41 lines
1.0 KiB
Python
41 lines
1.0 KiB
Python
|
import numpy as np
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
num_meas = 4
|
||
|
|
|
||
|
|
# speed of sound m/s
|
||
|
|
sos = 1500
|
||
|
|
|
||
|
|
# depth of the tub in mm
|
||
|
|
tub_depth = 690
|
||
|
|
|
||
|
|
# distances from the top measured at in mm
|
||
|
|
distance = [0,138,276,414]
|
||
|
|
|
||
|
|
# distance from the bottom
|
||
|
|
distance_to_bot = np.empty(num_meas)
|
||
|
|
for i in range(num_meas):
|
||
|
|
distance_to_bot[i] = tub_depth - distance[i]
|
||
|
|
|
||
|
|
# time from pulse to reflection measuredin uS
|
||
|
|
tof = [912,758,614,486]
|
||
|
|
|
||
|
|
# tot distance traveled
|
||
|
|
distance_traveled = np.empty(num_meas)
|
||
|
|
for i in range(num_meas):
|
||
|
|
distance_traveled[i] = (690*2) - (distance[i] * 2)
|
||
|
|
|
||
|
|
# ideal time from pulse to reflection measured uS
|
||
|
|
tof_expected = np.empty(num_meas)
|
||
|
|
for i in range(num_meas):
|
||
|
|
tof_expected[i] = ((distance_traveled[i] * 1e-3) / sos) * 1e6
|
||
|
|
|
||
|
|
plt.figure(1)
|
||
|
|
plt.plot(distance_to_bot, tof, label = 'tof measured in uS')
|
||
|
|
plt.plot(distance_to_bot, tof_expected, label = 'tof expected in uS')
|
||
|
|
plt.xlabel('Distance to bottom [mm]')
|
||
|
|
plt.ylabel('Time [uS]')
|
||
|
|
plt.title('Tof Data')
|
||
|
|
plt.xlim(max(distance_to_bot),min(distance_to_bot))
|
||
|
|
plt.legend()
|