A flexible analytics platform for sports and fitness data visualization

SportMetrics transforms your training session data into interactive, filterable charts and insights. Built with a modular architecture to support multiple data sources and visualization types.

• Docker and Docker Compose
• Fitness session JSON files in ./sessions/ directory

# Build the application
make build

# Process sessions and generate charts
./manage.sh process --sport RUNNING --output /app/output/running.json

# Start web server
./manage.sh web start

# Access at http://localhost:13000

# Web server controls
./manage.sh web start     # Start web server
./manage.sh web stop      # Stop web server  
./manage.sh web status    # Check status
./manage.sh web restart   # Restart web server

# Data processing
./manage.sh process --help                    # Show processing options
./manage.sh process --sport CYCLING          # Process cycling sessions
./manage.sh process --min-distance 20000     # Long distance sessions

• Auto-registration: Charts automatically register in charts.json when generated
• Dynamic navbar: Web interface loads available charts dynamically
• Tag-based organization: Charts organized by sport, distance, activity type
• Metadata tracking: Session count, date ranges, sports, distance ranges

• Supports running, cycling, and other fitness activities
• Sport-specific filtering and visualization
• Metadata extraction (distance, duration, sport, ascent)
• Autolap timing analysis

• Search: Find sessions by name/date
• Distance range: Dual-range slider for distance filtering
• Sport filtering: Multi-sport data organization
• Reset functionality: Quick filter reset

• Responsive design: Works on desktop and mobile
• Interactive charts: Chart.js powered visualizations
• Loading states: Clean loading without layout glitches
• Custom legends: Enhanced session management

• frontend/public/index.html - Main HTML structure
• frontend/public/css/styles.css - Responsive styling
• frontend/public/js/chart.js - Chart logic and interactivity
• frontend/public/data/charts.json - Chart configuration registry

• backend/src/session_processor.py - Session processing and Chart.js export
• Automatic chart registration with metadata extraction
• Data flows: ./sessions/*.json → processing → frontend/public/data/*.json

• processor: Python backend for session processing
• web: Nginx serving frontend with data access
• Volume mounting for data persistence

# Process all running sessions with distance filter
./manage.sh process --sport RUNNING --min-distance 20000 --output /app/output/long-runs.json

# Process cycling sessions from specific date range  
./manage.sh process --sport CYCLING --start-date 2024-01-01 --min-distance 15000

# Process all sessions for comprehensive overview
./manage.sh process --output /app/output/all-sessions.json

# Force rebuild image with latest code changes
make build-force

• --sport: Filter by sport type (RUNNING, CYCLING, etc.)
• --start-date / --end-date: Date range (YYYY-MM-DD)
• --min-duration: Minimum duration in seconds
• --min-distance: Minimum distance in meters
• --min-calories: Minimum calories burned
• --output: Output file path (within container: /app/output/filename.json)

• Automatic registration: Charts register when processed
• Metadata tracking: Session counts, date ranges, sports
• Tag system: Flexible organization (running, endurance, distance, etc.)
• Default chart: Configurable landing page chart

# Build management
make build              # Build image
make build-force        # Force rebuild (no cache)
make clean             # Remove images

# Service management  
./manage.sh web start   # Start web interface
./manage.sh web stop    # Stop services
./manage.sh process     # Run data processing

# Direct docker-compose (advanced)
docker-compose --profile web up -d
docker-compose --profile process run --rm processor --help

1. Extend session_processor.py with new export methods
2. Charts auto-register with appropriate tags and metadata
3. Frontend dynamically loads new chart types

• Modular processor design supports different input formats
• Easy to extend beyond fitness data to other analytics
• Tag system supports arbitrary categorization

• Current: search, distance range, sport filtering
• Extensible to: time-based, performance metrics, custom attributes
• Frontend filter system designed for easy expansion

SportMetrics generates Chart.js-compatible JSON files with:

• Interactive line chart configurations
• Dataset per session with timing data
• Rich metadata for filtering and organization
• Responsive chart options optimized for web display
• Custom legend integration for enhanced UX

SportMetrics: Transform your training data into actionable insights 🏃📊