7fe4ae5365
✨ Major Features Added: - Complete magical theming and rebranding from LifeRPG to The Wizard's Grimoire - Production-grade React frontend with Tailwind CSS v4 and magical aesthetics - Comprehensive analytics dashboard with Recharts integration (ScryingPortal) - Push notifications system with PWA service worker support - Drag & drop functionality using @dnd-kit for habit reordering - Social features with friends system and leaderboards - Performance optimization tools and monitoring - Mobile app enhancement with PWA installation support 🏗️ Technical Infrastructure: - Advanced service worker with offline support and background sync - Zustand state management for scalable application state - Production-ready UI component system with enhanced Button, Card, Input - Progressive Web App (PWA) with manifest and app installation - FastAPI backend with comprehensive API endpoints - Docker containerization and CI/CD pipeline setup 📱 Progressive Web App Features: - Offline functionality with intelligent caching - Push notification support for habit reminders - App installation on mobile and desktop platforms - Background sync for offline data management - Performance monitoring and optimization tools 🎨 User Experience: - Magical wizard/grimoire theming throughout application - Responsive design optimized for all device sizes - Drag & drop habit management with smooth animations - Interactive analytics with multiple chart types - Social connectivity with friends and competitive features - Comprehensive notification and performance settings 🔧 Developer Experience: - Modern development stack with Vite and React - Comprehensive testing setup and CI/CD pipelines - Code quality tools with pre-commit hooks - Docker development environment - Detailed documentation and implementation guides This represents a complete transformation from prototype to production-ready application with enterprise-grade features and magical user experience.
326 lines
11 KiB
Python
326 lines
11 KiB
Python
"""
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Analytics module for LifeRPG - habit tracking insights and visualizations.
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"""
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from datetime import datetime, timedelta, timezone
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from typing import Dict, List, Optional
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from sqlalchemy.orm import Session
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from sqlalchemy import func, and_
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import models
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import json
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def get_habit_heatmap(db: Session, user_id: int, days: int = 365) -> Dict:
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"""Generate habit completion heatmap data for the last N days."""
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end_date = datetime.now(timezone.utc)
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start_date = end_date - timedelta(days=days)
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# Get all completions in the date range
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completions = db.query(
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func.date(models.Log.timestamp).label('date'),
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func.count(models.Log.id).label('count')
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).filter(
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models.Log.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= start_date
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).group_by(
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func.date(models.Log.timestamp)
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).all()
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# Create a map of date -> completion count
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completion_map = {str(comp.date): comp.count for comp in completions}
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# Generate full date range with completion counts
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heatmap_data = []
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current_date = start_date.date()
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end_date_only = end_date.date()
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while current_date <= end_date_only:
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date_str = current_date.isoformat()
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count = completion_map.get(date_str, 0)
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heatmap_data.append({
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'date': date_str,
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'count': count,
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'level': min(4, count) # 0-4 intensity levels for visualization
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})
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current_date += timedelta(days=1)
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return {
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'data': heatmap_data,
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'total_days': days,
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'completion_days': len(completion_map),
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'total_completions': sum(completion_map.values()),
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'start_date': start_date.date().isoformat(),
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'end_date': end_date.date().isoformat()
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}
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def get_habit_trends(db: Session, user_id: int, habit_id: Optional[int] = None, days: int = 30) -> Dict:
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"""Get habit completion trends over time."""
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end_date = datetime.now(timezone.utc)
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start_date = end_date - timedelta(days=days)
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# Base query
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query = db.query(
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func.date(models.Log.timestamp).label('date'),
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func.count(models.Log.id).label('completions')
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).filter(
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models.Log.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= start_date
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)
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# Filter by specific habit if provided
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if habit_id:
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query = query.filter(models.Log.habit_id == habit_id)
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trends = query.group_by(
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func.date(models.Log.timestamp)
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).order_by(
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func.date(models.Log.timestamp)
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).all()
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# Fill in missing dates with 0
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trend_data = []
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current_date = start_date.date()
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trend_map = {str(trend.date): trend.completions for trend in trends}
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while current_date <= end_date.date():
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date_str = current_date.isoformat()
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trend_data.append({
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'date': date_str,
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'completions': trend_map.get(date_str, 0)
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})
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current_date += timedelta(days=1)
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# Calculate some basic stats
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total_completions = sum(trend_map.values())
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active_days = len([d for d in trend_data if d['completions'] > 0])
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avg_per_day = total_completions / days if days > 0 else 0
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return {
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'data': trend_data,
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'stats': {
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'total_completions': total_completions,
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'active_days': active_days,
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'average_per_day': round(avg_per_day, 2),
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'completion_rate': round((active_days / days) * 100, 1) if days > 0 else 0
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},
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'period': {
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'days': days,
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'start_date': start_date.date().isoformat(),
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'end_date': end_date.date().isoformat()
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}
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}
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def get_habit_breakdown(db: Session, user_id: int, days: int = 30) -> Dict:
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"""Get breakdown of completions by habit."""
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end_date = datetime.now(timezone.utc)
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start_date = end_date - timedelta(days=days)
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# Get completions by habit
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results = db.query(
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models.Habit.id,
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models.Habit.title,
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func.count(models.Log.id).label('completions')
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).join(
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models.Log, models.Habit.id == models.Log.habit_id
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).filter(
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models.Habit.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= start_date
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).group_by(
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models.Habit.id, models.Habit.title
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).order_by(
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func.count(models.Log.id).desc()
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).all()
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habit_data = []
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total_completions = 0
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for result in results:
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completions = result.completions
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total_completions += completions
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habit_data.append({
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'habit_id': result.id,
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'habit_title': result.title,
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'completions': completions
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})
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# Calculate percentages
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for habit in habit_data:
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habit['percentage'] = round((habit['completions'] / total_completions) * 100, 1) if total_completions > 0 else 0
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return {
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'habits': habit_data,
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'total_completions': total_completions,
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'period': {
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'days': days,
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'start_date': start_date.date().isoformat(),
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'end_date': end_date.date().isoformat()
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}
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}
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def get_streak_history(db: Session, user_id: int, days: int = 90) -> Dict:
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"""Calculate streak history over time."""
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end_date = datetime.now(timezone.utc)
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start_date = end_date - timedelta(days=days)
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# Get all completion dates
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completion_dates = db.query(
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func.date(models.Log.timestamp).label('date')
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).filter(
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models.Log.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= start_date
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).group_by(
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func.date(models.Log.timestamp)
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).order_by(
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func.date(models.Log.timestamp)
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).all()
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# Convert to set for fast lookup
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completion_dates_set = {comp.date for comp in completion_dates}
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# Calculate streak for each day
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streak_data = []
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current_date = start_date.date()
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current_streak = 0
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while current_date <= end_date.date():
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if current_date in completion_dates_set:
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current_streak += 1
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else:
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current_streak = 0
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streak_data.append({
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'date': current_date.isoformat(),
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'streak': current_streak,
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'completed': current_date in completion_dates_set
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})
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current_date += timedelta(days=1)
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# Find longest streak in period
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max_streak = max((day['streak'] for day in streak_data), default=0)
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return {
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'data': streak_data,
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'max_streak': max_streak,
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'current_streak': streak_data[-1]['streak'] if streak_data else 0,
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'period': {
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'days': days,
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'start_date': start_date.date().isoformat(),
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'end_date': end_date.date().isoformat()
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}
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}
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def get_weekly_summary(db: Session, user_id: int, weeks: int = 12) -> Dict:
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"""Get weekly completion summary."""
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end_date = datetime.now(timezone.utc)
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start_date = end_date - timedelta(weeks=weeks)
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# Get completions grouped by week
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results = db.query(
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func.strftime('%Y-%W', models.Log.timestamp).label('week'),
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func.count(models.Log.id).label('completions')
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).filter(
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models.Log.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= start_date
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).group_by(
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func.strftime('%Y-%W', models.Log.timestamp)
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).order_by(
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func.strftime('%Y-%W', models.Log.timestamp)
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).all()
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weekly_data = []
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for result in results:
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# Parse week string (YYYY-WW format)
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year_week = result.week
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completions = result.completions
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weekly_data.append({
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'week': year_week,
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'completions': completions
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})
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return {
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'data': weekly_data,
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'total_weeks': weeks,
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'period': {
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'weeks': weeks,
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'start_date': start_date.date().isoformat(),
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'end_date': end_date.date().isoformat()
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}
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}
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def get_performance_insights(db: Session, user_id: int) -> Dict:
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"""Generate performance insights and recommendations."""
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# Get basic stats
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total_habits = db.query(models.Habit).filter(models.Habit.user_id == user_id).count()
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active_habits = db.query(models.Habit).filter(
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models.Habit.user_id == user_id,
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models.Habit.status == 'active'
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).count()
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# Get completion data for last 30 days
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thirty_days_ago = datetime.now(timezone.utc) - timedelta(days=30)
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recent_completions = db.query(models.Log).filter(
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models.Log.user_id == user_id,
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models.Log.action == 'complete',
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models.Log.timestamp >= thirty_days_ago
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).count()
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# Calculate completion rate
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expected_completions = active_habits * 30 # Assuming daily habits
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completion_rate = (recent_completions / expected_completions) * 100 if expected_completions > 0 else 0
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# Get streak info
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from . import gamification
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current_streak = gamification.calculate_current_streak(db, user_id)
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longest_streak = gamification.calculate_longest_streak(db, user_id)
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# Generate insights
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insights = []
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if completion_rate < 50:
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insights.append({
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'type': 'warning',
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'title': 'Low Completion Rate',
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'message': f'Your completion rate is {completion_rate:.1f}%. Consider reducing the number of active habits or adjusting your routine.',
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'action': 'Review your habits and focus on the most important ones.'
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})
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elif completion_rate > 80:
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insights.append({
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'type': 'success',
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'title': 'Excellent Performance',
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'message': f'Great job! You have a {completion_rate:.1f}% completion rate.',
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'action': 'Consider adding new challenges or increasing habit difficulty.'
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})
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if current_streak == 0 and longest_streak > 0:
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insights.append({
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'type': 'motivation',
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'title': 'Get Back on Track',
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'message': f'You had a {longest_streak}-day streak before. You can do it again!',
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'action': 'Start with one small habit to rebuild momentum.'
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})
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if current_streak >= 7:
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insights.append({
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'type': 'celebration',
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'title': 'Great Streak!',
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'message': f'You\'re on a {current_streak}-day streak. Keep it up!',
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'action': 'Maintain consistency to reach the next milestone.'
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})
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return {
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'stats': {
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'total_habits': total_habits,
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'active_habits': active_habits,
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'completion_rate': round(completion_rate, 1),
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'recent_completions': recent_completions,
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'current_streak': current_streak,
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'longest_streak': longest_streak
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},
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'insights': insights
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}
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