Major progress on VLSI lab 1&2 and ECOMMS lab 1

7th-Semester-Fall-2024/ECOMMS/labs/lab1/part3.py

@@ -1,54 +1,93 @@
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib as mpl
+from cycler import cycler
 import scipy as sp
 
-f_m = 5E3
-f_c = 25E3
 
-f_s = 50*f_c
+def add_noise(s, SNR):
+    var_s = np.cov(s)
+    var_noise = var_s/(10**(SNR/10))
+    noise = var_noise**0.5 * np.random.randn(len(s))
+    return s + noise
 
-T_m = 1/f_m
-T_c = 1/f_c
-T_s = 1/f_s
+def plot_at_snrs(t, f, s_am, s_fm):
+    stem_colors = ['k', 'b', 'r']
+    ci = 0
+    stem_color = stem_colors[ci]
 
-A_c = 10
-A_m = 1
+    am_time = plt.figure()
+    am_freq = plt.figure()
+    fm_time = plt.figure()
+    fm_freq = plt.figure()
 
-t = np.arange(0,2*T_m,T_s)
-f = t*f_s/(2*T_m)
+    for SNR in range(10,30,10):
+        m_am = add_noise(s_am,SNR)
+        m_fm = add_noise(s_fm,SNR)
 
-# ===== AM =====
-s = A_c * (1 + A_m*np.cos(2*np.pi*f_m*t)) * np.cos(2*np.pi*f_c*t)
+        plt.figure(am_time)
+        plt.plot(t, m_am, label=f"SNR = {SNR}")
 
-var_s = np.cov(s)
+        plt.figure(am_freq)
+        plt.stem(f, abs(sp.fft.fft(m_am)), label=f"SNR = {SNR}", markerfmt=stem_color+"o", linefmt=stem_color+"-")
 
-SNR = 10
-var_snr = var_s/(10**(SNR/10))
-noise_snr = (var_snr**0.5) * np.random.randn(len(s))
-m = s+noise_snr
+        plt.figure(fm_time)
+        plt.plot(t, m_fm, label=f"SNR = {SNR}")
 
-S = sp.fft.fft(s)
-M = sp.fft.fft(m)
+        plt.figure(fm_freq)
+        plt.stem(f, abs(sp.fft.fft(m_fm)), label=f"SNR = {SNR}", markerfmt=stem_color+"o", linefmt=stem_color+"-")
 
-plt.subplot(211)
-plt.stem(f, S)
-plt.subplot(212)
-plt.stem(f, M)
-plt.show()
+        ci += 1
+        stem_color = stem_colors[ci]
 
-# ===== FM =====
-beta_f = 10
-s = A_c*np.cos(2*np.pi*f_c*t + beta_f*A_m*np.sin(2*np.pi*f_m*t))
+    plt.figure(am_time)
+    plt.plot(t, s_am, label="Pure signal")
+    plt.legend(loc="upper right")
+    plt.savefig("part-3-am-time")
 
-var_snr = var_s/(10**(SNR/10))
-noise_snr = (var_snr**0.5) * np.random.randn(len(s))
-m = s+noise_snr
+    plt.figure(am_freq)
+    plt.stem(f, abs(sp.fft.fft(s_am)), label="Pure signal", markerfmt=stem_color+"o", linefmt=stem_color+"-")
+    plt.xlim((0, 2E5))
+    plt.legend(loc="upper right")
+    plt.savefig("part-3-am-freq")
 
-S = sp.fft.fft(s)
-M = sp.fft.fft(m)
+    plt.figure(fm_time)
+    plt.plot(t, s_fm, label="Pure signal")
+    plt.legend(loc="upper right")
+    plt.savefig("part-3-fm-time")
 
-plt.subplot(211)
-plt.stem(f, S)
-plt.subplot(212)
-plt.stem(f, M)
-plt.show()
+    plt.figure(fm_freq)
+    plt.stem(f, abs(sp.fft.fft(s_fm)), label="Pure signal", markerfmt=stem_color+"o", linefmt=stem_color+"-")
+    plt.xlim((0, 2E5))
+    plt.legend(loc="upper right")
+    plt.savefig("part-3-fm-freq")
+
+    plt.show()
+
+
+def main():
+    f_m = 5E3
+    f_c = 125E3
+    f_s = 125E4
+
+    T_m = 1/f_m
+    T_c = 1/f_c
+    T_s = 1/f_s
+
+    A_c = 10
+    A_m = 1
+
+    t = np.arange(0,2*T_m,T_s)      # Time domain
+    f = t*f_s/(2*T_m)               # Frequency domain
+
+    beta_f = 10                     # Modulation index
+
+    s_am = A_c * (1 + A_m*np.cos(2*np.pi*f_m*t)) * np.cos(2*np.pi*f_c*t)
+    s_fm = A_c*np.cos(2*np.pi*f_c*t + beta_f*A_m*np.sin(2*np.pi*f_m*t))
+    plot_at_snrs(t, f, s_am, s_fm)
+
+
+
+if __name__ == "__main__":
+    mpl.rcParams['axes.prop_cycle'] = cycler(color=['k', 'b', 'r'])
+    main()

7th-Semester-Fall-2024/ECOMMS/labs/lab1/part4.py

@@ -0,0 +1 @@
+