Lars
bf84e3c2a5
- Refactored the `calculate_proxy_internal_load_7d` function to `calculate_proxy_internal_load_window`, allowing for dynamic day range input. - Introduced new functions for calculating training volume deltas and building fitness progress insights, enhancing user feedback on training metrics. - Updated the fitness dashboard to include new charts for quality sessions and load monitoring, improving data visualization. - Integrated these new metrics into the fitness dashboard overview, providing users with comprehensive insights into their training performance. - Streamlined the router to utilize the new chart-building functions, ensuring consistency and maintainability across the application.
284 lines
9.7 KiB
Python
284 lines
9.7 KiB
Python
"""
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KPI-Kacheln für Layer-2b Fitness-Dashboard (Issue #53).
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Ausgabe für KpiTilesOverview; ``keys`` = Platzhalter-Registry-Referenzen.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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def _verdict(status: str) -> str:
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if status == "good":
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return "Gut"
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if status == "warn":
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return "Hinweis"
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return "Achtung"
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def _minutes_status(minutes: Optional[int]) -> str:
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if minutes is None:
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return "warn"
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if 150 <= minutes <= 300:
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return "good"
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if minutes < 150:
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return "warn" if minutes >= 90 else "bad"
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return "warn"
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def _quality_status(pct: Optional[int]) -> str:
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if pct is None:
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return "warn"
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if pct >= 60:
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return "good"
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if pct >= 40:
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return "warn"
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return "bad"
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def _score_status(score: Optional[int]) -> str:
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if score is None:
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return "warn"
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if score >= 70:
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return "good"
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if score >= 50:
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return "warn"
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return "bad"
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def _vo2_status(trend: Optional[float]) -> str:
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if trend is None:
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return "warn"
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if trend > 0.5:
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return "good"
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if trend >= -0.5:
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return "warn"
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return "bad"
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def _vol_delta_status(delta_pct: Optional[float], prior7: int, last7: int) -> str:
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if delta_pct is None:
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if last7 > 0 and prior7 == 0:
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return "good"
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return "warn"
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if delta_pct >= 5:
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return "good"
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if delta_pct >= -10:
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return "warn"
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return "bad"
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def build_fitness_progress_insights(
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vol_delta: Dict[str, Any],
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load_meta: Dict[str, Any],
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quality_pct: Optional[int],
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) -> List[Dict[str, Any]]:
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"""
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Kurz-Aussagen für die UI (Layer 2b), keine zweite Datenquelle.
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"""
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out: List[Dict[str, Any]] = []
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if vol_delta.get("has_data"):
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last7 = int(vol_delta.get("last7_min") or 0)
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prev7 = int(vol_delta.get("prior7_min") or 0)
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d = vol_delta.get("delta_pct")
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if d is not None:
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sign = "+" if d > 0 else ""
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body = (
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f"Trainingsminuten letzte 7 Tage ({last7} min) vs. Vorwoche ({prev7} min): "
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f"{sign}{d} %."
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)
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elif last7 > 0 and prev7 == 0:
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body = f"Mehr Volumen als in der Vorwoche: zuletzt {last7} min (Vorwoche 0 min)."
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else:
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body = "Zu wenig Daten für einen Vorwochen-Vergleich."
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out.append(
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{
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"key": "ins_vol_trend",
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"tone": _vol_delta_status(
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float(d) if d is not None else None, prev7, last7
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),
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"title": "Volumen-Trend",
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"body": body,
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}
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)
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acwr = load_meta.get("acwr")
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st = load_meta.get("acwr_status")
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if acwr is not None and isinstance(load_meta, dict) and load_meta.get("data_points", 0) > 0:
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if st == "optimal":
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tone = "good"
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hint = "Akute zu chronischer Last (ACWR) liegt im oft empfohlenen Bereich (ca. 0,8–1,3)."
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else:
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tone = "warn"
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hint = (
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"ACWR außerhalb des häufig genannten Zielkorridors — bei anhaltender Belastung "
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"Erholung oder Volumen prüfen (Proxy-Modell)."
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)
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out.append(
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{
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"key": "ins_acwr",
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"tone": tone,
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"title": "Belastungsverhältnis (ACWR)",
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"body": f"Verhältnis akut (7 Tage) zu chronisch (28 Tage): {float(acwr):.2f}. {hint}",
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}
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)
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if quality_pct is not None:
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tone = "good" if quality_pct >= 60 else "warn" if quality_pct >= 40 else "bad"
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out.append(
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{
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"key": "ins_quality",
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"tone": tone,
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"title": "Session-Qualität",
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"body": f"{quality_pct} % der Sessions sind als «gut» oder besser eingestuft — Grundlage für progressive Belastung.",
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}
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)
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return out
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def build_fitness_dashboard_kpi_tiles(
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summary: Dict[str, Any],
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minutes_7d: Optional[int],
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quality_pct: Optional[int],
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quality_window_days: int,
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activity_score: Optional[int],
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vo2_trend: Optional[float],
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top_focus: Optional[Dict[str, Any]],
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vol_delta: Optional[Dict[str, Any]] = None,
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) -> List[Dict[str, Any]]:
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spw = summary.get("sessions_per_week")
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try:
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spw_f = float(spw) if spw is not None else None
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except (TypeError, ValueError):
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spw_f = None
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spw_s = f"{spw_f:.1f}".replace(".", ",") if spw_f is not None else "—"
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m_status = _minutes_status(minutes_7d)
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q_status = _quality_status(quality_pct)
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s_status = _score_status(activity_score)
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v_status = _vo2_status(vo2_trend)
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tiles: List[Dict[str, Any]] = []
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if vol_delta and vol_delta.get("has_data"):
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d = vol_delta.get("delta_pct")
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last7 = int(vol_delta.get("last7_min") or 0)
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prev7 = int(vol_delta.get("prior7_min") or 0)
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if d is not None:
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sign = "+" if float(d) > 0 else ""
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v_s = f"{sign}{d:.1f} %".replace(".", ",")
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sub = f"{last7} min vs. {prev7} min (7-Tage-Fenster)"
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elif last7 > 0 and prev7 == 0:
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v_s = "neu"
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sub = f"{last7} min letzte Woche"
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else:
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v_s = "—"
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sub = "Vergleich Vorwoche"
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vd_st = _vol_delta_status(float(d) if d is not None else None, prev7, last7)
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tiles.append(
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{
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"key": "volume_vs_prior_week",
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"category": "Volumen vs. Vorwoche",
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"icon": "📈",
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"value": v_s,
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"sublabel": sub,
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"status": vd_st,
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"verdict": _verdict(vd_st),
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"hoverTop": "Fortschritt Trainingsminuten",
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"hoverBody": "Letzte 7 Kalendertage vs. die 7 Tage davor (activity_log).",
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"keys": ["training_minutes_week", "activity_summary"],
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}
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)
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tiles.extend(
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[
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{
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"key": "minutes_week",
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"category": "Minuten (7 Tage)",
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"icon": "⏱",
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"value": f"{minutes_7d} min" if minutes_7d is not None else "—",
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"sublabel": "WHO: 150–300 min/Woche",
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"status": m_status,
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"verdict": _verdict(m_status),
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"hoverTop": "Summe Trainingsminuten (letzte 7 Tage)",
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"hoverBody": "Gleiche Quelle wie Platzhalter training_minutes_week.",
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"keys": ["training_minutes_week", "activity_score"],
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},
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{
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"key": "sessions_per_week",
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"category": "Sessions / Woche",
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"icon": "📅",
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"value": spw_s,
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"sublabel": f"Fenster: {summary.get('days_analyzed', '—')} Tage",
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"status": "good",
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"verdict": "Gut",
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"hoverTop": "Durchschnittliche Sessions pro Woche",
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"hoverBody": "Aus activity_summary (activity_log im gewählten Zeitraum).",
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"keys": ["activity_summary"],
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},
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{
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"key": "quality_pct",
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"category": "Qualitätssessions",
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"icon": "✓",
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"value": f"{quality_pct} %" if quality_pct is not None else "—",
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"sublabel": f"Anteil «gut+» · {quality_window_days} Tage",
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"status": q_status,
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"verdict": _verdict(q_status),
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"hoverTop": "Anteil Sessions mit guter Qualitätslabel-Klassifikation",
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"hoverBody": "Entspricht quality_sessions_pct (Fenster wie gewählt).",
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"keys": ["quality_sessions_pct"],
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},
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{
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"key": "activity_score",
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"category": "Activity-Score",
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"icon": "🎯",
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"value": str(activity_score) if activity_score is not None else "—",
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"sublabel": "Ausrichtung an gewichteten Fokusbereichen",
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"status": s_status,
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"verdict": _verdict(s_status) if activity_score is not None else "Hinweis",
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"hoverTop": "Gewichteter Score (0–100)",
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"hoverBody": "Ohne gewichtete Aktivitäts-Fokusbereiche kein Score.",
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"keys": ["activity_score"],
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},
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{
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"key": "vo2_trend",
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"category": "VO₂max-Trend",
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"icon": "🫁",
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"value": f"{vo2_trend:+.1f}" if vo2_trend is not None else "—",
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"sublabel": "28-Tage-Trend (geschätzt)",
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"status": v_status,
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"verdict": _verdict(v_status) if vo2_trend is not None else "Hinweis",
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"hoverTop": "Trend der VO₂max-Schätzung aus Aktivitätsdaten",
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"hoverBody": "Wie vo2max_trend_28d im Data Layer.",
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"keys": ["vo2max_trend_28d"],
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},
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]
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)
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if top_focus:
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prog = top_focus.get("progress")
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prog_s = f"{prog} %" if prog is not None else "—"
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w = top_focus.get("weight")
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try:
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w_s = f"{float(w):.0f} %" if w is not None else "—"
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except (TypeError, ValueError):
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w_s = "—"
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tiles.append(
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{
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"key": "top_focus",
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"category": "Schwerpunkt-Fokus",
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"icon": "🔭",
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"value": str(top_focus.get("label") or "—"),
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"sublabel": f"Fortschritt {prog_s} · Gewicht {w_s}",
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"status": "good",
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"verdict": "Gut",
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"hoverTop": "Höchstgewichteter Fokusbereich",
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"hoverBody": "Aus focus_area_definitions + Nutzer-Gewichtungen.",
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"keys": ["top_focus_area_name", "top_focus_area_progress"],
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}
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)
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return tiles
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