Lars
61738cecb7
- Added an `include_avg_sleep_kpi` parameter to the `build_recovery_dashboard_kpi_tiles` function to conditionally include average sleep data in the dashboard. - Updated the `get_recovery_dashboard_viz_bundle` function to pass the new parameter, ensuring flexibility in data presentation. - Refactored the insights generation in the `vitals_fitness_insights.py` file to utilize a new structured approach for better organization of heart and VO2 insights. - Introduced new components in the frontend for displaying insights, improving the user experience and clarity of vital metrics.
401 lines
14 KiB
Python
401 lines
14 KiB
Python
"""
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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
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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 _trailing_window_means(vals: List[float], window: int = 7) -> List[float]:
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"""Gleitender Mittelwert über die letzten bis zu `window` aufeinanderfolgenden Messungen (nicht Kalendertage)."""
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out: List[float] = []
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for i in range(len(vals)):
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chunk = vals[max(0, i - window + 1) : i + 1]
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out.append(round(statistics.mean(chunk), 2))
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return out
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def _de_num(x: float) -> str:
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"""Dezimalzahl mit Komma für Fließtext."""
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return f"{x:.1f}".replace(".", ",")
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def _de_num_signed(x: float) -> str:
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"""Wie _de_num, mit explizitem Vorzeichen (für %-Abweichungen)."""
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return f"{x:+.1f}".replace(".", ",")
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def _ins(
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key: str,
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section: str,
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title_de: str,
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body: str,
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tone: str = "neutral",
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) -> Dict[str, Any]:
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"""Ein strukturierter Hinweis für UI-Platzierung (section: heart | vo2)."""
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return {"key": key, "section": section, "title_de": title_de, "body": body, "tone": tone}
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def _build_section_insights(
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series: Dict[str, Any],
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hrv_vs_baseline_pct: Optional[float],
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rhr_vs_baseline_pct: Optional[float],
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r_pearson: Optional[float],
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pairs_n: int,
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) -> List[Dict[str, Any]]:
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"""
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Gleiche Inhalte wie früher konsolidierter Fließtext, aber nach UI-Bereich getrennt.
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section: heart = Herz/Kreislauf/Training-Folge; vo2 = VO2max-Verlauf.
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"""
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out: List[Dict[str, Any]] = []
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basis_bits: List[str] = []
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if hrv_vs_baseline_pct is not None:
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basis_bits.append(
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f"HRV gegenüber älterer Referenz: {_de_num_signed(float(hrv_vs_baseline_pct))} %"
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)
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if rhr_vs_baseline_pct is not None:
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basis_bits.append(
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f"Ruhepuls relativ zur Referenz: {_de_num_signed(float(rhr_vs_baseline_pct))} %"
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)
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if basis_bits:
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out.append(
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_ins(
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"heart_baseline",
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"heart",
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"Kurzfristiges Mittel vs. ältere Basis",
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" ".join(basis_bits)
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+ " — Vergleich letzter Tage zum älteren Referenzmittel; individuell interpretieren (keine Diagnose).",
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"neutral",
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)
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)
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rhr = series.get("resting_hr")
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hrv_s = series.get("hrv")
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rhr_short_body = ""
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r_short_tone = "neutral"
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if rhr and rhr.get("points") and len(rhr["points"]) >= 10:
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pts = rhr["points"]
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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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rhr_short_body = (
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f"Die letzten 7 Messungen liegen im Mittel ca. {_de_num(diff)} bpm über dem vorangehenden Fenster — "
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"kann mit Belastung, Stress, Schlaf oder Infekt zusammenhängen."
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)
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r_short_tone = "warn"
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elif diff < -3:
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rhr_short_body = (
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"Der Ruhepuls liegt im kurzen Vergleich unter dem vorherigen Mittel — oft mit Entlastung oder "
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"besserer Regeneration vereinbar (individuell)."
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)
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r_short_tone = "good"
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rhr_var_sentence = ""
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if rhr and rhr.get("stdev") is not None and rhr.get("n", 0) >= 6:
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rhr_var_sentence = (
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f"Ruhepuls: Standardabweichung im Fenster ca. {_de_num(float(rhr['stdev']))} bpm — kurzfristige Schwankungen "
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"sind normal; extreme Sprünge mit Kontext (Training, Schlaf) betrachten."
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)
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hrv_var_sentence = ""
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if hrv_s and hrv_s.get("stdev") is not None and hrv_s.get("n", 0) >= 6:
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hrv_var_sentence = (
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f"HRV: σ im Fenster ca. {_de_num(float(hrv_s['stdev']))} ms — "
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"Vergleich mit der eigenen Basis ist aussagekräftiger als Einzelwerte."
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)
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ma_hint = (
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"Einzelwerte können stark springen; die gestrichelte Linie in den Verläufen zeigt einen gleitenden Mittelwert "
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"über bis zu sieben aufeinanderfolgende Messungen (nicht Kalendertage)."
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)
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streuung_parts: List[str] = [ma_hint]
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if rhr_var_sentence:
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streuung_parts.append(rhr_var_sentence)
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if hrv_var_sentence:
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streuung_parts.append(hrv_var_sentence)
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if rhr or hrv_s:
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out.append(
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_ins(
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"heart_streuung_ma",
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"heart",
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"Streuung & gleitender Mittelwert",
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" ".join(streuung_parts),
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"neutral",
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)
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)
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if rhr_short_body:
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out.append(_ins("heart_rhr_kurz", "heart", "Ruhepuls: Kurzvergleich", rhr_short_body, r_short_tone))
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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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out.append(
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_ins(
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"vo2_trend_up",
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"vo2",
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"VO2max-Verlauf",
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"Im gewählten Fenster steigt der erfasste VO2max tendenziell — häufig mit Trainingsreiz oder "
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"besserer Datenlage vereinbar.",
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"good",
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)
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)
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elif s < -0.002:
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out.append(
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_ins(
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"vo2_trend_down",
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"vo2",
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"VO2max-Verlauf",
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"VO2max zeigt im Fenster einen fallenden Trend — kann z. B. durch Pause, Krankheit oder Messrauschen "
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"entstehen; Verlauf beobachten.",
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"warn",
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)
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)
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if r_pearson is not None and pairs_n >= 8:
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if r_pearson > 0.35:
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out.append(
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_ins(
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"heart_load_rhr",
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"heart",
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"Training und Folge-Ruhepuls",
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(
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"An Tagen nach höherer Trainingsdauer (Minuten-Summe) steigt der Ruhepuls am nächsten Morgen in deinen "
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"Daten tendenziell — typisches Muster während Erholungsreaktion (kein Kausalbeweis). "
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f"Korrelation (Trainingsminuten am Tag → Ruhepuls am Folgetag): r ≈ {r_pearson:.2f} bei n = {pairs_n} Paaren."
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),
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"warn",
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)
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)
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elif r_pearson < -0.25:
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out.append(
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_ins(
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"heart_load_rhr_neg",
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"heart",
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"Training und Folge-Ruhepuls",
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"Es zeigt sich ein leicht negatives Zusammenspiel zwischen Tages-Belastung und Folge-Ruhepuls in diesem "
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f"Fenster — stark von Datenlage und Ausreißern abhängig. r ≈ {r_pearson:.2f}, n = {pairs_n} Paare.",
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"neutral",
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)
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)
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return out
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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(
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profile_id: str,
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days: int,
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hrv_vs_baseline_pct: Optional[float] = None,
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rhr_vs_baseline_pct: Optional[float] = None,
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) -> Dict[str, Any]:
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"""
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Zeitreihen pro Kennzahl (eigene Einheit / eigene Skala im Frontend) + zusammengefasste Einordnung.
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Optional: Abweichung HRV/Ruhepuls zur älteren Basis — für einen Absatz statt doppelter KPI-Texte.
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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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ma_vals = _trailing_window_means(vals, window=7)
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points_ma7 = [
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{"date": pts[i]["date"], "value": ma_vals[i]} for i in range(len(pts))
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]
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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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"points_ma7": points_ma7,
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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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# 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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pairs_n = len(pairs_load)
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section_insights = _build_section_insights(
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series,
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hrv_vs_baseline_pct,
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rhr_vs_baseline_pct,
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r_pearson,
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pairs_n,
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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": {
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"bullets": [],
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"consolidated_paragraphs": [],
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"section_insights": section_insights,
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},
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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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"load_rhr_pairs_n": pairs_n,
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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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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": {
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"bullets": [],
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"consolidated_paragraphs": [],
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"section_insights": section_insights,
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},
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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": pairs_n,
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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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