epoxy_transducer/tof_data.py

@@ -1,40 +0,0 @@
-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()

scope_fft/scope_fft.py

@@ -1,45 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-
-def sig_fft(file1, file2):
-    # Load data from CSV files
-    data = np.genfromtxt(file1, delimiter=',')[2:]
-    data2 = np.genfromtxt(file2, delimiter=',')[2:]
-    
-    # find where data is nan
-    n_index = np.argmax(np.isnan(data[:,1]))
-    n_index2 = np.argmax(np.isnan(data2[:,1]))
-    # Define each column
-    time = data[:n_index, 0]
-    rx_signal = data[:n_index, 1]
-    tx_signal = data[:n_index, 2]
-    num_samples = time.size
-    fs1 = 1 / (time[1] - time[0])  # Set sampling frequency
-    
-    # Define each column for data 2
-    time2 = data2[:n_index2, 0]
-    rx_signal2 = data2[:n_index2, 1]
-    tx_signal2 = data2[:n_index2, 2]
-    num_samples2 = time2.size
-    fs2 = 1 / (time2[1] - time2[0])  # Set sampling frequency
-    # Plot raw data
-    plt.figure(1)
-    plt.plot(time, rx_signal, label='Data 1')
-    plt.plot(time2, rx_signal2, label='Data 2')
-    plt.title('Unfiltered Data')
-    plt.legend()
-    
-    # fft for data 1
-    sigfft = np.fft.fft(rx_signal)
-    freq = np.fft.fftfreq(rx_signal.shape[-1], 1/(fs1))
-    # fft for data 2
-    sigfft2 = np.fft.fft(rx_signal2)
-    freq2 = np.fft.fftfreq(rx_signal2.shape[-1], 1/(fs2))
-    # plot fft
-    plt.figure(2)
-    plt.plot(freq, np.abs(sigfft), label='Data 1')
-    plt.plot(freq2, np.abs(sigfft2), label='Data 2')
-    plt.title('FFT of Data')
-    plt.xlim(0, 20000)
-    
-    plt.show()