Execute each algorithm once

src/execution.py

@@ -20,8 +20,7 @@ def create_dataframes():
 def process_output(results):
     distances = []
     time = []
-    for element in results:
-        for line in element:
+    for line in results:
         if line.startswith(bytes("Total distance:", encoding="utf-8")):
             line_elements = line.split(sep=bytes(":", encoding="utf-8"))
             distances.append(float(line_elements[1]))
@@ -31,8 +30,8 @@ def process_output(results):
     return distances, time
 
 
-def populate_dataframe(df, output_list, dataset):
-    distances, time = process_output(output_list)
+def populate_dataframe(df, output_cmd, dataset):
+    distances, time = process_output(output_cmd)
     data_dict = {
         "dataset": dataset.removeprefix("data/"),
         "media distancia": mean(distances),
@@ -44,7 +43,7 @@ def populate_dataframe(df, output_list, dataset):
     return df
 
 
-def script_execution(filenames, df_list, iterations=2):
+def script_execution(filenames, df_list):
     script = "src/main.py"
     parameters = [
         ["genetic", "uniform", "generational"],
@@ -59,13 +58,10 @@ def script_execution(filenames, df_list, iterations=2):
         print(f"Running on dataset {dataset}")
         for index, params in zip(range(4), parameters):
             print(f"Running {params} algorithm")
-            output_list = []
-            for _ in range(iterations):
             output_cmd = run(
                 [executable, script, dataset, *params], capture_output=True
             ).stdout.splitlines()
-                output_list.append(output_cmd)
-            df_list[index] = populate_dataframe(df_list[index], output_list, dataset)
+            df_list[index] = populate_dataframe(df_list[index], output_cmd, dataset)
     return df_list