Lars
b5c5f2f612
- Added new functions to build fitness dashboard visualizations, including weekly training volume and training type distribution charts. - Updated the `charts.py` router to include a new endpoint for the fitness dashboard, integrating data from activity metrics. - Refactored existing activity-related functions to improve modularity and maintainability. - Updated frontend components to reflect the new fitness terminology and integrate the fitness dashboard overview, enhancing user experience.
126 lines
3.9 KiB
Python
126 lines
3.9 KiB
Python
"""
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Layer 2b: Fitness-Hub — ein Bundle für die Aktivitäts-/Fitness-UI (Issue #53).
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Single Source: activity_metrics + dieselben Hilfsfunktionen wie Chart-Endpunkte A1/A2.
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"""
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from __future__ import annotations
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from typing import Any, Dict, Optional
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from db import get_db, get_cursor
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from data_layer.activity_metrics import (
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build_training_type_distribution_chart_payload,
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build_training_volume_chart_payload,
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calculate_activity_score,
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calculate_training_minutes_week,
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calculate_quality_sessions_pct,
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calculate_vo2max_trend_28d,
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get_activity_summary_data,
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)
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from data_layer.fitness_interpretation import build_fitness_dashboard_kpi_tiles
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from data_layer.scores import get_top_focus_area
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def _iso(d: Any) -> Optional[str]:
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if d is None:
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return None
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if hasattr(d, "isoformat"):
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return d.isoformat()[:10]
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return str(d)[:10]
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def _has_activity_entries(profile_id: str) -> bool:
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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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"SELECT 1 FROM activity_log WHERE profile_id=%s LIMIT 1",
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(profile_id,),
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)
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return cur.fetchone() is not None
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def _last_activity_date(profile_id: str) -> Optional[str]:
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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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"SELECT MAX(date) AS d FROM activity_log WHERE profile_id=%s",
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(profile_id,),
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)
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row = cur.fetchone()
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if not row or row["d"] is None:
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return None
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return _iso(row["d"])
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def get_fitness_dashboard_viz_bundle(profile_id: str, days: int) -> Dict[str, Any]:
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"""
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Bundle für Fitness-Übersicht: KPI-Kacheln + eingebettete Chart-Payloads (Chart.js-Format).
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``days``: Analysefenster für Zusammenfassung; >=9999 = lange Historie (max. 3650 Tage).
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"""
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if not _has_activity_entries(profile_id):
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return {
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"confidence": "insufficient",
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"has_activity_entries": False,
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"message": "Noch keine Aktivitätsdaten",
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"kpi_tiles": [],
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"summary": {},
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"charts": {},
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"meta": {"layer_1": "activity_metrics", "layer_2b": "fitness_viz"},
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}
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all_history = days >= 9999
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eff_days = 3650 if all_history else max(7, min(int(days), 3650))
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summary = get_activity_summary_data(profile_id, eff_days)
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weeks_vol = max(4, min(52, (min(eff_days, 365) + 6) // 7))
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dist_days = min(90, max(7, min(eff_days, 365)))
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volume_chart = build_training_volume_chart_payload(profile_id, weeks_vol)
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type_chart = build_training_type_distribution_chart_payload(profile_id, dist_days)
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quality_days = dist_days
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quality_pct = calculate_quality_sessions_pct(profile_id, quality_days)
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minutes_7d = calculate_training_minutes_week(profile_id)
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activity_score = calculate_activity_score(profile_id)
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vo2_trend = calculate_vo2max_trend_28d(profile_id)
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top_focus = get_top_focus_area(profile_id)
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kpi_tiles = build_fitness_dashboard_kpi_tiles(
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summary,
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minutes_7d,
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quality_pct,
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quality_days,
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activity_score,
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vo2_trend,
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top_focus,
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)
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conf = summary.get("confidence") or "medium"
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if summary.get("activity_count", 0) == 0:
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conf = "insufficient"
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return {
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"confidence": conf,
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"has_activity_entries": True,
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"days_requested": days,
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"effective_window_days": eff_days,
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"training_volume_weeks_used": weeks_vol,
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"training_type_dist_days_used": dist_days,
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"last_updated": _last_activity_date(profile_id),
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"summary": summary,
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"kpi_tiles": kpi_tiles,
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"interpretation_tiles": [],
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"charts": {
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"training_volume": volume_chart,
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"training_type_distribution": type_chart,
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},
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"meta": {
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"layer_1": "activity_metrics",
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"layer_2b": "fitness_viz",
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"issue": "53-layer-2b-fitness",
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},
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}
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