719cd5f743
Merge feature/habit-tracker into master (squashed): ✨ Habit Tracker Features: - Bead chain visualization (30-day history) - Weekly lives recovery system (+1 life/week) - Lucide icons (zap, shield) replacing emoji - Responsive layout (mobile-optimized) - Navigation links added to all dashboard pages 📚 Knowledge Base: - 40+ trading basics articles with metadata - Daily notes (2026-02-10, 2026-02-11) - Health & insights content - KB index restructuring 🧪 Tests: - Comprehensive test suite (4 test files) - Integration tests for lives recovery - 28/29 tests passing Commits squashed: - feat(habits): bead chain visualization + weekly lives recovery + nav integration - docs(memory): update KB content + daily notes - chore(data): update habits and status data Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
388 lines
13 KiB
Python
388 lines
13 KiB
Python
"""
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Habit Tracker Helper Functions
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This module provides core helper functions for calculating streaks,
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checking relevance, and computing stats for habits.
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"""
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from datetime import datetime, timedelta
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from typing import Dict, List, Any, Optional
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def calculate_streak(habit: Dict[str, Any]) -> int:
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"""
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Calculate the current streak for a habit based on its frequency type.
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Skips maintain the streak (don't break it) but don't count toward the total.
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Args:
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habit: Dict containing habit data with frequency, completions, etc.
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Returns:
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int: Current streak count (days, weeks, or months depending on frequency)
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"""
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frequency_type = habit.get("frequency", {}).get("type", "daily")
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completions = habit.get("completions", [])
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if not completions:
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return 0
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# Sort completions by date (newest first)
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sorted_completions = sorted(
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[c for c in completions if c.get("date")],
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key=lambda x: x["date"],
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reverse=True
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)
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if not sorted_completions:
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return 0
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if frequency_type == "daily":
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return _calculate_daily_streak(sorted_completions)
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elif frequency_type == "specific_days":
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return _calculate_specific_days_streak(habit, sorted_completions)
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elif frequency_type == "x_per_week":
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return _calculate_x_per_week_streak(habit, sorted_completions)
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elif frequency_type == "weekly":
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return _calculate_weekly_streak(sorted_completions)
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elif frequency_type == "monthly":
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return _calculate_monthly_streak(sorted_completions)
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elif frequency_type == "custom":
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return _calculate_custom_streak(habit, sorted_completions)
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return 0
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def _calculate_daily_streak(completions: List[Dict[str, Any]]) -> int:
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"""
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Calculate streak for daily habits (consecutive days).
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Skips maintain the streak (don't break it) but don't count toward the total.
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"""
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streak = 0
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today = datetime.now().date()
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expected_date = today
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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completion_type = completion.get("type", "check")
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if completion_date == expected_date:
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# Only count 'check' completions toward streak total
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# 'skip' completions maintain the streak but don't extend it
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if completion_type == "check":
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streak += 1
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expected_date = completion_date - timedelta(days=1)
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elif completion_date < expected_date:
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# Gap found, streak breaks
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break
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return streak
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def _calculate_specific_days_streak(habit: Dict[str, Any], completions: List[Dict[str, Any]]) -> int:
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"""Calculate streak for specific days habits (only count relevant days)."""
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relevant_days = set(habit.get("frequency", {}).get("days", []))
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if not relevant_days:
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return 0
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streak = 0
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today = datetime.now().date()
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current_date = today
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# Find the most recent relevant day
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while current_date.weekday() not in relevant_days:
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current_date -= timedelta(days=1)
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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if completion_date == current_date:
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streak += 1
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# Move to previous relevant day
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current_date -= timedelta(days=1)
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while current_date.weekday() not in relevant_days:
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current_date -= timedelta(days=1)
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elif completion_date < current_date:
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# Check if we missed a relevant day
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temp_date = current_date
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found_gap = False
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while temp_date > completion_date:
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if temp_date.weekday() in relevant_days:
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found_gap = True
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break
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temp_date -= timedelta(days=1)
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if found_gap:
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break
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return streak
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def _calculate_x_per_week_streak(habit: Dict[str, Any], completions: List[Dict[str, Any]]) -> int:
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"""Calculate streak for x_per_week habits (consecutive days with check-ins).
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For x_per_week habits, streak counts consecutive DAYS with check-ins,
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not consecutive weeks meeting the target. The weekly target (e.g., 4/week)
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is a goal, but streak measures the chain of check-in days.
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"""
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# Use the same logic as daily habits - count consecutive check-in days
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return _calculate_daily_streak(completions)
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def _calculate_weekly_streak(completions: List[Dict[str, Any]]) -> int:
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"""Calculate streak for weekly habits (consecutive days with check-ins).
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For weekly habits, streak counts consecutive DAYS with check-ins,
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just like daily habits. The weekly frequency just means you should
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check in at least once per week.
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"""
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return _calculate_daily_streak(completions)
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def _calculate_monthly_streak(completions: List[Dict[str, Any]]) -> int:
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"""Calculate streak for monthly habits (consecutive days with check-ins).
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For monthly habits, streak counts consecutive DAYS with check-ins,
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just like daily habits. The monthly frequency just means you should
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check in at least once per month.
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"""
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return _calculate_daily_streak(completions)
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def _calculate_custom_streak(habit: Dict[str, Any], completions: List[Dict[str, Any]]) -> int:
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"""Calculate streak for custom interval habits (every X days)."""
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interval = habit.get("frequency", {}).get("interval", 1)
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if interval <= 0:
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return 0
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streak = 0
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expected_date = datetime.now().date()
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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# Allow completion within the interval window
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days_diff = (expected_date - completion_date).days
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if 0 <= days_diff <= interval - 1:
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streak += 1
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expected_date = completion_date - timedelta(days=interval)
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else:
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break
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return streak
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def should_check_today(habit: Dict[str, Any]) -> bool:
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"""
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Check if a habit is relevant for today based on its frequency type.
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Args:
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habit: Dict containing habit data with frequency settings
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Returns:
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bool: True if the habit should be checked today
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"""
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frequency_type = habit.get("frequency", {}).get("type", "daily")
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today = datetime.now().date()
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weekday = today.weekday() # 0=Monday, 6=Sunday
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if frequency_type == "daily":
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return True
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elif frequency_type == "specific_days":
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relevant_days = set(habit.get("frequency", {}).get("days", []))
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return weekday in relevant_days
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elif frequency_type == "x_per_week":
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# Always relevant for x_per_week (can check any day)
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return True
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elif frequency_type == "weekly":
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# Always relevant (can check any day of the week)
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return True
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elif frequency_type == "monthly":
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# Always relevant (can check any day of the month)
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return True
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elif frequency_type == "custom":
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# Check if enough days have passed since last completion
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completions = habit.get("completions", [])
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if not completions:
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return True
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interval = habit.get("frequency", {}).get("interval", 1)
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last_completion = max(completions, key=lambda x: x.get("date", ""))
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last_date = datetime.fromisoformat(last_completion["date"]).date()
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days_since = (today - last_date).days
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return days_since >= interval
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return False
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def get_completion_rate(habit: Dict[str, Any], days: int = 30) -> float:
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"""
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Calculate the completion rate as a percentage over the last N days.
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Args:
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habit: Dict containing habit data
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days: Number of days to look back (default 30)
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Returns:
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float: Completion rate as percentage (0-100)
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"""
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frequency_type = habit.get("frequency", {}).get("type", "daily")
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completions = habit.get("completions", [])
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today = datetime.now().date()
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start_date = today - timedelta(days=days - 1)
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# Count relevant days and checked days
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relevant_days = 0
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checked_dates = set()
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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if start_date <= completion_date <= today:
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checked_dates.add(completion_date)
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# Calculate relevant days based on frequency type
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if frequency_type == "daily":
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relevant_days = days
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elif frequency_type == "specific_days":
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relevant_day_set = set(habit.get("frequency", {}).get("days", []))
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current = start_date
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while current <= today:
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if current.weekday() in relevant_day_set:
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relevant_days += 1
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current += timedelta(days=1)
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elif frequency_type == "x_per_week":
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target_per_week = habit.get("frequency", {}).get("count", 1)
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num_weeks = days // 7
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relevant_days = num_weeks * target_per_week
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elif frequency_type == "weekly":
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num_weeks = days // 7
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relevant_days = num_weeks
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elif frequency_type == "monthly":
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num_months = days // 30
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relevant_days = num_months
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elif frequency_type == "custom":
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interval = habit.get("frequency", {}).get("interval", 1)
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relevant_days = days // interval if interval > 0 else 0
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if relevant_days == 0:
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return 0.0
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checked_days = len(checked_dates)
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return (checked_days / relevant_days) * 100
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def get_weekly_summary(habit: Dict[str, Any]) -> Dict[str, str]:
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"""
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Get a summary of the current week showing status for each day.
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Args:
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habit: Dict containing habit data
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Returns:
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Dict mapping day names to status: "checked", "skipped", "missed", or "upcoming"
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"""
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frequency_type = habit.get("frequency", {}).get("type", "daily")
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completions = habit.get("completions", [])
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today = datetime.now().date()
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# Start of current week (Monday)
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start_of_week = today - timedelta(days=today.weekday())
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# Create completion map
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completion_map = {}
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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if completion_date >= start_of_week:
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completion_type = completion.get("type", "check")
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completion_map[completion_date] = completion_type
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# Build summary for each day of the week
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summary = {}
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day_names = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"]
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for i, day_name in enumerate(day_names):
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day_date = start_of_week + timedelta(days=i)
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if day_date > today:
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summary[day_name] = "upcoming"
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elif day_date in completion_map:
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if completion_map[day_date] == "skip":
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summary[day_name] = "skipped"
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else:
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summary[day_name] = "checked"
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else:
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# Check if this day was relevant
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if frequency_type == "specific_days":
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relevant_days = set(habit.get("frequency", {}).get("days", []))
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if day_date.weekday() not in relevant_days:
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summary[day_name] = "not_relevant"
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else:
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summary[day_name] = "missed"
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else:
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summary[day_name] = "missed"
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return summary
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def check_and_award_weekly_lives(habit: Dict[str, Any]) -> tuple[int, bool]:
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"""
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Check if habit qualifies for weekly lives recovery and award +1 life if eligible.
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Awards +1 life if:
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- At least one check-in in the previous week (Monday-Sunday)
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- Not already awarded this week
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Args:
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habit: Dict containing habit data with completions and lastLivesAward
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Returns:
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tuple[int, bool]: (new_lives_count, was_awarded)
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"""
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completions = habit.get("completions", [])
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current_lives = habit.get("lives", 3)
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today = datetime.now().date()
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# Calculate current week start (Monday 00:00)
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current_week_start = today - timedelta(days=today.weekday())
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# Check if already awarded this week
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last_lives_award = habit.get("lastLivesAward")
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if last_lives_award:
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last_award_date = datetime.fromisoformat(last_lives_award).date()
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if last_award_date >= current_week_start:
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# Already awarded this week
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return (current_lives, False)
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# Calculate previous week boundaries
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previous_week_start = current_week_start - timedelta(days=7)
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previous_week_end = current_week_start - timedelta(days=1)
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# Count check-ins in previous week
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checkins_in_previous_week = 0
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for completion in completions:
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completion_date = datetime.fromisoformat(completion["date"]).date()
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completion_type = completion.get("type", "check")
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if previous_week_start <= completion_date <= previous_week_end:
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if completion_type == "check":
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checkins_in_previous_week += 1
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# Award life if at least 1 check-in found
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if checkins_in_previous_week >= 1:
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new_lives = current_lives + 1
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return (new_lives, True)
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return (current_lives, False)
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