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