281 lines
10 KiB
Python
281 lines
10 KiB
Python
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"""
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Vitalwerte: Zeitreihen + einfache Fitness-/Recovery-Einordnung (Layer 1, Issue 53).
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Keine Diagnose — deskriptive Trends, Korrelationen und Varianz-Hinweise.
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"""
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from __future__ import annotations
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import statistics
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Sequence, Tuple
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from db import get_db, get_cursor
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from data_layer.utils import safe_float, serialize_dates
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SERIES_CONFIG = (
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("resting_hr", "Ruhepuls", "bpm", "#3B82F6"),
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("hrv", "HRV", "ms", "#1D9E75"),
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("vo2_max", "VO2max", "ml/kg/min", "#8B5CF6"),
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("spo2", "SpO2", "%", "#0EA5E9"),
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("respiratory_rate", "Atemfrequenz", "/min", "#F59E0B"),
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)
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def _date_to_ord(d: Any) -> float:
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if hasattr(d, "toordinal"):
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return float(d.toordinal())
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if isinstance(d, str):
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return float(datetime.fromisoformat(d[:10]).date().toordinal())
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return 0.0
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def _linear_slope(dates: Sequence[Any], values: Sequence[float]) -> float:
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if len(values) < 3 or len(dates) != len(values):
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return 0.0
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xs = [_date_to_ord(d) for d in dates]
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ys = list(values)
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n = len(xs)
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mx = sum(xs) / n
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my = sum(ys) / n
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den = sum((x - mx) ** 2 for x in xs)
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if den < 1e-9:
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return 0.0
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return sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / den
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def _pearson(xs: Sequence[float], ys: Sequence[float]) -> Optional[float]:
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n = len(xs)
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if n < 5 or len(ys) != n:
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return None
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mx = statistics.mean(xs)
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my = statistics.mean(ys)
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sx = statistics.pstdev(xs) if n > 1 else 0.0
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sy = statistics.pstdev(ys) if n > 1 else 0.0
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if sx < 1e-9 or sy < 1e-9:
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return None
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cov = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) / n
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return cov / (sx * sy)
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def _daily_training_load(cur: Any, profile_id: str, cutoff: str) -> Dict[str, float]:
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"""Summe Trainingsminuten pro Kalendertag als Belastungs-Proxy."""
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cur.execute(
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"""
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SELECT date::text AS d, COALESCE(SUM(duration_min), 0)::float AS minutes
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FROM activity_log
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WHERE profile_id = %s AND date >= %s::date AND duration_min IS NOT NULL AND duration_min > 0
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GROUP BY date
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ORDER BY date
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""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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return {r["d"]: float(r["minutes"]) for r in rows}
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def _rhr_by_date(cur: Any, profile_id: str, cutoff: str) -> Dict[str, float]:
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cur.execute(
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"""
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SELECT date::text AS d, resting_hr::float AS rhr
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FROM vitals_baseline
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WHERE profile_id = %s AND date >= %s::date AND resting_hr IS NOT NULL
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ORDER BY date
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""",
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(profile_id, cutoff),
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)
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return {r["d"]: float(r["rhr"]) for r in cur.fetchall()}
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def build_vitals_history_and_analytics(profile_id: str, days: int) -> Dict[str, Any]:
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"""
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Zeitreihen pro Kennzahl (eigene Einheit / eigene Skala im Frontend) + Kurz-Analytik.
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"""
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if days < 7:
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days = 7
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if days > 365:
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days = 365
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""
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SELECT date, resting_hr, hrv, vo2_max, spo2, respiratory_rate
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FROM vitals_baseline
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WHERE profile_id = %s AND date >= %s
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ORDER BY date ASC
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""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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series: Dict[str, Any] = {}
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for key, label_de, unit, color in SERIES_CONFIG:
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pts: List[Dict[str, Any]] = []
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dates: List[Any] = []
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vals: List[float] = []
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for r in rows:
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v = r.get(key)
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if v is None:
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continue
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fv = safe_float(v)
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d = r["date"]
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d_iso = d.isoformat() if hasattr(d, "isoformat") else str(d)[:10]
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pts.append({"date": d_iso, "value": round(fv, 2)})
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dates.append(d)
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vals.append(fv)
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if pts:
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series[key] = {
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"key": key,
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"label_de": label_de,
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"unit": unit,
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"color": color,
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"points": pts,
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"n": len(pts),
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"last": vals[-1] if vals else None,
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"mean": round(statistics.mean(vals), 2) if len(vals) >= 1 else None,
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"stdev": round(statistics.pstdev(vals), 2) if len(vals) >= 2 else None,
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"slope_per_day": round(_linear_slope(dates, vals), 6) if len(vals) >= 3 else None,
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}
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bullets: List[Dict[str, Any]] = []
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# VO2max-Trend
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vo2 = series.get("vo2_max")
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if vo2 and vo2.get("n", 0) >= 4 and vo2.get("slope_per_day") is not None:
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s = vo2["slope_per_day"]
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if s > 0.002:
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bullets.append(
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{
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"key": "vo2_trend_up",
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"tone": "good",
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"title": "VO2max-Verlauf",
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"body": "Im gewählten Fenster steigt der erfasste VO2max tendenziell — häufig mit Trainingsreiz oder besserer Datenlage vereinbar.",
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}
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)
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elif s < -0.002:
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bullets.append(
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{
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"key": "vo2_trend_down",
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"tone": "warn",
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"title": "VO2max-Verlauf",
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"body": "VO2max zeigt im Fenster einen fallenden Trend — kann z. B. durch Pause, Krankheit oder Messrauschen entstehen; Verlauf beobachten.",
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}
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)
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# Ruhepuls: letzte 7 vs davor (wenn genug Punkte)
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rhr = series.get("resting_hr")
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if rhr and rhr.get("points"):
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pts = rhr["points"]
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if len(pts) >= 10:
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last7 = [p["value"] for p in pts[-7:]]
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before = [p["value"] for p in pts[:-7][-14:]] if len(pts) > 7 else []
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if before:
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m7 = statistics.mean(last7)
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mb = statistics.mean(before)
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diff = m7 - mb
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if diff > 3:
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bullets.append(
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{
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"key": "rhr_short_high",
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"tone": "warn",
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"title": "Ruhepuls zuletzt höher",
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"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.",
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}
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)
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elif diff < -3:
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bullets.append(
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{
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"key": "rhr_short_low",
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"tone": "good",
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"title": "Ruhepuls zuletzt niedriger",
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"body": "Der Ruhepuls liegt im kurzen Vergleich unter dem vorherigen Mittel — oft mit Entlastung oder besserer Regeneration vereinbar (individuell).",
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}
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)
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if rhr.get("stdev") is not None and rhr["n"] >= 6:
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bullets.append(
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{
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"key": "rhr_var",
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"tone": "neutral",
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"title": "Schwankung Ruhepuls",
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"body": f"Standardabweichung im Fenster ca. {rhr['stdev']} bpm — kurzfristige Schwankungen sind normal; extreme Sprünge mit Kontext (Training, Schlaf) betrachten.",
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}
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)
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# HRV: Varianz-Hinweis
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hrv_s = series.get("hrv")
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if hrv_s and hrv_s.get("stdev") and hrv_s["n"] >= 6:
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bullets.append(
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{
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"key": "hrv_var",
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"tone": "neutral",
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"title": "HRV-Schwankung",
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"body": f"HRV schwankt im Fenster (σ ≈ {hrv_s['stdev']} ms). Vergleich mit der eigenen Basis ist aussagekräftiger als Einzelwerte.",
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}
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)
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# Belastung (Activity) vs Ruhepuls am Folgetag
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with get_db() as conn:
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cur = get_cursor(conn)
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load_by_d = _daily_training_load(cur, profile_id, cutoff)
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rhr_by_d = _rhr_by_date(cur, profile_id, cutoff)
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pairs_load: List[float] = []
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pairs_rhr: List[float] = []
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for d_str, load_min in load_by_d.items():
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try:
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d0 = datetime.fromisoformat(d_str[:10]).date()
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except ValueError:
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continue
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d1 = (d0 + timedelta(days=1)).isoformat()
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if d1 in rhr_by_d and load_min > 0:
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pairs_load.append(load_min)
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pairs_rhr.append(rhr_by_d[d1])
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r_pearson = _pearson(pairs_load, pairs_rhr) if len(pairs_load) >= 8 else None
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if r_pearson is not None:
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if r_pearson > 0.35:
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bullets.append(
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{
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"key": "load_rhr_pos",
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"tone": "warn",
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"title": "Belastung und Ruhepuls (Folgetag)",
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"body": "An Tagen nach höherer Trainingsdauer (Minuten-Summe) steigt der Ruhepuls am nächsten Morgen in deinen Daten tendenziell — typisches Muster während Erholungsreaktion (kein Kausalbeweis).",
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}
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)
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elif r_pearson < -0.25:
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bullets.append(
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{
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"key": "load_rhr_neg",
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"tone": "neutral",
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"title": "Belastung und Ruhepuls",
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"body": "Es zeigt sich ein leicht negatives Zusammenspiel zwischen Tages-Belastung und Folge-Ruhepuls in diesem Fenster — stark von Datenlage und Ausreißern abhängig.",
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}
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)
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if not series:
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return {
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"chart_type": "vitals_dashboard",
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"window_days": days,
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"series": {},
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"analytics": {"bullets": []},
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"metadata": {
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"confidence": "insufficient",
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"message": "Keine Vital-Zeitreihen im Fenster",
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},
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}
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return {
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"chart_type": "vitals_dashboard",
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"window_days": days,
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"series": serialize_dates(series),
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"analytics": {"bullets": bullets},
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"metadata": {
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"confidence": "medium",
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"note": "Deskriptive Auswertung; keine medizinische Diagnose.",
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"load_rhr_pairs_n": len(pairs_load),
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"load_rhr_correlation": round(r_pearson, 3) if r_pearson is not None else None,
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},
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}
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