feat: update app version to 0.9t and enhance nutrition visualization
- Bumped application version to 0.9t and updated changelog with new features. - Integrated new chart payloads for energy balance, protein adequacy, and nutrition adherence to optimize data retrieval and reduce HTTP requests. - Updated NutritionCharts component to utilize prefetched chart payloads, improving loading efficiency and user experience. - Refactored History page to pass chart payloads, enhancing the visualization of nutrition trends without additional requests.
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backend/data_layer/nutrition_chart_payloads.py
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backend/data_layer/nutrition_chart_payloads.py
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@ -0,0 +1,404 @@
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"""
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Chart.js-kompatible Payloads für Ernährungs-Charts (E1, E2, E4).
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Gleiche Logik wie ``routers/charts.py`` — hier zentral, damit ``nutrition_viz``
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und die API dieselbe Berechnung nutzen (Phase C, Issue 53).
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from typing import Any, Dict
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from db import get_db, get_cursor
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from data_layer.nutrition_metrics import (
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get_energy_balance_data,
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get_protein_adequacy_data,
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get_protein_targets_data,
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)
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from data_layer.utils import calculate_confidence, safe_float, serialize_dates
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def build_energy_balance_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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"""E1 Energiebilanz — identisch zu GET /api/charts/energy-balance."""
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balance_meta = get_energy_balance_data(profile_id, days)
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with get_db() as conn:
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cur = get_cursor(conn)
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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cur.execute(
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"""SELECT date, SUM(kcal)::float AS kcal
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FROM nutrition_log
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WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL
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GROUP BY date
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows or len(rows) < 3:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": len(rows) if rows else 0,
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"message": "Nicht genug Ernährungsdaten (min. 3 Tage)",
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},
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}
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estimated_tdee = balance_meta.get("estimated_tdee") or 0
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if estimated_tdee <= 0:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": len(rows),
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"message": "Kein Gewicht für TDEE-Schätzung (weight_log erforderlich)",
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},
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}
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labels = []
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daily_values = []
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avg_7d = []
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avg_14d = []
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for i, row in enumerate(rows):
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labels.append(row["date"].isoformat())
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daily_values.append(safe_float(row["kcal"]))
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start_7d = max(0, i - 6)
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window_7d = [safe_float(rows[j]["kcal"]) for j in range(start_7d, i + 1)]
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avg_7d.append(round(sum(window_7d) / len(window_7d), 1) if window_7d else None)
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start_14d = max(0, i - 13)
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window_14d = [safe_float(rows[j]["kcal"]) for j in range(start_14d, i + 1)]
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avg_14d.append(round(sum(window_14d) / len(window_14d), 1) if window_14d else None)
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avg_intake = float(
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balance_meta.get("avg_intake")
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or (sum(daily_values) / len(daily_values) if daily_values else 0)
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)
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energy_balance = float(
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balance_meta.get("energy_balance") or (avg_intake - estimated_tdee)
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)
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balance_status = balance_meta.get("status") or (
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"deficit"
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if energy_balance < -200
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else "surplus"
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if energy_balance > 200
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else "maintenance"
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)
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datasets = [
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{
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"label": "Kalorien (täglich)",
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"data": daily_values,
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"borderColor": "#1D9E7599",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 1.5,
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"tension": 0.2,
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"fill": False,
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"pointRadius": 2,
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},
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{
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"label": "Ø 7 Tage",
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"data": avg_7d,
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"borderColor": "#1D9E75",
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"borderWidth": 2.5,
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"tension": 0.3,
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"fill": False,
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"pointRadius": 0,
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},
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{
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"label": "Ø 14 Tage",
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"data": avg_14d,
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"borderColor": "#085041",
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"borderWidth": 2,
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"tension": 0.3,
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"fill": False,
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"pointRadius": 0,
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"borderDash": [6, 3],
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},
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{
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"label": "TDEE (geschätzt)",
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"data": [estimated_tdee] * len(labels),
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"borderColor": "#888",
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"borderWidth": 1,
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"borderDash": [5, 5],
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"fill": False,
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"pointRadius": 0,
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},
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]
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confidence = balance_meta.get("confidence") or "low"
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return {
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"chart_type": "line",
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"data": {"labels": labels, "datasets": datasets},
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"metadata": serialize_dates(
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{
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"confidence": confidence,
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"data_points": len(rows),
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"avg_kcal": round(avg_intake, 1),
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"estimated_tdee": estimated_tdee,
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"energy_balance": round(energy_balance, 1),
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"balance_status": balance_status,
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"first_date": rows[0]["date"],
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"last_date": rows[-1]["date"],
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}
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),
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}
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def build_protein_adequacy_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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"""E2 Protein Adequacy — identisch zu GET /api/charts/protein-adequacy."""
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targets = get_protein_targets_data(profile_id)
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with get_db() as conn:
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cur = get_cursor(conn)
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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cur.execute(
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"""SELECT date, SUM(protein_g)::float AS protein_g
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FROM nutrition_log
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WHERE profile_id=%s AND date >= %s AND protein_g IS NOT NULL
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GROUP BY date
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows or len(rows) < 3:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": len(rows) if rows else 0,
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"message": "Nicht genug Protein-Daten (min. 3 Tage)",
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},
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}
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labels = []
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daily_values = []
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avg_7d = []
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avg_28d = []
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for i, row in enumerate(rows):
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labels.append(row["date"].isoformat())
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daily_values.append(safe_float(row["protein_g"]))
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start_7d = max(0, i - 6)
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window_7d = [safe_float(rows[j]["protein_g"]) for j in range(start_7d, i + 1)]
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avg_7d.append(round(sum(window_7d) / len(window_7d), 1) if window_7d else None)
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start_28d = max(0, i - 27)
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window_28d = [safe_float(rows[j]["protein_g"]) for j in range(start_28d, i + 1)]
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avg_28d.append(round(sum(window_28d) / len(window_28d), 1) if window_28d else None)
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target_low = targets["protein_target_low"]
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target_high = targets["protein_target_high"]
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datasets = [
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{
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"label": "Protein (täglich)",
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"data": daily_values,
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"borderColor": "#1D9E7599",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 1.5,
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"tension": 0.2,
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"fill": False,
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"pointRadius": 2,
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},
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{
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"label": "Ø 7 Tage",
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"data": avg_7d,
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"borderColor": "#1D9E75",
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"borderWidth": 2.5,
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"tension": 0.3,
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"fill": False,
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"pointRadius": 0,
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},
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{
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"label": "Ø 28 Tage",
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"data": avg_28d,
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"borderColor": "#085041",
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"borderWidth": 2,
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"tension": 0.3,
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"fill": False,
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"pointRadius": 0,
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"borderDash": [6, 3],
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},
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{
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"label": "Ziel Min",
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"data": [target_low] * len(labels),
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"borderColor": "#888",
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"borderWidth": 1,
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"borderDash": [5, 5],
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"fill": False,
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"pointRadius": 0,
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},
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]
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datasets.append(
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{
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"label": "Ziel Max",
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"data": [target_high] * len(labels),
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"borderColor": "#888",
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"borderWidth": 1,
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"borderDash": [5, 5],
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"fill": False,
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"pointRadius": 0,
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}
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)
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confidence = calculate_confidence(len(rows), days, "general")
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days_in_target = sum(1 for v in daily_values if target_low <= v <= target_high)
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return {
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"chart_type": "line",
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"data": {"labels": labels, "datasets": datasets},
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"metadata": serialize_dates(
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{
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"confidence": confidence,
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"data_points": len(rows),
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"target_low": round(target_low, 1),
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"target_high": round(target_high, 1),
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"days_in_target": days_in_target,
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"target_compliance_pct": round(
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days_in_target / len(daily_values) * 100, 1
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)
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if daily_values
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else 0,
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"first_date": rows[0]["date"],
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"last_date": rows[-1]["date"],
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}
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),
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}
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def build_nutrition_adherence_score_payload(profile_id: str, days: int) -> Dict[str, Any]:
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"""E4 Adhärenz — identisch zu GET /api/charts/nutrition-adherence-score."""
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute("SELECT goal_mode FROM profiles WHERE id = %s", (profile_id,))
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profile_row = cur.fetchone()
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goal_mode = (
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profile_row["goal_mode"]
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if profile_row and profile_row["goal_mode"]
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else "health"
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)
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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cur.execute(
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"""WITH daily AS (
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SELECT date,
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COALESCE(SUM(kcal), 0)::float AS dk,
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COALESCE(SUM(protein_g), 0)::float AS dp,
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COALESCE(SUM(carbs_g), 0)::float AS dc,
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COALESCE(SUM(fat_g), 0)::float AS df FROM nutrition_log
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WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL
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GROUP BY date
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)
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SELECT COUNT(*)::int AS cnt,
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AVG(dk) AS avg_kcal,
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STDDEV(dk) AS std_kcal,
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AVG(dp) AS avg_protein,
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AVG(dc) AS avg_carbs,
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AVG(df) AS avg_fat
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FROM daily""",
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(profile_id, cutoff),
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)
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stats = cur.fetchone()
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if not stats or stats["cnt"] < 7:
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return {
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"score": 0,
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"components": {},
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"metadata": {
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"confidence": "insufficient",
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"message": "Nicht genug Daten (min. 7 Tage)",
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},
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}
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protein_data = get_protein_adequacy_data(profile_id, days)
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calorie_adherence = 70.0
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protein_adequacy_pct = protein_data.get("adequacy_score", 0)
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protein_adherence = min(100, protein_adequacy_pct)
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kcal_cv = (
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(safe_float(stats["std_kcal"]) / safe_float(stats["avg_kcal"]) * 100)
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if safe_float(stats["avg_kcal"]) > 0
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else 100
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)
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intake_consistency = max(0, 100 - kcal_cv)
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food_quality = 60.0
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if goal_mode == "weight_loss":
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weights = {
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"calorie": 0.35,
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"protein": 0.25,
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"consistency": 0.20,
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"quality": 0.20,
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}
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elif goal_mode == "strength":
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weights = {
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"calorie": 0.25,
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"protein": 0.35,
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"consistency": 0.20,
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"quality": 0.20,
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}
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elif goal_mode == "endurance":
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weights = {
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"calorie": 0.30,
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"protein": 0.20,
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"consistency": 0.20,
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"quality": 0.30,
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}
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else:
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weights = {
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"calorie": 0.25,
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"protein": 0.25,
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"consistency": 0.25,
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"quality": 0.25,
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}
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final_score = (
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calorie_adherence * weights["calorie"]
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+ protein_adherence * weights["protein"]
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+ intake_consistency * weights["consistency"]
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+ food_quality * weights["quality"]
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)
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components = {
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"calorie_adherence": round(calorie_adherence, 1),
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"protein_adherence": round(protein_adherence, 1),
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"intake_consistency": round(intake_consistency, 1),
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"food_quality": round(food_quality, 1),
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}
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weak_areas = [k for k, v in components.items() if v < 60]
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if weak_areas:
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recommendation = f"Verbesserungspotenzial: {', '.join(weak_areas)}"
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else:
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recommendation = "Gute Adhärenz, weiter so!"
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return {
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"score": round(final_score, 1),
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"components": components,
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"goal_mode": goal_mode,
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"weights": weights,
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"recommendation": recommendation,
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"metadata": {
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"confidence": calculate_confidence(stats["cnt"], days, "general"),
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"data_points": stats["cnt"],
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"days_analyzed": days,
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},
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}
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@ -17,6 +17,11 @@ from data_layer.nutrition_interpretation import (
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build_nutrition_correlation_heuristic_items,
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build_nutrition_history_kpi_tiles,
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)
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from data_layer.nutrition_chart_payloads import (
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build_energy_balance_chart_payload,
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build_nutrition_adherence_score_payload,
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build_protein_adequacy_chart_payload,
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)
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from data_layer.nutrition_metrics import (
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estimate_tdee_kcal_from_latest_weight,
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get_energy_availability_warning_payload,
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@ -244,6 +249,8 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
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"calorie_balance_daily": [],
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"protein_vs_lean_mass": {"points": [], "protein_target_low_g": None},
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"nutrition_correlation_heuristics": [],
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"chart_payloads": {},
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"chart_payloads_days": None,
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"meta": {"layer_1": "nutrition_metrics", "layer_2b": "nutrition_viz"},
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}
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@ -312,6 +319,20 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
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weeks_for_weekly = max(4, min(52, (chart_days_for_pipeline + 6) // 7))
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weekly_chart = get_weekly_macro_distribution_chart_data(profile_id, weeks_for_weekly)
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# E1/E2/E4 Chart.js-Payloads — gleiche Funktionen wie /api/charts/* (kein zweiter HTTP-Roundtrip im Verlauf)
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days_for_embedded_charts = max(7, min(int(chart_days_for_pipeline), 90))
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chart_payloads = {
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"energy_balance": build_energy_balance_chart_payload(
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profile_id, days_for_embedded_charts
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),
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"protein_adequacy": build_protein_adequacy_chart_payload(
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profile_id, days_for_embedded_charts
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),
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"nutrition_adherence": build_nutrition_adherence_score_payload(
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profile_id, days_for_embedded_charts
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),
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}
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conf = navg.get("confidence") or "medium"
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if targets.get("confidence") == "insufficient":
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conf = "insufficient"
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@ -362,6 +383,8 @@ def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, An
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"protein_target_low_g": pt_low if pt_low > 0 else None,
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},
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"nutrition_correlation_heuristics": nutrition_correlation_heuristics,
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"chart_payloads": chart_payloads,
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"chart_payloads_days": days_for_embedded_charts,
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"meta": {
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"layer_1": "nutrition_metrics",
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"layer_2b": "nutrition_viz",
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@ -46,9 +46,7 @@ from data_layer.recovery_chart_payloads import (
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|||
from data_layer.nutrition_metrics import (
|
||||
get_nutrition_average_data,
|
||||
get_protein_targets_data,
|
||||
get_protein_adequacy_data,
|
||||
get_macro_consistency_data,
|
||||
get_energy_balance_data,
|
||||
get_weekly_macro_distribution_chart_data,
|
||||
get_energy_availability_warning_payload,
|
||||
)
|
||||
|
|
@ -77,6 +75,11 @@ from data_layer.correlations import (
|
|||
calculate_top_drivers
|
||||
)
|
||||
from data_layer.utils import serialize_dates, safe_float, calculate_confidence
|
||||
from data_layer.nutrition_chart_payloads import (
|
||||
build_energy_balance_chart_payload,
|
||||
build_protein_adequacy_chart_payload,
|
||||
build_nutrition_adherence_score_payload,
|
||||
)
|
||||
|
||||
router = APIRouter(prefix="/api/charts", tags=["charts"])
|
||||
|
||||
|
|
@ -462,136 +465,7 @@ def get_energy_balance_chart(
|
|||
Chart.js line chart with multiple datasets
|
||||
"""
|
||||
profile_id = session['profile_id']
|
||||
|
||||
balance_meta = get_energy_balance_data(profile_id, days)
|
||||
|
||||
from db import get_db, get_cursor
|
||||
with get_db() as conn:
|
||||
cur = get_cursor(conn)
|
||||
cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
||||
|
||||
cur.execute(
|
||||
"""SELECT date, SUM(kcal)::float AS kcal
|
||||
FROM nutrition_log
|
||||
WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL
|
||||
GROUP BY date
|
||||
ORDER BY date""",
|
||||
(profile_id, cutoff),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
|
||||
if not rows or len(rows) < 3:
|
||||
return {
|
||||
"chart_type": "line",
|
||||
"data": {
|
||||
"labels": [],
|
||||
"datasets": []
|
||||
},
|
||||
"metadata": {
|
||||
"confidence": "insufficient",
|
||||
"data_points": len(rows) if rows else 0,
|
||||
"message": "Nicht genug Ernährungsdaten (min. 3 Tage)"
|
||||
}
|
||||
}
|
||||
|
||||
estimated_tdee = balance_meta.get("estimated_tdee") or 0
|
||||
if estimated_tdee <= 0:
|
||||
return {
|
||||
"chart_type": "line",
|
||||
"data": {
|
||||
"labels": [],
|
||||
"datasets": []
|
||||
},
|
||||
"metadata": {
|
||||
"confidence": "insufficient",
|
||||
"data_points": len(rows),
|
||||
"message": "Kein Gewicht für TDEE-Schätzung (weight_log erforderlich)"
|
||||
}
|
||||
}
|
||||
|
||||
labels = []
|
||||
daily_values = []
|
||||
avg_7d = []
|
||||
avg_14d = []
|
||||
|
||||
for i, row in enumerate(rows):
|
||||
labels.append(row['date'].isoformat())
|
||||
daily_values.append(safe_float(row['kcal']))
|
||||
|
||||
start_7d = max(0, i - 6)
|
||||
window_7d = [safe_float(rows[j]['kcal']) for j in range(start_7d, i + 1)]
|
||||
avg_7d.append(round(sum(window_7d) / len(window_7d), 1) if window_7d else None)
|
||||
|
||||
start_14d = max(0, i - 13)
|
||||
window_14d = [safe_float(rows[j]['kcal']) for j in range(start_14d, i + 1)]
|
||||
avg_14d.append(round(sum(window_14d) / len(window_14d), 1) if window_14d else None)
|
||||
|
||||
avg_intake = float(balance_meta.get("avg_intake") or (sum(daily_values) / len(daily_values) if daily_values else 0))
|
||||
energy_balance = float(balance_meta.get("energy_balance") or (avg_intake - estimated_tdee))
|
||||
balance_status = balance_meta.get("status") or (
|
||||
"deficit" if energy_balance < -200 else "surplus" if energy_balance > 200 else "maintenance"
|
||||
)
|
||||
|
||||
datasets = [
|
||||
{
|
||||
"label": "Kalorien (täglich)",
|
||||
"data": daily_values,
|
||||
"borderColor": "#1D9E7599",
|
||||
"backgroundColor": "rgba(29, 158, 117, 0.1)",
|
||||
"borderWidth": 1.5,
|
||||
"tension": 0.2,
|
||||
"fill": False,
|
||||
"pointRadius": 2
|
||||
},
|
||||
{
|
||||
"label": "Ø 7 Tage",
|
||||
"data": avg_7d,
|
||||
"borderColor": "#1D9E75",
|
||||
"borderWidth": 2.5,
|
||||
"tension": 0.3,
|
||||
"fill": False,
|
||||
"pointRadius": 0
|
||||
},
|
||||
{
|
||||
"label": "Ø 14 Tage",
|
||||
"data": avg_14d,
|
||||
"borderColor": "#085041",
|
||||
"borderWidth": 2,
|
||||
"tension": 0.3,
|
||||
"fill": False,
|
||||
"pointRadius": 0,
|
||||
"borderDash": [6, 3]
|
||||
},
|
||||
{
|
||||
"label": "TDEE (geschätzt)",
|
||||
"data": [estimated_tdee] * len(labels),
|
||||
"borderColor": "#888",
|
||||
"borderWidth": 1,
|
||||
"borderDash": [5, 5],
|
||||
"fill": False,
|
||||
"pointRadius": 0
|
||||
}
|
||||
]
|
||||
|
||||
confidence = balance_meta.get("confidence") or "low"
|
||||
|
||||
return {
|
||||
"chart_type": "line",
|
||||
"data": {
|
||||
"labels": labels,
|
||||
"datasets": datasets
|
||||
},
|
||||
"metadata": serialize_dates({
|
||||
"confidence": confidence,
|
||||
"data_points": len(rows),
|
||||
"avg_kcal": round(avg_intake, 1),
|
||||
"estimated_tdee": estimated_tdee,
|
||||
"energy_balance": round(energy_balance, 1),
|
||||
"balance_status": balance_status,
|
||||
"first_date": rows[0]['date'],
|
||||
"last_date": rows[-1]['date']
|
||||
})
|
||||
}
|
||||
return build_energy_balance_chart_payload(profile_id, days)
|
||||
|
||||
|
||||
@router.get("/macro-distribution")
|
||||
|
|
@ -706,136 +580,7 @@ def get_protein_adequacy_chart(
|
|||
Chart.js line chart with protein intake + averages + target bands
|
||||
"""
|
||||
profile_id = session['profile_id']
|
||||
|
||||
# Get protein targets
|
||||
targets = get_protein_targets_data(profile_id)
|
||||
|
||||
from db import get_db, get_cursor
|
||||
with get_db() as conn:
|
||||
cur = get_cursor(conn)
|
||||
cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
||||
|
||||
cur.execute(
|
||||
"""SELECT date, SUM(protein_g)::float AS protein_g
|
||||
FROM nutrition_log
|
||||
WHERE profile_id=%s AND date >= %s AND protein_g IS NOT NULL
|
||||
GROUP BY date
|
||||
ORDER BY date""",
|
||||
(profile_id, cutoff),
|
||||
)
|
||||
rows = cur.fetchall()
|
||||
|
||||
if not rows or len(rows) < 3:
|
||||
return {
|
||||
"chart_type": "line",
|
||||
"data": {
|
||||
"labels": [],
|
||||
"datasets": []
|
||||
},
|
||||
"metadata": {
|
||||
"confidence": "insufficient",
|
||||
"data_points": len(rows) if rows else 0,
|
||||
"message": "Nicht genug Protein-Daten (min. 3 Tage)"
|
||||
}
|
||||
}
|
||||
|
||||
# Prepare data
|
||||
labels = []
|
||||
daily_values = []
|
||||
avg_7d = []
|
||||
avg_28d = []
|
||||
|
||||
for i, row in enumerate(rows):
|
||||
labels.append(row['date'].isoformat())
|
||||
daily_values.append(safe_float(row['protein_g']))
|
||||
|
||||
# 7d rolling average
|
||||
start_7d = max(0, i - 6)
|
||||
window_7d = [safe_float(rows[j]['protein_g']) for j in range(start_7d, i + 1)]
|
||||
avg_7d.append(round(sum(window_7d) / len(window_7d), 1) if window_7d else None)
|
||||
|
||||
# 28d rolling average
|
||||
start_28d = max(0, i - 27)
|
||||
window_28d = [safe_float(rows[j]['protein_g']) for j in range(start_28d, i + 1)]
|
||||
avg_28d.append(round(sum(window_28d) / len(window_28d), 1) if window_28d else None)
|
||||
|
||||
# Add target range bands
|
||||
target_low = targets['protein_target_low']
|
||||
target_high = targets['protein_target_high']
|
||||
|
||||
datasets = [
|
||||
{
|
||||
"label": "Protein (täglich)",
|
||||
"data": daily_values,
|
||||
"borderColor": "#1D9E7599",
|
||||
"backgroundColor": "rgba(29, 158, 117, 0.1)",
|
||||
"borderWidth": 1.5,
|
||||
"tension": 0.2,
|
||||
"fill": False,
|
||||
"pointRadius": 2
|
||||
},
|
||||
{
|
||||
"label": "Ø 7 Tage",
|
||||
"data": avg_7d,
|
||||
"borderColor": "#1D9E75",
|
||||
"borderWidth": 2.5,
|
||||
"tension": 0.3,
|
||||
"fill": False,
|
||||
"pointRadius": 0
|
||||
},
|
||||
{
|
||||
"label": "Ø 28 Tage",
|
||||
"data": avg_28d,
|
||||
"borderColor": "#085041",
|
||||
"borderWidth": 2,
|
||||
"tension": 0.3,
|
||||
"fill": False,
|
||||
"pointRadius": 0,
|
||||
"borderDash": [6, 3]
|
||||
},
|
||||
{
|
||||
"label": "Ziel Min",
|
||||
"data": [target_low] * len(labels),
|
||||
"borderColor": "#888",
|
||||
"borderWidth": 1,
|
||||
"borderDash": [5, 5],
|
||||
"fill": False,
|
||||
"pointRadius": 0
|
||||
}
|
||||
]
|
||||
|
||||
datasets.append({
|
||||
"label": "Ziel Max",
|
||||
"data": [target_high] * len(labels),
|
||||
"borderColor": "#888",
|
||||
"borderWidth": 1,
|
||||
"borderDash": [5, 5],
|
||||
"fill": False,
|
||||
"pointRadius": 0
|
||||
})
|
||||
|
||||
from data_layer.utils import calculate_confidence
|
||||
confidence = calculate_confidence(len(rows), days, "general")
|
||||
|
||||
days_in_target = sum(1 for v in daily_values if target_low <= v <= target_high)
|
||||
|
||||
return {
|
||||
"chart_type": "line",
|
||||
"data": {
|
||||
"labels": labels,
|
||||
"datasets": datasets
|
||||
},
|
||||
"metadata": serialize_dates({
|
||||
"confidence": confidence,
|
||||
"data_points": len(rows),
|
||||
"target_low": round(target_low, 1),
|
||||
"target_high": round(target_high, 1),
|
||||
"days_in_target": days_in_target,
|
||||
"target_compliance_pct": round(days_in_target / len(daily_values) * 100, 1) if daily_values else 0,
|
||||
"first_date": rows[0]['date'],
|
||||
"last_date": rows[-1]['date']
|
||||
})
|
||||
}
|
||||
return build_protein_adequacy_chart_payload(profile_id, days)
|
||||
|
||||
|
||||
@router.get("/nutrition-consistency")
|
||||
|
|
@ -963,138 +708,7 @@ def get_nutrition_adherence_score(
|
|||
}
|
||||
"""
|
||||
profile_id = session['profile_id']
|
||||
|
||||
from db import get_db, get_cursor
|
||||
from data_layer.nutrition_metrics import (
|
||||
get_protein_adequacy_data,
|
||||
calculate_macro_consistency_score
|
||||
)
|
||||
|
||||
# Get user's goal mode (weight_loss, strength, endurance, etc.)
|
||||
with get_db() as conn:
|
||||
cur = get_cursor(conn)
|
||||
cur.execute("SELECT goal_mode FROM profiles WHERE id = %s", (profile_id,))
|
||||
profile_row = cur.fetchone()
|
||||
goal_mode = profile_row['goal_mode'] if profile_row and profile_row['goal_mode'] else 'health'
|
||||
|
||||
cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
||||
|
||||
# Get nutrition data
|
||||
cur.execute(
|
||||
"""WITH daily AS (
|
||||
SELECT date,
|
||||
COALESCE(SUM(kcal), 0)::float AS dk,
|
||||
COALESCE(SUM(protein_g), 0)::float AS dp,
|
||||
COALESCE(SUM(carbs_g), 0)::float AS dc,
|
||||
COALESCE(SUM(fat_g), 0)::float AS df FROM nutrition_log
|
||||
WHERE profile_id=%s AND date >= %s AND kcal IS NOT NULL
|
||||
GROUP BY date
|
||||
)
|
||||
SELECT COUNT(*)::int AS cnt,
|
||||
AVG(dk) AS avg_kcal,
|
||||
STDDEV(dk) AS std_kcal,
|
||||
AVG(dp) AS avg_protein,
|
||||
AVG(dc) AS avg_carbs,
|
||||
AVG(df) AS avg_fat
|
||||
FROM daily""",
|
||||
(profile_id, cutoff),
|
||||
)
|
||||
stats = cur.fetchone()
|
||||
|
||||
if not stats or stats['cnt'] < 7:
|
||||
return {
|
||||
"score": 0,
|
||||
"components": {},
|
||||
"metadata": {
|
||||
"confidence": "insufficient",
|
||||
"message": "Nicht genug Daten (min. 7 Tage)"
|
||||
}
|
||||
}
|
||||
|
||||
# Get protein adequacy
|
||||
protein_data = get_protein_adequacy_data(profile_id, days)
|
||||
|
||||
# Calculate components based on goal mode
|
||||
components = {}
|
||||
|
||||
# 1. Calorie adherence (placeholder, needs goal-specific logic)
|
||||
calorie_adherence = 70.0 # TODO: Calculate based on TDEE target
|
||||
|
||||
# 2. Protein adherence
|
||||
protein_adequacy_pct = protein_data.get('adequacy_score', 0)
|
||||
protein_adherence = min(100, protein_adequacy_pct)
|
||||
|
||||
# 3. Intake consistency (low volatility = good)
|
||||
kcal_cv = (safe_float(stats['std_kcal']) / safe_float(stats['avg_kcal']) * 100) if safe_float(stats['avg_kcal']) > 0 else 100
|
||||
intake_consistency = max(0, 100 - kcal_cv) # Invert: low CV = high score
|
||||
|
||||
# 4. Food quality (placeholder for fiber/sugar analysis)
|
||||
food_quality = 60.0 # TODO: Calculate from fiber/sugar data
|
||||
|
||||
# Goal-specific weighting (from concept E4)
|
||||
if goal_mode == 'weight_loss':
|
||||
weights = {
|
||||
'calorie': 0.35,
|
||||
'protein': 0.25,
|
||||
'consistency': 0.20,
|
||||
'quality': 0.20
|
||||
}
|
||||
elif goal_mode == 'strength':
|
||||
weights = {
|
||||
'calorie': 0.25,
|
||||
'protein': 0.35,
|
||||
'consistency': 0.20,
|
||||
'quality': 0.20
|
||||
}
|
||||
elif goal_mode == 'endurance':
|
||||
weights = {
|
||||
'calorie': 0.30,
|
||||
'protein': 0.20,
|
||||
'consistency': 0.20,
|
||||
'quality': 0.30
|
||||
}
|
||||
else: # health, recomposition
|
||||
weights = {
|
||||
'calorie': 0.25,
|
||||
'protein': 0.25,
|
||||
'consistency': 0.25,
|
||||
'quality': 0.25
|
||||
}
|
||||
|
||||
# Calculate weighted score
|
||||
final_score = (
|
||||
calorie_adherence * weights['calorie'] +
|
||||
protein_adherence * weights['protein'] +
|
||||
intake_consistency * weights['consistency'] +
|
||||
food_quality * weights['quality']
|
||||
)
|
||||
|
||||
components = {
|
||||
'calorie_adherence': round(calorie_adherence, 1),
|
||||
'protein_adherence': round(protein_adherence, 1),
|
||||
'intake_consistency': round(intake_consistency, 1),
|
||||
'food_quality': round(food_quality, 1)
|
||||
}
|
||||
|
||||
# Generate recommendation
|
||||
weak_areas = [k for k, v in components.items() if v < 60]
|
||||
if weak_areas:
|
||||
recommendation = f"Verbesserungspotenzial: {', '.join(weak_areas)}"
|
||||
else:
|
||||
recommendation = "Gute Adhärenz, weiter so!"
|
||||
|
||||
return {
|
||||
"score": round(final_score, 1),
|
||||
"components": components,
|
||||
"goal_mode": goal_mode,
|
||||
"weights": weights,
|
||||
"recommendation": recommendation,
|
||||
"metadata": {
|
||||
"confidence": calculate_confidence(stats['cnt'], days, "general"),
|
||||
"data_points": stats['cnt'],
|
||||
"days_analyzed": days
|
||||
}
|
||||
}
|
||||
return build_nutrition_adherence_score_payload(profile_id, days)
|
||||
|
||||
|
||||
@router.get("/energy-availability-warning")
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ Semantic Versioning: MAJOR.MINOR.PATCH
|
|||
- PATCH: Bugfix, kleine Änderung, Refactor
|
||||
"""
|
||||
|
||||
APP_VERSION = "0.9s"
|
||||
APP_VERSION = "0.9t"
|
||||
BUILD_DATE = "2026-04-20"
|
||||
DB_SCHEMA_VERSION = "20260409c" # 048/049 vitals_baseline.source csv + SAVEPOINT Import
|
||||
|
||||
|
|
@ -36,6 +36,14 @@ MODULE_VERSIONS = {
|
|||
}
|
||||
|
||||
CHANGELOG = [
|
||||
{
|
||||
"version": "0.9t",
|
||||
"date": "2026-04-20",
|
||||
"changes": [
|
||||
"Phase C: data_layer/nutrition_chart_payloads (E1/E2/E4) — gemeinsam mit /api/charts/*",
|
||||
"nutrition-history-viz: chart_payloads + chart_payloads_days; Verlauf NutritionCharts ohne 3 Extra-HTTP-Calls",
|
||||
],
|
||||
},
|
||||
{
|
||||
"version": "0.9s",
|
||||
"date": "2026-04-20",
|
||||
|
|
|
|||
|
|
@ -205,12 +205,14 @@ export function WeeklyMacroDistributionPanel({ macroWeeklyData, loading, error }
|
|||
|
||||
/**
|
||||
* Nutrition Charts (E1–E5). Verlauf: `showWeeklyMacroDistribution={false}` wenn E3 separat (z. B. neben Donut) gerendert wird.
|
||||
* @param {object} [prefetchedChartPayloads] — aus GET /charts/nutrition-history-viz (`chart_payloads`): E1/E2/E4 ohne Extra-Requests (Phase C).
|
||||
*/
|
||||
export default function NutritionCharts({
|
||||
days = 28,
|
||||
showWeeklyMacroDistribution = true,
|
||||
/** Verlauf: E5-Kachel liegt in nutrition-history-viz KPIs — doppelte Karte ausblenden */
|
||||
hideEnergyAvailabilityCard = false,
|
||||
prefetchedChartPayloads = null,
|
||||
}) {
|
||||
const [energyData, setEnergyData] = useState(null)
|
||||
const [proteinData, setProteinData] = useState(null)
|
||||
|
|
@ -226,20 +228,44 @@ export default function NutritionCharts({
|
|||
|
||||
useEffect(() => {
|
||||
loadCharts()
|
||||
}, [days, showWeeklyMacroDistribution, hideEnergyAvailabilityCard])
|
||||
}, [days, showWeeklyMacroDistribution, hideEnergyAvailabilityCard, prefetchedChartPayloads])
|
||||
|
||||
const loadCharts = async () => {
|
||||
const tasks = [
|
||||
loadEnergyBalance(),
|
||||
loadProteinAdequacy(),
|
||||
loadAdherence(),
|
||||
]
|
||||
const p = prefetchedChartPayloads
|
||||
const tasks = []
|
||||
|
||||
if (p?.energy_balance) {
|
||||
setEnergyData(p.energy_balance)
|
||||
setLoading((l) => ({ ...l, energy: false }))
|
||||
setErrors((e) => ({ ...e, energy: null }))
|
||||
} else {
|
||||
tasks.push(loadEnergyBalance())
|
||||
}
|
||||
|
||||
if (p?.protein_adequacy) {
|
||||
setProteinData(p.protein_adequacy)
|
||||
setLoading((l) => ({ ...l, protein: false }))
|
||||
setErrors((e) => ({ ...e, protein: null }))
|
||||
} else {
|
||||
tasks.push(loadProteinAdequacy())
|
||||
}
|
||||
|
||||
if (showWeeklyMacroDistribution) {
|
||||
tasks.push(loadMacroWeekly())
|
||||
}
|
||||
|
||||
if (p?.nutrition_adherence) {
|
||||
setAdherenceData(p.nutrition_adherence)
|
||||
setLoading((l) => ({ ...l, adherence: false }))
|
||||
setErrors((e) => ({ ...e, adherence: null }))
|
||||
} else {
|
||||
tasks.push(loadAdherence())
|
||||
}
|
||||
|
||||
if (!hideEnergyAvailabilityCard) {
|
||||
tasks.push(loadWarning())
|
||||
}
|
||||
if (showWeeklyMacroDistribution) {
|
||||
tasks.splice(2, 0, loadMacroWeekly())
|
||||
}
|
||||
|
||||
await Promise.all(tasks)
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1120,7 +1120,12 @@ function NutritionSection({ insights, onRequest, loadingSlug, filterActiveSlugs
|
|||
<div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8, marginTop: 4 }}>
|
||||
Zeitverläufe (Energie & Protein)
|
||||
</div>
|
||||
<NutritionCharts days={chartDays} showWeeklyMacroDistribution={false} hideEnergyAvailabilityCard />
|
||||
<NutritionCharts
|
||||
days={chartDays}
|
||||
showWeeklyMacroDistribution={false}
|
||||
hideEnergyAvailabilityCard
|
||||
prefetchedChartPayloads={viz.chart_payloads}
|
||||
/>
|
||||
|
||||
<InsightBox insights={insights} slugs={filterActiveSlugs(['ernaehrung'])} onRequest={onRequest} loading={loadingSlug}/>
|
||||
</div>
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user