"""
Recovery Metrics Data Layer

Provides structured data for recovery tracking and analysis.

Functions:
    - get_sleep_duration_data(): Average sleep duration
    - get_sleep_quality_data(): Sleep quality score (Deep+REM %)
    - get_rest_days_data(): Rest day count and types

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_sleep_duration_data(
    profile_id: str,
    days: int = 7
) -> Dict:
    """
    Calculate average sleep duration.

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

    Returns:
        {
            "avg_duration_hours": float,
            "avg_duration_minutes": int,
            "total_nights": int,
            "nights_with_data": int,
            "confidence": str,
            "days_analyzed": int
        }

    Migration from Phase 0b:
        OLD: get_sleep_avg_duration(pid, days) formatted string
        NEW: Structured data with hours and minutes
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT sleep_segments FROM sleep_log
               WHERE profile_id=%s AND date >= %s
               ORDER BY date DESC""",
            (profile_id, cutoff)
        )
        rows = cur.fetchall()

        if not rows:
            return {
                "avg_duration_hours": 0.0,
                "avg_duration_minutes": 0,
                "total_nights": 0,
                "nights_with_data": 0,
                "confidence": "insufficient",
                "days_analyzed": days
            }

        total_minutes = 0
        nights_with_data = 0

        for row in rows:
            segments = row['sleep_segments']
            if segments:
                night_minutes = sum(seg.get('duration_min', 0) for seg in segments)
                if night_minutes > 0:
                    total_minutes += night_minutes
                    nights_with_data += 1

        if nights_with_data == 0:
            return {
                "avg_duration_hours": 0.0,
                "avg_duration_minutes": 0,
                "total_nights": len(rows),
                "nights_with_data": 0,
                "confidence": "insufficient",
                "days_analyzed": days
            }

        avg_minutes = int(total_minutes / nights_with_data)
        avg_hours = avg_minutes / 60

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

        return {
            "avg_duration_hours": round(avg_hours, 1),
            "avg_duration_minutes": avg_minutes,
            "total_nights": len(rows),
            "nights_with_data": nights_with_data,
            "confidence": confidence,
            "days_analyzed": days
        }


def get_sleep_quality_data(
    profile_id: str,
    days: int = 7
) -> Dict:
    """
    Calculate sleep quality score (Deep+REM percentage).

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

    Returns:
        {
            "quality_score": float,          # 0-100, Deep+REM percentage
            "avg_deep_rem_minutes": int,
            "avg_total_minutes": int,
            "avg_light_minutes": int,
            "avg_awake_minutes": int,
            "nights_analyzed": int,
            "confidence": str,
            "days_analyzed": int
        }

    Migration from Phase 0b:
        OLD: get_sleep_avg_quality(pid, days) formatted string
        NEW: Complete sleep phase breakdown
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        cur.execute(
            """SELECT sleep_segments FROM sleep_log
               WHERE profile_id=%s AND date >= %s
               ORDER BY date DESC""",
            (profile_id, cutoff)
        )
        rows = cur.fetchall()

        if not rows:
            return {
                "quality_score": 0.0,
                "avg_deep_rem_minutes": 0,
                "avg_total_minutes": 0,
                "avg_light_minutes": 0,
                "avg_awake_minutes": 0,
                "nights_analyzed": 0,
                "confidence": "insufficient",
                "days_analyzed": days
            }

        total_quality = 0
        total_deep_rem = 0
        total_light = 0
        total_awake = 0
        total_all = 0
        count = 0

        for row in rows:
            segments = row['sleep_segments']
            if segments:
                # Note: segments use 'phase' key, stored lowercase (deep, rem, light, awake)
                deep_rem_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') in ['deep', 'rem'])
                light_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') == 'light')
                awake_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') == 'awake')
                total_min = sum(s.get('duration_min', 0) for s in segments)

                if total_min > 0:
                    quality_pct = (deep_rem_min / total_min) * 100
                    total_quality += quality_pct
                    total_deep_rem += deep_rem_min
                    total_light += light_min
                    total_awake += awake_min
                    total_all += total_min
                    count += 1

        if count == 0:
            return {
                "quality_score": 0.0,
                "avg_deep_rem_minutes": 0,
                "avg_total_minutes": 0,
                "avg_light_minutes": 0,
                "avg_awake_minutes": 0,
                "nights_analyzed": 0,
                "confidence": "insufficient",
                "days_analyzed": days
            }

        avg_quality = total_quality / count
        avg_deep_rem = int(total_deep_rem / count)
        avg_total = int(total_all / count)
        avg_light = int(total_light / count)
        avg_awake = int(total_awake / count)

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

        return {
            "quality_score": round(avg_quality, 1),
            "avg_deep_rem_minutes": avg_deep_rem,
            "avg_total_minutes": avg_total,
            "avg_light_minutes": avg_light,
            "avg_awake_minutes": avg_awake,
            "nights_analyzed": count,
            "confidence": confidence,
            "days_analyzed": days
        }


def get_rest_days_data(
    profile_id: str,
    days: int = 30
) -> Dict:
    """
    Get rest days count and breakdown by type.

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

    Returns:
        {
            "total_rest_days": int,
            "rest_types": {
                "muscle_recovery": int,
                "cardio_recovery": int,
                "mental_rest": int,
                "deload": int,
                "injury": int
            },
            "rest_frequency": float,  # days per week
            "confidence": str,
            "days_analyzed": int
        }

    Migration from Phase 0b:
        OLD: get_rest_days_count(pid, days) formatted string
        NEW: Complete breakdown by rest type
    """
    with get_db() as conn:
        cur = get_cursor(conn)
        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')

        # Get total distinct rest days
        cur.execute(
            """SELECT COUNT(DISTINCT date) as count FROM rest_days
               WHERE profile_id=%s AND date >= %s""",
            (profile_id, cutoff)
        )
        total_row = cur.fetchone()
        total_count = total_row['count'] if total_row else 0

        # Get breakdown by focus type
        cur.execute(
            """SELECT focus, COUNT(*) as count FROM rest_days
               WHERE profile_id=%s AND date >= %s
               GROUP BY focus""",
            (profile_id, cutoff)
        )
        type_rows = cur.fetchall()

        rest_types = {
            "muscle_recovery": 0,
            "cardio_recovery": 0,
            "mental_rest": 0,
            "deload": 0,
            "injury": 0
        }

        for row in type_rows:
            focus = row['focus']
            if focus in rest_types:
                rest_types[focus] = row['count']

        # Calculate frequency (rest days per week)
        rest_frequency = (total_count / days * 7) if days > 0 else 0.0

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

        return {
            "total_rest_days": total_count,
            "rest_types": rest_types,
            "rest_frequency": round(rest_frequency, 1),
            "confidence": confidence,
            "days_analyzed": days
        }