homework commit

HW4/game_data.py

@@ -0,0 +1,61 @@
+import json
+import random
+import pandas as pd
+#load moves from json file
+#eclude moves with power equal to null,
+# do not consider the effect,effects or changes fields
+available_moves = dict()
+
+keys_to_skip = ['effect','effects','changes']
+with open('moves.json', 'r') as file:
+    for line in file:
+        m = json.loads(line)
+        
+        if m['power'] == None or m['accuracy'] == None:
+            continue
+        else:
+            
+            available_moves[m['name']] = {}
+            m_keys = list(m.keys())
+            for k in m_keys:
+                if k in keys_to_skip:
+                    continue
+                else:
+                    available_moves[m['name']][k] = m[k]
+                    continue
+    available_moves_df = pd.DataFrame.from_dict(available_moves,orient='index')
+
+#loading pokemons from json file
+#assume levell = 1 for all
+pokemons_dict = dict()
+with open('pokemons.json', 'r') as file:
+    for line in file:
+        p = json.loads(line)
+        p['level'] = 1
+        pokemons_dict[p['name']] = p
+        #pokemons_dict[p['name']]['level'] = 1
+
+        current_pokemon_types = p['types'] + ['normal']
+        compatible_moves = []
+        for m in available_moves.keys():
+            if available_moves[m]['type'] in current_pokemon_types:
+                compatible_moves.append(m)
+        #select two random moves
+        selected_moves = [random.choice(compatible_moves) for _ in range(2)]
+        pokemons_dict[p['name']]['moves'] = selected_moves
+pokemons_df = pd.DataFrame.from_dict(pokemons_dict,orient='index')
+
+
+available_pokemon_names = list(pokemons_dict.keys())
+available_starters = ['bulbasaur', 'charmander', 'squirtle']
+
+#type effectiveness
+type_effectiveness = []
+with open('type_effectiveness.json', 'r') as file:
+    for line in 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.