feat: Phase 0c - migrate activity_metrics calculations to data_layer (20 functions)

- Migrated all 20 calculation functions from calculations/activity_metrics.py to data_layer/activity_metrics.py
- Functions: Training volume (minutes/week, frequency, quality sessions %)
- Functions: Intensity distribution (proxy-based until HR zones available)
- Functions: Ability balance (strength, endurance, mental, coordination, mobility)
- Functions: Load monitoring (internal load proxy, monotony score, strain score)
- Functions: Activity scoring (main score with focus weights, strength/cardio/balance helpers)
- Functions: Rest day compliance
- Functions: VO2max trend (28d)
- Functions: Data quality assessment
- Updated data_layer/__init__.py with 17 new exports
- Refactored placeholder_resolver.py to import activity_metrics from data_layer

Module 3/6 complete. Single Source of Truth for activity metrics established.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/data_layer/__init__.py

@@ -90,11 +90,30 @@ __all__ = [
     'calculate_sugar_avg_7d',
     'calculate_nutrition_data_quality',
 
-    # Activity Metrics
+    # Activity Metrics (Basic)
     'get_activity_summary_data',
     'get_activity_detail_data',
     'get_training_type_distribution_data',
 
+    # Activity Metrics (Calculated)
+    'calculate_training_minutes_week',
+    'calculate_training_frequency_7d',
+    'calculate_quality_sessions_pct',
+    'calculate_intensity_proxy_distribution',
+    'calculate_ability_balance',
+    'calculate_ability_balance_strength',
+    'calculate_ability_balance_endurance',
+    'calculate_ability_balance_mental',
+    'calculate_ability_balance_coordination',
+    'calculate_ability_balance_mobility',
+    'calculate_proxy_internal_load_7d',
+    'calculate_monotony_score',
+    'calculate_strain_score',
+    'calculate_activity_score',
+    'calculate_rest_day_compliance',
+    'calculate_vo2max_trend_28d',
+    'calculate_activity_data_quality',
+
     # Recovery Metrics
     'get_sleep_duration_data',
     'get_sleep_quality_data',

backend/data_layer/activity_metrics.py

@@ -275,3 +275,631 @@ def get_training_type_distribution_data(
             "confidence": confidence,
             "days_analyzed": days
         }
+
+
+# ============================================================================
+# Calculated Metrics (migrated from calculations/activity_metrics.py)
+# ============================================================================
+# These functions return simple values for placeholders and scoring.
+# Use get_*_data() functions above for structured chart data.
+
+def calculate_training_minutes_week(profile_id: str) -> Optional[int]:
+    """Calculate total training minutes last 7 days"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT SUM(duration_min) as total_minutes
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+        """, (profile_id,))
+
+        row = cur.fetchone()
+        return int(row['total_minutes']) if row and row['total_minutes'] else None
+
+
+def calculate_training_frequency_7d(profile_id: str) -> Optional[int]:
+    """Calculate number of training sessions last 7 days"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT COUNT(*) as session_count
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+        """, (profile_id,))
+
+        row = cur.fetchone()
+        return int(row['session_count']) if row else None
+
+
+def calculate_quality_sessions_pct(profile_id: str) -> Optional[int]:
+    """Calculate percentage of quality sessions (good or better) last 28 days"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT
+                COUNT(*) as total,
+                COUNT(*) FILTER (WHERE quality_label IN ('excellent', 'very_good', 'good')) as quality_count
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+        """, (profile_id,))
+
+        row = cur.fetchone()
+        if not row or row['total'] == 0:
+            return None
+
+        pct = (row['quality_count'] / row['total']) * 100
+        return int(pct)
+
+
+# ============================================================================
+# A2: Intensity Distribution (Proxy-based)
+# ============================================================================
+
+def calculate_intensity_proxy_distribution(profile_id: str) -> Optional[Dict]:
+    """
+    Calculate intensity distribution (proxy until HR zones available)
+    Returns dict: {'low': X, 'moderate': Y, 'high': Z} in minutes
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT duration_min, hr_avg, hr_max
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+        """, (profile_id,))
+
+        activities = cur.fetchall()
+
+        if not activities:
+            return None
+
+        low_min = 0
+        moderate_min = 0
+        high_min = 0
+
+        for activity in activities:
+            duration = activity['duration_min']
+            avg_hr = activity['hr_avg']
+            max_hr = activity['hr_max']
+
+            # Simple proxy classification
+            if avg_hr:
+                # Rough HR-based classification (assumes max HR ~190)
+                if avg_hr < 120:
+                    low_min += duration
+                elif avg_hr < 150:
+                    moderate_min += duration
+                else:
+                    high_min += duration
+            else:
+                # Fallback: assume moderate
+                moderate_min += duration
+
+        return {
+            'low': low_min,
+            'moderate': moderate_min,
+            'high': high_min
+        }
+
+
+# ============================================================================
+# A4: Ability Balance Calculations
+# ============================================================================
+
+def calculate_ability_balance(profile_id: str) -> Optional[Dict]:
+    """
+    Calculate ability balance from training_types.abilities
+    Returns dict with scores per ability dimension (0-100)
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT a.duration_min, tt.abilities
+            FROM activity_log a
+            JOIN training_types tt ON a.training_category = tt.category
+            WHERE a.profile_id = %s
+              AND a.date >= CURRENT_DATE - INTERVAL '28 days'
+              AND tt.abilities IS NOT NULL
+        """, (profile_id,))
+
+        activities = cur.fetchall()
+
+        if not activities:
+            return None
+
+        # Accumulate ability load (duration × ability weight)
+        ability_loads = {
+            'strength': 0,
+            'endurance': 0,
+            'mental': 0,
+            'coordination': 0,
+            'mobility': 0
+        }
+
+        for activity in activities:
+            duration = activity['duration_min']
+            abilities = activity['abilities']  # JSONB
+
+            if not abilities:
+                continue
+
+            for ability, weight in abilities.items():
+                if ability in ability_loads:
+                    ability_loads[ability] += duration * weight
+
+        # Normalize to 0-100 scale
+        max_load = max(ability_loads.values()) if ability_loads else 1
+        if max_load == 0:
+            return None
+
+        normalized = {
+            ability: int((load / max_load) * 100)
+            for ability, load in ability_loads.items()
+        }
+
+        return normalized
+
+
+def calculate_ability_balance_strength(profile_id: str) -> Optional[int]:
+    """Get strength ability score"""
+    balance = calculate_ability_balance(profile_id)
+    return balance['strength'] if balance else None
+
+
+def calculate_ability_balance_endurance(profile_id: str) -> Optional[int]:
+    """Get endurance ability score"""
+    balance = calculate_ability_balance(profile_id)
+    return balance['endurance'] if balance else None
+
+
+def calculate_ability_balance_mental(profile_id: str) -> Optional[int]:
+    """Get mental ability score"""
+    balance = calculate_ability_balance(profile_id)
+    return balance['mental'] if balance else None
+
+
+def calculate_ability_balance_coordination(profile_id: str) -> Optional[int]:
+    """Get coordination ability score"""
+    balance = calculate_ability_balance(profile_id)
+    return balance['coordination'] if balance else None
+
+
+def calculate_ability_balance_mobility(profile_id: str) -> Optional[int]:
+    """Get mobility ability score"""
+    balance = calculate_ability_balance(profile_id)
+    return balance['mobility'] if balance else None
+
+
+# ============================================================================
+# A5: Load Monitoring (Proxy-based)
+# ============================================================================
+
+def calculate_proxy_internal_load_7d(profile_id: str) -> Optional[int]:
+    """
+    Calculate proxy internal load (last 7 days)
+    Formula: duration × intensity_factor × quality_factor
+    """
+    intensity_factors = {'low': 1.0, 'moderate': 1.5, 'high': 2.0}
+    quality_factors = {
+        'excellent': 1.15,
+        'very_good': 1.05,
+        'good': 1.0,
+        'acceptable': 0.9,
+        'poor': 0.75,
+        'excluded': 0.0
+    }
+
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT duration_min, hr_avg, rpe
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+        """, (profile_id,))
+
+        activities = cur.fetchall()
+
+        if not activities:
+            return None
+
+        total_load = 0
+
+        for activity in activities:
+            duration = activity['duration_min']
+            avg_hr = activity['hr_avg']
+            # Map RPE to quality (rpe 8-10 = excellent, 6-7 = good, 4-5 = moderate, <4 = poor)
+            rpe = activity.get('rpe')
+            if rpe and rpe >= 8:
+                quality = 'excellent'
+            elif rpe and rpe >= 6:
+                quality = 'good'
+            elif rpe and rpe >= 4:
+                quality = 'moderate'
+            else:
+                quality = 'good'  # default
+
+            # Determine intensity
+            if avg_hr:
+                if avg_hr < 120:
+                    intensity = 'low'
+                elif avg_hr < 150:
+                    intensity = 'moderate'
+                else:
+                    intensity = 'high'
+            else:
+                intensity = 'moderate'
+
+            load = float(duration) * intensity_factors[intensity] * quality_factors.get(quality, 1.0)
+            total_load += load
+
+        return int(total_load)
+
+
+def calculate_monotony_score(profile_id: str) -> Optional[float]:
+    """
+    Calculate training monotony (last 7 days)
+    Monotony = mean daily load / std dev daily load
+    Higher = more monotonous
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT date, SUM(duration_min) as daily_duration
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+            GROUP BY date
+            ORDER BY date
+        """, (profile_id,))
+
+        daily_loads = [float(row['daily_duration']) for row in cur.fetchall() if row['daily_duration']]
+
+        if len(daily_loads) < 4:
+            return None
+
+        mean_load = sum(daily_loads) / len(daily_loads)
+        std_dev = statistics.stdev(daily_loads)
+
+        if std_dev == 0:
+            return None
+
+        monotony = mean_load / std_dev
+        return round(monotony, 2)
+
+
+def calculate_strain_score(profile_id: str) -> Optional[int]:
+    """
+    Calculate training strain (last 7 days)
+    Strain = weekly load × monotony
+    """
+    weekly_load = calculate_proxy_internal_load_7d(profile_id)
+    monotony = calculate_monotony_score(profile_id)
+
+    if weekly_load is None or monotony is None:
+        return None
+
+    strain = weekly_load * monotony
+    return int(strain)
+
+
+# ============================================================================
+# A6: Activity Goal Alignment Score (Dynamic Focus Areas)
+# ============================================================================
+
+def calculate_activity_score(profile_id: str, focus_weights: Optional[Dict] = None) -> Optional[int]:
+    """
+    Activity goal alignment score 0-100
+    Weighted by user's activity-related focus areas
+    """
+    if focus_weights is None:
+        from calculations.scores import get_user_focus_weights
+        focus_weights = get_user_focus_weights(profile_id)
+
+    # Activity-related focus areas (English keys from DB)
+    # Strength training
+    strength = focus_weights.get('strength', 0)
+    strength_endurance = focus_weights.get('strength_endurance', 0)
+    power = focus_weights.get('power', 0)
+    total_strength = strength + strength_endurance + power
+
+    # Endurance training
+    aerobic = focus_weights.get('aerobic_endurance', 0)
+    anaerobic = focus_weights.get('anaerobic_endurance', 0)
+    cardiovascular = focus_weights.get('cardiovascular_health', 0)
+    total_cardio = aerobic + anaerobic + cardiovascular
+
+    # Mobility/Coordination
+    flexibility = focus_weights.get('flexibility', 0)
+    mobility = focus_weights.get('mobility', 0)
+    balance = focus_weights.get('balance', 0)
+    reaction = focus_weights.get('reaction', 0)
+    rhythm = focus_weights.get('rhythm', 0)
+    coordination = focus_weights.get('coordination', 0)
+    total_ability = flexibility + mobility + balance + reaction + rhythm + coordination
+
+    total_activity_weight = total_strength + total_cardio + total_ability
+
+    if total_activity_weight == 0:
+        return None  # No activity goals
+
+    components = []
+
+    # 1. Weekly minutes (general activity volume)
+    minutes = calculate_training_minutes_week(profile_id)
+    if minutes is not None:
+        # WHO: 150-300 min/week
+        if 150 <= minutes <= 300:
+            minutes_score = 100
+        elif minutes < 150:
+            minutes_score = max(40, (minutes / 150) * 100)
+        else:
+            minutes_score = max(80, 100 - ((minutes - 300) / 10))
+
+        # Volume relevant for all activity types (20% base weight)
+        components.append(('minutes', minutes_score, total_activity_weight * 0.2))
+
+    # 2. Quality sessions (always relevant)
+    quality_pct = calculate_quality_sessions_pct(profile_id)
+    if quality_pct is not None:
+        # Quality gets 10% base weight
+        components.append(('quality', quality_pct, total_activity_weight * 0.1))
+
+    # 3. Strength presence (if strength focus active)
+    if total_strength > 0:
+        strength_score = _score_strength_presence(profile_id)
+        if strength_score is not None:
+            components.append(('strength', strength_score, total_strength))
+
+    # 4. Cardio presence (if cardio focus active)
+    if total_cardio > 0:
+        cardio_score = _score_cardio_presence(profile_id)
+        if cardio_score is not None:
+            components.append(('cardio', cardio_score, total_cardio))
+
+    # 5. Ability balance (if mobility/coordination focus active)
+    if total_ability > 0:
+        balance_score = _score_ability_balance(profile_id)
+        if balance_score is not None:
+            components.append(('balance', balance_score, total_ability))
+
+    if not components:
+        return None
+
+    # Weighted average
+    total_score = sum(score * weight for _, score, weight in components)
+    total_weight = sum(weight for _, _, weight in components)
+
+    return int(total_score / total_weight)
+
+
+def _score_strength_presence(profile_id: str) -> Optional[int]:
+    """Score strength training presence (0-100)"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT COUNT(DISTINCT date) as strength_days
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+              AND training_category = 'strength'
+        """, (profile_id,))
+
+        row = cur.fetchone()
+        if not row:
+            return None
+
+        strength_days = row['strength_days']
+
+        # Target: 2-4 days/week
+        if 2 <= strength_days <= 4:
+            return 100
+        elif strength_days == 1:
+            return 60
+        elif strength_days == 5:
+            return 85
+        elif strength_days == 0:
+            return 0
+        else:
+            return 70
+
+
+def _score_cardio_presence(profile_id: str) -> Optional[int]:
+    """Score cardio training presence (0-100)"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT COUNT(DISTINCT date) as cardio_days, SUM(duration_min) as cardio_minutes
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '7 days'
+              AND training_category = 'cardio'
+        """, (profile_id,))
+
+        row = cur.fetchone()
+        if not row:
+            return None
+
+        cardio_days = row['cardio_days']
+        cardio_minutes = row['cardio_minutes'] or 0
+
+        # Target: 3-5 days/week, 150+ minutes
+        day_score = min(100, (cardio_days / 4) * 100)
+        minute_score = min(100, (cardio_minutes / 150) * 100)
+
+        return int((day_score + minute_score) / 2)
+
+
+def _score_ability_balance(profile_id: str) -> Optional[int]:
+    """Score ability balance (0-100)"""
+    balance = calculate_ability_balance(profile_id)
+
+    if not balance:
+        return None
+
+    # Good balance = all abilities > 40, std_dev < 30
+    values = list(balance.values())
+    min_value = min(values)
+    std_dev = statistics.stdev(values) if len(values) > 1 else 0
+
+    # Score based on minimum coverage and balance
+    min_score = min(100, min_value * 2)  # Want all > 50
+    balance_score = max(0, 100 - (std_dev * 2))  # Want low std_dev
+
+    return int((min_score + balance_score) / 2)
+
+
+# ============================================================================
+# A7: Rest Day Compliance
+# ============================================================================
+
+def calculate_rest_day_compliance(profile_id: str) -> Optional[int]:
+    """
+    Calculate rest day compliance percentage (last 28 days)
+    Returns percentage of planned rest days that were respected
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+
+        # Get planned rest days
+        cur.execute("""
+            SELECT date, rest_config->>'focus' as rest_type
+            FROM rest_days
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+        """, (profile_id,))
+
+        rest_days = {row['date']: row['rest_type'] for row in cur.fetchall()}
+
+        if not rest_days:
+            return None
+
+        # Check if training occurred on rest days
+        cur.execute("""
+            SELECT date, training_category
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+        """, (profile_id,))
+
+        training_days = {}
+        for row in cur.fetchall():
+            if row['date'] not in training_days:
+                training_days[row['date']] = []
+            training_days[row['date']].append(row['training_category'])
+
+        # Count compliance
+        compliant = 0
+        total = len(rest_days)
+
+        for rest_date, rest_type in rest_days.items():
+            if rest_date not in training_days:
+                # Full rest = compliant
+                compliant += 1
+            else:
+                # Check if training violates rest type
+                categories = training_days[rest_date]
+                if rest_type == 'strength_rest' and 'strength' not in categories:
+                    compliant += 1
+                elif rest_type == 'cardio_rest' and 'cardio' not in categories:
+                    compliant += 1
+                # If rest_type == 'recovery', any training = non-compliant
+
+        compliance_pct = (compliant / total) * 100
+        return int(compliance_pct)
+
+
+# ============================================================================
+# A8: VO2max Development
+# ============================================================================
+
+def calculate_vo2max_trend_28d(profile_id: str) -> Optional[float]:
+    """Calculate VO2max trend (change over 28 days)"""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute("""
+            SELECT vo2_max, date
+            FROM vitals_baseline
+            WHERE profile_id = %s
+              AND vo2_max IS NOT NULL
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+            ORDER BY date DESC
+        """, (profile_id,))
+
+        measurements = cur.fetchall()
+
+        if len(measurements) < 2:
+            return None
+
+        recent = measurements[0]['vo2_max']
+        oldest = measurements[-1]['vo2_max']
+
+        change = recent - oldest
+        return round(change, 1)
+
+
+# ============================================================================
+# Data Quality Assessment
+# ============================================================================
+
+def calculate_activity_data_quality(profile_id: str) -> Dict[str, any]:
+    """
+    Assess data quality for activity metrics
+    Returns dict with quality score and details
+    """
+    with get_db() as conn:
+        cur = get_cursor(conn)
+
+        # Activity entries last 28 days
+        cur.execute("""
+            SELECT COUNT(*) as total,
+                   COUNT(hr_avg) as with_hr,
+                   COUNT(rpe) as with_quality
+            FROM activity_log
+            WHERE profile_id = %s
+              AND date >= CURRENT_DATE - INTERVAL '28 days'
+        """, (profile_id,))
+
+        counts = cur.fetchone()
+
+    total_entries = counts['total']
+    hr_coverage = counts['with_hr'] / total_entries if total_entries > 0 else 0
+    quality_coverage = counts['with_quality'] / total_entries if total_entries > 0 else 0
+
+    # Score components
+    frequency_score = min(100, (total_entries / 15) * 100)  # 15 = ~4 sessions/week
+    hr_score = hr_coverage * 100
+    quality_score = quality_coverage * 100
+
+    # Overall score
+    overall_score = int(
+        frequency_score * 0.5 +
+        hr_score * 0.25 +
+        quality_score * 0.25
+    )
+
+    if overall_score >= 80:
+        confidence = "high"
+    elif overall_score >= 60:
+        confidence = "medium"
+    else:
+        confidence = "low"
+
+    return {
+        "overall_score": overall_score,
+        "confidence": confidence,
+        "measurements": {
+            "activities_28d": total_entries,
+            "hr_coverage_pct": int(hr_coverage * 100),
+            "quality_coverage_pct": int(quality_coverage * 100)
+        },
+        "component_scores": {
+            "frequency": int(frequency_score),
+            "hr": int(hr_score),
+            "quality": int(quality_score)
+        }
+    }

backend/placeholder_resolver.py

@@ -417,8 +417,8 @@ def _safe_int(func_name: str, profile_id: str) -> str:
     import traceback
     try:
         # Import calculations dynamically to avoid circular imports
-        from calculations import scores, activity_metrics, recovery_metrics, correlation_metrics
+        from calculations import scores, recovery_metrics, correlation_metrics
-        from data_layer import body_metrics, nutrition_metrics
+        from data_layer import body_metrics, nutrition_metrics, activity_metrics
 
         # Map function names to actual functions
         func_map = {
@@ -480,8 +480,8 @@ def _safe_float(func_name: str, profile_id: str, decimals: int = 1) -> str:
     """
     import traceback
     try:
-        from calculations import activity_metrics, recovery_metrics, scores
+        from calculations import recovery_metrics, scores
-        from data_layer import body_metrics, nutrition_metrics
+        from data_layer import body_metrics, nutrition_metrics, activity_metrics
 
         func_map = {
             'weight_7d_median': body_metrics.calculate_weight_7d_median,