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How to Build a Python Code Dependency Visualizer

Create a user-friendly tool that analyzes Python projects and generates interactive visualizations of code dependencies. This visualizer will help developers understand complex codebases, identify potential issues, and optimize their project structure for better maintainability and performance.

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Simple Summary

A powerful Code Dependency Visualizer for Python Projects that helps developers understand and optimize their codebase structure through intuitive graphical representations.

Product Requirements Document (PRD)

Goals:

  • Develop a tool that automatically analyzes Python project structures
  • Generate clear, interactive visualizations of code dependencies
  • Provide insights to help developers optimize their codebase

Target Audience:

  • Python developers
  • Software development teams
  • Code reviewers and architects

Key Features:

  1. Project import and analysis
  2. Interactive dependency graph generation
  3. Module and function-level dependency tracking
  4. Circular dependency detection
  5. Export and sharing of visualizations
  6. Integration with popular IDEs and version control systems

User Requirements:

  • Intuitive interface for project import and analysis
  • Clear, zoomable, and interactive dependency graphs
  • Ability to filter and search within large codebases
  • Detailed information on specific dependencies when selected
  • Easy export and sharing of visualizations
  • Performance optimization for large projects

User Flows

  1. Project Import and Analysis:

    • User uploads or selects a Python project
    • System analyzes the codebase and generates a dependency graph
    • User is presented with an interactive visualization

  2. Exploring Dependencies:

    • User navigates the graph by zooming and panning
    • Clicking on nodes reveals detailed information about modules/functions
    • User can filter dependencies based on specific criteria

  3. Optimization and Sharing:

    • System highlights potential issues (e.g., circular dependencies)
    • User makes changes to the codebase based on insights
    • User exports the visualization or shares it with team members

Technical Specifications

  • Frontend: React.js with D3.js for visualizations
  • Backend: Python (Flask or FastAPI) for analysis engine
  • Database: PostgreSQL for storing project metadata and analysis results
  • Version Control: Git integration
  • APIs: RESTful API for communication between frontend and backend
  • Authentication: JWT-based authentication
  • Deployment: Docker containers for easy deployment and scaling

API Endpoints

  • POST /api/projects/analyze: Upload and analyze a new project
  • GET /api/projects/{id}: Retrieve project analysis results
  • GET /api/projects/{id}/dependencies: Get dependency data for visualization
  • POST /api/projects/{id}/export: Export visualization data
  • GET /api/users/{id}/projects: List user's analyzed projects

Database Schema

Tables:

  1. Users (id, username, email, password_hash)
  2. Projects (id, user_id, name, description, created_at, updated_at)
  3. Modules (id, project_id, name, file_path)
  4. Dependencies (id, source_module_id, target_module_id, type)
  5. AnalysisResults (id, project_id, result_data, created_at)

File Structure

/src /frontend /components /pages /utils /styles /backend /analysis /api /models /utils /tests /public /assets /docs README.md requirements.txt Dockerfile docker-compose.yml

Implementation Plan

  1. Set up project structure and version control
  2. Implement backend analysis engine
    • Develop Python code parsing and dependency tracking
    • Create API endpoints for project analysis
  3. Build frontend visualization components
    • Implement D3.js graph rendering
    • Create user interface for project import and interaction
  4. Integrate frontend and backend
  5. Implement user authentication and project management
  6. Add advanced features (circular dependency detection, filtering)
  7. Optimize performance for large projects
  8. Implement export and sharing functionality
  9. Write tests and perform QA
  10. Create documentation and user guide
  11. Prepare for deployment

Deployment Strategy

  1. Containerize application using Docker
  2. Set up CI/CD pipeline (e.g., GitHub Actions, GitLab CI)
  3. Deploy to cloud provider (e.g., AWS, Google Cloud)
  4. Use managed database service for PostgreSQL
  5. Implement CDN for static assets
  6. Set up monitoring and logging (e.g., Prometheus, ELK stack)
  7. Configure auto-scaling for backend services
  8. Implement regular backups for database and user data
  9. Use HTTPS and implement security best practices

Design Rationale

  • React.js chosen for its component-based architecture and large ecosystem
  • D3.js selected for its powerful data visualization capabilities
  • Python backend leverages existing libraries for code analysis
  • PostgreSQL provides robust storage for structured project data
  • Docker ensures consistent deployment across environments
  • RESTful API allows for future expansion (e.g., IDE plugins, CI integration)
  • Modular file structure promotes maintainability and scalability
  • Focus on performance optimization necessary for handling large codebases