"""
Vitalwerte: Zeitreihen + einfache Fitness-/Recovery-Einordnung (Layer 1, Issue 53).

Keine Diagnose — deskriptive Trends, Korrelationen und Varianz-Hinweise.
"""

from __future__ import annotations

import statistics
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Sequence, Tuple

from db import get_db, get_cursor
from data_layer.utils import safe_float, serialize_dates

SERIES_CONFIG = (
    ("resting_hr", "Ruhepuls", "bpm", "#3B82F6"),
    ("hrv", "HRV", "ms", "#1D9E75"),
    ("vo2_max", "VO2max", "ml/kg/min", "#8B5CF6"),
    ("spo2", "SpO2", "%", "#0EA5E9"),
    ("respiratory_rate", "Atemfrequenz", "/min", "#F59E0B"),
)


def _date_to_ord(d: Any) -> float:
    if hasattr(d, "toordinal"):
        return float(d.toordinal())
    if isinstance(d, str):
        return float(datetime.fromisoformat(d[:10]).date().toordinal())
    return 0.0


def _linear_slope(dates: Sequence[Any], values: Sequence[float]) -> float:
    if len(values) < 3 or len(dates) != len(values):
        return 0.0
    xs = [_date_to_ord(d) for d in dates]
    ys = list(values)
    n = len(xs)
    mx = sum(xs) / n
    my = sum(ys) / n
    den = sum((x - mx) ** 2 for x in xs)
    if den < 1e-9:
        return 0.0
    return sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / den


def _pearson(xs: Sequence[float], ys: Sequence[float]) -> Optional[float]:
    n = len(xs)
    if n < 5 or len(ys) != n:
        return None
    mx = statistics.mean(xs)
    my = statistics.mean(ys)
    sx = statistics.pstdev(xs) if n > 1 else 0.0
    sy = statistics.pstdev(ys) if n > 1 else 0.0
    if sx < 1e-9 or sy < 1e-9:
        return None
    cov = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / n
    return cov / (sx * sy)


def _daily_training_load(cur: Any, profile_id: str, cutoff: str) -> Dict[str, float]:
    """Summe Trainingsminuten pro Kalendertag als Belastungs-Proxy."""
    cur.execute(
        """
        SELECT date::text AS d, COALESCE(SUM(duration_min), 0)::float AS minutes
        FROM activity_log
        WHERE profile_id = %s AND date >= %s::date AND duration_min IS NOT NULL AND duration_min > 0
        GROUP BY date
        ORDER BY date
        """,
        (profile_id, cutoff),
    )
    rows = cur.fetchall()
    return {r["d"]: float(r["minutes"]) for r in rows}


def _rhr_by_date(cur: Any, profile_id: str, cutoff: str) -> Dict[str, float]:
    cur.execute(
        """
        SELECT date::text AS d, resting_hr::float AS rhr
        FROM vitals_baseline
        WHERE profile_id = %s AND date >= %s::date AND resting_hr IS NOT NULL
        ORDER BY date
        """,
        (profile_id, cutoff),
    )
    return {r["d"]: float(r["rhr"]) for r in cur.fetchall()}


def build_vitals_history_and_analytics(profile_id: str, days: int) -> Dict[str, Any]:
    """
    Zeitreihen pro Kennzahl (eigene Einheit / eigene Skala im Frontend) + Kurz-Analytik.
    """
    if days < 7:
        days = 7
    if days > 365:
        days = 365
    cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")

    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute(
            """
            SELECT date, resting_hr, hrv, vo2_max, spo2, respiratory_rate
            FROM vitals_baseline
            WHERE profile_id = %s AND date >= %s
            ORDER BY date ASC
            """,
            (profile_id, cutoff),
        )
        rows = cur.fetchall()

    series: Dict[str, Any] = {}
    for key, label_de, unit, color in SERIES_CONFIG:
        pts: List[Dict[str, Any]] = []
        dates: List[Any] = []
        vals: List[float] = []
        for r in rows:
            v = r.get(key)
            if v is None:
                continue
            fv = safe_float(v)
            d = r["date"]
            d_iso = d.isoformat() if hasattr(d, "isoformat") else str(d)[:10]
            pts.append({"date": d_iso, "value": round(fv, 2)})
            dates.append(d)
            vals.append(fv)
        if pts:
            series[key] = {
                "key": key,
                "label_de": label_de,
                "unit": unit,
                "color": color,
                "points": pts,
                "n": len(pts),
                "last": vals[-1] if vals else None,
                "mean": round(statistics.mean(vals), 2) if len(vals) >= 1 else None,
                "stdev": round(statistics.pstdev(vals), 2) if len(vals) >= 2 else None,
                "slope_per_day": round(_linear_slope(dates, vals), 6) if len(vals) >= 3 else None,
            }

    bullets: List[Dict[str, Any]] = []

    # VO2max-Trend
    vo2 = series.get("vo2_max")
    if vo2 and vo2.get("n", 0) >= 4 and vo2.get("slope_per_day") is not None:
        s = vo2["slope_per_day"]
        if s > 0.002:
            bullets.append(
                {
                    "key": "vo2_trend_up",
                    "tone": "good",
                    "title": "VO2max-Verlauf",
                    "body": "Im gewählten Fenster steigt der erfasste VO2max tendenziell — häufig mit Trainingsreiz oder besserer Datenlage vereinbar.",
                }
            )
        elif s < -0.002:
            bullets.append(
                {
                    "key": "vo2_trend_down",
                    "tone": "warn",
                    "title": "VO2max-Verlauf",
                    "body": "VO2max zeigt im Fenster einen fallenden Trend — kann z. B. durch Pause, Krankheit oder Messrauschen entstehen; Verlauf beobachten.",
                }
            )

    # Ruhepuls: letzte 7 vs davor (wenn genug Punkte)
    rhr = series.get("resting_hr")
    if rhr and rhr.get("points"):
        pts = rhr["points"]
        if len(pts) >= 10:
            last7 = [p["value"] for p in pts[-7:]]
            before = [p["value"] for p in pts[:-7][-14:]] if len(pts) > 7 else []
            if before:
                m7 = statistics.mean(last7)
                mb = statistics.mean(before)
                diff = m7 - mb
                if diff > 3:
                    bullets.append(
                        {
                            "key": "rhr_short_high",
                            "tone": "warn",
                            "title": "Ruhepuls zuletzt höher",
                            "body": f"Die letzten 7 Messungen liegen im Mittel ca. {diff:.1f} bpm über dem vorangehenden Fenster — kann mit Belastung, Stress, Schlaf oder Infekt zusammenhängen.",
                        }
                    )
                elif diff < -3:
                    bullets.append(
                        {
                            "key": "rhr_short_low",
                            "tone": "good",
                            "title": "Ruhepuls zuletzt niedriger",
                            "body": "Der Ruhepuls liegt im kurzen Vergleich unter dem vorherigen Mittel — oft mit Entlastung oder besserer Regeneration vereinbar (individuell).",
                        }
                    )
        if rhr.get("stdev") is not None and rhr["n"] >= 6:
            bullets.append(
                {
                    "key": "rhr_var",
                    "tone": "neutral",
                    "title": "Schwankung Ruhepuls",
                    "body": f"Standardabweichung im Fenster ca. {rhr['stdev']} bpm — kurzfristige Schwankungen sind normal; extreme Sprünge mit Kontext (Training, Schlaf) betrachten.",
                }
            )

    # HRV: Varianz-Hinweis
    hrv_s = series.get("hrv")
    if hrv_s and hrv_s.get("stdev") and hrv_s["n"] >= 6:
        bullets.append(
            {
                "key": "hrv_var",
                "tone": "neutral",
                "title": "HRV-Schwankung",
                "body": f"HRV schwankt im Fenster (σ ≈ {hrv_s['stdev']} ms). Vergleich mit der eigenen Basis ist aussagekräftiger als Einzelwerte.",
            }
        )

    # Belastung (Activity) vs Ruhepuls am Folgetag
    with get_db() as conn:
        cur = get_cursor(conn)
        load_by_d = _daily_training_load(cur, profile_id, cutoff)
        rhr_by_d = _rhr_by_date(cur, profile_id, cutoff)

    pairs_load: List[float] = []
    pairs_rhr: List[float] = []
    for d_str, load_min in load_by_d.items():
        try:
            d0 = datetime.fromisoformat(d_str[:10]).date()
        except ValueError:
            continue
        d1 = (d0 + timedelta(days=1)).isoformat()
        if d1 in rhr_by_d and load_min > 0:
            pairs_load.append(load_min)
            pairs_rhr.append(rhr_by_d[d1])

    r_pearson = _pearson(pairs_load, pairs_rhr) if len(pairs_load) >= 8 else None
    if r_pearson is not None:
        if r_pearson > 0.35:
            bullets.append(
                {
                    "key": "load_rhr_pos",
                    "tone": "warn",
                    "title": "Belastung und Ruhepuls (Folgetag)",
                    "body": "An Tagen nach höherer Trainingsdauer (Minuten-Summe) steigt der Ruhepuls am nächsten Morgen in deinen Daten tendenziell — typisches Muster während Erholungsreaktion (kein Kausalbeweis).",
                }
            )
        elif r_pearson < -0.25:
            bullets.append(
                {
                    "key": "load_rhr_neg",
                    "tone": "neutral",
                    "title": "Belastung und Ruhepuls",
                    "body": "Es zeigt sich ein leicht negatives Zusammenspiel zwischen Tages-Belastung und Folge-Ruhepuls in diesem Fenster — stark von Datenlage und Ausreißern abhängig.",
                }
            )

    if not series:
        return {
            "chart_type": "vitals_dashboard",
            "window_days": days,
            "series": {},
            "analytics": {"bullets": []},
            "metadata": {
                "confidence": "insufficient",
                "message": "Keine Vital-Zeitreihen im Fenster",
            },
        }

    return {
        "chart_type": "vitals_dashboard",
        "window_days": days,
        "series": serialize_dates(series),
        "analytics": {"bullets": bullets},
        "metadata": {
            "confidence": "medium",
            "note": "Deskriptive Auswertung; keine medizinische Diagnose.",
            "load_rhr_pairs_n": len(pairs_load),
            "load_rhr_correlation": round(r_pearson, 3) if r_pearson is not None else None,
        },
    }