python-maze/PLAN.md

Maze Generator - Project Plan

Project Overview

A comprehensive Python-based maze generation and solving application featuring multiple algorithms, interactive web interface, and advanced analysis tools with containerized deployment.

Technology Stack

• Backend: Python 3.11+
• Web Framework: Flask or FastAPI
• Frontend: HTML5, CSS3, JavaScript (possibly React or Vue.js for interactivity)
• Testing: pytest, pytest-cov for coverage
• Containerization: Docker, Docker Compose
• Visualization: Pillow (PIL) for image generation, Canvas API for web rendering
• Package Management: pip, requirements.txt

Project Structure

mazer/
├── src/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── maze.py                 # Core Maze class
│   │   └── cell.py                 # Cell representation
│   ├── generators/
│   │   ├── __init__.py
│   │   ├── base.py                 # Base generator interface
│   │   ├── recursive_backtracking.py
│   │   ├── kruskal.py
│   │   ├── prim.py
│   │   ├── sidewinder.py
│   │   ├── hunt_and_kill.py
│   │   ├── eller.py
│   │   ├── wilson.py
│   │   └── aldous_broder.py
│   ├── solvers/
│   │   ├── __init__.py
│   │   ├── base.py                 # Base solver interface
│   │   ├── dfs.py                  # Depth-First Search
│   │   └── bfs.py                  # Breadth-First Search
│   ├── visualization/
│   │   ├── __init__.py
│   │   ├── image_renderer.py       # PNG/JPG generation
│   │   └── web_renderer.py         # JSON format for web canvas
│   ├── analysis/
│   │   ├── __init__.py
│   │   ├── analyzer.py             # Maze metrics and statistics
│   │   └── benchmark.py            # Algorithm performance testing
│   └── storage/
│       ├── __init__.py
│       └── file_handler.py         # Save/Load maze data
├── api/
│   ├── __init__.py
│   ├── app.py                      # Main Flask/FastAPI application
│   └── routes/
│       ├── __init__.py
│       ├── generate.py             # Generation endpoints
│       ├── solve.py                # Solving endpoints
│       └── analyze.py              # Analysis endpoints
├── web/
│   ├── static/
│   │   ├── css/
│   │   │   └── styles.css
│   │   ├── js/
│   │   │   ├── app.js
│   │   │   ├── visualizer.js
│   │   │   └── controls.js
│   │   └── images/
│   └── templates/
│       └── index.html
├── tests/
│   ├── __init__.py
│   ├── conftest.py                 # Pytest fixtures
│   ├── unit/
│   │   ├── test_generators.py
│   │   ├── test_solvers.py
│   │   ├── test_maze.py
│   │   └── test_analysis.py
│   ├── integration/
│   │   ├── test_api.py
│   │   └── test_workflow.py
│   └── performance/
│       └── test_benchmarks.py
├── docker/
│   ├── Dockerfile
│   └── docker-compose.yml
├── requirements.txt
├── requirements-dev.txt
├── pytest.ini
├── .dockerignore
├── .gitignore
├── README.md
└── PLAN.md

Phase 1: Core Foundation (Days 1-3)

1.1 Project Setup

• Initialize project structure
• Create virtual environment setup
• Configure pytest with coverage
• Set up .gitignore for Python projects
• Create requirements.txt and requirements-dev.txt

1.2 Core Data Structures

• Implement Cell class (walls, visited status, coordinates)
• Implement Maze class (grid, dimensions, seed management)
• Add maze serialization/deserialization (JSON format)
• Write unit tests for core classes (>90% coverage)

1.3 File Storage System

• Implement save/load functionality (JSON format)
• Add validation for loaded maze data
• Create tests for file operations

Phase 2: Maze Generation Algorithms (Days 4-7)

2.1 Base Generator Interface

• Create abstract BaseGenerator class
• Define standard interface (generate, step_by_step)
• Implement timing/performance tracking

2.2 Implement Generation Algorithms

• Recursive Backtracking (stack-based)
• Kruskal's Algorithm (union-find)
• Prim's Algorithm (minimum spanning tree)
• Sidewinder Algorithm (row-by-row)
• Hunt-and-Kill Algorithm
• Eller's Algorithm (row-by-row with sets)
• Wilson's Algorithm (loop-erased random walk)
• Aldous-Broder Algorithm (random walk)

2.3 Testing

• Unit tests for each algorithm
• Verify maze validity (all cells reachable)
• Test seed reproducibility
• Performance tests (5x5 to 50x50)

Phase 3: Maze Solving (Days 8-9)

3.1 Solver Implementation

• Create abstract BaseSolver class
• Implement DFS solver with path tracking
• Implement BFS solver with path tracking
• Add step-by-step visualization support

3.2 Testing

• Unit tests for both solvers
• Verify solution correctness
• Test on various maze sizes
• Performance benchmarking

Phase 4: Visualization (Days 10-12)

4.1 Image Generation

• Implement PNG/JPG renderer using Pillow
• Support for different styles (walls, paths, solutions)
• Color coding for solution paths
• Configurable cell sizes and colors

4.2 Web Visualization

• Create JSON format for web rendering
• Implement Canvas-based renderer in JavaScript
• Add animation support for generation/solving
• Real-time step-by-step visualization

4.3 Testing

• Test image generation for various sizes
• Validate JSON output format
• Visual regression tests (if applicable)

Phase 5: Analysis & Benchmarking (Days 13-14)

5.1 Maze Analysis

• Calculate dead ends count
• Calculate longest path
• Calculate branching factor
• Calculate solution path length
• Generate complexity metrics

5.2 Benchmarking System

• Compare generation algorithms (time, memory)
• Compare solving algorithms
• Generate performance reports
• Create visualization of benchmark results

5.3 Testing

• Unit tests for analysis functions
• Validate benchmark accuracy
• Performance regression tests

Phase 6: Web API (Days 15-17)

6.1 API Development

• Set up Flask/FastAPI application
• Create RESTful endpoints:
  • POST /api/generate - Generate new maze
  • GET /api/maze/{id} - Retrieve maze
  • POST /api/solve - Solve maze
  • GET /api/analyze/{id} - Analyze maze
  • POST /api/benchmark - Run benchmarks
  • GET /api/download/{id} - Download as image
• Add request validation
• Implement error handling
• Add CORS support

6.2 API Testing

• Integration tests for all endpoints
• Test error cases
• Load testing (optional)

Phase 7: Web Interface (Days 18-21)

7.1 Frontend Development

• Create responsive HTML layout
• Implement Neo-Brutalism CSS styling:
  • Bold, thick borders (4-8px)
  • High contrast colors (black borders, bright backgrounds)
  • Flat design with no gradients or shadows (except hard drop shadows)
  • Offset/displaced drop shadows for depth
  • Chunky, bold typography
  • Asymmetric layouts and overlapping elements
  • Vibrant color palette (neon yellows, pinks, blues)
• Build JavaScript controls:
  1. Generate new maze (algorithm selector, size, seed)
  2. Visualize maze (canvas rendering)
  3. Download maze as image
  4. Save maze to file
  5. Load maze from file
  6. Solve maze (DFS) with visualization
  7. Solve maze (BFS) with visualization
  8. Analyze maze (show statistics)
  9. Benchmark algorithms (show results)

7.2 Interactive Features

• Real-time generation animation
• Step-by-step solving visualization
• Interactive controls (play/pause/speed)
• Results display panel

7.3 Testing

• Manual UI testing
• Cross-browser compatibility
• Responsive design testing

Phase 8: Containerization (Days 22-23)

8.1 Docker Setup

• Create Dockerfile (multi-stage build)
• Create docker-compose.yml
• Configure environment variables
• Optimize image size

8.2 Docker Testing

• Test container builds
• Test container deployment
• Verify all features work in container
• Document deployment process

Phase 9: Testing & Quality Assurance (Days 24-25)

9.1 Comprehensive Testing

• Achieve >90% code coverage
• Run full test suite
• Fix any failing tests
• Performance testing across all algorithms

9.2 Code Quality

• Run linting (pylint/flake8)
• Format code (black)
• Type checking (mypy - optional)
• Code review

Phase 10: Documentation & Deployment (Days 26-27)

10.1 Documentation

• Complete README.md with:
  • Installation instructions
  • Usage examples
  • API documentation
  • Algorithm descriptions
• Add inline code documentation
• Create user guide
• Add deployment guide

10.2 Final Deployment

• Final testing in production-like environment
• Performance optimization
• Security review
• Deployment checklist

Key Technical Requirements

Maze Generation Requirements

• Support for 8 different algorithms
• Maze dimensions: 5x5 to 50x50
• Seed-based reproducibility
• Performance tracking (milliseconds)

Testing Requirements

• Automated unit tests (pytest)
• Integration tests for API
• Code coverage >90%
• Performance benchmarks
• Self-contained test suite

Containerization Requirements

• Docker container with all dependencies
• Docker Compose for easy deployment
• Environment-based configuration
• Health checks and logging

UI/UX Requirements

• Neo-Brutalism design aesthetic:
  • Thick black borders (4-8px) on all UI elements
  • High contrast color scheme
  • Hard drop shadows with offset (not soft/blurred)
  • Bold, sans-serif typography (e.g., Space Grotesk, Inter Bold)
  • Flat colors, no gradients
  • Asymmetric, grid-breaking layouts
  • Vibrant accent colors (neon pink, yellow, cyan)
  • Raw, unpolished aesthetic with intentional roughness

Performance Targets

• 5x5 maze: <10ms generation
• 25x25 maze: <100ms generation
• 50x50 maze: <1s generation
• API response time: <2s for all operations

Testing Strategy

Unit Tests

• All generator algorithms
• All solver algorithms
• Core maze operations
• Analysis functions
• File I/O operations

Integration Tests

• API endpoints
• End-to-end workflows
• File save/load roundtrips

Performance Tests

• Algorithm benchmarking
• Scalability testing (5x5 to 50x50)
• Memory usage monitoring

Test Coverage Goals

• Overall: >90%
• Core modules: >95%
• Generators: >90%
• Solvers: >90%
• API: >85%

Risk Mitigation

Technical Risks

1. Algorithm Complexity: Start with simpler algorithms (Recursive Backtracking)
2. Performance Issues: Implement early benchmarking
3. Browser Compatibility: Use standard Canvas API, test on major browsers
4. Container Size: Use multi-stage builds, alpine base images

Timeline Risks

1. Scope Creep: Stick to defined features
2. Testing Overhead: Write tests alongside implementation
3. Integration Issues: Regular integration testing

Success Criteria

• All 8 generation algorithms implemented and tested
• Both solving algorithms (DFS, BFS) working correctly
• Web interface with all 9 features functional
• UI follows Neo-Brutalism design concept
• Test coverage >90%
• Successful Docker deployment
• All mazes generated are valid (fully connected)
• Performance targets met
• Complete documentation

Future Enhancements (Post-MVP)

• Additional solving algorithms (A*, Dijkstra)
• 3D maze support
• Multi-start/multi-end points
• Maze difficulty ratings
• User accounts and saved mazes
• REST API rate limiting
• WebSocket support for real-time updates
• Mobile app version

Notes

• Prioritize code quality and test coverage
• Keep algorithms modular and extensible
• Document algorithm time/space complexity
• Use type hints throughout Python code
• Follow PEP 8 style guidelines
• Implement proper logging for debugging