calculated heelstrike')
plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
# plt.plot(df.loop_time, df.gyro_z/100, label='gyro z')
plt.plot(df.loop_time, df.accel_z,'b-', label='acc z')
plt.plot(df.loop_time, np.array(filtered_gyro)/100, label='filtered gyro z')
plt.legend()
# plt.plot(df.loop_time, np.array(heelstrike)*6,'g-', label='calculated heelstrike')
# plt.plot(df.loop_time, 5*df.did_heel_strike, 'r-',label='heel_strike')
# # plt.plot(df.loop_time, df.gyro_z/100, label='gyro z')
# plt.plot(df.loop_time, df.accel_z,'b-', label='acc z')
# plt.plot(df.loop_time, np.array(filtered_gyro)/100, label='filtered gyro z')
# plt.legend()

plt.show()
# plt.show()

##################
# # # plt.plot(df.loop_time, df.accel_y, 'k-')
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