Finished first rough implementation of show_BER_curves()

sw/main.py

@@ -9,75 +9,58 @@ from itertools import chain
 from timeit import default_timer
 
 from decoders import proximal, maximum_likelihood
-from utility import simulations, codes
+from utility import simulations, codes, visualization
 
 
 # TODO: Fix spacing between axes and margins
 def plot_results():
     results_dir = "sim_results"
 
-    code_paths = {}
+    # Read data from files
+
+    data = []
     for file in os.listdir(results_dir):
         if file.endswith(".csv"):
-            code_paths[file.replace(".csv", "")] = os.path.join(results_dir, file)
+            df = pd.read_csv(os.path.join(results_dir, file))
+            df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
+            data.append(df)
+
+    # Create and show graphs
 
     sns.set_theme()
-
-    fig, axes = plt.subplots(2, len(code_paths) // 2, figsize=(12, 6))
-    fig.suptitle("Bit-Error-Rates of various decoders for different codes")
-
-    axes = list(chain.from_iterable(axes))
-
-    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()
-
+    fig = visualization.show_BER_curves(data)
     plt.show()
 
 
 def main():
-    Path("sim_results").mkdir(parents=True, exist_ok=True)
-
-    # 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)
-
-    decoders = [
-        maximum_likelihood.MLDecoder(G, H),
-        proximal.ProximalDecoder(H, gamma=0.01),
-        proximal.ProximalDecoder(H, gamma=0.05),
-        proximal.ProximalDecoder(H, gamma=0.15)
-    ]
-
-    k, n = G.shape
-    SNRs, BERs = simulations.test_decoders(n, k, decoders, N_max=30000, target_frame_errors=100)
-
-    df = pd.DataFrame({"SNR": SNRs})
-    df["BER_ML"] = BERs[0]
-    df["BER_prox_0_01"] = BERs[0]
-    df["BER_prox_0_05"] = BERs[1]
-    df["BER_prox_0_15"] = BERs[2]
-
-    df.to_csv(f"sim_results/{used_code}.csv")
+    # Path("sim_results").mkdir(parents=True, exist_ok=True)
+    #
+    # # 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)
+    #
+    # decoders = [
+    #     maximum_likelihood.MLDecoder(G, H),
+    #     proximal.ProximalDecoder(H, gamma=0.01),
+    #     proximal.ProximalDecoder(H, gamma=0.05),
+    #     proximal.ProximalDecoder(H, gamma=0.15)
+    # ]
+    #
+    # k, n = G.shape
+    # SNRs, BERs = simulations.test_decoders(n, k, decoders, N_max=30000, target_frame_errors=100)
+    #
+    # df = pd.DataFrame({"SNR": SNRs})
+    # df["BER_ML"] = BERs[0]
+    # df["BER_prox_0_01"] = BERs[0]
+    # df["BER_prox_0_05"] = BERs[1]
+    # df["BER_prox_0_15"] = BERs[2]
+    #
+    # df.to_csv(f"sim_results/{used_code}.csv")
 
     plot_results()
 

sw/utility/visualization.py

@@ -0,0 +1,52 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+import typing
+from itertools import chain
+
+
+def _get_num_rows(num_graphs: int, num_cols: int) -> int:
+    """Get the minimum number of rows needed to show a certain number of graphs,
+    given a certain number of columns.
+
+    :param num_graphs: Number of graphs
+    :param num_cols: Number of columns
+    :return: Number of rows
+    """
+    return num_graphs // num_cols + 1
+
+
+# TODO: Calculate fig size in relation to the number of rows and columns
+# TODO: Set proper line labels
+# TODO: Set proper axis titles
+# TODO: Should unnamed columns be dropped by this function or by the caller?
+def show_BER_curves(data: typing.List[pd.DataFrame], num_cols: int = 3) -> plt.figure:
+    """This function creates a matplotlib figure containing a number of BER curves.
+
+    :param data: List of pandas DataFrames containing the data to be plotted. Each element in the list is plotted in
+    a new graph. Each dataframe is assumed to contain a column named "SNR" which is used as the x-axis
+    :param num_cols: Number of columns in which the graphs should be arranged in the resulting figure
+    :return: Matplotlib figure
+    """
+    num_graphs = len(data)
+    num_rows = _get_num_rows(num_cols, num_cols)
+
+    fig, axes = plt.subplots(num_rows, num_cols)
+    fig.suptitle("Bit-Error-Rates of various decoders for different codes")
+
+    axes = list(chain.from_iterable(axes))[:num_graphs]  # Flatten the 2d axes array
+
+    for axis, df in zip(axes, data):
+        column_names = [column for column in df.columns.values.tolist() if not column == "SNR"]
+
+        for column in column_names:
+            sns.lineplot(ax=axis, data=df, x="SNR", y=column, label=column)
+
+        #axis.set_title(code)
+        axis.set(yscale="log")
+        axis.set_xlabel("SNR")
+        axis.set_ylabel("BER")
+        axis.set_yticks([10e-5, 10e-4, 10e-3, 10e-2, 10e-1, 10e0])
+        axis.legend()
+
+    return fig