RF Mind Map Visualizer

An interactive web-based visualization tool for exploring structured knowledge in RF communications and related engineering domains. It renders a mind map from CSV datasets describing topics, their relationships, and associated multimedia resources (e.g., YouTube links).

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🚀 Features

• Dynamic Mind Map Visualization Built with D3.js, the app displays interconnected RF topics as an explorable graph.

• Contextual Details Panel Clicking a topic or relationship opens a side panel showing:

  • Descriptions and key concepts
  • Related topics and their relation types
  • Linked YouTube resources with thumbnails

• Data-Driven Structure Topics, links, and URLs are loaded directly from CSV files located in the data/ directory.

• Offline & Client-Side Entirely browser-based — no backend required. Data editing (e.g., adding URLs) is done by re-generating and downloading updated CSVs.

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📂 Project Structure

.github/
  copilot-instructions.md      # AI assistant code conventions

data/
  topics.csv                   # List of topics and subtopics
  links.csv                    # Relationships between topics
  urls.csv                     # URLs associated with topics
  yt-playlists.url             # YouTube playlists to crawl

scripts/
  calculate_tokens.py          # Token estimator for Gemini AI context
  check_models.py              # Lists Gemini models supporting content generation
  check_limits.py              # Prints available models’ context limits
  enrich_urls.py               # Matches topics to YouTube videos using Gemini AI

src/
  index.html                   # Main application HTML (with inline CSS)
  main.js                      # D3.js logic and UI interaction code

.env.example                   # Example environment file (YouTube + Gemini API keys)
.gitignore                     # Git ignore rules
requirements.txt               # Python dependencies
todo.txt                       # Developer task notes

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🧠 How It Works

1. Data Preparation

   • topics.csv: hierarchical list of RF topics
   • links.csv: defines cross-topic relations (e.g., depends on, extends)
   • urls.csv: connects topics/links to videos or web resources

2. Visualization

   • Open src/index.html in a browser
   • The app loads data from /data/
   • Interactive D3.js graph is generated dynamically

3. URL Enrichment (Optional)

   • scripts/enrich_urls.py uses Gemini AI + YouTube Data API
   • It reads YouTube playlists and matches videos to topics automatically
   • Generates/updates urls.csv with best-matching resources

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⚙️ Setup

1. Clone the Repository

git clone https://github.com/<yourusername>/rf-mindmap.git
cd rf-mindmap

2. Install Python Dependencies

pip install -r requirements.txt

3. Configure Environment

Create a .env file (based on .env.example) and add:

YOUTUBE_API_KEY=your_youtube_api_key
GEMINI_API_KEY=your_gemini_api_key

4. Run Scripts (Optional)

• Check Model Limits

  python scripts/check_limits.py

• Estimate Token Usage

  python scripts/calculate_tokens.py

• Enrich URLs Automatically

  python scripts/enrich_urls.py

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🌐 Using the Visualizer

1. Open src/index.html in your browser.
2. Explore the mind map — click on nodes or links to view details.
3. Modify data files in /data/ to expand or update the graph.
4. Use the “Save & Download urls.csv” button to export updated data.

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🧩 Data File Formats

File	Description
topics.csv	Topic index, title, and description
links.csv	Relationships between topics
urls.csv	URLs mapped to topics or links
yt-playlists.url	List of playlists for enrichment script

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🛠️ Technologies

• Frontend: D3.js (v7), HTML5, CSS3
• Backend Scripts: Python 3.10
• APIs: YouTube Data API, Google Gemini AI
• Data Format: CSV

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🧑‍💻 Development Notes

See .github/copilot-instructions.md for code conventions and AI assistant guidance. This ensures generated code remains clean, modular, and compatible with the project’s structure.

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📜 License

This project is released under the No License. Feel free to use, modify, and adapt it for educational or visualization purposes.

this project was made using Google AI studio