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code/Animation_ChatGPT.py

@@ -9,12 +9,14 @@ import numpy as np
 import matplotlib.pyplot as plt
 from scipy.signal import welch
 from matplotlib.animation import FuncAnimation, PillowWriter
+import useful_functions as f
 
 # Generate distances and corresponding frequencies
-distances = np.arange(-400, 451, 1)
+distances = np.arange(-400, 2000, 1)
 f1 = 800
 f2 = f1 + distances
 
+
 # Time parameters
 dt = 0.00001
 t = np.arange(0, 2, dt)
@@ -27,37 +29,45 @@ axs[1].set_xlabel('Frequency [Hz]')
 axs[1].set_ylabel('Power [1/Hz]')
 axs[1].set_xlim(0, 1500)
 
-# Function to compute and plot the power spectrum
-def plot_powerspectrum(i):
+
+
+ # Generate the signal as a sum of two sine waves
+def plot_powerspectrum_2(i):
     # Clear the previous plots
     axs[0].cla()
     axs[1].cla()
     
-    # Generate the signal
-    x = np.sin(2*np.pi*f1*t) + 0.2 * np.sin(2*np.pi*f2[i]*t)
-    x[x < 0] = 0  # Apply half-wave rectification
-
+    # Generate the signal as a sum of two sine waves
+    x = np.sin(2 * np.pi * f1 * t) + 0.8 * np.sin(2 * np.pi * f2[i] * t)  # Second wave is 20% as strong
+    
+    
     # Plot the signal (first 20 ms for clarity)
     axs[0].plot(t[t < 0.02], x[t < 0.02])
     axs[0].set_title(f"Signal (f2={f2[i]} Hz)")
     axs[0].set_xlabel('Time [s]')
     axs[0].set_ylabel('Amplitude')
-    axs[0].set_ylim(0, 1.2)
+    axs[0].set_ylim(-2, 2)
 
+    x[x < 0] = 0  # Apply half-wave rectification (optional)
     # Compute power spectrum
     freq, power = welch(x, fs=1/dt, nperseg=2**16)
-
+    pref = np.max(power)
+    decibel_power = 10 * np.log10(power/pref)
+    AM = f.find_AM(f1, 0.5 * f1, f2[i])
     # Plot the power spectrum
     axs[1].plot(freq, power)
-    axs[1].set_xlim(0, 1500)
-    axs[1].set_ylim(0, 0.05)
+    axs[1].set_xlim(0, 3000)
     axs[1].set_title(f'Power Spectrum (f2={f2[i]} Hz)')
     axs[1].set_xlabel('Frequency [Hz]')
     axs[1].set_ylabel('Power [1/Hz]')
+    #axs[1].set_ylim(0, 0.00007)
+    axs[1].plot(f1, power[np.argmin(np.abs(freq-f1))], 'o')
+    axs[1].plot(f2[i], power[np.argmin(np.abs(freq-f2[i]))], 'd')
+    axs[1].plot(AM, power[np.argmin(np.abs(freq-AM))], '*')
+    axs[1].axvline(AM, alpha = 0.5, color = 'r')
 
 # Create the animation
-ani = FuncAnimation(fig, plot_powerspectrum, frames=len(distances), interval=500)
+ani = FuncAnimation(fig, plot_powerspectrum_2, frames=len(distances), interval=500)
 
-# Display the animation
-ani.save("signal_animation.gif", writer=PillowWriter(fps=30))
-plt.show()
+# Save the animation as a GIF file (optional)
+ani.save("sum_of_sinewaves.gif", writer=PillowWriter(fps=30))