Lars
d7304c1a44
- Added `get_energy_availability_warning_payload` function to assess energy availability and provide contextual warnings based on multiple health indicators. - Integrated energy availability KPI tile into the nutrition history visualization, enhancing user insights on energy balance. - Updated frontend components to conditionally display the energy availability warning, improving user experience and data interpretation. - Refactored existing logic in `charts.py` to utilize the new energy availability functionality, streamlining data handling.
220 lines
8.0 KiB
Python
220 lines
8.0 KiB
Python
"""
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Interpretation + KPI-Kacheln für Layer 2b Ernährungs-Verlauf.
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Gleiche Schwellen wie zuvor im Frontend (History.jsx); Ausgabe strukturiert
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für KpiTilesOverview (keys = related_placeholder_keys).
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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 build_nutrition_history_kpi_tiles(
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navg: Dict[str, Any],
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targets: Dict[str, Any],
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date_span_label: str,
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n_days_with_entries: int,
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) -> List[Dict[str, Any]]:
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"""
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KPI-Kacheln wie buildNutritionKpiTiles im Frontend (Kalorien/KH/Fett + Regeln).
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"""
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kcal_avg = round(float(navg.get("kcal_avg") or 0))
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avg_carbs = round(float(navg.get("carbs_avg") or 0) * 10) / 10
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avg_fat = round(float(navg.get("fat_avg") or 0) * 10) / 10
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avg_protein = round(float(navg.get("protein_avg") or 0) * 10) / 10
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pt_low = round(float(targets.get("protein_target_low") or 0))
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pt_high = round(float(targets.get("protein_target_high") or 0))
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targets_ok = targets.get("confidence") != "insufficient" and pt_low > 0
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protein_ok = targets_ok and avg_protein >= pt_low
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total_macro_kcal = avg_protein * 4 + avg_carbs * 4 + avg_fat * 9
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prot_pct = (
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round(avg_protein * 4 / total_macro_kcal * 100)
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if total_macro_kcal > 0
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else 0
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)
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kh_pct = (
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round(avg_carbs * 4 / total_macro_kcal * 100)
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if total_macro_kcal > 0
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else 0
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)
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fat_pct = (
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round(avg_fat * 9 / total_macro_kcal * 100)
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if total_macro_kcal > 0
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else 0
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)
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tiles: List[Dict[str, Any]] = [
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{
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"key": "kcal",
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"category": "Kalorien (Ø)",
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"icon": "🔥",
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"value": f"{kcal_avg} kcal",
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"sublabel": date_span_label,
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"status": "good",
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"verdict": "Gut",
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"hoverTop": "Durchschnittliche tägliche Energie",
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"hoverBody": f"Mittel über {n_days_with_entries} Tage mit Ernährungseinträgen im gewählten Zeitraum.",
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"keys": ["nutrition_score"],
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},
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{
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"key": "carbs",
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"category": "KH (Ø)",
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"icon": "🌾",
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"value": f"{avg_carbs} g",
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"sublabel": "Kohlenhydrate / Tag",
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"status": "good",
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"verdict": "Gut",
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"hoverTop": "Durchschnittliche Kohlenhydrate",
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"hoverBody": "Summe der täglichen Werte im Zeitraum, gemittelt.",
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"keys": ["nutrition_summary"],
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},
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{
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"key": "fat",
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"category": "Fett (Ø)",
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"icon": "🧈",
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"value": f"{avg_fat} g",
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"sublabel": "Fett / Tag",
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"status": "good",
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"verdict": "Gut",
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"hoverTop": "Durchschnittliches Fett",
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"hoverBody": "Summe der täglichen Werte im Zeitraum, gemittelt.",
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"keys": ["nutrition_summary"],
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},
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]
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if not targets_ok:
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tiles.append(
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{
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"key": "eval-protein",
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"category": "Protein",
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"icon": "🥩",
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"value": f"{avg_protein}g",
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"sublabel": "Referenzgewicht fehlt",
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"status": "warn",
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"verdict": _verdict("warn"),
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"hint": "Ohne aktuelles Körpergewicht lässt sich das Protein-Ziel (g/kg) nicht bewerten.",
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"hoverTop": "Protein-Ziel nicht berechenbar",
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"hoverBody": "Für 1,6–2,2 g/kg wird ein aktuelles Körpergewicht benötigt.",
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"keys": ["protein_adequacy"],
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}
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)
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elif not protein_ok:
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miss = max(0, pt_low - round(avg_protein))
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tiles.append(
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{
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"key": "eval-protein",
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"category": "Protein",
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"icon": "🥩",
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"value": f"{avg_protein}g",
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"sublabel": f"Unterversorgung: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
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"status": "bad",
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"verdict": _verdict("bad"),
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"hint": (
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f"~{miss} g Protein/Tag fehlen – bei Defizit Muskelerhalt gefährdet."
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),
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"hoverTop": f"Unterversorgung: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
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"hoverBody": (
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f"1,6–2,2g/kg KG. Fehlend: ~{miss}g täglich. "
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"Konsequenz: Muskelverlust bei Defizit."
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),
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"keys": ["protein_adequacy", "nutrition_score"],
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}
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)
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else:
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tiles.append(
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{
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"key": "eval-protein",
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"category": "Protein",
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"icon": "🥩",
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"value": f"{avg_protein}g",
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"sublabel": f"Gut: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
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"status": "good",
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"verdict": _verdict("good"),
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"hoverTop": f"Gut: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
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"hoverBody": "Ausreichend für Muskelerhalt und -aufbau.",
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"keys": ["protein_adequacy", "nutrition_score"],
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}
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)
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if prot_pct < 20 and total_macro_kcal > 0:
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tiles.append(
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{
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"key": "eval-macro-pct",
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"category": "Makro-Anteil",
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"icon": "📊",
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"value": f"{prot_pct}%",
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"sublabel": f"Protein-Anteil niedrig: {prot_pct}% der Kalorien",
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"status": "warn",
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"verdict": _verdict("warn"),
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"hint": (
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f"Protein-Kalorienanteil niedrig (P {prot_pct} % / KH {kh_pct} % / F {fat_pct} %); "
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"Ziel oft 25–35 %."
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),
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"hoverTop": f"Protein-Anteil niedrig: {prot_pct}% der Kalorien",
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"hoverBody": (
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f"Empfehlung oft 25–35%. Aktuell: {prot_pct}% P / {kh_pct}% KH / {fat_pct}% F"
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),
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"keys": ["nutrition_summary"],
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}
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)
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return tiles
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def build_energy_availability_kpi_tile(ea: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""E5: nur bei caution/warning — gleiche Daten wie /charts/energy-availability-warning."""
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level = str(ea.get("warning_level") or "none").strip().lower()
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if level == "none":
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return None
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triggers: List[str] = list(ea.get("triggers") or [])
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msg = str(ea.get("message") or "").strip()
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st = "bad" if level == "warning" else "warn"
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first = triggers[0] if triggers else msg
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if len(first) > 90:
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first = first[:87] + "…"
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meta = ea.get("metadata") if isinstance(ea.get("metadata"), dict) else {}
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note = str(meta.get("note") or "")
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hover_lines = [msg] + [f"• {t}" for t in triggers]
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if note:
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hover_lines.append(note)
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return {
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"key": "energy-availability-e5",
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"category": "Energieverfügbarkeit",
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"icon": "⚡",
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"value": "Achtung" if level == "warning" else "Hinweis",
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"sublabel": first or "Signale prüfen",
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"status": st,
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"verdict": _verdict(st),
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"hint": msg,
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"hoverTop": "Energieverfügbarkeit (Heuristik)",
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"hoverBody": "\n".join(hover_lines),
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"keys": ["nutrition_score"],
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}
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def build_macro_donut_from_averages(navg: Dict[str, Any]) -> Optional[List[Dict[str, Any]]]:
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"""Anteile in % der Makro-kcal + Gramm für Legende."""
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p = float(navg.get("protein_avg") or 0)
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c = float(navg.get("carbs_avg") or 0)
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f = float(navg.get("fat_avg") or 0)
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pkcal, ckcal, fkcal = p * 4, c * 4, f * 9
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tot = pkcal + ckcal + fkcal
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if tot <= 0:
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return None
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return [
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{"name": "Protein", "value": round(pkcal / tot * 100), "color": "#4a8f72", "grams": round(p, 1)},
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{"name": "KH", "value": round(ckcal / tot * 100), "color": "#c17d45", "grams": round(c, 1)},
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{"name": "Fett", "value": round(fkcal / tot * 100), "color": "#6e8eb8", "grams": round(f, 1)},
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]
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