Add plots as function of weights. Add -S to set random seed.

libraries/base/common/python/try_round_weight.py

@@ -48,47 +48,82 @@ import common as cm
 
 # Parse arguments to derive user parameters
 _parser = argparse.ArgumentParser('try_round_weight')
+_parser.add_argument('-S', default=0, type=int, help='Random number seed')
 _parser.add_argument('-N', default=1000, type=int, help='Number of input samples')
-_parser.add_argument('--weight_lo', default=0.3, type=float, help='Lowest weight')
-_parser.add_argument('--weight_hi', default=2.0, type=float, help='Highest weight')
-_parser.add_argument('--weight_step', default=0.1, type=float, help='Step weight')
+_parser.add_argument('--s_lo', default=0.1, type=float, help='Lowest sigma')
+_parser.add_argument('--s_hi', default=25.0, type=float, help='Highest sigma')
+_parser.add_argument('--s_step', default=0.1, type=float, help='Step sigma')
+_parser.add_argument('--w_lo', default=0.3, type=float, help='Lowest weight')
+_parser.add_argument('--w_hi', default=2.0, type=float, help='Highest weight')
+_parser.add_argument('--w_step', default=0.1, type=float, help='Step weight')
 args = _parser.parse_args()
 
-N_samples = args.N
-weight_lo = args.weight_lo
-weight_hi = args.weight_hi
-weight_step = args.weight_step
+np.random.seed(args.S)
 
-# Prepare signals
+# Prepare noise signal
+N_samples = args.N
+noise = np.random.randn(N_samples)
 noise = np.random.randn(N_samples)
 noise /= np.std(noise)
 
-# Noise level range, 1 unit = 1 LSbit
-sigma_lo = 0.1
-sigma_hi = 25
-sigmas = np.arange(sigma_lo, sigma_hi, 0.1)
+# Noise levels range, 1 unit = 1 LSbit
+sigma_lo = args.s_lo
+sigma_hi = args.s_hi
+sigma_step = args.s_step
+sigmas = np.arange(sigma_lo, sigma_hi, sigma_step)
 N_sigmas = len(sigmas)
 
-# Weight range, unit weight = 1
+# Weights range, unit weight = 1
+weight_lo = args.w_lo
+weight_hi = args.w_hi
+weight_step = args.w_step
 weights = np.arange(weight_lo, weight_hi, weight_step)
 N_weights = len(weights)
 
 # Determine weighted rounded noise sigma / weighted noise sigma for range of weights and input noise sigmas
 sigmas_ratio = np.nan * np.zeros((N_weights, N_sigmas))  # w rows, s cols
+sigmas_qq = np.zeros((N_weights, N_sigmas))
+sigmas_sq = np.zeros((N_weights, N_sigmas))
 for s, sigma in enumerate(sigmas):
    noise_s = noise * sigma
    noise_q = np.round(noise_s)
    for w, weight in enumerate(weights):
        noise_q_weighted_q = np.round(noise_q * weight)  # apply weight to rounded noise
        noise_s_weighted_q = np.round(noise_s * weight)  # apply weight to original noise
-       sigma_qq = np.std(noise_q_weighted_q)
-       sigma_sq = np.std(noise_s_weighted_q)
-       if sigma_sq != 0:
-           sigmas_ratio[w][s] = sigma_qq / sigma_sq  # weighted rounded noise sigma / weighted noise sigma
+       s_qq = np.std(noise_q_weighted_q)
+       s_sq = np.std(noise_s_weighted_q)
+       sigmas_qq[w][s] = s_qq
+       sigmas_sq[w][s] = s_sq
+       if s_sq != 0:
+           sigmas_ratio[w][s] = s_qq / s_sq  # weighted rounded noise sigma / weighted noise sigma
+# Transpose [w][s] to have index ranges [s][w]
+sigmas_ratio_T = sigmas_ratio.transpose()
+sigmas_qq_T = sigmas_qq.transpose()
+sigmas_sq_T = sigmas_sq.transpose()
 
 # Plot results
 figNr = 0
 
+figNr += 1
+plt.figure(figNr)
+for s, sigma in enumerate(sigmas):
+    plt.plot(weights, sigmas_qq_T[s], label='s = %4.2f' % sigma)
+plt.title("Sigma of weighted quantized noise")
+plt.xlabel("Weight")
+plt.ylabel("Sigma")
+plt.legend(loc='upper right')
+plt.grid()
+
+figNr += 1
+plt.figure(figNr)
+for s, sigma in enumerate(sigmas):
+    plt.plot(weights, sigmas_ratio_T[s], label='s = %4.2f' % sigma)
+plt.title("Relative sigma difference of weighting after / before quantisation")
+plt.xlabel("Weight")
+plt.ylabel("Relative sigma difference")
+plt.legend(loc='upper right')
+plt.grid()
+
 figNr += 1
 plt.figure(figNr)
 for w, weight in enumerate(weights):