Lars
8cb5ad992f
- Updated the `build_vital_signs_matrix_chart_payload` function to accept optional keys for omitting specific snapshot data, improving flexibility in data presentation. - Enhanced the `build_recovery_dashboard_kpi_tiles` function to conditionally merge heart and autonomic tiles based on new parameters, refining the dashboard's insights. - Integrated new analytics features in the `RecoveryDashboardOverview` component, including consolidated paragraphs for better narrative context and visual representation of trends. - Improved the handling of vital signs data in the frontend, ensuring clearer messaging and enhanced user experience when displaying vital metrics.
300 lines
10 KiB
Python
300 lines
10 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 _build_consolidated_paragraphs(
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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[str]:
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"""Eine zusammenhängende Einordnung statt vieler einzelner Karten zu Puls/HRV/Basis."""
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paras: List[str] = []
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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 liegt gegenüber der älteren Referenz bei {hrv_vs_baseline_pct:+.1f} %".replace(".", ",")
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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 bei {rhr_vs_baseline_pct:+.1f} %".replace(".", ",")
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)
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if basis_bits:
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paras.append(
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" ".join(basis_bits)
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+ " (Vergleich kurzfristiges Mittel vs. ältere Basis — individuell interpretieren)."
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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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var_bits: List[str] = []
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if rhr and rhr.get("stdev") is not None and rhr.get("n", 0) >= 3:
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var_bits.append(f"Ruhepuls Schwankungsbreite im Fenster etwa σ = {rhr['stdev']} bpm")
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if hrv_s and hrv_s.get("stdev") is not None and hrv_s.get("n", 0) >= 3:
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var_bits.append(f"HRV etwa σ = {hrv_s['stdev']} ms")
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if var_bits:
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paras.append(
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"Einzelwerte können stark springen; die gestrichelte Linie zeigt einen gleitenden Mittelwert (max. 7 Messungen). "
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+ "Im Fenster: " + "; ".join(var_bits) + "."
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)
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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 abs(diff) > 3:
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paras.append(
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f"Kurzfristig liegt der Ruhepuls im Mittel der letzten 7 Messungen "
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f"{'über' if diff > 0 else 'unter'} dem vorherigen Fenster (Δ ca. {abs(diff):.1f} bpm) — Kontext: Belastung, Schlaf, Stress."
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)
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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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paras.append(
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"VO2max steigt im gewählten Fenster tendenziell — oft mit Trainingsreiz oder stabilen Messungen vereinbar."
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)
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elif s < -0.002:
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paras.append(
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"VO2max fällt im Fenster leicht — kann Pause, Krankheit oder Messrauschen widerspiegeln."
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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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paras.append(
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f"Korrelation Trainingsminuten (Tag) → Ruhepuls (Folgetag): r ≈ {r_pearson:.2f} (n = {pairs_n} Paare). "
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"Höhere Belastung und etwas höherer Ruhepuls am nächsten Morgen kommen in den Daten häufig zusammen — kein Kausalbeweis."
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)
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elif r_pearson < -0.25:
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paras.append(
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f"Zwischen Tages-Belastung und Folge-Ruhepuls zeigt sich ein leicht negatives Zusammenspiel (r ≈ {r_pearson:.2f}, n = {pairs_n}). "
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"Stark von Ausreißern und Datenlücken abhängig."
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)
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return [p for p in paras if p]
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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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consolidated = _build_consolidated_paragraphs(
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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": {"bullets": [], "consolidated_paragraphs": consolidated},
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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": consolidated,
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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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