"""
Nutrition Metrics Data Layer

Provides structured data for nutrition tracking and analysis.

Functions:
    - get_nutrition_average_data(): Average calor

ies, protein, carbs, fat
    - get_nutrition_days_data(): Number of days with nutrition data
    - get_protein_targets_data(): Protein targets based on weight
    - get_energy_balance_data(): Energy balance calculation
    - get_protein_adequacy_data(): Protein adequacy score
    - get_macro_consistency_data(): Macro consistency analysis

All functions return structured data (dict) without formatting.
Use placeholder_resolver.py for formatted strings for AI.

Phase 0c: Multi-Layer Architecture
Version: 1.0
"""

from typing import Dict, List, Optional
from datetime import datetime, timedelta, date
from db import get_db, get_cursor, r2d
from data_layer.utils import calculate_confidence, safe_float, safe_int


def get_nutrition_average_data(
    profile_id: str,
    days: int = 30
) -> Dict:
    """
    Get average nutrition values for all macros.

    Args:
        profile_id: User profile ID
        days: Analysis window (default 30)

    Returns:
        {
            "kcal_avg": float,
            "protein_avg": float,
            "carbs_avg": float,
            "fat_avg": float,
            "data_points": int,
            "confidence": str,
            "days_analyzed": int
        }

    Migration from Phase 0b:
        OLD: get_nutrition_avg(pid, field, days) per field
        NEW: All macros in one call
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT
                AVG(kcal) as kcal_avg,
                AVG(protein_g) as protein_avg,
                AVG(carbs_g) as carbs_avg,
                AVG(fat_g) as fat_avg,
                COUNT(*) as data_points
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s""",
            (profile_id, cutoff)
        )
        row = cur.fetchone()

        if not row or row['data_points'] == 0:
            return {
                "kcal_avg": 0.0,
                "protein_avg": 0.0,
                "carbs_avg": 0.0,
                "fat_avg": 0.0,
                "data_points": 0,
                "confidence": "insufficient",
                "days_analyzed": days
            }

        data_points = row['data_points']
        confidence = calculate_confidence(data_points, days, "general")

        return {
            "kcal_avg": safe_float(row['kcal_avg']),
            "protein_avg": safe_float(row['protein_avg']),
            "carbs_avg": safe_float(row['carbs_avg']),
            "fat_avg": safe_float(row['fat_avg']),
            "data_points": data_points,
            "confidence": confidence,
            "days_analyzed": days
        }


def get_nutrition_days_data(
    profile_id: str,
    days: int = 30
) -> Dict:
    """
    Count days with nutrition data.

    Args:
        profile_id: User profile ID
        days: Analysis window (default 30)

    Returns:
        {
            "days_with_data": int,
            "days_analyzed": int,
            "coverage_pct": float,
            "confidence": str
        }
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT COUNT(DISTINCT date) as days
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s""",
            (profile_id, cutoff)
        )
        row = cur.fetchone()
        days_with_data = row['days'] if row else 0

        coverage_pct = (days_with_data / days * 100) if days > 0 else 0
        confidence = calculate_confidence(days_with_data, days, "general")

        return {
            "days_with_data": days_with_data,
            "days_analyzed": days,
            "coverage_pct": coverage_pct,
            "confidence": confidence
        }


def get_protein_targets_data(
    profile_id: str
) -> Dict:
    """
    Calculate protein targets based on current weight.

    Targets:
        - Low: 1.6 g/kg (maintenance)
        - High: 2.2 g/kg (muscle building)

    Args:
        profile_id: User profile ID

    Returns:
        {
            "current_weight": float,
            "protein_target_low": float,   # 1.6 g/kg
            "protein_target_high": float,  # 2.2 g/kg
            "confidence": str
        }
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute(
            """SELECT weight FROM weight_log
               WHERE profile_id=%s ORDER BY date DESC LIMIT 1""",
            (profile_id,)
        )
        row = cur.fetchone()

        if not row:
            return {
                "current_weight": 0.0,
                "protein_target_low": 0.0,
                "protein_target_high": 0.0,
                "confidence": "insufficient"
            }

        weight = safe_float(row['weight'])

        return {
            "current_weight": weight,
            "protein_target_low": weight * 1.6,
            "protein_target_high": weight * 2.2,
            "confidence": "high"
        }


def get_energy_balance_data(
    profile_id: str,
    days: int = 7
) -> Dict:
    """
    Calculate energy balance (intake - estimated expenditure).

    Note: This is a simplified calculation.
    For accurate TDEE, use profile-based calculations.

    Args:
        profile_id: User profile ID
        days: Analysis window (default 7)

    Returns:
        {
            "energy_balance": float,        # kcal/day (negative = deficit)
            "avg_intake": float,
            "estimated_tdee": float,
            "status": str,                  # "deficit" | "surplus" | "maintenance"
            "confidence": str,
            "days_analyzed": int,
            "data_points": int
        }
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        # Get average intake
        cur.execute(
            """SELECT AVG(kcal) as avg_kcal, COUNT(*) as cnt
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL""",
            (profile_id, cutoff)
        )
        row = cur.fetchone()

        if not row or row['cnt'] == 0:
            return {
                "energy_balance": 0.0,
                "avg_intake": 0.0,
                "estimated_tdee": 0.0,
                "status": "unknown",
                "confidence": "insufficient",
                "days_analyzed": days,
                "data_points": 0
            }

        avg_intake = safe_float(row['avg_kcal'])
        data_points = row['cnt']

        # Simple TDEE estimation (this should be improved with profile data)
        # For now, use a rough estimate: 2500 kcal for average adult
        estimated_tdee = 2500.0  # TODO: Calculate from profile (weight, height, age, activity)

        energy_balance = avg_intake - estimated_tdee

        # Determine status
        if energy_balance < -200:
            status = "deficit"
        elif energy_balance > 200:
            status = "surplus"
        else:
            status = "maintenance"

        confidence = calculate_confidence(data_points, days, "general")

        return {
            "energy_balance": energy_balance,
            "avg_intake": avg_intake,
            "estimated_tdee": estimated_tdee,
            "status": status,
            "confidence": confidence,
            "days_analyzed": days,
            "data_points": data_points
        }


def get_protein_adequacy_data(
    profile_id: str,
    days: int = 28
) -> Dict:
    """
    Calculate protein adequacy score (0-100).

    Score based on:
    - Daily protein intake vs. target (1.6-2.2 g/kg)
    - Consistency across days

    Args:
        profile_id: User profile ID
        days: Analysis window (default 28)

    Returns:
        {
            "adequacy_score": int,          # 0-100
            "avg_protein_g": float,
            "target_protein_low": float,
            "target_protein_high": float,
            "protein_g_per_kg": float,
            "days_in_target": int,
            "days_with_data": int,
            "confidence": str
        }
    """
    # Get protein targets
    targets = get_protein_targets_data(profile_id)

    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT
                AVG(protein_g) as avg_protein,
                COUNT(*) as cnt,
                SUM(CASE WHEN protein_g >= %s AND protein_g <= %s THEN 1 ELSE 0 END) as days_in_target
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s AND protein_g IS NOT NULL""",
            (targets['protein_target_low'], targets['protein_target_high'], profile_id, cutoff)
        )
        row = cur.fetchone()

        if not row or row['cnt'] == 0:
            return {
                "adequacy_score": 0,
                "avg_protein_g": 0.0,
                "target_protein_low": targets['protein_target_low'],
                "target_protein_high": targets['protein_target_high'],
                "protein_g_per_kg": 0.0,
                "days_in_target": 0,
                "days_with_data": 0,
                "confidence": "insufficient"
            }

        avg_protein = safe_float(row['avg_protein'])
        days_with_data = row['cnt']
        days_in_target = row['days_in_target']

        protein_g_per_kg = avg_protein / targets['current_weight'] if targets['current_weight'] > 0 else 0.0

        # Calculate adequacy score
        # 100 = always in target range
        # Scale based on percentage of days in target + average relative to target
        target_pct = (days_in_target / days_with_data * 100) if days_with_data > 0 else 0

        # Bonus/penalty for average protein level
        target_mid = (targets['protein_target_low'] + targets['protein_target_high']) / 2
        avg_vs_target = (avg_protein / target_mid) if target_mid > 0 else 0

        # Weighted score: 70% target days, 30% average level
        adequacy_score = int(target_pct * 0.7 + min(avg_vs_target * 100, 100) * 0.3)
        adequacy_score = max(0, min(100, adequacy_score))  # Clamp to 0-100

        confidence = calculate_confidence(days_with_data, days, "general")

        return {
            "adequacy_score": adequacy_score,
            "avg_protein_g": avg_protein,
            "target_protein_low": targets['protein_target_low'],
            "target_protein_high": targets['protein_target_high'],
            "protein_g_per_kg": protein_g_per_kg,
            "days_in_target": days_in_target,
            "days_with_data": days_with_data,
            "confidence": confidence
        }


def get_macro_consistency_data(
    profile_id: str,
    days: int = 28
) -> Dict:
    """
    Calculate macro consistency score (0-100).

    Measures how consistent macronutrient ratios are across days.
    High consistency = predictable nutrition, easier to track progress.

    Args:
        profile_id: User profile ID
        days: Analysis window (default 28)

    Returns:
        {
            "consistency_score": int,       # 0-100 (100 = very consistent)
            "avg_protein_pct": float,
            "avg_carbs_pct": float,
            "avg_fat_pct": float,
            "std_dev_protein": float,       # Standard deviation
            "std_dev_carbs": float,
            "std_dev_fat": float,
            "confidence": str,
            "data_points": int
        }
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT
                protein_g, carbs_g, fat_g, kcal
               FROM nutrition_log
               WHERE profile_id=%s
                 AND date >= %s
                 AND protein_g IS NOT NULL
                 AND carbs_g IS NOT NULL
                 AND fat_g IS NOT NULL
                 AND kcal > 0
               ORDER BY date""",
            (profile_id, cutoff)
        )
        rows = cur.fetchall()

        if len(rows) < 3:
            return {
                "consistency_score": 0,
                "avg_protein_pct": 0.0,
                "avg_carbs_pct": 0.0,
                "avg_fat_pct": 0.0,
                "std_dev_protein": 0.0,
                "std_dev_carbs": 0.0,
                "std_dev_fat": 0.0,
                "confidence": "insufficient",
                "data_points": len(rows)
            }

        # Calculate macro percentages for each day
        import statistics

        protein_pcts = []
        carbs_pcts = []
        fat_pcts = []

        for row in rows:
            total_kcal = safe_float(row['kcal'])
            if total_kcal == 0:
                continue

            # Convert grams to kcal (protein=4, carbs=4, fat=9)
            protein_kcal = safe_float(row['protein_g']) * 4
            carbs_kcal = safe_float(row['carbs_g']) * 4
            fat_kcal = safe_float(row['fat_g']) * 9

            macro_kcal_total = protein_kcal + carbs_kcal + fat_kcal

            if macro_kcal_total > 0:
                protein_pcts.append(protein_kcal / macro_kcal_total * 100)
                carbs_pcts.append(carbs_kcal / macro_kcal_total * 100)
                fat_pcts.append(fat_kcal / macro_kcal_total * 100)

        if len(protein_pcts) < 3:
            return {
                "consistency_score": 0,
                "avg_protein_pct": 0.0,
                "avg_carbs_pct": 0.0,
                "avg_fat_pct": 0.0,
                "std_dev_protein": 0.0,
                "std_dev_carbs": 0.0,
                "std_dev_fat": 0.0,
                "confidence": "insufficient",
                "data_points": len(protein_pcts)
            }

        # Calculate averages and standard deviations
        avg_protein_pct = statistics.mean(protein_pcts)
        avg_carbs_pct = statistics.mean(carbs_pcts)
        avg_fat_pct = statistics.mean(fat_pcts)

        std_protein = statistics.stdev(protein_pcts) if len(protein_pcts) > 1 else 0.0
        std_carbs = statistics.stdev(carbs_pcts) if len(carbs_pcts) > 1 else 0.0
        std_fat = statistics.stdev(fat_pcts) if len(fat_pcts) > 1 else 0.0

        # Consistency score: inverse of average standard deviation
        # Lower std_dev = higher consistency
        avg_std = (std_protein + std_carbs + std_fat) / 3

        # Score: 100 - (avg_std * scale_factor)
        # avg_std of 5% = score 75, avg_std of 10% = score 50, avg_std of 20% = score 0
        consistency_score = max(0, min(100, int(100 - (avg_std * 5))))

        confidence = calculate_confidence(len(protein_pcts), days, "general")

        return {
            "consistency_score": consistency_score,
            "avg_protein_pct": avg_protein_pct,
            "avg_carbs_pct": avg_carbs_pct,
            "avg_fat_pct": avg_fat_pct,
            "std_dev_protein": std_protein,
            "std_dev_carbs": std_carbs,
            "std_dev_fat": std_fat,
            "confidence": confidence,
            "data_points": len(protein_pcts)
        }


# ============================================================================
# Calculated Metrics (migrated from calculations/nutrition_metrics.py)
# ============================================================================
# These functions return simple values for placeholders.
# Use get_*_data() functions above for structured chart data.


def calculate_energy_balance_7d(profile_id: str) -> Optional[float]:
    """
    Calculate 7-day average energy balance (kcal/day)
    Positive = surplus, Negative = deficit

    Migration from Phase 0b:
        Used by placeholders that need single balance value
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("""
            SELECT kcal
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '7 days'
            ORDER BY date DESC
        """, (profile_id,))

        calories = [row['kcal'] for row in cur.fetchall()]

        if len(calories) < 4:  # Need at least 4 days
            return None

        avg_intake = float(sum(calories) / len(calories))

        # Get estimated TDEE (simplified - could use Harris-Benedict)
        # For now, use weight-based estimate
        cur.execute("""
            SELECT weight
            FROM weight_log
            WHERE profile_id = %s
            ORDER BY date DESC
            LIMIT 1
        """, (profile_id,))

        weight_row = cur.fetchone()
        if not weight_row:
            return None

        # Simple TDEE estimate: bodyweight (kg) × 30-35
        # TODO: Improve with activity level, age, gender
        estimated_tdee = float(weight_row['weight']) * 32.5

        balance = avg_intake - estimated_tdee

        return round(balance, 0)


def calculate_energy_deficit_surplus(profile_id: str, days: int = 7) -> Optional[str]:
    """
    Classify energy balance as deficit/maintenance/surplus
    Returns: 'deficit', 'maintenance', 'surplus', or None
    """
    balance = calculate_energy_balance_7d(profile_id)

    if balance is None:
        return None

    if balance < -200:
        return 'deficit'
    elif balance > 200:
        return 'surplus'
    else:
        return 'maintenance'


def calculate_protein_g_per_kg(profile_id: str) -> Optional[float]:
    """Calculate average protein intake in g/kg bodyweight (last 7 days)"""
    with get_db() as conn:
        cur = get_cursor(conn)

        # Get recent weight
        cur.execute("""
            SELECT weight
            FROM weight_log
            WHERE profile_id = %s
            ORDER BY date DESC
            LIMIT 1
        """, (profile_id,))

        weight_row = cur.fetchone()
        if not weight_row:
            return None

        weight = float(weight_row['weight'])

        # Get protein intake
        cur.execute("""
            SELECT protein_g
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '7 days'
              AND protein_g IS NOT NULL
            ORDER BY date DESC
        """, (profile_id,))

        protein_values = [row['protein_g'] for row in cur.fetchall()]

        if len(protein_values) < 4:
            return None

        avg_protein = float(sum(protein_values) / len(protein_values))
        protein_per_kg = avg_protein / weight

        return round(protein_per_kg, 2)


def calculate_protein_days_in_target(profile_id: str, target_low: float = 1.6, target_high: float = 2.2) -> Optional[str]:
    """
    Calculate how many days in last 7 were within protein target
    Returns: "5/7" format or None
    """
    with get_db() as conn:
        cur = get_cursor(conn)

        # Get recent weight
        cur.execute("""
            SELECT weight
            FROM weight_log
            WHERE profile_id = %s
            ORDER BY date DESC
            LIMIT 1
        """, (profile_id,))

        weight_row = cur.fetchone()
        if not weight_row:
            return None

        weight = float(weight_row['weight'])

        # Get protein intake last 7 days
        cur.execute("""
            SELECT protein_g, date
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '7 days'
              AND protein_g IS NOT NULL
            ORDER BY date DESC
        """, (profile_id,))

        protein_data = cur.fetchall()

        if len(protein_data) < 4:
            return None

        # Count days in target range
        days_in_target = 0
        total_days = len(protein_data)

        for row in protein_data:
            protein_per_kg = float(row['protein_g']) / weight
            if target_low <= protein_per_kg <= target_high:
                days_in_target += 1

        return f"{days_in_target}/{total_days}"


def calculate_protein_adequacy_28d(profile_id: str) -> Optional[int]:
    """
    Protein adequacy score 0-100 (last 28 days)
    Based on consistency and target achievement
    """
    import statistics

    with get_db() as conn:
        cur = get_cursor(conn)

        # Get average weight (28d)
        cur.execute("""
            SELECT AVG(weight) as avg_weight
            FROM weight_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
        """, (profile_id,))

        weight_row = cur.fetchone()
        if not weight_row or not weight_row['avg_weight']:
            return None

        weight = float(weight_row['avg_weight'])

        # Get protein intake (28d)
        cur.execute("""
            SELECT protein_g
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
              AND protein_g IS NOT NULL
        """, (profile_id,))

        protein_values = [float(row['protein_g']) for row in cur.fetchall()]

        if len(protein_values) < 18:  # 60% coverage
            return None

        # Calculate metrics
        protein_per_kg_values = [p / weight for p in protein_values]
        avg_protein_per_kg = sum(protein_per_kg_values) / len(protein_per_kg_values)

        # Target range: 1.6-2.2 g/kg for active individuals
        target_mid = 1.9

        # Score based on distance from target
        if 1.6 <= avg_protein_per_kg <= 2.2:
            base_score = 100
        elif avg_protein_per_kg < 1.6:
            # Below target
            base_score = max(40, 100 - ((1.6 - avg_protein_per_kg) * 40))
        else:
            # Above target (less penalty)
            base_score = max(80, 100 - ((avg_protein_per_kg - 2.2) * 10))

        # Consistency bonus/penalty
        std_dev = statistics.stdev(protein_per_kg_values)
        if std_dev < 0.3:
            consistency_bonus = 10
        elif std_dev < 0.5:
            consistency_bonus = 0
        else:
            consistency_bonus = -10

        final_score = min(100, max(0, base_score + consistency_bonus))

        return int(final_score)


def calculate_macro_consistency_score(profile_id: str) -> Optional[int]:
    """
    Macro consistency score 0-100 (last 28 days)
    Lower variability = higher score
    """
    import statistics

    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("""
            SELECT kcal, protein_g, fat_g, carbs_g
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
              AND kcal IS NOT NULL
            ORDER BY date DESC
        """, (profile_id,))

        data = cur.fetchall()

        if len(data) < 18:
            return None

        # Calculate coefficient of variation for each macro
        def cv(values):
            """Coefficient of variation (std_dev / mean)"""
            if not values or len(values) < 2:
                return None
            mean = sum(values) / len(values)
            if mean == 0:
                return None
            std_dev = statistics.stdev(values)
            return std_dev / mean

        calories_cv = cv([d['kcal'] for d in data])
        protein_cv = cv([d['protein_g'] for d in data if d['protein_g']])
        fat_cv = cv([d['fat_g'] for d in data if d['fat_g']])
        carbs_cv = cv([d['carbs_g'] for d in data if d['carbs_g']])

        cv_values = [v for v in [calories_cv, protein_cv, fat_cv, carbs_cv] if v is not None]

        if not cv_values:
            return None

        avg_cv = sum(cv_values) / len(cv_values)

        # Score: lower CV = higher score
        # CV < 0.2 = excellent consistency
        # CV > 0.5 = poor consistency
        if avg_cv < 0.2:
            score = 100
        elif avg_cv < 0.3:
            score = 85
        elif avg_cv < 0.4:
            score = 70
        elif avg_cv < 0.5:
            score = 55
        else:
            score = max(30, 100 - (avg_cv * 100))

        return int(score)


def calculate_intake_volatility(profile_id: str) -> Optional[str]:
    """
    Classify intake volatility: 'stable', 'moderate', 'high'
    """
    consistency = calculate_macro_consistency_score(profile_id)

    if consistency is None:
        return None

    if consistency >= 80:
        return 'stable'
    elif consistency >= 60:
        return 'moderate'
    else:
        return 'high'


def calculate_nutrition_score(profile_id: str, focus_weights: Optional[Dict] = None) -> Optional[int]:
    """
    Nutrition adherence score 0-100
    Weighted by user's nutrition-related focus areas
    """
    if focus_weights is None:
        # Import here to avoid circular dependency
        from data_layer.scores import get_user_focus_weights
        focus_weights = get_user_focus_weights(profile_id)

    # Nutrition-related focus areas (English keys from DB)
    protein_intake = focus_weights.get('protein_intake', 0)
    calorie_balance = focus_weights.get('calorie_balance', 0)
    macro_consistency = focus_weights.get('macro_consistency', 0)
    meal_timing = focus_weights.get('meal_timing', 0)
    hydration = focus_weights.get('hydration', 0)

    total_nutrition_weight = protein_intake + calorie_balance + macro_consistency + meal_timing + hydration

    if total_nutrition_weight == 0:
        return None  # No nutrition goals

    components = []

    # 1. Calorie target adherence (if calorie_balance goal active)
    if calorie_balance > 0:
        calorie_score = _score_calorie_adherence(profile_id)
        if calorie_score is not None:
            components.append(('calories', calorie_score, calorie_balance))

    # 2. Protein target adherence (if protein_intake goal active)
    if protein_intake > 0:
        protein_score = calculate_protein_adequacy_28d(profile_id)
        if protein_score is not None:
            components.append(('protein', protein_score, protein_intake))

    # 3. Intake consistency (if macro_consistency goal active)
    if macro_consistency > 0:
        consistency_score = calculate_macro_consistency_score(profile_id)
        if consistency_score is not None:
            components.append(('consistency', consistency_score, macro_consistency))

    # 4. Macro balance (always relevant if any nutrition goal)
    if total_nutrition_weight > 0:
        macro_score = _score_macro_balance(profile_id)
        if macro_score is not None:
            # Use 20% of total weight for macro balance
            components.append(('macros', macro_score, total_nutrition_weight * 0.2))

    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_calorie_adherence(profile_id: str) -> Optional[int]:
    """Score calorie target adherence (0-100)"""
    # Check for energy balance goal
    # For now, use energy balance calculation
    balance = calculate_energy_balance_7d(profile_id)

    if balance is None:
        return None

    # Score based on whether deficit/surplus aligns with goal
    # Simplified: assume weight loss goal = deficit is good
    # TODO: Check actual goal type

    abs_balance = abs(balance)

    # Moderate deficit/surplus = good
    if 200 <= abs_balance <= 500:
        return 100
    elif 100 <= abs_balance <= 700:
        return 85
    elif abs_balance <= 900:
        return 70
    elif abs_balance <= 1200:
        return 55
    else:
        return 40


def _score_macro_balance(profile_id: str) -> Optional[int]:
    """Score macro balance (0-100)"""
    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("""
            SELECT protein_g, fat_g, carbs_g, kcal
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
              AND protein_g IS NOT NULL
              AND fat_g IS NOT NULL
              AND carbs_g IS NOT NULL
            ORDER BY date DESC
        """, (profile_id,))

        data = cur.fetchall()

        if len(data) < 18:
            return None

        # Calculate average macro percentages
        macro_pcts = []
        for row in data:
            total_kcal = (row['protein_g'] * 4) + (row['fat_g'] * 9) + (row['carbs_g'] * 4)
            if total_kcal == 0:
                continue

            protein_pct = (row['protein_g'] * 4 / total_kcal) * 100
            fat_pct = (row['fat_g'] * 9 / total_kcal) * 100
            carbs_pct = (row['carbs_g'] * 4 / total_kcal) * 100

            macro_pcts.append((protein_pct, fat_pct, carbs_pct))

        if not macro_pcts:
            return None

        avg_protein_pct = sum(p for p, _, _ in macro_pcts) / len(macro_pcts)
        avg_fat_pct = sum(f for _, f, _ in macro_pcts) / len(macro_pcts)
        avg_carbs_pct = sum(c for _, _, c in macro_pcts) / len(macro_pcts)

        # Reasonable ranges:
        # Protein: 20-35%
        # Fat: 20-35%
        # Carbs: 30-55%

        score = 100

        # Protein score
        if not (20 <= avg_protein_pct <= 35):
            if avg_protein_pct < 20:
                score -= (20 - avg_protein_pct) * 2
            else:
                score -= (avg_protein_pct - 35) * 1

        # Fat score
        if not (20 <= avg_fat_pct <= 35):
            if avg_fat_pct < 20:
                score -= (20 - avg_fat_pct) * 2
            else:
                score -= (avg_fat_pct - 35) * 2

        # Carbs score
        if not (30 <= avg_carbs_pct <= 55):
            if avg_carbs_pct < 30:
                score -= (30 - avg_carbs_pct) * 1.5
            else:
                score -= (avg_carbs_pct - 55) * 1.5

        return max(40, min(100, int(score)))


def calculate_energy_availability_warning(profile_id: str) -> Optional[Dict]:
    """
    Heuristic energy availability warning
    Returns dict with warning level and reasons
    """
    warnings = []
    severity = 'none'  # none, low, medium, high

    # 1. Check for sustained large deficit
    balance = calculate_energy_balance_7d(profile_id)
    if balance and balance < -800:
        warnings.append('Anhaltend großes Energiedefizit (>800 kcal/Tag)')
        severity = 'medium'

        if balance < -1200:
            warnings.append('Sehr großes Energiedefizit (>1200 kcal/Tag)')
            severity = 'high'

    # 2. Check recovery score
    from data_layer.recovery_metrics import calculate_recovery_score_v2
    recovery = calculate_recovery_score_v2(profile_id)
    if recovery and recovery < 50:
        warnings.append('Recovery Score niedrig (<50)')
        if severity == 'none':
            severity = 'low'
        elif severity == 'medium':
            severity = 'high'

    # 3. Check LBM trend
    from data_layer.body_metrics import calculate_lbm_28d_change
    lbm_change = calculate_lbm_28d_change(profile_id)
    if lbm_change and lbm_change < -1.0:
        warnings.append('Magermasse sinkt (>1kg in 28 Tagen)')
        if severity == 'none':
            severity = 'low'
        elif severity in ['low', 'medium']:
            severity = 'high'

    # 4. Check sleep quality
    from data_layer.recovery_metrics import calculate_sleep_quality_7d
    sleep_quality = calculate_sleep_quality_7d(profile_id)
    if sleep_quality and sleep_quality < 60:
        warnings.append('Schlafqualität verschlechtert')
        if severity == 'none':
            severity = 'low'

    if not warnings:
        return None

    return {
        'severity': severity,
        'warnings': warnings,
        'recommendation': _get_energy_warning_recommendation(severity)
    }


def _get_energy_warning_recommendation(severity: str) -> str:
    """Get recommendation text based on severity"""
    if severity == 'high':
        return ("Mögliche Unterversorgung erkannt. Erwäge eine Reduktion des Energiedefizits, "
                "Erhöhung der Proteinzufuhr und mehr Erholung. Dies ist keine medizinische Diagnose.")
    elif severity == 'medium':
        return ("Hinweise auf aggressives Defizit. Beobachte Recovery, Schlaf und Magermasse genau.")
    else:
        return ("Leichte Hinweise auf Belastung. Monitoring empfohlen.")


def calculate_fiber_avg_7d(profile_id: str) -> Optional[float]:
    """Calculate average fiber intake (g/day) last 7 days"""
    # TODO: Implement when fiber column added to nutrition_log
    return None


def calculate_sugar_avg_7d(profile_id: str) -> Optional[float]:
    """Calculate average sugar intake (g/day) last 7 days"""
    # TODO: Implement when sugar column added to nutrition_log
    return None


def calculate_nutrition_data_quality(profile_id: str) -> Dict[str, any]:
    """
    Assess data quality for nutrition metrics
    Returns dict with quality score and details
    """
    with get_db() as conn:
        cur = get_cursor(conn)

        # Nutrition entries last 28 days
        cur.execute("""
            SELECT COUNT(*) as total,
                   COUNT(protein_g) as with_protein,
                   COUNT(fat_g) as with_fat,
                   COUNT(carbs_g) as with_carbs
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
        """, (profile_id,))

        counts = cur.fetchone()

    total_entries = counts['total']
    protein_coverage = counts['with_protein'] / total_entries if total_entries > 0 else 0
    macro_coverage = min(counts['with_fat'], counts['with_carbs']) / total_entries if total_entries > 0 else 0

    # Score components
    frequency_score = min(100, (total_entries / 21) * 100)  # 21 = 75% of 28 days
    protein_score = protein_coverage * 100
    macro_score = macro_coverage * 100

    # Overall score (frequency 50%, protein 30%, macros 20%)
    overall_score = int(
        frequency_score * 0.5 +
        protein_score * 0.3 +
        macro_score * 0.2
    )

    # Confidence level
    if overall_score >= 80:
        confidence = "high"
    elif overall_score >= 60:
        confidence = "medium"
    else:
        confidence = "low"

    return {
        "overall_score": overall_score,
        "confidence": confidence,
        "measurements": {
            "entries_28d": total_entries,
            "protein_coverage_pct": int(protein_coverage * 100),
            "macro_coverage_pct": int(macro_coverage * 100)
        },
        "component_scores": {
            "frequency": int(frequency_score),
            "protein": int(protein_score),
            "macros": int(macro_score)
        }
    }