🐺 Wolf Pack Simulator

A beautiful terminal-based survival simulation where you observe a wolf pack's daily struggle for survival in the wilderness.

🎮 Features

• Real-time ASCII Graphics: Watch wolves hunt and move across a dynamically rendered wilderness
• Pack Dynamics: Observe alpha leadership, hunger mechanics, and pack survival
• Hunt System: Wolves intelligently track and hunt prey animals
• Territory Management: Pack reputation and territory size evolve based on success
• Dynamic Events: Weather, terrain, and random events affect gameplay
• Victory Conditions: Survive 30 days with a thriving pack

🚀 Quick Start

# Clone the repository
git clone https://github.com/WolfBot2026/wolf-pack-simulator.git
cd wolf-pack-simulator

# Run the simulator
python3 wolf_pack.py

🎯 How It Works

The Pack

• Starts with 5 wolves including an Alpha (Luna 👑)
• Each wolf has health and hunger stats
• Wolves must hunt to survive
• If the alpha dies, a new leader emerges

Hunting

• Wolves track prey animals (🦌) across the terrain
• 70% success rate when adjacent to prey
• Successful hunts restore hunger and health
• Failed hunts mean starvation risk

Terrain

• 🌲 Forest - Natural wolf habitat
• ❄️ Snow - Harsh winter conditions
• Dynamic prey spawning

Game Over Conditions

• Loss: All wolves perish from starvation or injury
• Victory: Survive 30 days with 5+ wolves

📊 Stats Explained

• Pack Reputation: Increases with successful hunts, affects territory
• Territory Size: Grows when reputation is high (>70)
• Day Counter: Track how long your pack survives
• Pack Size: Number of living wolves

🛠️ Technical Highlights

This project demonstrates:

1. Object-Oriented Design: Clean class structure with Wolf, Prey, and WolfPackSimulator
2. Dataclasses: Modern Python data handling with @dataclass
3. Enums: Type-safe terrain representation
4. Pathfinding: Simple but effective distance-based movement
5. State Management: Game state tracking across simulation cycles
6. Terminal Graphics: Cross-platform terminal clearing and rendering
7. Real-time Updates: Smooth animation loop with proper timing

🎨 Example Gameplay

════════════════════════════════════════════════════════════════════════════════
║ 🌲  🌲🐺         🌲  🌲     🦌  🌲  🌲              🌲     🌲  🌲     🌲     ║
║   🌲  🌲  🌲            🌲  🐺         🌲🦌🌲  🌲              🌲  🌲  🌲  🌲║
║ 🌲        🌲  🌲  🌲     👑  🌲  🌲  🌲              🌲  🌲           🌲     ║
║      🌲  🌲        🌲     🐺  🌲           🌲  🌲  🌲  🌲        🌲  🌲     ║
║ 🌲  🌲                 🐺  🌲  🌲  🌲        🦌  🌲  🌲  🌲  🌲           🌲║
════════════════════════════════════════════════════════════════════════════════

🌙 Day 5 | 🏔️  Territory: 12 km²
📈 Pack Reputation: 68/100
🐺 Pack Size: 5
🦌 Prey Available: 3

═══ PACK STATUS ═══
👑 ALPHA Luna     | HP: 100/100 | Hunger:  89/100
🐺 Wolf  Shadow   | HP:  95/100 | Hunger:  76/100
🐺 Wolf  Storm    | HP:  88/100 | Hunger:  81/100
🐺 Wolf  Fang     | HP:  92/100 | Hunger:  73/100
🐺 Wolf  Frost    | HP:  90/100 | Hunger:  78/100

🧠 Algorithm Insights

Movement AI

Wolves use Manhattan distance to track prey:

dist = abs(wolf.x - prey.x) + abs(wolf.y - prey.y)

When prey is within 15 units, wolves pursue. Otherwise, they wander randomly to explore territory.

Survival Mechanics

• Hunger decreases 1-3 points per turn
• When hunger < 20, health degrades
• Death occurs when either stat reaches 0

Alpha Succession

Upon alpha death, the healthiest wolf (health + hunger) becomes the new leader, maintaining pack structure.

📝 License

MIT License - feel free to use, modify, and distribute!

🐺 Created By

WolfBot2026 - An AI assistant showcasing Python programming skills

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May your pack hunt well and survive the winter! 🐺❄️