Mice!

Mice! is a strategic game where players must kill rats with bombs before they reproduce and become too numerous. The game is a clone of the classic game Rats! for Windows 95.

Compatibility

It's developed in Python 3.13, please use it

Features

• Maze Generation: Randomly generated mazes using Depth First Search (DFS) algorithm.
• Units: Different types of units such as rats, bombs, and points with specific behaviors.
• Graphics: Custom graphics for maze tiles, units, and effects.
• Sound Effects: Audio feedback for various game events.
• Scoring: Points system to track player progress.
• Performance: Optimized collision detection system supporting 200+ simultaneous units using NumPy vectorization.

Engine Architecture

The Mice! game engine is built on a modular architecture designed for flexibility and maintainability. The engine follows a component-based design pattern where different systems handle specific aspects of the game.

Core Engine Components

1. Collision System (engine/collision_system.py)

• CollisionSystem Class: High-performance collision detection using NumPy vectorization
• Features:
  • Spatial hashing with grid-based lookups (O(1) average case)
  • Support for 6 collision layers (RAT, BOMB, GAS, MINE, POINT, EXPLOSION)
  • Hybrid approach: simple iteration for <10 candidates, NumPy vectorization for ≥10
  • Pre-allocated arrays with capacity management to minimize overhead
  • Area queries for explosion damage (get_units_in_area)
  • Cell-based queries for gas/mine detection (get_units_in_cell)
• Performance:
  • Handles 200+ units at ~3ms per frame
  • Reduces collision checks from O(n²) to O(n) using spatial partitioning
  • Vectorized distance calculations for massive parallel processing

2. Rendering System (engine/sdl2.py)

• GameWindow Class: Central rendering manager using SDL2
• Features:
  • Hardware-accelerated rendering via SDL2
  • Texture management and caching
  • Sprite rendering with transparency support (SDL_BLENDMODE_BLEND for alpha blending)
  • Text rendering with custom fonts
  • Resolution-independent scaling
  • Fullscreen/windowed mode switching
  • Blood stain rendering with RGBA format and proper alpha channel
• Optimizations:
  • Cached viewport bounds to avoid repeated calculations
  • Pre-cached image sizes for all assets at startup
  • Blood overlay layer system (no background regeneration needed)
  • Pre-generated blood stain pool (10 variants) for instant spawning
• Implementation:
  • Uses SDL2 renderer for efficient GPU-accelerated drawing
  • Implements double buffering for smooth animation
  • Manages texture atlas for optimized memory usage
  • Handles viewport transformations for different screen resolutions

3. Input System (engine/controls.py)

• KeyBindings Class: Handles all user input
• Features:
  • Keyboard input mapping and handling
  • Joystick/gamepad support
  • Configurable key bindings
  • Input state management
• Implementation:
  • Event-driven input processing
  • Key state buffering for smooth movement
  • Support for multiple input devices simultaneously
  • Customizable control schemes

3. Map System (engine/maze.py)

• Map Class: Manages the game world structure
• Features:
  • Maze data loading and parsing
  • Collision detection system
  • Tile-based world representation
  • Pathfinding support for AI units
• Implementation:
  • Grid-based coordinate system
  • Efficient collision detection using spatial partitioning
  • Support for different tile types (walls, floors, special tiles)
  • Integration with maze generation algorithms

4. Audio System

• Sound Management: Handles all audio playback
• Features:
  • Sound effect playback
  • Background music support
  • Volume control
  • Multiple audio channels
• Implementation:
  • Uses subprocess module for audio playback
  • Asynchronous sound loading and playing
  • Audio file format support (WAV, MP3, OGG)

Game Loop Architecture

The main game loop follows an optimized 4-pass pattern:

1. Pre-Registration Phase: Populate collision system with unit positions before movement
2. Update Phase: Execute unit logic and movement (bombs/gas can now query collision system)
3. Re-Registration Phase: Update collision system with new positions after movement
4. Collision & Render Phase: Check collisions and draw all game objects

Pre-Register → Move → Re-Register → Collisions → Render → Present → Repeat

This architecture ensures weapons (bombs, gas) can detect victims during their execution phase while maintaining accurate collision data.

Units Implementation

The game uses an object-oriented approach for all game entities. Each unit type inherits from a base unit class and implements specific behaviors.

Base Unit Architecture

All units share common properties and methods:

• Position and Movement: 2D coordinates with movement capabilities
• Unique Identification: UUID-based unique identifiers
• Collision Detection: Bounding box collision system
• State Management: Current state tracking (alive, dead, exploding, etc.)
• Rendering: Sprite-based visual representation

Unit Types Implementation

1. Rat Units (units/rat.py)

Base Rat Class:

• AI Behavior: Implements pathfinding using A* algorithm
• Movement: Grid-based movement with smooth interpolation
• State Machine: Multiple states (wandering, fleeing, reproducing)

Male Rat Class:

• Reproduction Logic: Seeks female rats for mating
• Territorial Behavior: Defends territory from other males
• Lifespan Management: Age-based death system

Female Rat Class:

• Pregnancy System: Gestation period simulation
• Offspring Generation: Creates new rat units
• Maternal Behavior: Protects offspring from threats

Implementation Details:

# Optimized rat behavior with pre-calculated render positions
class Rat:
    def move(self):
        self.process_ai()           # Decision making
        self.handle_movement()      # Position updates
        self._update_render_position() # Cache render coordinates
    
    def collisions(self):
        # Use optimized collision system with vectorization
        collisions = self.game.collision_system.get_collisions_for_unit(
            self.id, self.bbox, self.collision_layer
        )
        # Process only Rat-to-Rat collisions
        for _, other_id in collisions:
            other_unit = self.game.get_unit_by_id(other_id)
            if isinstance(other_unit, Rat):
                self.handle_rat_collision(other_unit)
    
    def draw(self):
        # Use cached render positions (no recalculation)
        self.game.render_engine.draw_image(
            self.render_x, self.render_y, self.sprite, tag="unit"
        )

2. Bomb Units (units/bomb.py)

Bomb Class:

• Timer System: Countdown mechanism before explosion
• Placement Logic: Player-controlled positioning
• Damage Calculation: Blast radius and damage computation

Explosion Class:

• Visual Effects: Animated explosion graphics
• Damage Dealing: Affects units within blast radius
• Temporary Entity: Self-destructs after animation

Implementation Details:

• State Machine: Armed → Countdown → Exploding → Cleanup
• Optimized Damage System: Uses collision_system.get_units_in_area() with vectorized distance calculations
• Effect Propagation: Chain reaction support for multiple bombs
• Area Query Example:

def die(self):
    # Collect explosion positions
    explosion_positions = self.calculate_blast_radius()
    # Query all rats in blast area using vectorized collision system
    victims = self.game.collision_system.get_units_in_area(
        explosion_positions, 
        layer_filter=CollisionLayer.RAT
    )
    for unit_id in victims:
        rat = self.game.get_unit_by_id(unit_id)
        if rat:
            rat.die()

3. Point Units (units/points.py)

Point Class:

• Collection Mechanics: Player interaction system
• Value System: Different point values for different achievements
• Visual Feedback: Pickup animations and effects

Unit Interaction System

Units interact through a centralized collision and event system:

1. Collision Detection:

   • Spatial hashing: Grid-based broad phase with O(1) lookups
   • NumPy vectorization: Parallel distance calculations for large candidate sets
   • Hybrid approach: Direct iteration for <10 candidates, vectorization for ≥10
   • Layer filtering: Efficient collision filtering by unit type (RAT, BOMB, GAS, etc.)
   • Area queries: Optimized explosion and gas effect calculations

2. Event System:

   • Unit death events
   • Reproduction events
   • Explosion events (with area damage)
   • Point collection events (90 frames lifetime ~1.5s at 60 FPS)

3. AI Communication:

   • Shared pathfinding data
   • Pheromone trail system for rat behavior
   • Danger awareness (bombs, explosions)

4. Spawn Protection:

   • Rats won't spawn on cells occupied by weapons (mines, bombs, gas)
   • Automatic fallback to adjacent cells if primary position blocked
   • Prevents unfair early-game deaths

Technical Details

• Language: Python 3.13
• Libraries:
  • numpy 2.3.4 for vectorized collision detection
  • sdl2 for graphics and window management
  • Pillow for image processing
  • uuid for unique unit identification
  • subprocess for playing sound effects
  • tkinter for maze generation visualization
• Performance Optimizations:
  • Collision System: NumPy-based spatial hashing reducing O(n²) to O(n)
  • Rendering Cache: Pre-calculated render positions, viewport bounds, and image sizes
  • Blood Overlay: Separate sprite layer eliminates background regeneration
  • Hybrid Processing: Automatic switching between direct iteration and vectorization
  • Pre-allocated Arrays: Capacity-based resizing minimizes NumPy vstack overhead
  • Texture Atlasing: Reduced memory usage and GPU calls
  • Object Pooling: Blood stain pool (10 pre-generated variants)
  • Delta Time Updates: Frame rate independence
• Memory Management:
  • Automatic cleanup of dead units
  • Texture caching and reuse
  • Efficient data structures for 200+ simultaneous units
  • Blood stain sprite pool to avoid runtime generation

Environment Variables

• SDL_VIDEODRIVER: Set the video driver (x11, wayland, etc.)
• RESOLUTION: Set the screen resolution (format: WIDTHxHEIGHT)
• FULLSCREEN: Enable/disable fullscreen mode (true/false)
• SOUND_ENABLED: Enable/disable sound effects (true/false)

Installation

1. Clone the repository: bash git clone https://github.com/yourusername/mice-maze-game.git cd mice-maze-game
2. Create a virtual environment: bash python3 -m venv venv source venv/bin/activate
3. Install the dependencies: bash pip install -r requirements.txt
4. Run the game: bash python rats.py

Project Structure

mice/
├── engine/                     # Core engine components
│   ├── collision_system.py    # NumPy-based vectorized collision detection
│   ├── controls.py            # Input handling system
│   ├── graphics.py            # Blood overlay and rendering optimizations
│   ├── maze.py                # Map and collision system
│   ├── sdl2.py                # Rendering and window management
│   └── unit_manager.py        # Unit spawning and lifecycle management
├── units/                      # Game entity implementations
│   ├── unit.py                # Base unit class with collision layers
│   ├── bomb.py                # Bomb and explosion logic with area damage
│   ├── gas.py                 # Gas weapon with cell-based detection
│   ├── mine.py                # Proximity mine with trigger system
│   ├── rat.py                 # Rat AI with optimized rendering cache
│   └── points.py              # Collectible points (90 frames lifetime)
├── assets/                    # Game resources
│   ├── images/                # Sprites and textures
│   └── fonts/                 # Text rendering fonts
├── sound/                     # Audio files
├── maze.py                    # Maze generation algorithms
├── rats.py                    # Main game entry point with 4-pass game loop
├── requirements.txt           # Python dependencies (including numpy)
├── .env                       # Environment configuration
└── README.md                  # This documentation

Game Files Details

• maze.py: Contains the MazeGenerator class implementing DFS algorithm for procedural maze generation
• rats.py: Main game controller with 4-pass optimized game loop, manages collision system and unit lifecycle
• engine/collision_system.py: NumPy-based spatial hashing system supporting 200+ units at 3ms/frame
• engine/graphics.py: Blood overlay system with pre-generated stain pool and rendering optimizations
• engine/controls.py: Input abstraction layer with configurable key bindings
• engine/maze.py: World representation with collision detection and pathfinding support
• engine/sdl2.py: Low-level graphics interface wrapping SDL2 with alpha blending and texture caching
• engine/unit_manager.py: Centralized unit spawning with weapon collision avoidance
• units/unit.py: Base unit class with collision layer support
• units/bomb.py: Explosive units with vectorized area damage calculations
• units/gas.py: Area denial weapon using cell-based victim detection
• units/mine.py: Proximity-triggered explosives
• units/rat.py: AI-driven entities with cached render positions and collision filtering
• units/points.py: Collectible scoring items (90 frame lifetime, ~1.5s at 60 FPS)
• assets/: Game resources including sprites, textures, and fonts
• sound/: Audio assets for game events and feedback
• scores.txt: Persistent high score storage
• .env: Runtime configuration and environment settings
• .gitignore: Version control exclusion rules