Restructured main.py

sw/main.py

@@ -2,75 +2,102 @@ import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
-from timeit import default_timer as timer
+import typing
+from pathlib import Path
+import os
+from itertools import chain
 
 from decoders import proximal, naive_soft_decision
-from utility import noise, simulations, encoders, codes
+from utility import simulations, encoders, codes
 
 
-def main():
-    # used_code = "Hamming_7_4"
-    # used_code = "Golay_24_12"
-    # used_code = "BCH_31_16"
-    # used_code = "BCH_31_21"
-    used_code = "BCH_63_16"
-
-    G = codes.Gs[used_code]
-    H = codes.get_systematic_H(G)
-    R = codes.get_systematic_R(G)
-
-    # Define encoder and decoders
-
-    encoder = encoders.Encoder(G)
-
-    decoders = {
-                # "naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H, R),
-                "proximal_0_01": proximal.ProximalDecoder(H, R, K=100, gamma=0.01),
-                "proximal_0_05": proximal.ProximalDecoder(H, R, K=100, gamma=0.05),
-                "proximal_0_15": proximal.ProximalDecoder(H, R, K=100, gamma=0.15),
-                }
-
-    # Test decoders
-
+def test_decoders(G, encoder, decoders: typing.List) -> pd.DataFrame:
     k, n = G.shape
     d = np.zeros(k)  # All-zeros assumption
 
-    SNRs = np.linspace(1, 8, 9)
+    SNRs = np.linspace(1, 8, 8)
     data = pd.DataFrame({"SNR": SNRs})
 
-    start_time = timer()
-
     for decoder_name in decoders:
         decoder = decoders[decoder_name]
         _, BERs_sd = simulations.test_decoder(encoder=encoder,
                                               decoder=decoder,
                                               d=d,
                                               SNRs=SNRs,
-                                              target_bit_errors=500,
-                                              N_max=10000)
+                                              target_bit_errors=100,
+                                              N_max=30000)
 
         data[f"BER_{decoder_name}"] = BERs_sd
 
-    stop_time = timer()
-    print(f"Elapsed time: {stop_time - start_time:2f}")
+    return data
 
-    # Plot results
+
+# TODO: Fix spacing between axes and margins
+def plot_results():
+    results_dir = "sim_results"
+
+    code_paths = {}
+    for file in os.listdir(results_dir):
+        if file.endswith(".csv"):
+            code_paths[file.replace(".csv", "")] = os.path.join(results_dir, file)
 
     sns.set_theme()
 
-    fig, axes = plt.subplots(1, 1)
-    fig.suptitle("Bit-Error-Rates of various decoders")
+    fig, axes = plt.subplots(2, len(code_paths) // 2, figsize=(12, 6))
+    fig.suptitle("Bit-Error-Rates of various decoders for different codes")
 
-    for decoder_name in decoders:
-        ax = sns.lineplot(data=data, x="SNR", y=f"BER_{decoder_name}", label=f"{decoder_name}")
+    axes = list(chain.from_iterable(axes))
 
-    ax.set_title(used_code)
-    ax.set(yscale="log")
-    ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
-    ax.legend()
+    for i, code in enumerate(code_paths):
+        data = pd.read_csv(code_paths[code])
+
+        column_names = [column for column in data.columns.values.tolist()
+                        if column.startswith("BER")]
+
+        ax = axes[i]
+
+        for column in column_names:
+            sns.lineplot(ax=ax, data=data, x="SNR", y=column, label=column.lstrip("BER_"))
+
+        ax.set_title(code)
+        ax.set(yscale="log")
+        ax.set_xlabel("SNR")
+        ax.set_ylabel("BER")
+        ax.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
+        ax.legend()
 
     plt.show()
 
 
+def main():
+    Path("sim_results").mkdir(parents=True, exist_ok=True)
+
+    used_codes = [
+        "Hamming_7_4",
+        "Golay_24_12",
+        # "BCH_31_16",
+        # "BCH_31_21",
+        # "BCH_63_16",
+    ]
+
+    for used_code in used_codes:
+        G = codes.Gs[used_code]
+        H = codes.get_systematic_H(G)
+        R = codes.get_systematic_R(G)
+
+        encoder = encoders.Encoder(G)
+        decoders = {
+            "naive_soft_decision": naive_soft_decision.SoftDecisionDecoder(G, H, R),
+            "proximal_0_01": proximal.ProximalDecoder(H, R, K=100, gamma=0.01),
+            "proximal_0_05": proximal.ProximalDecoder(H, R, K=100, gamma=0.05),
+            "proximal_0_15": proximal.ProximalDecoder(H, R, K=100, gamma=0.15),
+        }
+
+        # data = test_decoders(G, encoder, decoders)
+        # data.to_csv(f"sim_results/{used_code}.csv")
+
+    plot_results()
+
+
 if __name__ == "__main__":
     main()