Update

automated_plots.py

@@ -78,7 +78,6 @@ for metric in metrics_choices:
     r = []
     for i in range(len(names)):
         r.append(i)
-    #print(r)
 
     colors=['black', 'red', 'green', 'blue', 'cyan']
     
@@ -101,6 +100,7 @@ for metric in metrics_choices:
     plt.show()
 
     # Automatic saving
+    if Prompt.ask("Do you want to save the plot ?", choices=['y','n'], default='n'):
         date = datetime.datetime.now()
         plt.savefig('plots/'+metric+'-'+str(date.year)+str(date.month)+str(date.day)+'-'+str(date.hour)+str(date.minute)+'.png')
         

scenario_study.py

@@ -42,6 +42,8 @@ def normalize_data(DATA):
 
 #### Cost function
 import matplotlib.pyplot as plt
+import datetime
+
 def cost_scenario(scenario, data):
     """
     Calculate and visualize the cost of scenarios based on the provided data.
@@ -53,6 +55,7 @@ def cost_scenario(scenario, data):
     Returns:
         None.
     """
+
     data = normalize_data(data)
     # we prepare the weight
     WEIGHT = {}
@@ -86,7 +89,6 @@ def cost_scenario(scenario, data):
         
         for i in range(1, len(data_list), 3) :
             cost = 0
-            # x_data = [algo,key_length,block_length,operation,integrity,implementation_size,ram_consumption,throughput,execution_time,memory_usage,encrypted_file_size]
             encryption_data = data_list[i]
             decryption_data = data_list[i+1]
             cost += WEIGHT["end_user1"]["implementation_size"]*encryption_data[5]
@@ -101,20 +103,28 @@ def cost_scenario(scenario, data):
             cost += WEIGHT["end_user2"]["execution_time"]*decryption_data[8]
             cost += WEIGHT["end_user2"]["encrypted_file_size"]*decryption_data[10]
             costs[mode].append(cost)
-    ## pas fini, faut tracer mtn, 
 
     plt.barh(algo['stream'], costs["stream"])
     plt.barh(algo['block'], costs["block"])
 
-    plt.title(f'Cost in the scenario : {scenario}')
+    #plt.title(f'Cost in the scenario : {scenario}')
     plt.show()
 
 
-    # On explicite les meilleur
+    # On explicite les meilleurs
     best_cost = [min(costs["stream"]),min(costs["block"])]
     best_algo_index = [costs["stream"].index(best_cost[0]), costs["block"].index(best_cost[1])]
-    print(f"The best for stream is {algo['stream'][best_algo_index[0]]}")
-    print(f"\nThe best for block is {algo['block'][best_algo_index[1]]}")
+    print(f"The best for stream is {algo['stream'][best_algo_index[0]]} with a cost of {best_cost[0]}")
+    print(f"\nThe best for block is {algo['block'][best_algo_index[1]]} with a cost of {best_cost[1]}")
+
+    # Automatic saving
+    if Prompt.ask("Do you want to save the plot ?", choices=['y','n'], default='n'):
+        date = datetime.datetime.now()
+        plt.savefig('plots/'+'cost'+'-'+str(date.year)+str(date.month)+str(date.day)+'-'+str(date.hour)+str(date.minute)+'.png')
+        
+        # Clear after saving
+        plt.clf()
+        plt.cla()
 
 
 ##################################################################################