remove nearest bomb

src/environment.py

@@ -13,5 +13,5 @@ def generate_field() -> List[List[Tile]]:
 
 class Environment:
     def __init__(self, field: List[List[Tile]] = None):
-        self.field = generate_field()
+        self.field = field if field else generate_field()
         self.mine_count = sum(sum(1 for tile in row if tile.mine) for row in self.field)

src/main.py

@@ -2,7 +2,7 @@ import pygame as pg
 
 from agent import Agent
 from game_ui import GameUi
-from const import ICON, IMAGES
+from const import ICON, IMAGES, DEBUG_FIELD
 from environment import Environment
 from search_algoritms.BFS import breadth_first_search, breadth_first_search_for_a_star
 from src.search_algoritms.a_star import a_star
@@ -41,7 +41,7 @@ def main():
     pg.display.set_icon(pg.image.load(ICON))
     pg.fastevent.init()
 
-    env = Environment()
+    env = Environment(DEBUG_FIELD)
     agent = Agent()
     game_ui = GameUi(agent, env)
     game_ui.update()

src/search_algoritms/a_star.py

@@ -1,7 +1,5 @@
 from typing import List, Tuple
 
-from ap_mine import APMine
-from at_mine import ATMine
 from src.node import Node
 from src.tile import Tile
 from .helpers import successor, get_path_actions_a_star, calculate_priority
@@ -13,7 +11,6 @@ def a_star(field: List[List[Tile]], start_x: int, start_y: int, start_direction:
 
     while node_queue:
         node = node_queue.pop(0)
-        # przy get wyciąga element z najniższą wartością priorytetu
 
         if (node.x, node.y) == goal:
             return get_path_actions_a_star(node, field)
@@ -33,18 +30,3 @@ def a_star(field: List[List[Tile]], start_x: int, start_y: int, start_direction:
                 node_queue.sort()
 
     return False, False
-
-
-def nearest_bomb(a: Tuple[int, int], field: List[List[Tile]]):
-    min = 20
-    min_x = 0
-    min_y = 0
-    for x in range(10):
-        for y in range(10):
-            if isinstance(field[y][x].mine, (APMine, ATMine)):
-                distance = abs(a[0] - x) + abs(a[1] - y)
-                if distance < min:
-                    min = distance
-                    min_x = x
-                    min_y = y
-    return min_x, min_y

src/search_algoritms/helpers.py

@@ -6,6 +6,16 @@ from src.node import Node
 from src.tile import Tile
 
 
+def get_path_actions(node: Node):
+    path = []
+    actions = []
+    while node.parent:
+        path.append((node.x, node.y))
+        actions.append(node.action)
+        node = node.parent
+    return path[::-1], actions[::-1]
+
+
 def successor(field: List[List[Tile]], x: int, y: int, direction: int):
     """
     kody klawiszy:
@@ -28,28 +38,6 @@ def successor(field: List[List[Tile]], x: int, y: int, direction: int):
     return neighbours
 
 
-def get_path_actions(node: Node, field: List[List[Tile]]):
-    path = []
-    actions = []
-    while node.parent:
-        path.append((node.x, node.y))
-        actions.append(node.action)
-        node = node.parent
-    return path[::-1], actions[::-1]
-
-def get_path_actions_a_star(node: Node, field: List[List[Tile]]):
-    path = []
-    actions = []
-    while node.parent:
-        path.append((node.x, node.y))
-        print(field[node.y][node.x], field[node.y][node.x].weight, node.x, node.y, field[node.y][node.x].mine)
-        if field[node.y][node.x].mine:
-            actions.append(32)
-        actions.append(node.action)
-        node = node.parent
-    return path[::-1], actions[::-1]
-
-
 def cost(field: List[List[Tile]], node):
     return 1 if (node.parent.x, node.parent.y) == (node.x, node.y) else field[node.y][node.x].weight
 
@@ -60,3 +48,15 @@ def calculate_priority(field: List[List[Tile]], node, goal_test:  Tuple[int, int
 
 def heuristic(a, b):
     return abs(a.x - b[0]) + abs(a.y - b[1])
+
+
+def get_path_actions_a_star(node: Node, field: List[List[Tile]]):
+    path = []
+    actions = []
+    while node.parent:
+        path.append((node.x, node.y))
+        if field[node.y][node.x].mine:
+            actions.append(32)
+        actions.append(node.action)
+        node = node.parent
+    return path[::-1], actions[::-1]