final corrections before submission

HW4/game_data.py

@@ -56,6 +56,5 @@ with open('type_effectiveness.json', 'r') as file:
         type_effectiveness.append(json.loads(line))
 type_effectiveness_df = pd.DataFrame.from_dict(type_effectiveness)
 #NOTE: from a practical point of view, since type_effectiveness is accessed many times
-#in the main code, using pandas DataFrame is not efficient. I decided to keep using dictionaries
-#for the type effectiveness. The justification arises especially if we consider 
-#how long data generation will take.
+#in the main code I decided to keep using dictionaries
+#for the type effectiveness

HW4/hw4.py

@@ -7,7 +7,6 @@ import pickle
 import matplotlib.pyplot as plt
 import numpy as np
 import os
-np.seterr(divide='raise', invalid='raise')
 
 #if the results folder does not exist, create it
 if not os.path.exists('results'):
@@ -220,17 +219,19 @@ def make_plots(Ngames,Nbattles):
     plt.savefig('plots/pokemon_types_distribution.pdf')
     
 
-
 if __name__ == "__main__":
     Ngames = 1000
     Nbattles = 150
+
     print("Generating data")
     for i in range(Ngames):
+        #automatic player creation (choose a random starter pokemon)
         starter_choice = random.choice(gd.available_starters)
-        #print(f"Generating data for {starter_choice}, game {i}...")
+        print(f"Generating data for {starter_choice}, game {i}...")
         results = Main(starter_choice = starter_choice, Nbattles=Nbattles)
         pickle_out = open(f"results/{starter_choice}_game_{i}_{Nbattles}_battles.pickle",'wb')
         pickle.dump(results,pickle_out)
         pickle_out.close()
     print("Generating plots")
-    make_plots(Ngames, Nbattles)
+    make_plots(Ngames, Nbattles)
+    print("Finished!")