'P:/Motiv/1030_Robotics_Development_IRAD/02_Engineering/IRAD Actuator/Single_Axis_Testing/Testing_Data/Weighted_Testing_PosvsStrain/07272017_/'
+ load_amt + '/' + load_amt + '_125_0Hz_' + load_case + '_wTorque.csv',
delimiter=',',
skip_header=True)
filtered_wave = adjust_load(load_wave,
calibration_wave=cal_wave,
width=w,
sigma=sig)
torque_wave = np.genfromtxt(
'P:/Motiv/1030_Robotics_Development_IRAD/02_Engineering/IRAD Actuator/Single_Axis_Testing/Testing_Data/Weighted_Testing_PosvsStrain/07272017_/'
+ load_amt + '/' + load_amt + '_125_0Hz_' + load_case + '_wTorque.csv',
delimiter=',',
skip_header=True)
torque_wave = torque_wave[:, [0, 2]]
torque_wave[:, 1] = torque_wave[:, 1] * (144.15 / 2048)
smooth_wave = moving_avg(filtered_wave)
#waves = [filtered_wave, smooth_wave, torque_wave]
#labels = ['23.5Nm_filtered', '23.5Nm_moving_average', 'Torque_Sensor']
#markers = ['bo', 'ro', 'g1']
#scaling = [False, False, False]
#
#plot_num += 1
#
#plot_wave(waves, labels, markers, plot_num, scaling)
#
#waves = [torque_wave]
#labels = [ 'Torque_Sensor']
#markers = ['g1']
#scaling = [False]
#
for i in range(0, np.size(pos)):
torque_wave[i] = delta_amp * np.sin(
(np.pi / load_phase) +
(2 * np.pi / pos_num) * pos[i]) + np.random.randint(
delta_low, delta_high)
torque_wave = np.column_stack((pos, torque_wave))
#plot raw loaded wave
plt.figure(3)
plt.plot(pos, load_sin, 'b1')
#build 2-D array for loaded wave
load_wave = np.column_stack((pos, load_sin))
#generate filtered wave from calibration wave
filtered_wave = adjust_load(wave=load_wave,
calibration_wave=cal_wave,
width=w_,
sigma=sig_)
#generate moving average from filtered wave
smooth_wave = moving_avg(filtered_wave, win_len=wl_)
plt.figure(4)
plt.plot(torque_wave[:, 0], torque_wave[:, 1], 'r1')
plt.figure(5)
plt.plot(filtered_wave[:, 0], filtered_wave[:, 1], 'r1')
plt.plot(torque_wave[:, 0], torque_wave[:, 1], 'y1')
plt.plot(smooth_wave[:, 0], smooth_wave[:, 1], 'g1')
corr_100_100 = np.corrcoef(calib_100[:, 1], calib_100[:, 1])
print('30_30', corr_30_30)
print('30_45', corr_30_45)
print('30_100', corr_30_100)
print('45_45', corr_45_45)
print('45_100', corr_45_100)
print('100_100', corr_100_100)
#Use new calibration curve to generate a filtered curve
new_30 = adjust_load(f_load_30, calib_100)
new_45 = adjust_load(f_load_45, calib_100)
new_100 = adjust_load(f_load_100, calib_100)
#Get moving average
smooth_30 = moving_avg(new_30)
smooth_45 = moving_avg(new_45)
smooth_100 = moving_avg(new_100)
#Plot curves
#30ks curves
waves = [new_30, smooth_30]
labels = ['30ks_adjusted', '30ks_moving_average']
markers = ['b1', 'r1']
scale = [True, True]
plot_num += 1
plot_wave(waves, labels, markers, plot_num, scale)
print_min_max(smooth_30)
#45ks curves