320 lines
8.5 KiB
Markdown
320 lines
8.5 KiB
Markdown
# Maze Generator
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A comprehensive Python-based maze generation and solving application featuring **8 different algorithms**, an interactive **Neo-Brutalism web interface**, and advanced analysis tools.
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## Features
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### 🎨 8 Maze Generation Algorithms
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- **Recursive Backtracking** - Depth-first search with backtracking
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- **Kruskal's Algorithm** - Minimum spanning tree using union-find
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- **Prim's Algorithm** - Greedy minimum spanning tree
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- **Sidewinder Algorithm** - Row-by-row generation with horizontal bias
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- **Hunt-and-Kill Algorithm** - Random walks with hunting phase
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- **Eller's Algorithm** - Memory-efficient row-by-row generation
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- **Wilson's Algorithm** - Loop-erased random walks (uniform spanning trees)
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- **Aldous-Broder Algorithm** - Random walk-based generation
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### 🔍 2 Solving Algorithms
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- **Depth-First Search (DFS)** - Memory-efficient pathfinding
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- **Breadth-First Search (BFS)** - Guaranteed shortest path
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### 🎯 Interactive Web Interface
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Built with **Neo-Brutalism** design aesthetics featuring:
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- Bold, thick borders (6px)
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- High contrast neon colors (yellow, pink, cyan, green)
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- Hard drop shadows
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- Chunky typography (Space Grotesk font)
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- Asymmetric layouts
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- **Animated solving** with pause/stop controls and adjustable speed
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### 📊 Analysis Tools
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- Dead end counting and percentage
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- Longest path detection
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- Branching factor calculation
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- Algorithm performance benchmarking
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### 🖼️ Visualization
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- Real-time canvas rendering
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- PNG image export
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- Solution path highlighting
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- Visited cell tracking
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### 💾 Persistence
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- Save/load mazes as JSON
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- File management system
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- Reproducible generation with seeds
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## Installation
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### Local Setup
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1. **Clone the repository:**
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```bash
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git clone <repository-url>
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cd mazer
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```
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2. **Create virtual environment:**
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```bash
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python -m venv venv
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# Windows
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venv\Scripts\activate
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# Linux/Mac
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source venv/bin/activate
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```
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3. **Install dependencies:**
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```bash
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pip install -r requirements.txt
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```
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4. **Generate SEO images:**
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```bash
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python generate_images.py
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```
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This creates favicons, social media previews, and PWA icons.
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See `GENERATE_IMAGES_INSTRUCTIONS.md` for details.
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5. **Run the application:**
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```bash
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python api/app.py
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```
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6. **Open your browser:**
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Navigate to `http://localhost:5000`
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### Docker Setup
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1. **Build and run with Docker Compose:**
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```bash
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cd docker
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docker-compose up --build
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```
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2. **Access the application:**
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Navigate to `http://localhost:5000`
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## Usage
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### Web Interface
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The web interface provides 9 main operations:
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1. **Generate Maze** - Create a new maze with selected algorithm and dimensions
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2. **Visualize** - Display the maze on canvas
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3. **Download Image** - Save maze as PNG file
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4. **Save to File** - Persist maze as JSON
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5. **Load from File** - Restore saved maze
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6. **Solve (DFS)** - Find path using Depth-First Search with animated visualization
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7. **Solve (BFS)** - Find shortest path using Breadth-First Search with animated visualization
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8. **Analyze** - Compute maze statistics and metrics
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9. **Benchmark** - Compare algorithm performance
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#### Animation Controls
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When solving a maze, you can:
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- **Pause/Resume** - Pause the animation and resume where you left off
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- **Stop** - Stop the animation and show the final result
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- **Adjust Speed** - Use the slider to control animation speed (slow to fast)
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### API Endpoints
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#### Generate Maze
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```bash
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POST /api/generate
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Content-Type: application/json
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{
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"algorithm": "recursive_backtracking",
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"rows": 15,
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"cols": 15,
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"seed": 42
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}
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```
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#### Solve Maze
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```bash
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POST /api/solve
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Content-Type: application/json
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{
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"maze_id": 0,
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"algorithm": "bfs"
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}
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```
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#### Analyze Maze
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```bash
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GET /api/analyze/<maze_id>
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```
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#### Download Maze Image
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```bash
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GET /api/download/<maze_id>?solution=true&solver=bfs&format=png
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# Parameters:
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# - solution: true/false (include solution path)
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# - solver: dfs/bfs (which algorithm to use)
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# - format: png/jpg (image format, defaults to png)
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```
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#### Benchmark Algorithms
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```bash
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POST /api/benchmark
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Content-Type: application/json
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{
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"type": "quick"
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}
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```
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### Python API
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```python
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from src.generators import RecursiveBacktrackingGenerator
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from src.solvers import BFSSolver
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from src.analysis.analyzer import MazeAnalyzer
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# Generate a maze
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generator = RecursiveBacktrackingGenerator()
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maze = generator.generate(rows=20, cols=20, seed=42)
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# Solve the maze
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solver = BFSSolver()
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solution = solver.solve(maze)
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# Analyze the maze
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analysis = MazeAnalyzer.analyze(maze)
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print(f"Dead ends: {analysis['dead_ends']}")
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print(f"Longest path: {analysis['longest_path_length']}")
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```
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## Testing
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Run the test suite with coverage:
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```bash
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# Run all tests
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pytest
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# Run with coverage report
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pytest --cov=src --cov-report=html
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# Run specific test file
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pytest tests/unit/test_maze.py
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# Run specific test
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pytest tests/unit/test_generators.py::TestGenerators::test_generator_creates_valid_maze
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```
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### Test Coverage
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- Overall: >90%
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- Core modules: >95%
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- Generators: >90%
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- Solvers: >90%
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- API: >85%
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## Project Structure
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```
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mazer/
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├── src/ # Core application code
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│ ├── core/ # Maze and Cell classes
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│ ├── generators/ # 8 generation algorithms
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│ ├── solvers/ # DFS and BFS solvers
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│ ├── visualization/ # Image and web rendering
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│ ├── analysis/ # Analysis and benchmarking
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│ └── storage/ # File I/O operations
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├── api/ # Flask web API
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│ └── app.py # Main application
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├── web/ # Frontend
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│ ├── static/
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│ │ ├── css/ # Neo-Brutalism styles
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│ │ └── js/ # Interactive controls
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│ └── templates/ # HTML templates
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├── tests/ # Test suite
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│ ├── unit/ # Unit tests
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│ └── integration/ # Integration tests
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├── docker/ # Docker configuration
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├── requirements.txt # Python dependencies
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└── README.md # This file
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```
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## Algorithm Complexity
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| Algorithm | Time Complexity | Space Complexity | Characteristics |
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|-----------|----------------|------------------|-----------------|
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| Recursive Backtracking | O(n) | O(n) | Long winding paths |
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| Kruskal's | O(E log E) | O(V) | Many short paths |
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| Prim's | O(E log V) | O(V) | Short dead ends |
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| Sidewinder | O(n) | O(cols) | Horizontal bias |
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| Hunt-and-Kill | O(n²) | O(1) | Few dead ends |
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| Eller's | O(n) | O(cols) | Memory efficient |
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| Wilson's | O(n) expected | O(n) | Uniform spanning tree |
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| Aldous-Broder | O(n log n) | O(1) | Uniform, slow |
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## Performance Targets
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- 5×5 maze: <10ms generation
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- 25×25 maze: <100ms generation
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- 50×50 maze: <1s generation
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- API response time: <2s for all operations
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## Technologies Used
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- **Backend:** Python 3.11+
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- **Web Framework:** Flask 3.0
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- **Image Processing:** Pillow
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- **Testing:** pytest, pytest-cov
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- **Containerization:** Docker, Docker Compose
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- **Frontend:** HTML5, CSS3, JavaScript
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- **Design:** Neo-Brutalism aesthetic
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- **SEO:** Comprehensive meta tags, structured data, sitemap, robots.txt
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## SEO Features
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The site is fully optimized for search engines:
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- ✅ Comprehensive meta tags (title, description, keywords)
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- ✅ Open Graph tags for social media sharing
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- ✅ Twitter Card support
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- ✅ Schema.org structured data (WebApplication)
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- ✅ XML sitemap with image sitemap
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- ✅ robots.txt for crawler guidance
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- ✅ PWA manifest for mobile app experience
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- ✅ Semantic HTML with ARIA roles
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- ✅ Mobile-optimized and responsive
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- ✅ Fast loading with resource hints
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See `SEO_OPTIMIZATION.md` for complete details.
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## Contributing
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1. Fork the repository
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2. Create a feature branch (`git checkout -b feature/amazing-feature`)
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3. Commit your changes (`git commit -m 'Add amazing feature'`)
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4. Push to the branch (`git push origin feature/amazing-feature`)
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5. Open a Pull Request
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## License
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This project is licensed under the MIT License.
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## Acknowledgments
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- Maze generation algorithms based on ["Mazes for Programmers" by Jamis Buck](http://www.mazesforprogrammers.com/)
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- Neo-Brutalism design inspiration from [neobrutalism.dev](https://neobrutalism.dev/)
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## Future Enhancements
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- Additional solving algorithms (A*, Dijkstra)
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- 3D maze support
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- Multi-start/multi-end points
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- Difficulty ratings
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- WebSocket real-time updates
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- Mobile app version
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