Implement optimized collision detection system using NumPy

- Introduced a hybrid collision detection approach that utilizes NumPy for vectorized operations, improving performance for games with many entities (200+).
- Added a spatial grid for efficient lookups and AABB (Axis-Aligned Bounding Box) collision detection.
- Implemented a new `CollisionSystem` class with methods for registering units, checking collisions, and managing spatial data.
- Created performance tests to benchmark the new collision system against the old O(n²) method, demonstrating significant speed improvements.
- Updated existing code to integrate the new collision detection system and ensure compatibility with game logic.

units/points.py

@@ -6,14 +6,20 @@ import uuid
 AGE_THRESHOLD = 200
 
 
+from .unit import Unit
+from engine.collision_system import CollisionLayer
+
+
 class Point(Unit):
-    def __init__(self, game, position=(0,0), id=None, value=5):
-        super().__init__(game, position, id)
-        # Specific attributes for points
-        self.speed = 4  # Points age faster
-        self.fight = False
+    """
+    Represents a collectible point in the game.
+    Appears when a rat dies and can be collected by the player.
+    """
+    
+    def __init__(self, game, position=(0,0), id=None, value=10):
+        super().__init__(game, position, id, collision_layer=CollisionLayer.POINT)
         self.value = value
-        self.game.add_point(self.value)
+        self.speed = 1  # Points don't move but need speed for draw timing
 
     def move(self):
         self.age += self.speed