Made the way simulations are done and plotted more scalable

sw/main.py

@@ -21,30 +21,44 @@ def main():
                   [0, 1, 1, 0, 0, 1, 1],
                   [0, 0, 0, 1, 1, 1, 1]])
 
+    # Define encoder and decoders
+
     encoder = channel.Encoder(G)
 
-    proximal_decoder = proximal.ProximalDecoder(H, K=100, gamma=0.01)
+    decoders = {"naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H),
-    soft_decision_decoder = naive_soft_decision.SoftDecisionDecoder(G, H)
+                # "proximal": proximal.ProximalDecoder(H, K=100, gamma=0.01),
+                }
 
-    # Test decoder
+    # Test decoders
 
     k, n = G.shape
     d = np.zeros(k)  # All-zeros assumption
 
-    SNRs_sd, BERs_sd = utility.test_decoder(encoder=encoder,
+    SNRs = np.linspace(1, 7, 9)
-                                            decoder=soft_decision_decoder,
+    data = pd.DataFrame({"SNR": SNRs})
-                                            d=d,
-                                            SNRs=np.linspace(1, 7, 9),
-                                            target_bit_errors=500)
 
-    data = pd.DataFrame({"SNR": SNRs_sd, "BER_sd": BERs_sd})
+    for decoder_name in decoders:
+        decoder = decoders[decoder_name]
+        _, BERs_sd = utility.test_decoder(encoder=encoder,
+                                          decoder=decoder,
+                                          d=d,
+                                          SNRs=SNRs)
+
+        data[f"BER_{decoder_name}"] = BERs_sd
 
     # Plot results
 
-    ax = sns.lineplot(data=data, x="SNR", y="BER_sd")
+    fig, axes = plt.subplots(1, 1)
+    fig.suptitle("Bit-Error-Rates of various decoders")
+
+    for decoder_name in decoders:
+        ax = sns.lineplot(data=data, x="SNR", y=f"BER_{decoder_name}", label=f"{decoder_name}")
+
+    ax.set_title("Hamming(7,4) Code")
     ax.set(yscale="log")
     ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
-    # ax.set_ylim([10e-6, 10e0])
+    ax.legend()
+
     plt.show()