Finished with improvements, continued on experimental resuls

data/errorrates/2bit_temp/misc/processed/find_best_configuration.py

@@ -0,0 +1,46 @@
+import pandas as pd
+import glob
+
+# Function to find the best configuration based on parameter value 99
+def find_best_configuration(file_paths):
+    dfs = []
+    for file_path in file_paths:
+        df = pd.read_csv(file_path, header=None, names=['Parameter', 'Bit Error Rate'])
+        # Extract configuration from file name
+        config = file_path.split('_')[1:3]
+        df['config'] = f"{config[0]}_{config[1]}"
+        # Filter by parameter value 99
+        df = df[df['Parameter'] == 99]
+        dfs.append(df)
+
+    # Concatenate all dataframes
+    combined_df = pd.concat(dfs, ignore_index=True)
+
+    # Find the configuration with the overall best (lowest) bit error rate for parameter 99
+    
+    #best_config = combined_df.groupby('config')['Bit Error Rate'].mean().idxmin()
+   
+    sorted_configs = combined_df.groupby('config')['Bit Error Rate'].mean().sort_values().reset_index()
+    return sorted_configs
+
+# Example usage
+
+tmps = ["5", "15", "25", "35", "45", "55"]
+
+paths = []
+
+for tmp1 in tmps: 
+    for tmp2 in tmps:
+        paths.append("./errorrates_" + tmp1 + "_" + tmp2 + "_" + "processed.csv") 
+
+file_paths = [
+    "/path/to/errorrates_5_5_processed.csv",
+    "/path/to/errorrates_5_15_processed.csv",
+    "/path/to/errorrates_5_25_processed.csv",
+    "/path/to/errorrates_5_35_processed.csv",
+    "/path/to/errorrates_5_45_processed.csv"
+]
+
+best_config, combined_df = find_best_configuration(paths)
+print("Best configuration:", best_config)
+

data/errorrates/2bit_temp/misc/processed/find_best_configuration2.py

@@ -0,0 +1,44 @@
+import pandas as pd
+
+# Function to find configurations based on parameter value 99 and sort them
+def find_sorted_configurations(file_paths):
+    dfs = []
+    for file_path in file_paths:
+        df = pd.read_csv(file_path, header=None, names=['Parameter', 'Bit Error Rate'])
+        # Extract configuration from file name
+        config = file_path.split('_')[1:3]
+        df['config'] = f"{config[0]}_{config[1]}"
+        # Filter by parameter value 99
+        df = df[df['Parameter'] == 99]
+        dfs.append(df)
+
+    # Concatenate all dataframes
+    combined_df = pd.concat(dfs, ignore_index=True)
+
+    # Group by configuration and calculate the mean bit error rate
+    sorted_configs = combined_df.groupby('config')['Bit Error Rate'].mean().sort_values().reset_index()
+    
+    # Add a column for the absolute difference of the two configuration parameters
+    sorted_configs['Abs Difference'] = sorted_configs['config'].apply(lambda x: abs(int(x.split('_')[0]) - int(x.split('_')[1])))
+
+    return sorted_configs  
+
+tmps = ["5", "15", "25", "35", "45", "55"]
+paths = []
+
+for tmp1 in tmps:
+    for tmp2 in tmps:
+        paths.append("./errorrates_" + tmp1 + "_" + tmp2 + "_processed.csv")
+
+# Example usage
+file_paths = [
+    "/path/to/errorrates_5_5_processed.csv",
+    "/path/to/errorrates_5_15_processed.csv",
+    "/path/to/errorrates_5_25_processed.csv",
+    "/path/to/errorrates_5_35_processed.csv",
+    "/path/to/errorrates_5_45_processed.csv"
+]
+
+sorted_configurations = find_sorted_configurations(paths)
+print("Sorted configurations:\n", sorted_configurations)
+