Lars
3f6673b636
- 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.
405 lines
12 KiB
Python
405 lines
12 KiB
Python
"""
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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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