KKTIX Timeline Visualization

A Python tool that processes KKTIX event data to analyze event history, expenses, and learning hours.

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Overview

This project provides data processing for KKTIX event data, featuring:

• Event data extraction
• Expense calculations
• Learning hours tracking
• Data visualization

Setup

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation Steps

1. Clone the repository

git clone https://github.com/fantasybz/kktix-timeline.git

2. Navigate to the project directory

cd kktix-timeline

3. Install required packages

pip install -r requirements.txt

Environment Configuration

1. Create and Configure Environment File

   # Create .env file in project root
   touch .env

   Add the following to your .env file:

   KKTIX_EMAIL=your_email@example.com
   KKTIX_PASSWORD=your_password
   KKTIX_HEADLESS=true
   KKTIX_DEBUG=false

   Environment variables explained:

   • KKTIX_EMAIL: Your KKTIX account email
   • KKTIX_PASSWORD: Your KKTIX account password
   • KKTIX_HEADLESS: Run browser in headless mode (no GUI)
   • KKTIX_DEBUG: Enable/disable debug logging

Running the Script

1. Execute the Python Script

   python3 src/main.py

   The script will process your KKTIX data and generate output files

Troubleshooting

1. Environment Issues

   # Verify environment variables are loaded
   python3 -c "import os; print(os.getenv('KKTIX_EMAIL'))"

2. Authentication Issues

   • Verify your KKTIX credentials are correct
   • Check if KKTIX service is accessible
   • Ensure no special characters in .env file are causing issues

3. Browser Automation Issues

   # Try disabling headless mode
   # Set in .env:
   KKTIX_HEADLESS=false
   KKTIX_DEBUG=true

Features

1. Data Processing

• Event data extraction
• Date and time parsing
• Price calculation
• Learning hours computation

2. Data Analysis

• Total expenses calculation
• Learning hours tracking
• Event count
• Location statistics

3. Data Visualization

• Interactive timeline of attended events
• Top 5 expense hosts by attended events
• Top 5 locations by attended events

Technical Details

Technologies Used

• Python 3.8+
• Selenium for web scraping
• D3.js for data visualization