def __init__(self):
# Get activation signal a
emg_data = load_data('./data/ta_vs_gait.csv')
emg_data = np.array(emg_data)
self.emg_function = get_norm_emg(emg_data)
emg_sol_data = load_data('./data/soleus_vs_gait.csv')
emg_sol_data = np.array(emg_sol_data)
self.emg_sol_function = get_norm_sol(emg_sol_data)
# Get ankle angle
ankle_data = load_data('./data/ankle_vs_gait.csv')
ankle_data = np.array(ankle_data)
self.ankle_function = get_regress_ankle(ankle_data)
# Get knee angle
knee_data = load_data('./data/knee_vs_gait.csv')
knee_data = np.array(knee_data)
self.knee_function = get_regress_general(knee_data)
# Get hip angle
hip_data = load_data('./data/hip_vs_gait.csv')
hip_data = np.array(hip_data)
self.hip_function = get_regress_hip(hip_data)
# Get shank velocity
x = np.arange(0.0, 100.0, 1.0)
shank_vel_data = 100 * derivative(self.knee_function, x, h=0.001)
shank_vel_data = np.transpose(np.array([x, shank_vel_data]))
self.shank_velocity_function = get_regress_general(shank_vel_data)
# Get thigh velocity
thigh_vel_data = 100 * derivative(self.hip_function, x, h=0.001)
thigh_vel_data = np.transpose(np.array([x, thigh_vel_data]))
self.thigh_velocity_function = get_regress_hip(thigh_vel_data)
for j in range(len(above_0_plot[0])):
if above_0_plot[i][j] == 1:
viable.append([
rmse_toe_height_plot[i][j], independent_1[i][j],
independent_2[i][j]
])
# Sorts by first element (ie RMSE)
top_viable = sorted(viable)
if len(top_viable) >= 5:
top_viable = top_viable[:5]
# Find fatigues
emg_data = load_data('./data/ta_vs_gait.csv')
emg_data = np.array(emg_data)
emg_function = get_norm_emg(emg_data)
fatigues = []
all_fatigues = []
for i in range(len(top_viable)):
a = Activation(top_viable[i][1], top_viable[i][2], scaling,
non_linearity)
a.get_activation_signal(emg_function)
fatigues.append([a.get_fatigue(), i])
for i in range(len(viable)):
a = Activation(viable[i][1], viable[i][2], scaling, non_linearity)
a.get_activation_signal(emg_function)
all_fatigues.append([a.get_fatigue(), i])
# Sorts by first element (ie fatigue)
if __name__ == '__main__':
plot_curves(
) # plot CE,SE,PE force-length curves and CE force-velocity curve
print(get_velocity(1.0, np.array([1.0]), np.array(
[1.01]))) # calculate velocity given a=1.0,lm=1.0,ls=1.01
# Constants
max_isometric_force = 605.0
total_length = 0.44479194718764087
resting_muscle_length = .25 * total_length
resting_tendon_length = .75 * total_length
emg_data = load_data('./data/ta_vs_gait.csv')
emg_data = np.array(emg_data)
emg_data_regress = get_norm_emg(emg_data)
frequency, duty_cycle, scaling, non_linearity = 35, 0.5, 1, -1
a = Activation(frequency, duty_cycle, scaling, non_linearity)
a.get_activation_signal(emg_data_regress, shape="monophasic")
# Create an HillTypeMuscle using the given constants
muscle = HillTypeMuscle(max_isometric_force, resting_muscle_length,
resting_tendon_length)
# Dynamic equation
def f(t, x):
normalized_tendon_length = muscle.norm_tendon_length(total_length, x)
temp = get_velocity(a.get_amp(t / 100), np.array([x]),
np.array([normalized_tendon_length]))
return temp