"""
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
"""

import statistics
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

# Fallback TDEE (kcal/day) when demographics for Mifflin–St Jeor are incomplete.
TDEE_KCAL_PER_KG_BODYWEIGHT = 32.5
# PAL applied to MSJ BMR when height, sex, dob and weight are available (moderate activity).
TDEE_PAL_MODERATE = 1.55


def _age_years_from_dob(dob) -> Optional[int]:
    if dob is None:
        return None
    try:
        if isinstance(dob, str):
            birth = datetime.strptime(dob[:10], "%Y-%m-%d").date()
        else:
            birth = dob
        today = date.today()
        return today.year - birth.year - ((today.month, today.day) < (birth.month, birth.day))
    except Exception:
        return None


def _mifflin_st_jeor_bmr_kcal(
    weight_kg: float, height_cm: float, age_years: int, sex_is_male: bool
) -> float:
    if sex_is_male:
        return 10.0 * weight_kg + 6.25 * height_cm - 5.0 * age_years + 5.0
    return 10.0 * weight_kg + 6.25 * height_cm - 5.0 * age_years - 161.0


def estimate_tdee_kcal_from_latest_weight(profile_id: str) -> Optional[float]:
    """
    Estimated TDEE (kcal/day).

    Primary: Mifflin–St Jeor BMR × TDEE_PAL_MODERATE when latest weight plus
    profiles.height, profiles.sex, profiles.dob are usable.

    Fallback: latest weight (kg) × TDEE_KCAL_PER_KG_BODYWEIGHT (legacy heuristic).

    Returns None if no weight on record.
    """
    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,),
        )
        wrow = cur.fetchone()
        if not wrow or wrow["weight"] is None:
            return None
        weight_kg = float(wrow["weight"])

        cur.execute(
            "SELECT height, sex, dob FROM profiles WHERE id=%s",
            (profile_id,),
        )
        prow = cur.fetchone()

    if prow and prow.get("height") and prow.get("sex") is not None and prow.get("dob"):
        height_cm = float(prow["height"])
        age = _age_years_from_dob(prow["dob"])
        if age is not None and 10 < age < 120 and height_cm > 50:
            sex_raw = str(prow["sex"]).strip().lower()
            sex_is_male = sex_raw in ("m", "male", "männlich", "mann")
            bmr = _mifflin_st_jeor_bmr_kcal(weight_kg, height_cm, age, sex_is_male)
            if bmr > 400:
                return bmr * TDEE_PAL_MODERATE

    return weight_kg * TDEE_KCAL_PER_KG_BODYWEIGHT


def _get_profile_goal_mode(profile_id: str) -> str:
    """Strategic goal_mode from profiles (Phase 0a); defaults to health."""
    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("SELECT goal_mode FROM profiles WHERE id=%s", (profile_id,))
        row = cur.fetchone()
        if row and row.get("goal_mode"):
            g = str(row["goal_mode"]).strip().lower()
            if g:
                return g
    return "health"


def get_nutrition_average_data(
    profile_id: str,
    days: int = 30,
    *,
    all_history: bool = False,
) -> 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 = None if all_history else (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        # Mean over calendar days (per-day sums), not over raw log rows.
        if cutoff:
            inner_where = "WHERE profile_id=%s AND date >= %s"
            params = (profile_id, cutoff)
        else:
            inner_where = "WHERE profile_id=%s"
            params = (profile_id,)

        cur.execute(
            f"""SELECT
                AVG(daily_kcal) AS kcal_avg,
                AVG(daily_protein) AS protein_avg,
                AVG(daily_carbs) AS carbs_avg,
                AVG(daily_fat) AS fat_avg,
                COUNT(*)::int AS day_count
               FROM (
                SELECT date,
                    COALESCE(SUM(kcal), 0)::float AS daily_kcal,
                    COALESCE(SUM(protein_g), 0)::float AS daily_protein,
                    COALESCE(SUM(carbs_g), 0)::float AS daily_carbs,
                    COALESCE(SUM(fat_g), 0)::float AS daily_fat
                FROM nutrition_log
                {inner_where}
                GROUP BY date
               ) AS daily""",
            params,
        )
        row = cur.fetchone()

        if not row or row["day_count"] == 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["day_count"]
        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:
    """
    Energy balance (intake - estimated expenditure), kcal/day.

    Intake: mean of daily total kcal (sum per calendar day).
    TDEE: estimate_tdee_kcal_from_latest_weight (MSJ × PAL oder kg-Fallback).
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT date, SUM(kcal)::float AS daily_kcal
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL
               GROUP BY date
               ORDER BY date""",
            (profile_id, cutoff),
        )
        daily_rows = cur.fetchall()

    if not daily_rows:
        return {
            "energy_balance": 0.0,
            "avg_intake": 0.0,
            "estimated_tdee": 0.0,
            "status": "unknown",
            "confidence": "insufficient",
            "days_analyzed": days,
            "data_points": 0,
        }

    daily_totals = [safe_float(r["daily_kcal"]) for r in daily_rows]
    avg_intake = sum(daily_totals) / len(daily_totals)
    data_points = len(daily_totals)

    estimated_tdee = estimate_tdee_kcal_from_latest_weight(profile_id)
    if estimated_tdee is None:
        return {
            "energy_balance": 0.0,
            "avg_intake": avg_intake,
            "estimated_tdee": 0.0,
            "status": "unknown",
            "confidence": "insufficient",
            "days_analyzed": days,
            "data_points": data_points
        }

    energy_balance = avg_intake - estimated_tdee

    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
        }
    """
    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 COALESCE(SUM(protein_g), 0)::float AS daily_protein
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s
               GROUP BY date""",
            (profile_id, cutoff),
        )
        rows = cur.fetchall()

    if not rows or targets.get("confidence") == "insufficient" or targets["current_weight"] <= 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"
        }

    daily_totals = [safe_float(r["daily_protein"]) for r in rows]
    days_with_data = len(daily_totals)
    low = targets["protein_target_low"]
    high = targets["protein_target_high"]
    days_in_target = sum(1 for d in daily_totals if low <= d <= high)

    avg_protein = sum(daily_totals) / days_with_data
    protein_g_per_kg = avg_protein / targets["current_weight"] if targets["current_weight"] > 0 else 0.0

    target_pct = (days_in_target / days_with_data * 100) if days_with_data > 0 else 0
    target_mid = (low + high) / 2
    avg_vs_target = (avg_protein / target_mid) if target_mid > 0 else 0

    adequacy_score = int(target_pct * 0.7 + min(avg_vs_target * 100, 100) * 0.3)
    adequacy_score = max(0, min(100, adequacy_score))

    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
                COALESCE(SUM(kcal), 0)::float AS kcal,
                COALESCE(SUM(protein_g), 0)::float AS protein_g,
                COALESCE(SUM(carbs_g), 0)::float AS carbs_g,
                COALESCE(SUM(fat_g), 0)::float AS fat_g
               FROM nutrition_log
               WHERE profile_id=%s AND date >= %s
               GROUP BY date
               HAVING COALESCE(SUM(kcal), 0) > 0
                 AND COALESCE(SUM(protein_g), 0) > 0
                 AND COALESCE(SUM(carbs_g), 0) > 0
                 AND COALESCE(SUM(fat_g), 0) > 0""",
            (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)
            }

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

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

            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)
        }


def get_weekly_macro_distribution_chart_data(profile_id: str, weeks: int) -> Dict:
    """
    Chart E3: gestapelte Wochenbalken (Makro-%), gleiche Logik wie /charts/weekly-macro-distribution.
    """
    cutoff = (datetime.now() - timedelta(weeks=weeks)).strftime("%Y-%m-%d")

    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute(
            """SELECT date, 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 not rows or len(rows) < 7:
        return {
            "chart_type": "bar",
            "data": {
                "labels": [],
                "datasets": [],
            },
            "metadata": {
                "confidence": "insufficient",
                "data_points": len(rows) if rows else 0,
                "message": "Nicht genug Daten für Wochen-Analyse (min. 7 Tage)",
            },
        }

    weekly_data: Dict[str, Dict[str, List[float]]] = {}
    for row in rows:
        date_obj = row["date"] if isinstance(row["date"], datetime) else datetime.fromisoformat(str(row["date"]))
        iso_week = date_obj.strftime("%Y-W%V")

        if iso_week not in weekly_data:
            weekly_data[iso_week] = {
                "protein": [],
                "carbs": [],
                "fat": [],
                "kcal": [],
            }

        weekly_data[iso_week]["protein"].append(safe_float(row["protein_g"]))
        weekly_data[iso_week]["carbs"].append(safe_float(row["carbs_g"]))
        weekly_data[iso_week]["fat"].append(safe_float(row["fat_g"]))
        weekly_data[iso_week]["kcal"].append(safe_float(row["kcal"]))

    labels: List[str] = []
    protein_pcts: List[float] = []
    carbs_pcts: List[float] = []
    fat_pcts: List[float] = []

    for iso_week in sorted(weekly_data.keys())[-weeks:]:
        data = weekly_data[iso_week]

        avg_protein = sum(data["protein"]) / len(data["protein"]) if data["protein"] else 0
        avg_carbs = sum(data["carbs"]) / len(data["carbs"]) if data["carbs"] else 0
        avg_fat = sum(data["fat"]) / len(data["fat"]) if data["fat"] else 0

        protein_kcal = avg_protein * 4
        carbs_kcal = avg_carbs * 4
        fat_kcal = avg_fat * 9

        total_kcal = protein_kcal + carbs_kcal + fat_kcal

        if total_kcal > 0:
            labels.append(f"KW {iso_week[-2:]}")
            protein_pcts.append(round((protein_kcal / total_kcal) * 100, 1))
            carbs_pcts.append(round((carbs_kcal / total_kcal) * 100, 1))
            fat_pcts.append(round((fat_kcal / total_kcal) * 100, 1))

    protein_cv = (
        statistics.stdev(protein_pcts) / statistics.mean(protein_pcts) * 100
        if len(protein_pcts) > 1 and statistics.mean(protein_pcts) > 0
        else 0
    )
    carbs_cv = (
        statistics.stdev(carbs_pcts) / statistics.mean(carbs_pcts) * 100
        if len(carbs_pcts) > 1 and statistics.mean(carbs_pcts) > 0
        else 0
    )
    fat_cv = (
        statistics.stdev(fat_pcts) / statistics.mean(fat_pcts) * 100
        if len(fat_pcts) > 1 and statistics.mean(fat_pcts) > 0
        else 0
    )

    return {
        "chart_type": "bar",
        "data": {
            "labels": labels,
            "datasets": [
                {
                    "label": "Protein (%)",
                    "data": protein_pcts,
                    "backgroundColor": "#4a8f72",
                    "stack": "macro",
                },
                {
                    "label": "Kohlenhydrate (%)",
                    "data": carbs_pcts,
                    "backgroundColor": "#c17d45",
                    "stack": "macro",
                },
                {
                    "label": "Fett (%)",
                    "data": fat_pcts,
                    "backgroundColor": "#6e8eb8",
                    "stack": "macro",
                },
            ],
        },
        "metadata": {
            "confidence": calculate_confidence(len(rows), weeks * 7, "general"),
            "data_points": len(rows),
            "weeks_analyzed": len(labels),
            "avg_protein_pct": round(statistics.mean(protein_pcts), 1) if protein_pcts else 0,
            "avg_carbs_pct": round(statistics.mean(carbs_pcts), 1) if carbs_pcts else 0,
            "avg_fat_pct": round(statistics.mean(fat_pcts), 1) if fat_pcts else 0,
            "protein_cv": round(protein_cv, 1),
            "carbs_cv": round(carbs_cv, 1),
            "fat_cv": round(fat_cv, 1),
        },
    }


def get_energy_availability_warning_payload(profile_id: str, days: int = 14) -> Dict:
    """
    E5 Energieverfügbarkeit — gleiche Heuristik wie GET /charts/energy-availability-warning.
    """
    from data_layer.recovery_metrics import calculate_recovery_score_v2, calculate_sleep_quality_7d
    from data_layer.body_metrics import calculate_lbm_28d_change

    triggers: List[str] = []
    warning_level = "none"

    energy_data = get_energy_balance_data(profile_id, days)
    if energy_data.get("energy_balance", 0) < -500:
        triggers.append("Großes Energiedefizit (>500 kcal/Tag)")

    try:
        recovery_score = calculate_recovery_score_v2(profile_id)
        if recovery_score and recovery_score < 50:
            triggers.append("Recovery Score niedrig (<50)")
    except Exception:
        pass

    try:
        sleep_quality = calculate_sleep_quality_7d(profile_id)
        if sleep_quality and sleep_quality < 60:
            triggers.append("Schlafqualität reduziert (<60%)")
    except Exception:
        pass

    try:
        lbm_change = calculate_lbm_28d_change(profile_id)
        if lbm_change and lbm_change < -1.0:
            triggers.append("Magermasse sinkt (-{:.1f} kg)".format(abs(lbm_change)))
    except Exception:
        pass

    if len(triggers) >= 3:
        warning_level = "warning"
        message = (
            "⚠️ Hinweis auf mögliche Unterversorgung. Mehrere Indikatoren auffällig. "
            "Erwäge Defizit-Anpassung oder Regenerationswoche."
        )
    elif len(triggers) >= 2:
        warning_level = "caution"
        message = (
            "⚡ Beobachte folgende Signale genau. Aktuell noch kein Handlungsbedarf, aber Trend beachten."
        )
    elif len(triggers) >= 1:
        warning_level = "caution"
        message = "💡 Ein Indikator auffällig. Weiter beobachten."
    else:
        message = "✅ Energieverfügbarkeit unauffällig."

    return {
        "warning_level": warning_level,
        "triggers": triggers,
        "message": message,
        "metadata": {
            "days_analyzed": days,
            "trigger_count": len(triggers),
            "note": "Heuristische Einschätzung, keine medizinische Diagnose",
        },
    }


# ============================================================================
# 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]:
    """
    7-day mean energy balance (kcal/day), same rules as get_energy_balance_data(..., 7).
    """
    data = get_energy_balance_data(profile_id, 7)
    if data["data_points"] < 4:
        return None
    tdee = data.get("estimated_tdee") or 0
    if tdee <= 0:
        return None
    return round(float(data["energy_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 aggregated by day (SUM per day)
        cur.execute("""
            SELECT date, SUM(protein_g) as daily_protein
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '7 days'
              AND protein_g IS NOT NULL
            GROUP BY date
            ORDER BY date DESC
        """, (profile_id,))

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

        if len(daily_protein) < 4:  # At least 4 days with data
            return None

        avg_protein = sum(daily_protein) / len(daily_protein)
        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'])

        # Calculate protein target range (absolute values)
        target_low_g = target_low * weight
        target_high_g = target_high * weight

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

        daily_data = cur.fetchall()

        if len(daily_data) < 4:  # At least 4 days with data
            return None

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

        for row in daily_data:
            daily_protein = float(row['daily_protein'])
            if target_low_g <= daily_protein <= target_high_g:
                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).
    Uses per-calendar-day total protein vs. average weight in the window (g/kg per day).
    """
    import statistics

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

        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'])

        cur.execute("""
            SELECT COALESCE(SUM(protein_g), 0)::float AS daily_protein
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
            GROUP BY date
        """, (profile_id,))

        daily_totals = [float(row['daily_protein']) for row in cur.fetchall()]

        if len(daily_totals) < 18:
            return None

        protein_per_kg_values = [p / weight for p in daily_totals]
        avg_protein_per_kg = sum(protein_per_kg_values) / len(protein_per_kg_values)

        if 1.6 <= avg_protein_per_kg <= 2.2:
            base_score = 100
        elif avg_protein_per_kg < 1.6:
            base_score = max(40, 100 - ((1.6 - avg_protein_per_kg) * 40))
        else:
            base_score = max(80, 100 - ((avg_protein_per_kg - 2.2) * 10))

        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).
    CV of daily totals (kcal and macros), not raw log rows.
    """
    import statistics

    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("""
            SELECT
                COALESCE(SUM(kcal), 0)::float AS dk,
                COALESCE(SUM(protein_g), 0)::float AS dp,
                COALESCE(SUM(fat_g), 0)::float AS df,
                COALESCE(SUM(carbs_g), 0)::float AS dc
            FROM nutrition_log
            WHERE profile_id = %s
              AND date >= CURRENT_DATE - INTERVAL '28 days'
            GROUP BY date
            HAVING COALESCE(SUM(kcal), 0) > 0
        """, (profile_id,))

        data = cur.fetchall()

        if len(data) < 18:
            return None

        def cv(values):
            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['dk'] for d in data])
        protein_cv = cv([d['dp'] for d in data if d['dp']])
        fat_cv = cv([d['df'] for d in data if d['df']])
        carbs_cv = cv([d['dc'] for d in data if d['dc']])

        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)

        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; Gewichte immer float)
    protein_intake = float(focus_weights.get('protein_intake', 0) or 0)
    calorie_balance = float(focus_weights.get('calorie_balance', 0) or 0)
    macro_consistency = float(focus_weights.get('macro_consistency', 0) or 0)
    meal_timing = float(focus_weights.get('meal_timing', 0) or 0)
    hydration = float(focus_weights.get('hydration', 0) or 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 (float: DB-Werte können Decimal sein)
    total_score = sum(float(score) * float(weight) for _, score, weight in components)
    total_weight = sum(float(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) using 7d balance vs profiles.goal_mode."""
    balance = calculate_energy_balance_7d(profile_id)
    if balance is None:
        return None

    mode = _get_profile_goal_mode(profile_id)
    b = float(balance)

    def _weight_loss(x: float) -> int:
        if -550 <= x <= -250:
            return 100
        if x > 450:
            return 38
        if -750 <= x < -550 or -250 < x <= 120:
            return 82
        if x < -1200:
            return 52
        if -950 <= x < -750 or 120 < x <= 350:
            return 68
        return 58

    def _surplus_friendly(x: float) -> int:
        if 80 <= x <= 480:
            return 100
        if -120 <= x < 80 or 480 < x <= 700:
            return 86
        if -380 <= x < -120:
            return 68
        if x > 850:
            return 54
        if x < -650:
            return 44
        return 72

    def _maintenance(x: float) -> int:
        a = abs(x)
        if a <= 200:
            return 100
        if a <= 400:
            return 84
        if a <= 650:
            return 70
        if a <= 900:
            return 55
        return 40

    if mode == "weight_loss":
        return _weight_loss(b)
    if mode in ("strength", "recomposition"):
        return _surplus_friendly(b)
    return _maintenance(b)


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)
        }
    }