Added scope fft snippet

Used to compare the fft of signals taken from the DSOX2024A oscope

epoxy_transducer/tof_data.py

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+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

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+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()