other changes

testy/domfreq.py

@@ -0,0 +1,62 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.fft import fft
+
+# Define the sampling frequency and time vector
+fs = 500  # Sampling frequency
+t = np.arange(0, 1, 1/fs)  # Time vector
+
+# Define the frequencies
+f1 = 5    # Frequency that occurs most often but with lower amplitude
+f2 = 20   # Frequency with the highest amplitude
+
+# Creating the individual signals
+signal_f1 = 0.5 * np.sin(2 * np.pi * f1 * t)  # Signal with frequency f1
+signal_f2 = 2 * np.sin(2 * np.pi * f2 * t)    # Signal with frequency f2
+
+# Composite signal
+signal = signal_f1 + signal_f2
+
+# Performing a Fourier Transform
+freq = np.fft.fftfreq(len(t), 1/fs)
+fft_values = fft(signal)
+
+# Plotting all the signals and the frequency spectrum
+plt.figure(figsize=(14, 10))
+
+# Plot 1: Composite Signal
+plt.subplot(4, 1, 1)
+plt.plot(t, signal)
+plt.title('Composite Signal (f1 + f2)')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 2: Frequency f1 Signal
+plt.subplot(4, 1, 2)
+plt.plot(t, signal_f1)
+plt.title('Individual Frequency f1 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 3: Frequency f2 Signal
+plt.subplot(4, 1, 3)
+plt.plot(t, signal_f2)
+plt.title('Individual Frequency f2 Signal')
+plt.xlabel('Time [s]')
+plt.ylabel('Amplitude')
+
+# Plot 4: Frequency Spectrum
+plt.subplot(4, 1, 4)
+plt.plot(freq, np.abs(fft_values))
+plt.title('Frequency Spectrum of Composite Signal')
+plt.xlabel('Frequency [Hz]')
+plt.ylabel('Amplitude')
+plt.xlim([0, 30])
+
+# Highlighting the dominant frequencies in the spectrum
+plt.axvline(x=f1, color='green', linestyle='--', label='Frequency f1')
+plt.axvline(x=f2, color='red', linestyle='--', label='Frequency f2')
+
+plt.legend()
+plt.tight_layout()
+plt.show()