Merge pull request 'Ernährungsasuwertungen' (#94) from develop into main

Reviewed-on: #94

backend/data_layer/nutrition_interpretation.py

@@ -0,0 +1,219 @@
+"""
+Interpretation + KPI-Kacheln für Layer 2b Ernährungs-Verlauf.
+
+Gleiche Schwellen wie zuvor im Frontend (History.jsx); Ausgabe strukturiert
+für KpiTilesOverview (keys = related_placeholder_keys).
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+
+def _verdict(status: str) -> str:
+    if status == "good":
+        return "Gut"
+    if status == "warn":
+        return "Hinweis"
+    return "Achtung"
+
+
+def build_nutrition_history_kpi_tiles(
+    navg: Dict[str, Any],
+    targets: Dict[str, Any],
+    date_span_label: str,
+    n_days_with_entries: int,
+) -> List[Dict[str, Any]]:
+    """
+    KPI-Kacheln wie buildNutritionKpiTiles im Frontend (Kalorien/KH/Fett + Regeln).
+    """
+    kcal_avg = round(float(navg.get("kcal_avg") or 0))
+    avg_carbs = round(float(navg.get("carbs_avg") or 0) * 10) / 10
+    avg_fat = round(float(navg.get("fat_avg") or 0) * 10) / 10
+    avg_protein = round(float(navg.get("protein_avg") or 0) * 10) / 10
+
+    pt_low = round(float(targets.get("protein_target_low") or 0))
+    pt_high = round(float(targets.get("protein_target_high") or 0))
+    targets_ok = targets.get("confidence") != "insufficient" and pt_low > 0
+    protein_ok = targets_ok and avg_protein >= pt_low
+
+    total_macro_kcal = avg_protein * 4 + avg_carbs * 4 + avg_fat * 9
+    prot_pct = (
+        round(avg_protein * 4 / total_macro_kcal * 100)
+        if total_macro_kcal > 0
+        else 0
+    )
+    kh_pct = (
+        round(avg_carbs * 4 / total_macro_kcal * 100)
+        if total_macro_kcal > 0
+        else 0
+    )
+    fat_pct = (
+        round(avg_fat * 9 / total_macro_kcal * 100)
+        if total_macro_kcal > 0
+        else 0
+    )
+
+    tiles: List[Dict[str, Any]] = [
+        {
+            "key": "kcal",
+            "category": "Kalorien (Ø)",
+            "icon": "🔥",
+            "value": f"{kcal_avg} kcal",
+            "sublabel": date_span_label,
+            "status": "good",
+            "verdict": "Gut",
+            "hoverTop": "Durchschnittliche tägliche Energie",
+            "hoverBody": f"Mittel über {n_days_with_entries} Tage mit Ernährungseinträgen im gewählten Zeitraum.",
+            "keys": ["nutrition_score"],
+        },
+        {
+            "key": "carbs",
+            "category": "KH (Ø)",
+            "icon": "🌾",
+            "value": f"{avg_carbs} g",
+            "sublabel": "Kohlenhydrate / Tag",
+            "status": "good",
+            "verdict": "Gut",
+            "hoverTop": "Durchschnittliche Kohlenhydrate",
+            "hoverBody": "Summe der täglichen Werte im Zeitraum, gemittelt.",
+            "keys": ["nutrition_summary"],
+        },
+        {
+            "key": "fat",
+            "category": "Fett (Ø)",
+            "icon": "🧈",
+            "value": f"{avg_fat} g",
+            "sublabel": "Fett / Tag",
+            "status": "good",
+            "verdict": "Gut",
+            "hoverTop": "Durchschnittliches Fett",
+            "hoverBody": "Summe der täglichen Werte im Zeitraum, gemittelt.",
+            "keys": ["nutrition_summary"],
+        },
+    ]
+
+    if not targets_ok:
+        tiles.append(
+            {
+                "key": "eval-protein",
+                "category": "Protein",
+                "icon": "🥩",
+                "value": f"{avg_protein}g",
+                "sublabel": "Referenzgewicht fehlt",
+                "status": "warn",
+                "verdict": _verdict("warn"),
+                "hint": "Ohne aktuelles Körpergewicht lässt sich das Protein-Ziel (g/kg) nicht bewerten.",
+                "hoverTop": "Protein-Ziel nicht berechenbar",
+                "hoverBody": "Für 1,6–2,2 g/kg wird ein aktuelles Körpergewicht benötigt.",
+                "keys": ["protein_adequacy"],
+            }
+        )
+    elif not protein_ok:
+        miss = max(0, pt_low - round(avg_protein))
+        tiles.append(
+            {
+                "key": "eval-protein",
+                "category": "Protein",
+                "icon": "🥩",
+                "value": f"{avg_protein}g",
+                "sublabel": f"Unterversorgung: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
+                "status": "bad",
+                "verdict": _verdict("bad"),
+                "hint": (
+                    f"~{miss} g Protein/Tag fehlen – bei Defizit Muskelerhalt gefährdet."
+                ),
+                "hoverTop": f"Unterversorgung: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
+                "hoverBody": (
+                    f"1,6–2,2g/kg KG. Fehlend: ~{miss}g täglich. "
+                    "Konsequenz: Muskelverlust bei Defizit."
+                ),
+                "keys": ["protein_adequacy", "nutrition_score"],
+            }
+        )
+    else:
+        tiles.append(
+            {
+                "key": "eval-protein",
+                "category": "Protein",
+                "icon": "🥩",
+                "value": f"{avg_protein}g",
+                "sublabel": f"Gut: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
+                "status": "good",
+                "verdict": _verdict("good"),
+                "hoverTop": f"Gut: {avg_protein}g/Tag (Ziel {pt_low}–{pt_high}g)",
+                "hoverBody": "Ausreichend für Muskelerhalt und -aufbau.",
+                "keys": ["protein_adequacy", "nutrition_score"],
+            }
+        )
+
+    if prot_pct < 20 and total_macro_kcal > 0:
+        tiles.append(
+            {
+                "key": "eval-macro-pct",
+                "category": "Makro-Anteil",
+                "icon": "📊",
+                "value": f"{prot_pct}%",
+                "sublabel": f"Protein-Anteil niedrig: {prot_pct}% der Kalorien",
+                "status": "warn",
+                "verdict": _verdict("warn"),
+                "hint": (
+                    f"Protein-Kalorienanteil niedrig (P {prot_pct} % / KH {kh_pct} % / F {fat_pct} %); "
+                    "Ziel oft 25–35 %."
+                ),
+                "hoverTop": f"Protein-Anteil niedrig: {prot_pct}% der Kalorien",
+                "hoverBody": (
+                    f"Empfehlung oft 25–35%. Aktuell: {prot_pct}% P / {kh_pct}% KH / {fat_pct}% F"
+                ),
+                "keys": ["nutrition_summary"],
+            }
+        )
+
+    return tiles
+
+
+def build_energy_availability_kpi_tile(ea: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """E5: nur bei caution/warning — gleiche Daten wie /charts/energy-availability-warning."""
+    level = str(ea.get("warning_level") or "none").strip().lower()
+    if level == "none":
+        return None
+    triggers: List[str] = list(ea.get("triggers") or [])
+    msg = str(ea.get("message") or "").strip()
+    st = "bad" if level == "warning" else "warn"
+    first = triggers[0] if triggers else msg
+    if len(first) > 90:
+        first = first[:87] + "…"
+    meta = ea.get("metadata") if isinstance(ea.get("metadata"), dict) else {}
+    note = str(meta.get("note") or "")
+    hover_lines = [msg] + [f"• {t}" for t in triggers]
+    if note:
+        hover_lines.append(note)
+    return {
+        "key": "energy-availability-e5",
+        "category": "Energieverfügbarkeit",
+        "icon": "⚡",
+        "value": "Achtung" if level == "warning" else "Hinweis",
+        "sublabel": first or "Signale prüfen",
+        "status": st,
+        "verdict": _verdict(st),
+        "hint": msg,
+        "hoverTop": "Energieverfügbarkeit (Heuristik)",
+        "hoverBody": "\n".join(hover_lines),
+        "keys": ["nutrition_score"],
+    }
+
+
+def build_macro_donut_from_averages(navg: Dict[str, Any]) -> Optional[List[Dict[str, Any]]]:
+    """Anteile in % der Makro-kcal + Gramm für Legende."""
+    p = float(navg.get("protein_avg") or 0)
+    c = float(navg.get("carbs_avg") or 0)
+    f = float(navg.get("fat_avg") or 0)
+    pkcal, ckcal, fkcal = p * 4, c * 4, f * 9
+    tot = pkcal + ckcal + fkcal
+    if tot <= 0:
+        return None
+    return [
+        {"name": "Protein", "value": round(pkcal / tot * 100), "color": "#4a8f72", "grams": round(p, 1)},
+        {"name": "KH", "value": round(ckcal / tot * 100), "color": "#c17d45", "grams": round(c, 1)},
+        {"name": "Fett", "value": round(fkcal / tot * 100), "color": "#6e8eb8", "grams": round(f, 1)},
+    ]

backend/data_layer/nutrition_metrics.py

@@ -20,6 +20,7 @@ Phase 0c: Multi-Layer Architecture
 Version: 1.0
 """
 
+import statistics
 from typing import Dict, List, Optional
 from datetime import datetime, timedelta, date
 from db import get_db, get_cursor, r2d
@@ -110,7 +111,9 @@ def _get_profile_goal_mode(profile_id: str) -> str:
 
 def get_nutrition_average_data(
     profile_id: str,
-    days: int = 30
+    days: int = 30,
+    *,
+    all_history: bool = False,
 ) -> Dict:
     """
     Get average nutrition values for all macros.
@@ -136,11 +139,18 @@ def get_nutrition_average_data(
     """
     with get_db() as conn:
         cur = get_cursor(conn)
-        cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
+        cutoff = None if all_history else (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
 
         # Mean over calendar days (per-day sums), not over raw log rows.
+        if cutoff:
+            inner_where = "WHERE profile_id=%s AND date >= %s"
+            params = (profile_id, cutoff)
+        else:
+            inner_where = "WHERE profile_id=%s"
+            params = (profile_id,)
+
         cur.execute(
-            """SELECT
+            f"""SELECT
                 AVG(daily_kcal) AS kcal_avg,
                 AVG(daily_protein) AS protein_avg,
                 AVG(daily_carbs) AS carbs_avg,
@@ -153,10 +163,10 @@ def get_nutrition_average_data(
                     COALESCE(SUM(carbs_g), 0)::float AS daily_carbs,
                     COALESCE(SUM(fat_g), 0)::float AS daily_fat
                 FROM nutrition_log
-                WHERE profile_id=%s AND date >= %s
+                {inner_where}
                 GROUP BY date
                ) AS daily""",
-            (profile_id, cutoff),
+            params,
         )
         row = cur.fetchone()
 
@@ -494,8 +504,6 @@ def get_macro_consistency_data(
                 "data_points": len(rows)
             }
 
-        import statistics
-
         protein_pcts = []
         carbs_pcts = []
         fat_pcts = []
@@ -561,6 +569,200 @@ def get_macro_consistency_data(
         }
 
 
+def get_weekly_macro_distribution_chart_data(profile_id: str, weeks: int) -> Dict:
+    """
+    Chart E3: gestapelte Wochenbalken (Makro-%), gleiche Logik wie /charts/weekly-macro-distribution.
+    """
+    cutoff = (datetime.now() - timedelta(weeks=weeks)).strftime("%Y-%m-%d")
+
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            """SELECT date, protein_g, carbs_g, fat_g, kcal
+               FROM nutrition_log
+               WHERE profile_id=%s AND date >= %s
+                 AND protein_g IS NOT NULL AND carbs_g IS NOT NULL
+                 AND fat_g IS NOT NULL AND kcal > 0
+               ORDER BY date""",
+            (profile_id, cutoff),
+        )
+        rows = cur.fetchall()
+
+    if not rows or len(rows) < 7:
+        return {
+            "chart_type": "bar",
+            "data": {
+                "labels": [],
+                "datasets": [],
+            },
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": len(rows) if rows else 0,
+                "message": "Nicht genug Daten für Wochen-Analyse (min. 7 Tage)",
+            },
+        }
+
+    weekly_data: Dict[str, Dict[str, List[float]]] = {}
+    for row in rows:
+        date_obj = row["date"] if isinstance(row["date"], datetime) else datetime.fromisoformat(str(row["date"]))
+        iso_week = date_obj.strftime("%Y-W%V")
+
+        if iso_week not in weekly_data:
+            weekly_data[iso_week] = {
+                "protein": [],
+                "carbs": [],
+                "fat": [],
+                "kcal": [],
+            }
+
+        weekly_data[iso_week]["protein"].append(safe_float(row["protein_g"]))
+        weekly_data[iso_week]["carbs"].append(safe_float(row["carbs_g"]))
+        weekly_data[iso_week]["fat"].append(safe_float(row["fat_g"]))
+        weekly_data[iso_week]["kcal"].append(safe_float(row["kcal"]))
+
+    labels: List[str] = []
+    protein_pcts: List[float] = []
+    carbs_pcts: List[float] = []
+    fat_pcts: List[float] = []
+
+    for iso_week in sorted(weekly_data.keys())[-weeks:]:
+        data = weekly_data[iso_week]
+
+        avg_protein = sum(data["protein"]) / len(data["protein"]) if data["protein"] else 0
+        avg_carbs = sum(data["carbs"]) / len(data["carbs"]) if data["carbs"] else 0
+        avg_fat = sum(data["fat"]) / len(data["fat"]) if data["fat"] else 0
+
+        protein_kcal = avg_protein * 4
+        carbs_kcal = avg_carbs * 4
+        fat_kcal = avg_fat * 9
+
+        total_kcal = protein_kcal + carbs_kcal + fat_kcal
+
+        if total_kcal > 0:
+            labels.append(f"KW {iso_week[-2:]}")
+            protein_pcts.append(round((protein_kcal / total_kcal) * 100, 1))
+            carbs_pcts.append(round((carbs_kcal / total_kcal) * 100, 1))
+            fat_pcts.append(round((fat_kcal / total_kcal) * 100, 1))
+
+    protein_cv = (
+        statistics.stdev(protein_pcts) / statistics.mean(protein_pcts) * 100
+        if len(protein_pcts) > 1 and statistics.mean(protein_pcts) > 0
+        else 0
+    )
+    carbs_cv = (
+        statistics.stdev(carbs_pcts) / statistics.mean(carbs_pcts) * 100
+        if len(carbs_pcts) > 1 and statistics.mean(carbs_pcts) > 0
+        else 0
+    )
+    fat_cv = (
+        statistics.stdev(fat_pcts) / statistics.mean(fat_pcts) * 100
+        if len(fat_pcts) > 1 and statistics.mean(fat_pcts) > 0
+        else 0
+    )
+
+    return {
+        "chart_type": "bar",
+        "data": {
+            "labels": labels,
+            "datasets": [
+                {
+                    "label": "Protein (%)",
+                    "data": protein_pcts,
+                    "backgroundColor": "#4a8f72",
+                    "stack": "macro",
+                },
+                {
+                    "label": "Kohlenhydrate (%)",
+                    "data": carbs_pcts,
+                    "backgroundColor": "#c17d45",
+                    "stack": "macro",
+                },
+                {
+                    "label": "Fett (%)",
+                    "data": fat_pcts,
+                    "backgroundColor": "#6e8eb8",
+                    "stack": "macro",
+                },
+            ],
+        },
+        "metadata": {
+            "confidence": calculate_confidence(len(rows), weeks * 7, "general"),
+            "data_points": len(rows),
+            "weeks_analyzed": len(labels),
+            "avg_protein_pct": round(statistics.mean(protein_pcts), 1) if protein_pcts else 0,
+            "avg_carbs_pct": round(statistics.mean(carbs_pcts), 1) if carbs_pcts else 0,
+            "avg_fat_pct": round(statistics.mean(fat_pcts), 1) if fat_pcts else 0,
+            "protein_cv": round(protein_cv, 1),
+            "carbs_cv": round(carbs_cv, 1),
+            "fat_cv": round(fat_cv, 1),
+        },
+    }
+
+
+def get_energy_availability_warning_payload(profile_id: str, days: int = 14) -> Dict:
+    """
+    E5 Energieverfügbarkeit — gleiche Heuristik wie GET /charts/energy-availability-warning.
+    """
+    from data_layer.recovery_metrics import calculate_recovery_score_v2, calculate_sleep_quality_7d
+    from data_layer.body_metrics import calculate_lbm_28d_change
+
+    triggers: List[str] = []
+    warning_level = "none"
+
+    energy_data = get_energy_balance_data(profile_id, days)
+    if energy_data.get("energy_balance", 0) < -500:
+        triggers.append("Großes Energiedefizit (>500 kcal/Tag)")
+
+    try:
+        recovery_score = calculate_recovery_score_v2(profile_id)
+        if recovery_score and recovery_score < 50:
+            triggers.append("Recovery Score niedrig (<50)")
+    except Exception:
+        pass
+
+    try:
+        sleep_quality = calculate_sleep_quality_7d(profile_id)
+        if sleep_quality and sleep_quality < 60:
+            triggers.append("Schlafqualität reduziert (<60%)")
+    except Exception:
+        pass
+
+    try:
+        lbm_change = calculate_lbm_28d_change(profile_id)
+        if lbm_change and lbm_change < -1.0:
+            triggers.append("Magermasse sinkt (-{:.1f} kg)".format(abs(lbm_change)))
+    except Exception:
+        pass
+
+    if len(triggers) >= 3:
+        warning_level = "warning"
+        message = (
+            "⚠️ Hinweis auf mögliche Unterversorgung. Mehrere Indikatoren auffällig. "
+            "Erwäge Defizit-Anpassung oder Regenerationswoche."
+        )
+    elif len(triggers) >= 2:
+        warning_level = "caution"
+        message = (
+            "⚡ Beobachte folgende Signale genau. Aktuell noch kein Handlungsbedarf, aber Trend beachten."
+        )
+    elif len(triggers) >= 1:
+        warning_level = "caution"
+        message = "💡 Ein Indikator auffällig. Weiter beobachten."
+    else:
+        message = "✅ Energieverfügbarkeit unauffällig."
+
+    return {
+        "warning_level": warning_level,
+        "triggers": triggers,
+        "message": message,
+        "metadata": {
+            "days_analyzed": days,
+            "trigger_count": len(triggers),
+            "note": "Heuristische Einschätzung, keine medizinische Diagnose",
+        },
+    }
+
+
 # ============================================================================
 # Calculated Metrics (migrated from calculations/nutrition_metrics.py)
 # ============================================================================

backend/data_layer/nutrition_viz.py

@@ -0,0 +1,294 @@
+"""
+Layer 2b: Ernährungs-Verlauf — ein Bundle für die UI (Issue #53).
+
+Single Source: nutrition_metrics + dieselben Tabellen wie Ernährungs-Platzhalter.
+"""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta
+from typing import Any, Dict, List, Optional
+
+from db import get_db, get_cursor, r2d
+from data_layer.nutrition_interpretation import (
+    build_energy_availability_kpi_tile,
+    build_macro_donut_from_averages,
+    build_nutrition_history_kpi_tiles,
+)
+from data_layer.nutrition_metrics import (
+    estimate_tdee_kcal_from_latest_weight,
+    get_energy_availability_warning_payload,
+    get_energy_balance_data,
+    get_nutrition_average_data,
+    get_protein_targets_data,
+    get_weekly_macro_distribution_chart_data,
+)
+from data_layer.utils import safe_float
+
+
+def _cutoff_sql(days: int) -> Optional[str]:
+    if days >= 9999:
+        return None
+    return (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
+
+
+def _iso(d: Any) -> Optional[str]:
+    if d is None:
+        return None
+    if hasattr(d, "isoformat"):
+        return d.isoformat()[:10]
+    return str(d)[:10]
+
+
+def _rolling_avg(rows: List[Dict[str, Any]], key: str, window: int) -> List[Dict[str, Any]]:
+    out: List[Dict[str, Any]] = []
+    for i, d in enumerate(rows):
+        sl = rows[max(0, i - window + 1) : i + 1]
+        vals: List[float] = []
+        for x in sl:
+            v = safe_float(x.get(key))
+            if v is not None:
+                vals.append(v)
+        if not vals:
+            out.append({**d, f"{key}_avg": None})
+            continue
+        avg = round(sum(vals) / len(vals), 1)
+        out.append({**d, f"{key}_avg": avg})
+    return out
+
+
+def _has_nutrition_entries(profile_id: str) -> bool:
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            "SELECT 1 FROM nutrition_log WHERE profile_id=%s LIMIT 1",
+            (profile_id,),
+        )
+        return cur.fetchone() is not None
+
+
+def _last_nutrition_date(profile_id: str) -> Optional[str]:
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            "SELECT MAX(date) AS d FROM nutrition_log WHERE profile_id=%s",
+            (profile_id,),
+        )
+        row = cur.fetchone()
+        if not row or row["d"] is None:
+            return None
+        return _iso(row["d"])
+
+
+def _fetch_daily_macro_totals(profile_id: str, cutoff: Optional[str]) -> List[Dict[str, Any]]:
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        if cutoff:
+            cur.execute(
+                """SELECT date,
+                    COALESCE(SUM(kcal), 0)::float AS kcal,
+                    COALESCE(SUM(protein_g), 0)::float AS protein_g,
+                    COALESCE(SUM(carbs_g), 0)::float AS carbs_g,
+                    COALESCE(SUM(fat_g), 0)::float AS fat_g
+                   FROM nutrition_log
+                   WHERE profile_id=%s AND date >= %s
+                   GROUP BY date
+                   ORDER BY date ASC""",
+                (profile_id, cutoff),
+            )
+        else:
+            cur.execute(
+                """SELECT date,
+                    COALESCE(SUM(kcal), 0)::float AS kcal,
+                    COALESCE(SUM(protein_g), 0)::float AS protein_g,
+                    COALESCE(SUM(carbs_g), 0)::float AS carbs_g,
+                    COALESCE(SUM(fat_g), 0)::float AS fat_g
+                   FROM nutrition_log
+                   WHERE profile_id=%s
+                   GROUP BY date
+                   ORDER BY date ASC""",
+                (profile_id,),
+            )
+        return [r2d(r) for r in cur.fetchall()]
+
+
+def _kcal_weight_points_for_window(
+    profile_id: str, cutoff: Optional[str]
+) -> List[Dict[str, Any]]:
+    """Gemeinsame Tage: Tages-kcal vs. Gewicht; gleiche Idee wie /nutrition/correlations, gefiltert."""
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        if cutoff:
+            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""",
+                (profile_id, cutoff),
+            )
+        else:
+            cur.execute(
+                """SELECT date, SUM(kcal)::float AS kcal
+                   FROM nutrition_log
+                   WHERE profile_id=%s AND kcal IS NOT NULL
+                   GROUP BY date""",
+                (profile_id,),
+            )
+        nk = { _iso(r["date"]): safe_float(r["kcal"]) for r in cur.fetchall() }
+
+        if cutoff:
+            cur.execute(
+                "SELECT date, weight FROM weight_log WHERE profile_id=%s AND date >= %s ORDER BY date",
+                (profile_id, cutoff),
+            )
+        else:
+            cur.execute(
+                "SELECT date, weight FROM weight_log WHERE profile_id=%s ORDER BY date",
+                (profile_id,),
+            )
+        wk = { _iso(r["date"]): safe_float(r["weight"]) for r in cur.fetchall() if r.get("weight") is not None }
+
+    common = sorted(set(nk) & set(wk))
+    raw: List[Dict[str, Any]] = []
+    for ds in common:
+        raw.append({"date": ds, "kcal": nk[ds], "weight": wk[ds]})
+    rolled = _rolling_avg(raw, "kcal", 7)
+    out: List[Dict[str, Any]] = []
+    for r in rolled:
+        out.append(
+            {
+                "date": r["date"],
+                "kcal": r.get("kcal"),
+                "weight": r.get("weight"),
+                "kcal_avg": r.get("kcal_avg"),
+            }
+        )
+    return out
+
+
+def get_nutrition_history_viz_bundle(profile_id: str, days: int) -> Dict[str, Any]:
+    """
+    Layer 2b Bundle für Verlauf «Ernährung».
+
+    days: Analysefenster (>=9999 = gesamte Historie für Mittelwerte / Reihen).
+    """
+    if not _has_nutrition_entries(profile_id):
+        return {
+            "confidence": "insufficient",
+            "has_nutrition_entries": False,
+            "message": "Noch keine Ernährungsdaten",
+            "kpi_tiles": [],
+            "summary": {},
+            "daily_macros": [],
+            "donut_avg_pct": None,
+            "kcal_vs_weight": {"points": [], "tdee_reference_kcal": None, "common_days_count": 0},
+            "weekly_macro_chart": {},
+            "tdee_reference_kcal": None,
+            "energy_balance_meta": {},
+            "interpretation_tiles": [],
+            "energy_availability_warning": None,
+            "meta": {"layer_1": "nutrition_metrics", "layer_2b": "nutrition_viz"},
+        }
+
+    all_history = days >= 9999
+    eff_days = 3650 if all_history else max(7, min(int(days), 3650))
+    cutoff = _cutoff_sql(days)
+    chart_days_for_pipeline = 90 if all_history else max(7, min(eff_days, 365))
+
+    navg = get_nutrition_average_data(profile_id, eff_days, all_history=all_history)
+    targets = get_protein_targets_data(profile_id)
+    energy_days = eff_days if not all_history else min(9999, 3650)
+    energy_meta = get_energy_balance_data(profile_id, energy_days)
+    tdee = estimate_tdee_kcal_from_latest_weight(profile_id)
+    if tdee is None:
+        tdee = safe_float(energy_meta.get("estimated_tdee")) or None
+    else:
+        tdee = float(tdee)
+
+    daily_rows = _fetch_daily_macro_totals(profile_id, cutoff)
+    daily_macros: List[Dict[str, Any]] = []
+    for r in daily_rows:
+        daily_macros.append(
+            {
+                "date": _iso(r["date"]),
+                "kcal": round(safe_float(r.get("kcal")) or 0),
+                "Protein": round(safe_float(r.get("protein_g")) or 0),
+                "KH": round(safe_float(r.get("carbs_g")) or 0),
+                "Fett": round(safe_float(r.get("fat_g")) or 0),
+            }
+        )
+
+    date_span_label = ""
+    if daily_macros:
+        date_span_label = f"{daily_macros[0]['date']} – {daily_macros[-1]['date']}"
+
+    n_days = int(navg.get("data_points") or 0)
+    kpi_tiles = build_nutrition_history_kpi_tiles(
+        navg, targets, date_span_label or "—", max(1, n_days)
+    )
+
+    ea_days = min(28, max(7, chart_days_for_pipeline))
+    ea_payload = get_energy_availability_warning_payload(profile_id, ea_days)
+    ea_tile = build_energy_availability_kpi_tile(ea_payload)
+    kpi_tiles_out: List[Dict[str, Any]] = list(kpi_tiles)
+    if ea_tile:
+        kpi_tiles_out.append(ea_tile)
+
+    donut = build_macro_donut_from_averages(navg)
+
+    kw_points = _kcal_weight_points_for_window(profile_id, cutoff)
+    pt_low = round(float(targets.get("protein_target_low") or 0))
+
+    weeks_for_weekly = max(4, min(52, (chart_days_for_pipeline + 6) // 7))
+    weekly_chart = get_weekly_macro_distribution_chart_data(profile_id, weeks_for_weekly)
+
+    conf = navg.get("confidence") or "medium"
+    if targets.get("confidence") == "insufficient":
+        conf = "insufficient"
+
+    return {
+        "confidence": conf,
+        "has_nutrition_entries": True,
+        "days_requested": days,
+        "effective_window_days": eff_days,
+        "nutrition_charts_days": chart_days_for_pipeline,
+        "weekly_macro_weeks_used": weeks_for_weekly,
+        "last_updated": _last_nutrition_date(profile_id),
+        "summary": {
+            "kcal_avg": navg.get("kcal_avg"),
+            "protein_avg": navg.get("protein_avg"),
+            "carbs_avg": navg.get("carbs_avg"),
+            "fat_avg": navg.get("fat_avg"),
+            "data_points": navg.get("data_points"),
+            "days_analyzed": navg.get("days_analyzed"),
+            "protein_target_low": targets.get("protein_target_low"),
+            "protein_target_high": targets.get("protein_target_high"),
+            "reference_weight_kg": targets.get("current_weight"),
+        },
+        "kpi_tiles": kpi_tiles_out,
+        "interpretation_tiles": [],
+        "energy_availability_warning": ea_payload,
+        "daily_macros": daily_macros,
+        "donut_avg_pct": donut,
+        "protein_reference_line_g": pt_low,
+        "kcal_vs_weight": {
+            "points": kw_points,
+            "tdee_reference_kcal": tdee,
+            "common_days_count": len(kw_points),
+        },
+        "weekly_macro_chart": weekly_chart,
+        "tdee_reference_kcal": tdee,
+        "energy_balance_meta": {
+            "energy_balance": energy_meta.get("energy_balance"),
+            "avg_intake": energy_meta.get("avg_intake"),
+            "estimated_tdee": energy_meta.get("estimated_tdee"),
+            "status": energy_meta.get("status"),
+            "confidence": energy_meta.get("confidence"),
+            "data_points": energy_meta.get("data_points"),
+        },
+        "meta": {
+            "layer_1": "nutrition_metrics",
+            "layer_2b": "nutrition_viz",
+            "issue": "53-phase-0c",
+        },
+    }

backend/routers/charts.py

@@ -32,12 +32,15 @@ from data_layer.body_metrics import (
     get_circumference_summary_data
 )
 from data_layer.body_viz import get_body_history_viz_bundle
+from data_layer.nutrition_viz import get_nutrition_history_viz_bundle
 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,
 )
 from data_layer.activity_metrics import (
     get_activity_summary_data,
@@ -265,6 +268,26 @@ def get_body_history_viz(
     return serialize_dates(bundle)
 
 
+@router.get("/nutrition-history-viz")
+def get_nutrition_history_viz(
+    days: int = Query(
+        default=90,
+        ge=7,
+        le=9999,
+        description="Analysefenster in Tagen (9999 = gesamte Historie)",
+    ),
+    session: dict = Depends(require_auth),
+) -> Dict:
+    """
+    Layer 2b: Ein Bundle für Verlauf «Ernährung» — Kennzahlen, Reihen, TDEE-Referenz, Wochen-Chart.
+
+    Alle Kennzahlen aus nutrition_metrics (gleiche Logik wie Platzhalter / Chart-Endpunkte).
+    """
+    profile_id = session["profile_id"]
+    bundle = get_nutrition_history_viz_bundle(profile_id, days)
+    return serialize_dates(bundle)
+
+
 @router.get("/circumferences")
 def get_circumferences_chart(
     max_age_days: int = Query(default=90, ge=7, le=365),
@@ -830,136 +853,10 @@ def get_weekly_macro_distribution_chart(
     Weekly macro distribution (E3) - Konzept-konform.
 
     100%-gestapelter Wochenbalken statt Pie Chart.
-    Shows macro consistency across weeks, not just overall average.
+    Datenberechnung: data_layer.nutrition_metrics.get_weekly_macro_distribution_chart_data
-
-    Args:
-        weeks: Number of weeks to analyze (4-52, default 12)
-        session: Auth session (injected)
-
-    Returns:
-        Chart.js stacked bar chart with weekly macro percentages
     """
     profile_id = session['profile_id']
-
+    return get_weekly_macro_distribution_chart_data(profile_id, weeks)
-    from db import get_db, get_cursor
-    import statistics
-
-    with get_db() as conn:
-        cur = get_cursor(conn)
-        cutoff = (datetime.now() - timedelta(weeks=weeks)).strftime('%Y-%m-%d')
-
-        cur.execute(
-            """SELECT date, protein_g, carbs_g, fat_g, kcal
-               FROM nutrition_log
-               WHERE profile_id=%s AND date >= %s
-                 AND protein_g IS NOT NULL AND carbs_g IS NOT NULL
-                 AND fat_g IS NOT NULL AND kcal > 0
-               ORDER BY date""",
-            (profile_id, cutoff)
-        )
-        rows = cur.fetchall()
-
-    if not rows or len(rows) < 7:
-        return {
-            "chart_type": "bar",
-            "data": {
-                "labels": [],
-                "datasets": []
-            },
-            "metadata": {
-                "confidence": "insufficient",
-                "data_points": len(rows) if rows else 0,
-                "message": "Nicht genug Daten für Wochen-Analyse (min. 7 Tage)"
-            }
-        }
-
-    # Group by ISO week
-    weekly_data = {}
-    for row in rows:
-        date_obj = row['date'] if isinstance(row['date'], datetime) else datetime.fromisoformat(str(row['date']))
-        iso_week = date_obj.strftime('%Y-W%V')
-
-        if iso_week not in weekly_data:
-            weekly_data[iso_week] = {
-                'protein': [],
-                'carbs': [],
-                'fat': [],
-                'kcal': []
-            }
-
-        weekly_data[iso_week]['protein'].append(safe_float(row['protein_g']))
-        weekly_data[iso_week]['carbs'].append(safe_float(row['carbs_g']))
-        weekly_data[iso_week]['fat'].append(safe_float(row['fat_g']))
-        weekly_data[iso_week]['kcal'].append(safe_float(row['kcal']))
-
-    # Calculate weekly averages and percentages
-    labels = []
-    protein_pcts = []
-    carbs_pcts = []
-    fat_pcts = []
-
-    for iso_week in sorted(weekly_data.keys())[-weeks:]:
-        data = weekly_data[iso_week]
-
-        avg_protein = sum(data['protein']) / len(data['protein']) if data['protein'] else 0
-        avg_carbs = sum(data['carbs']) / len(data['carbs']) if data['carbs'] else 0
-        avg_fat = sum(data['fat']) / len(data['fat']) if data['fat'] else 0
-
-        # Convert to kcal
-        protein_kcal = avg_protein * 4
-        carbs_kcal = avg_carbs * 4
-        fat_kcal = avg_fat * 9
-
-        total_kcal = protein_kcal + carbs_kcal + fat_kcal
-
-        if total_kcal > 0:
-            labels.append(f"KW {iso_week[-2:]}")
-            protein_pcts.append(round((protein_kcal / total_kcal) * 100, 1))
-            carbs_pcts.append(round((carbs_kcal / total_kcal) * 100, 1))
-            fat_pcts.append(round((fat_kcal / total_kcal) * 100, 1))
-
-    # Calculate variation coefficient (Variationskoeffizient)
-    protein_cv = statistics.stdev(protein_pcts) / statistics.mean(protein_pcts) * 100 if len(protein_pcts) > 1 and statistics.mean(protein_pcts) > 0 else 0
-    carbs_cv = statistics.stdev(carbs_pcts) / statistics.mean(carbs_pcts) * 100 if len(carbs_pcts) > 1 and statistics.mean(carbs_pcts) > 0 else 0
-    fat_cv = statistics.stdev(fat_pcts) / statistics.mean(fat_pcts) * 100 if len(fat_pcts) > 1 and statistics.mean(fat_pcts) > 0 else 0
-
-    return {
-        "chart_type": "bar",
-        "data": {
-            "labels": labels,
-            "datasets": [
-                {
-                    "label": "Protein (%)",
-                    "data": protein_pcts,
-                    "backgroundColor": "#1D9E75",
-                    "stack": "macro"
-                },
-                {
-                    "label": "Kohlenhydrate (%)",
-                    "data": carbs_pcts,
-                    "backgroundColor": "#F59E0B",
-                    "stack": "macro"
-                },
-                {
-                    "label": "Fett (%)",
-                    "data": fat_pcts,
-                    "backgroundColor": "#EF4444",
-                    "stack": "macro"
-                }
-            ]
-        },
-        "metadata": {
-            "confidence": calculate_confidence(len(rows), weeks * 7, "general"),
-            "data_points": len(rows),
-            "weeks_analyzed": len(labels),
-            "avg_protein_pct": round(statistics.mean(protein_pcts), 1) if protein_pcts else 0,
-            "avg_carbs_pct": round(statistics.mean(carbs_pcts), 1) if carbs_pcts else 0,
-            "avg_fat_pct": round(statistics.mean(fat_pcts), 1) if fat_pcts else 0,
-            "protein_cv": round(protein_cv, 1),
-            "carbs_cv": round(carbs_cv, 1),
-            "fat_cv": round(fat_cv, 1)
-        }
-    }
 
 
 @router.get("/nutrition-adherence-score")
@@ -1130,87 +1027,10 @@ def get_energy_availability_warning(
     """
     Energy Availability Warning (E5) - Konzept-konform.
 
-    Heuristic warning for potential undernutrition/overtraining.
+    Datenberechnung: data_layer.nutrition_metrics.get_energy_availability_warning_payload
-
-    Checks:
-    - Persistent large deficit
-    - Recovery score declining
-    - Sleep quality declining
-    - LBM declining
-
-    Args:
-        days: Analysis window (7-28 days, default 14)
-        session: Auth session (injected)
-
-    Returns:
-        {
-            "warning_level": "none" | "caution" | "warning",
-            "triggers": [...],
-            "message": "..."
-        }
     """
     profile_id = session['profile_id']
-
+    return get_energy_availability_warning_payload(profile_id, days)
-    from db import get_db, get_cursor
-    from data_layer.nutrition_metrics import get_energy_balance_data
-    from data_layer.recovery_metrics import calculate_recovery_score_v2, calculate_sleep_quality_7d
-    from data_layer.body_metrics import calculate_lbm_28d_change
-
-    triggers = []
-    warning_level = "none"
-
-    # Check 1: Large energy deficit
-    energy_data = get_energy_balance_data(profile_id, days)
-    if energy_data.get('energy_balance', 0) < -500:
-        triggers.append("Großes Energiedefizit (>500 kcal/Tag)")
-
-    # Check 2: Recovery declining
-    try:
-        recovery_score = calculate_recovery_score_v2(profile_id)
-        if recovery_score and recovery_score < 50:
-            triggers.append("Recovery Score niedrig (<50)")
-    except:
-        pass
-
-    # Check 3: Sleep quality
-    try:
-        sleep_quality = calculate_sleep_quality_7d(profile_id)
-        if sleep_quality and sleep_quality < 60:
-            triggers.append("Schlafqualität reduziert (<60%)")
-    except:
-        pass
-
-    # Check 4: LBM declining
-    try:
-        lbm_change = calculate_lbm_28d_change(profile_id)
-        if lbm_change and lbm_change < -1.0:
-            triggers.append("Magermasse sinkt (-{:.1f} kg)".format(abs(lbm_change)))
-    except:
-        pass
-
-    # Determine warning level
-    if len(triggers) >= 3:
-        warning_level = "warning"
-        message = "⚠️ Hinweis auf mögliche Unterversorgung. Mehrere Indikatoren auffällig. Erwäge Defizit-Anpassung oder Regenerationswoche."
-    elif len(triggers) >= 2:
-        warning_level = "caution"
-        message = "⚡ Beobachte folgende Signale genau. Aktuell noch kein Handlungsbedarf, aber Trend beachten."
-    elif len(triggers) >= 1:
-        warning_level = "caution"
-        message = "💡 Ein Indikator auffällig. Weiter beobachten."
-    else:
-        message = "✅ Energieverfügbarkeit unauffällig."
-
-    return {
-        "warning_level": warning_level,
-        "triggers": triggers,
-        "message": message,
-        "metadata": {
-            "days_analyzed": days,
-            "trigger_count": len(triggers),
-            "note": "Heuristische Einschätzung, keine medizinische Diagnose"
-        }
-    }
 
 
 @router.get("/training-volume")

frontend/src/app.css

@@ -199,13 +199,16 @@ body { font-family: var(--font); background: var(--bg); color: var(--text1); -we
 .page-title { font-size: 20px; font-weight: 700; margin-bottom: 16px; }
 
 /* Verlauf: Mobile Tabs horizontale Leiste, Desktop vertikal links (P4 / RESPONSIVE_UI §5.2) */
-/* Körper-Verlauf: KPI-Übersicht (Hover = Details, kein Klick) */
+/* KPI-Kachel-Raster: gemeinsam für Verlauf Körper, Dashboard KPI-Board, …
+   Desktop: title-Tooltip; Touch: ℹ → Bottom-Sheet (siehe KpiTilesOverview.jsx) */
+.kpi-tiles-grid,
 .body-kpi-overview {
   display: grid;
   grid-template-columns: repeat(auto-fill, minmax(158px, 1fr));
   gap: 8px;
   margin-bottom: 12px;
 }
+.kpi-tiles-card,
 .body-kpi-card {
   background: var(--surface2);
   border-radius: 10px;
@@ -215,11 +218,190 @@ body { font-family: var(--font); background: var(--bg); color: var(--text1); -we
   text-align: left;
   transition: border-color 0.15s ease, box-shadow 0.15s ease;
 }
+@media (hover: none) {
+  .kpi-tiles-card,
+  .body-kpi-card {
+    cursor: default;
+  }
+}
+.kpi-tiles-card:hover,
 .body-kpi-card:hover {
   border-color: var(--border2);
   box-shadow: 0 1px 5px rgba(0, 0, 0, 0.07);
 }
 
+.kpi-tiles-info-btn,
+.body-kpi-info-btn {
+  position: absolute;
+  top: 6px;
+  right: 6px;
+  display: inline-flex;
+  align-items: center;
+  justify-content: center;
+  min-width: 36px;
+  min-height: 36px;
+  margin: 0;
+  padding: 0;
+  border: none;
+  border-radius: 8px;
+  background: transparent;
+  color: var(--text3);
+  cursor: pointer;
+  -webkit-tap-highlight-color: transparent;
+}
+.kpi-tiles-info-btn:active,
+.body-kpi-info-btn:active {
+  background: var(--surface);
+  color: var(--accent);
+}
+
+.kpi-tiles-touch-backdrop,
+.body-kpi-touch-backdrop {
+  position: fixed;
+  inset: 0;
+  z-index: 10050;
+  display: flex;
+  align-items: flex-end;
+  justify-content: center;
+  padding: 0 12px;
+  padding-bottom: max(12px, env(safe-area-inset-bottom));
+  background: rgba(0, 0, 0, 0.45);
+  animation: kpi-tiles-fade-in 0.15s ease;
+}
+
+@keyframes kpi-tiles-fade-in {
+  from { opacity: 0; }
+  to { opacity: 1; }
+}
+@keyframes body-kpi-fade-in {
+  from { opacity: 0; }
+  to { opacity: 1; }
+}
+
+.kpi-tiles-touch-sheet,
+.body-kpi-touch-sheet {
+  width: 100%;
+  max-width: 520px;
+  max-height: min(72vh, 560px);
+  overflow: auto;
+  margin: 0 auto;
+  padding: 14px 16px 18px;
+  border-radius: 16px 16px 0 0;
+  background: var(--surface);
+  border: 1px solid var(--border);
+  border-bottom: none;
+  box-shadow: 0 -8px 32px rgba(0, 0, 0, 0.18);
+}
+
+.kpi-tiles-touch-sheet__head,
+.body-kpi-touch-sheet__head {
+  display: flex;
+  align-items: flex-start;
+  justify-content: space-between;
+  gap: 12px;
+  margin-bottom: 10px;
+}
+
+.kpi-tiles-touch-sheet__title,
+.body-kpi-touch-sheet__title {
+  margin: 0;
+  font-size: 16px;
+  font-weight: 700;
+  color: var(--text1);
+  line-height: 1.3;
+  flex: 1;
+  min-width: 0;
+}
+
+.kpi-tiles-touch-sheet__close,
+.body-kpi-touch-sheet__close {
+  flex-shrink: 0;
+  width: 40px;
+  height: 40px;
+  margin: -6px -8px 0 0;
+  padding: 0;
+  border: none;
+  border-radius: 10px;
+  background: transparent;
+  color: var(--text2);
+  font-size: 26px;
+  line-height: 1;
+  cursor: pointer;
+  -webkit-tap-highlight-color: transparent;
+}
+
+.kpi-tiles-touch-sheet__close:active,
+.body-kpi-touch-sheet__close:active {
+  background: var(--surface2);
+}
+
+.kpi-tiles-touch-sheet__body,
+.body-kpi-touch-sheet__body {
+  font-size: 13px;
+  line-height: 1.5;
+  color: var(--text2);
+  white-space: pre-wrap;
+  word-break: break-word;
+}
+
+.kpi-tiles-touch-sheet__body--muted,
+.body-kpi-touch-sheet__body--muted {
+  color: var(--text3);
+  font-style: italic;
+}
+
+/* KPI: Kurz-Hinweis max. 2 Zeilen — Details weiter per ℹ */
+.kpi-tiles-card__hint {
+  max-height: 2.8em;
+}
+
+/* Verlauf Ernährung: Donut (Ø-Quote) + wöchentliche Makro-Verteilung (E3) */
+.nutrition-macro-pair {
+  display: grid;
+  gap: 12px;
+  margin-bottom: 12px;
+  align-items: stretch;
+}
+
+@media (min-width: 780px) {
+  .nutrition-macro-pair {
+    grid-template-columns: minmax(280px, 1fr) minmax(320px, 1.25fr);
+  }
+}
+
+.nutrition-macro-pair__weekly {
+  min-width: 0;
+}
+
+/* Einheitliche Chart-Höhe (Donut-Bereich ≈ E3-Balken) */
+.nutrition-macro-pair__chart-wrap {
+  width: 100%;
+  min-height: 260px;
+}
+
+.nutrition-macro-pair__donut-inner {
+  display: flex;
+  flex-direction: column;
+  gap: 12px;
+  width: 100%;
+}
+
+.nutrition-macro-pair__donut-chart {
+  width: 100%;
+  min-height: 260px;
+}
+
+.nutrition-macro-pair__legend {
+  width: 100%;
+  padding-top: 2px;
+}
+
+.nutrition-macro-pair .card.nutrition-macro-pair__donut,
+.nutrition-macro-pair .card.nutrition-macro-pair__weekly {
+  display: flex;
+  flex-direction: column;
+}
+
 .history-page__title {
   margin-bottom: 12px;
 }

frontend/src/components/KpiTilesOverview.jsx

@@ -0,0 +1,178 @@
+import { useState, useEffect, useId } from 'react'
+import { Info } from 'lucide-react'
+import { getStatusColor } from '../utils/interpret'
+
+/**
+ * Zerlegt eine KPI-Kachel für Bottom-Sheet / Tooltip.
+ * @param {{ hoverTop?: string, category?: string, hoverBody?: string, keys?: string[] }} t
+ */
+export function kpiTileDetailParts(t) {
+  const registryLine = t.keys?.length ? `Registry: ${t.keys.join(', ')}` : ''
+  const body = [t.hoverBody, registryLine].filter(Boolean).join('\n\n')
+  return { title: t.hoverTop || t.category || 'Kennzahl', body }
+}
+
+/** Ein Zeilentext wie natives `title` (Desktop-Hover). */
+export function buildKpiTileTitleString(t) {
+  return [t.hoverTop, t.hoverBody, t.keys?.length ? `Registry: ${t.keys.join(', ')}` : '']
+    .filter(Boolean)
+    .join('\n\n')
+}
+
+/**
+ * Standard-KPI-Kacheln: Desktop `title`-Tooltip, Touch ℹ → Bottom-Sheet (gleicher Inhalt).
+ *
+ * Erwartete Kachel-Felder:
+ * - `key` (string, eindeutig)
+ * - `category` (string) — Zeilenkopf
+ * - `value` (ReactNode) — Hauptwert
+ * - `status` — für Farbstreifen: `good` | `warn` | `bad`
+ * - optional: `icon`, `sublabel`, `verdict`, `valueColor`, `hoverTop`, `hoverBody`, `keys`
+ * - optional: `hint` — Kurz-Hinweis/Warnung direkt auf der Kachel (z. B. Ernährung bei warn/bad)
+ */
+export default function KpiTilesOverview({
+  tiles,
+  heading = 'Kennzahlen',
+  showTouchHint = true,
+  gridClassName = 'kpi-tiles-grid',
+  marginBottom = 12,
+}) {
+  const [touchUi, setTouchUi] = useState(false)
+  const [openKey, setOpenKey] = useState(null)
+  const sheetTitleId = useId()
+
+  useEffect(() => {
+    const mq = window.matchMedia('(hover: none)')
+    const apply = () => setTouchUi(mq.matches)
+    apply()
+    mq.addEventListener('change', apply)
+    return () => mq.removeEventListener('change', apply)
+  }, [])
+
+  useEffect(() => {
+    if (!openKey) return
+    const onKey = e => { if (e.key === 'Escape') setOpenKey(null) }
+    const prev = document.body.style.overflow
+    document.body.style.overflow = 'hidden'
+    window.addEventListener('keydown', onKey)
+    return () => {
+      document.body.style.overflow = prev
+      window.removeEventListener('keydown', onKey)
+    }
+  }, [openKey])
+
+  if (!tiles?.length) return null
+
+  const openTile = openKey ? tiles.find(x => x.key === openKey) : null
+  const openParts = openTile ? kpiTileDetailParts(openTile) : null
+
+  const showVerdict = (v) => v != null && String(v).trim() !== '' && String(v).trim() !== '—'
+
+  return (
+    <div style={{ marginBottom }}>
+      {heading ? (
+        <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8 }}>{heading}</div>
+      ) : null}
+      {showTouchHint && touchUi && (
+        <div style={{ fontSize: 10, color: 'var(--text3)', marginBottom: 8, lineHeight: 1.4 }}>
+          <Info size={12} style={{ display: 'inline', verticalAlign: 'middle', marginRight: 4 }} aria-hidden />
+          Auf dem Smartphone: <strong>ℹ</strong> für Erklärung und Details.
+        </div>
+      )}
+      <div className={gridClassName}>
+        {tiles.map(t => {
+          const accent = getStatusColor(t.status)
+          const tip = buildKpiTileTitleString(t)
+          const cardHint = t.hint ? String(t.hint) : null
+          return (
+            <div
+              key={t.key}
+              className="kpi-tiles-card"
+              style={{ borderLeft: `4px solid ${accent}`, position: 'relative' }}
+              title={touchUi ? undefined : tip}
+            >
+              {touchUi && (
+                <button
+                  type="button"
+                  className="kpi-tiles-info-btn"
+                  aria-label={`Details: ${t.category || t.hoverTop || 'Kennzahl'}`}
+                  aria-expanded={openKey === t.key}
+                  onClick={() => setOpenKey(k => (k === t.key ? null : t.key))}
+                >
+                  <Info size={16} strokeWidth={2.25} aria-hidden />
+                </button>
+              )}
+              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', gap: 6, paddingRight: touchUi ? 28 : 0 }}>
+                {t.icon != null && t.icon !== false ? (
+                  <span style={{ fontSize: 14, lineHeight: 1 }}>{t.icon}</span>
+                ) : (
+                  <span style={{ width: 0, flexShrink: 0 }} aria-hidden />
+                )}
+                <div style={{ flex: 1, minWidth: 0 }}>
+                  <div style={{ fontSize: 9, fontWeight: 600, color: 'var(--text3)', textTransform: 'uppercase', letterSpacing: '0.04em' }}>{t.category}</div>
+                  <div style={{ fontSize: 18, fontWeight: 700, color: t.valueColor || 'var(--text1)', marginTop: 2, lineHeight: 1.2 }}>{t.value}</div>
+                  {t.sublabel ? (
+                    <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 2, lineHeight: 1.25 }}>{t.sublabel}</div>
+                  ) : null}
+                </div>
+                {showVerdict(t.verdict) ? (
+                  <div style={{ textAlign: 'right', flexShrink: 0 }}>
+                    <div style={{ fontSize: 10, fontWeight: 700, color: accent, lineHeight: 1.2 }}>{t.verdict}</div>
+                  </div>
+                ) : null}
+              </div>
+              {cardHint ? (
+                <div
+                  className="kpi-tiles-card__hint"
+                  style={{
+                    marginTop: 6,
+                    paddingLeft: 8,
+                    borderLeft: `2px solid ${accent}`,
+                    fontSize: 9,
+                    lineHeight: 1.35,
+                    color: 'var(--text2)',
+                    display: '-webkit-box',
+                    WebkitLineClamp: 2,
+                    WebkitBoxOrient: 'vertical',
+                    overflow: 'hidden',
+                    wordBreak: 'break-word',
+                  }}
+                >
+                  {cardHint}
+                </div>
+              ) : null}
+            </div>
+          )
+        })}
+      </div>
+
+      {openParts && (
+        <div
+          className="kpi-tiles-touch-backdrop"
+          role="presentation"
+          onClick={() => setOpenKey(null)}
+        >
+          <div
+            className="kpi-tiles-touch-sheet"
+            role="dialog"
+            aria-modal="true"
+            aria-labelledby={sheetTitleId}
+            onClick={e => e.stopPropagation()}
+          >
+            <div className="kpi-tiles-touch-sheet__head">
+              <h3 id={sheetTitleId} className="kpi-tiles-touch-sheet__title">{openParts.title}</h3>
+              <button type="button" className="kpi-tiles-touch-sheet__close" onClick={() => setOpenKey(null)} aria-label="Schließen">
+                ×
+              </button>
+            </div>
+            {openParts.body ? (
+              <div className="kpi-tiles-touch-sheet__body">{openParts.body}</div>
+            ) : (
+              <div className="kpi-tiles-touch-sheet__body kpi-tiles-touch-sheet__body--muted">Keine weiteren Details.</div>
+            )}
+          </div>
+        </div>
+      )}
+    </div>
+  )
+}

frontend/src/components/NutritionCharts.jsx

@@ -1,9 +1,11 @@
 import { useState, useEffect } from 'react'
 import {
   LineChart, Line, BarChart, Bar,
-  XAxis, YAxis, Tooltip, ResponsiveContainer, CartesianGrid, Legend
+  XAxis, YAxis, Tooltip, ResponsiveContainer, CartesianGrid, Legend,
+  ComposedChart, ReferenceArea,
 } from 'recharts'
 import { api } from '../utils/api'
+import { MACRO_CHART, NUTRITION_MACRO_CHART_BLOCK_PX } from '../utils/macroChartTheme'
 import dayjs from 'dayjs'
 
 const fmtDate = d => dayjs(d).format('DD.MM')
@@ -135,16 +137,81 @@ function WarningCard({ title, warning_level, triggers, message }) {
   )
 }
 
+/** Wöchentliche Makro-Verteilung (E3) — für Verlauf neben Donut nutzbar. */
+export function WeeklyMacroDistributionPanel({ macroWeeklyData, loading, error }) {
+  if (loading) {
+    return (
+      <div style={{ display: 'flex', justifyContent: 'center', padding: 40 }}>
+        <div className="spinner" style={{ width: 32, height: 32 }} />
+      </div>
+    )
+  }
+  if (error) {
+    return (
+      <div style={{ padding: 20, textAlign: 'center', color: 'var(--text3)', fontSize: 12 }}>{error}</div>
+    )
+  }
+  if (!macroWeeklyData || macroWeeklyData.metadata?.confidence === 'insufficient') {
+    const msg = macroWeeklyData?.metadata?.message || 'Nicht genug Daten für Wochen-Analyse (min. 7 Tage)'
+    return (
+      <div style={{ padding: 20, textAlign: 'center', color: 'var(--text3)', fontSize: 12 }}>{msg}</div>
+    )
+  }
+
+  const chartData = macroWeeklyData.data.labels.map((label, i) => ({
+    week: label,
+    protein: macroWeeklyData.data.datasets[0]?.data[i],
+    carbs: macroWeeklyData.data.datasets[1]?.data[i],
+    fat: macroWeeklyData.data.datasets[2]?.data[i],
+  }))
+
+  const meta = macroWeeklyData.metadata
+
+  return (
+    <>
+      <div style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 10 }}>
+        Anteil der Kalorien aus jedem Makronährstoff pro Kalenderwoche (100&nbsp;% gestapelt). Gut vergleichbar mit der
+        Donut-Übersicht links.
+      </div>
+      <div className="nutrition-macro-pair__chart-wrap">
+        <ResponsiveContainer width="100%" height={NUTRITION_MACRO_CHART_BLOCK_PX}>
+          <BarChart data={chartData} margin={{ top: 8, right: 4, bottom: 4, left: -18 }} barCategoryGap="18%">
+            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" vertical={false} />
+            <XAxis
+              dataKey="week"
+              tick={{ fontSize: 9, fill: 'var(--text3)' }}
+              tickLine={false}
+              interval={Math.max(0, Math.floor(chartData.length / 8) - 1)}
+            />
+            <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={[0, 100]} ticks={[0, 25, 50, 75, 100]} />
+            <Tooltip
+              contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
+              formatter={(v, name) => [`${v}%`, name]}
+            />
+            <Legend wrapperStyle={{ fontSize: 10, paddingTop: 8 }} />
+            <Bar dataKey="protein" stackId="a" fill={MACRO_CHART.protein} name="Protein %" radius={[0, 0, 0, 0]} />
+            <Bar dataKey="fat" stackId="a" fill={MACRO_CHART.fat} name="Fett %" radius={[0, 0, 0, 0]} />
+            <Bar dataKey="carbs" stackId="a" fill={MACRO_CHART.carbs} name="KH %" radius={[6, 6, 0, 0]} />
+          </BarChart>
+        </ResponsiveContainer>
+      </div>
+      <div style={{ marginTop: 10, fontSize: 10, color: 'var(--text3)', textAlign: 'center', lineHeight: 1.5 }}>
+        Ø Verteilung: P {meta.avg_protein_pct}% · KH {meta.avg_carbs_pct}% · F {meta.avg_fat_pct}% · Variabilität (CV): P{' '}
+        {meta.protein_cv}% · KH {meta.carbs_cv}% · F {meta.fat_cv}%
+      </div>
+    </>
+  )
+}
+
 /**
- * Nutrition Charts Component (E1-E5) - Konzept-konform v2.0
+ * Nutrition Charts (E1–E5). Verlauf: `showWeeklyMacroDistribution={false}` wenn E3 separat (z. B. neben Donut) gerendert wird.
- *
- * E1: Energy Balance (mit 7d/14d Durchschnitten)
- * E2: Protein Adequacy (mit 7d/28d Durchschnitten)
- * E3: Weekly Macro Distribution (100% gestapelte Balken)
- * E4: Nutrition Adherence Score (0-100, goal-aware)
- * E5: Energy Availability Warning (Ampel-System)
  */
-export default function NutritionCharts({ days = 28 }) {
+export default function NutritionCharts({
+  days = 28,
+  showWeeklyMacroDistribution = true,
+  /** Verlauf: E5-Kachel liegt in nutrition-history-viz KPIs — doppelte Karte ausblenden */
+  hideEnergyAvailabilityCard = false,
+}) {
   const [energyData, setEnergyData] = useState(null)
   const [proteinData, setProteinData] = useState(null)
   const [macroWeeklyData, setMacroWeeklyData] = useState(null)
@@ -159,16 +226,21 @@ export default function NutritionCharts({ days = 28 }) {
 
   useEffect(() => {
     loadCharts()
-  }, [days])
+  }, [days, showWeeklyMacroDistribution, hideEnergyAvailabilityCard])
 
   const loadCharts = async () => {
-    await Promise.all([
+    const tasks = [
       loadEnergyBalance(),
       loadProteinAdequacy(),
-      loadMacroWeekly(),
       loadAdherence(),
-      loadWarning()
+    ]
-    ])
+    if (!hideEnergyAvailabilityCard) {
+      tasks.push(loadWarning())
+    }
+    if (showWeeklyMacroDistribution) {
+      tasks.splice(2, 0, loadMacroWeekly())
+    }
+    await Promise.all(tasks)
   }
 
   const loadEnergyBalance = async () => {
@@ -236,12 +308,13 @@ export default function NutritionCharts({ days = 28 }) {
     }
   }
 
-  // E1: Energy Balance Timeline (mit 7d/14d Durchschnitten)
+  // E1: Energy Balance — klare Farben (kein hellgraues Gewirr)
   const renderEnergyBalance = () => {
     if (!energyData || energyData.metadata?.confidence === 'insufficient') {
-      return <div style={{padding:20,textAlign:'center',color:'var(--text3)',fontSize:12}}>
+      const msg = energyData?.metadata?.message || 'Nicht genug Ernährungsdaten für die Energiebilanz.'
-        Nicht genug Ernährungsdaten (min. 7 Tage)
+      return (
-      </div>
+        <div style={{ padding: 20, textAlign: 'center', color: 'var(--text3)', fontSize: 12 }}>{msg}</div>
+      )
     }
 
     const chartData = energyData.data.labels.map((label, i) => ({
@@ -249,7 +322,7 @@ export default function NutritionCharts({ days = 28 }) {
       täglich: energyData.data.datasets[0]?.data[i],
       avg7d: energyData.data.datasets[1]?.data[i],
       avg14d: energyData.data.datasets[2]?.data[i],
-      tdee: energyData.data.datasets[3]?.data[i]
+      tdee: energyData.data.datasets[3]?.data[i],
     }))
 
     const balance = energyData.metadata?.energy_balance || 0
@@ -257,111 +330,90 @@ export default function NutritionCharts({ days = 28 }) {
 
     return (
       <>
-        <ResponsiveContainer width="100%" height={220}>
+        <div style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
-          <LineChart data={chartData} margin={{top:4,right:8,bottom:0,left:-20}}>
+          Tägliche Aufnahme, gleitende Mittel und geschätzter TDEE — Linien sind farblich getrennt (Legende unten).
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
+        </div>
-            <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
+        <ResponsiveContainer width="100%" height={240}>
-              interval={Math.max(0,Math.floor(chartData.length/6)-1)}/>
+          <LineChart data={chartData} margin={{ top: 6, right: 10, bottom: 4, left: -18 }}>
+            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" vertical={false} />
+            <XAxis
+              dataKey="date"
+              tick={{ fontSize: 9, fill: 'var(--text3)' }}
+              tickLine={false}
+              interval={Math.max(0, Math.floor(chartData.length / 6) - 1)}
+            />
             <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}/>
+            <Tooltip
-            <Legend wrapperStyle={{fontSize:10}}/>
+              contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
-            <Line type="monotone" dataKey="täglich" stroke="#ccc" strokeWidth={1.5} dot={{r:1.5}} name="Täglich"/>
+            />
-            <Line type="monotone" dataKey="avg7d" stroke="#1D9E75" strokeWidth={2.5} dot={false} name="Ø 7d"/>
+            <Legend wrapperStyle={{ fontSize: 10, paddingTop: 6 }} />
-            <Line type="monotone" dataKey="avg14d" stroke="#085041" strokeWidth={2} strokeDasharray="6 3" dot={false} name="Ø 14d"/>
+            <Line type="monotone" dataKey="täglich" stroke="#64748B" strokeWidth={2} dot={{ r: 2 }} name="Täglich kcal" />
-            <Line type="monotone" dataKey="tdee" stroke="#888" strokeWidth={1.5} strokeDasharray="3 3" dot={false} name="TDEE"/>
+            <Line type="monotone" dataKey="avg14d" stroke="#6366F1" strokeWidth={2.5} dot={false} name="Ø 14 Tage" />
+            <Line type="monotone" dataKey="avg7d" stroke="#10B981" strokeWidth={3} dot={false} name="Ø 7 Tage" />
+            <Line type="monotone" dataKey="tdee" stroke="#EA580C" strokeWidth={2.5} strokeDasharray="10 5" dot={false} name="TDEE (Referenz)" />
           </LineChart>
         </ResponsiveContainer>
-        <div style={{marginTop:8,fontSize:10,textAlign:'center'}}>
+        <div style={{ marginTop: 8, fontSize: 10, textAlign: 'center', lineHeight: 1.5 }}>
-          <span style={{color:'var(--text3)'}}>
+          <span style={{ color: 'var(--text3)' }}>Ø {energyData.metadata.avg_kcal} kcal/Tag ·</span>
-            Ø {energyData.metadata.avg_kcal} kcal/Tag ·
-          </span>
           <span style={{ color: balanceColor, fontWeight: 600, marginLeft: 4 }}>
-            Balance: {balance > 0 ? '+' : ''}{balance} kcal/Tag
+            Balance: {balance > 0 ? '+' : ''}
-          </span>
+            {balance} kcal/Tag
-          <span style={{color:'var(--text3)',marginLeft:8}}>
-            · {energyData.metadata.data_points} Tage
           </span>
+          <span style={{ color: 'var(--text3)', marginLeft: 8 }}>· {energyData.metadata.data_points} Tage</span>
         </div>
       </>
     )
   }
 
-  // E2: Protein Adequacy Timeline (mit 7d/28d Durchschnitten)
+  // E2: Protein — Zielzone als Fläche, Linien klar von E1 abgrenzbar
   const renderProteinAdequacy = () => {
     if (!proteinData || proteinData.metadata?.confidence === 'insufficient') {
-      return <div style={{padding:20,textAlign:'center',color:'var(--text3)',fontSize:12}}>
+      const msg = proteinData?.metadata?.message || 'Nicht genug Protein-Daten für dieses Diagramm.'
-        Nicht genug Protein-Daten (min. 7 Tage)
+      return (
-      </div>
+        <div style={{ padding: 20, textAlign: 'center', color: 'var(--text3)', fontSize: 12 }}>{msg}</div>
+      )
     }
 
+    const tl = proteinData.metadata.target_low
+    const th = proteinData.metadata.target_high
+
     const chartData = proteinData.data.labels.map((label, i) => ({
       date: fmtDate(label),
       täglich: proteinData.data.datasets[0]?.data[i],
       avg7d: proteinData.data.datasets[1]?.data[i],
       avg28d: proteinData.data.datasets[2]?.data[i],
-      targetLow: proteinData.data.datasets[3]?.data[i],
-      targetHigh: proteinData.data.datasets[4]?.data[i]
     }))
 
     return (
       <>
-        <ResponsiveContainer width="100%" height={220}>
+        <div style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
-          <LineChart data={chartData} margin={{top:4,right:8,bottom:0,left:-20}}>
+          Grüne Zone = empfohlenes Protein-Ziel (g/Tag). Tägliche Werte und Mittel — andere Farben als Energiebilanz oben.
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
+        </div>
-            <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
+        <ResponsiveContainer width="100%" height={250}>
-              interval={Math.max(0,Math.floor(chartData.length/6)-1)}/>
+          <ComposedChart data={chartData} margin={{ top: 6, right: 10, bottom: 4, left: -18 }}>
+            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" vertical={false} />
+            <XAxis
+              dataKey="date"
+              tick={{ fontSize: 9, fill: 'var(--text3)' }}
+              tickLine={false}
+              interval={Math.max(0, Math.floor(chartData.length / 6) - 1)}
+            />
             <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}/>
+            <Tooltip
-            <Legend wrapperStyle={{fontSize:10}}/>
+              contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
-            <Line type="monotone" dataKey="targetLow" stroke="#888" strokeWidth={1} strokeDasharray="5 5" dot={false} name="Ziel Min"/>
+            />
-            <Line type="monotone" dataKey="targetHigh" stroke="#888" strokeWidth={1} strokeDasharray="5 5" dot={false} name="Ziel Max"/>
+            {tl != null && th != null && (
-            <Line type="monotone" dataKey="täglich" stroke="#ccc" strokeWidth={1.5} dot={{r:1.5}} name="Täglich"/>
+              <ReferenceArea y1={tl} y2={th} fill="rgba(16, 185, 129, 0.14)" stroke="#10B981" strokeOpacity={0.35} />
-            <Line type="monotone" dataKey="avg7d" stroke="#1D9E75" strokeWidth={2.5} dot={false} name="Ø 7d"/>
+            )}
-            <Line type="monotone" dataKey="avg28d" stroke="#085041" strokeWidth={2} strokeDasharray="6 3" dot={false} name="Ø 28d"/>
+            <Legend wrapperStyle={{ fontSize: 10, paddingTop: 6 }} />
-          </LineChart>
+            <Line type="monotone" dataKey="avg28d" stroke="#7C3AED" strokeWidth={2.5} dot={false} name="Ø 28 Tage" />
+            <Line type="monotone" dataKey="avg7d" stroke="#059669" strokeWidth={3} dot={false} name="Ø 7 Tage" />
+            <Line type="monotone" dataKey="täglich" stroke="#0284C7" strokeWidth={2} dot={{ r: 2 }} name="Täglich g" />
+          </ComposedChart>
         </ResponsiveContainer>
-        <div style={{marginTop:8,fontSize:10,color:'var(--text3)',textAlign:'center'}}>
+        <div style={{ marginTop: 8, fontSize: 10, color: 'var(--text3)', textAlign: 'center', lineHeight: 1.5 }}>
-          {proteinData.metadata.days_in_target}/{proteinData.metadata.data_points} Tage im Zielbereich ({proteinData.metadata.target_compliance_pct}%)
+          Ziel {tl}–{th} g/Tag · {proteinData.metadata.days_in_target}/{proteinData.metadata.data_points} Tage im Zielbereich (
-        </div>
+          {proteinData.metadata.target_compliance_pct}%)
-      </>
-    )
-  }
-
-  // E3: Weekly Macro Distribution (100% gestapelte Balken)
-  const renderMacroWeekly = () => {
-    if (!macroWeeklyData || macroWeeklyData.metadata?.confidence === 'insufficient') {
-      return <div style={{padding:20,textAlign:'center',color:'var(--text3)',fontSize:12}}>
-        Nicht genug Daten für Wochen-Analyse (min. 7 Tage)
-      </div>
-    }
-
-    const chartData = macroWeeklyData.data.labels.map((label, i) => ({
-      week: label,
-      protein: macroWeeklyData.data.datasets[0]?.data[i],
-      carbs: macroWeeklyData.data.datasets[1]?.data[i],
-      fat: macroWeeklyData.data.datasets[2]?.data[i]
-    }))
-
-    const meta = macroWeeklyData.metadata
-
-    return (
-      <>
-        <ResponsiveContainer width="100%" height={240}>
-          <BarChart data={chartData} margin={{top:4,right:8,bottom:0,left:-20}}>
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
-            <XAxis dataKey="week" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
-              interval={Math.max(0,Math.floor(chartData.length/8)-1)}/>
-            <YAxis tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false} domain={[0,100]}/>
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}/>
-            <Legend wrapperStyle={{fontSize:10}}/>
-            <Bar dataKey="protein" stackId="a" fill="#1D9E75" name="Protein %"/>
-            <Bar dataKey="carbs" stackId="a" fill="#F59E0B" name="Kohlenhydrate %"/>
-            <Bar dataKey="fat" stackId="a" fill="#EF4444" name="Fett %"/>
-          </BarChart>
-        </ResponsiveContainer>
-        <div style={{marginTop:8,fontSize:10,color:'var(--text3)',textAlign:'center'}}>
-          Ø Verteilung: P {meta.avg_protein_pct}% · C {meta.avg_carbs_pct}% · F {meta.avg_fat_pct}% ·
-          Konsistenz (CV): P {meta.protein_cv}% · C {meta.carbs_cv}% · F {meta.fat_cv}%
         </div>
       </>
     )
@@ -414,20 +466,22 @@ export default function NutritionCharts({ days = 28 }) {
 
   return (
     <div>
-      <ChartCard title="📊 Energiebilanz (E1)" loading={loading.energy} error={errors.energy}>
+      <ChartCard title="📊 Energiebilanz" loading={loading.energy} error={errors.energy}>
         {renderEnergyBalance()}
       </ChartCard>
 
-      <ChartCard title="📊 Protein-Adequacy (E2)" loading={loading.protein} error={errors.protein}>
+      <ChartCard title="📊 Protein (Adequacy)" loading={loading.protein} error={errors.protein}>
         {renderProteinAdequacy()}
       </ChartCard>
 
-      <ChartCard title="📊 Wöchentliche Makro-Verteilung (E3)" loading={loading.macro} error={errors.macro}>
+      {showWeeklyMacroDistribution && (
-        {renderMacroWeekly()}
+        <ChartCard title="📊 Wöchentliche Makro-Verteilung" loading={loading.macro} error={errors.macro}>
+          <WeeklyMacroDistributionPanel macroWeeklyData={macroWeeklyData} loading={false} error={null} />
         </ChartCard>
+      )}
 
       {!loading.adherence && !errors.adherence && renderAdherence()}
-      {!loading.warning && !errors.warning && renderWarning()}
+      {!hideEnergyAvailabilityCard && !loading.warning && !errors.warning && renderWarning()}
     </div>
   )
 }

frontend/src/components/pilot/PilotKpiBoard.jsx

@@ -1,4 +1,4 @@
-import { useState, useEffect, useMemo, useCallback } from 'react'
+import { useState, useEffect, useMemo } from 'react'
 import { Link } from 'react-router-dom'
 import dayjs from 'dayjs'
 import { api } from '../../utils/api'
@@ -6,6 +6,7 @@ import { getBfCategory } from '../../utils/calc'
 import { useProfile } from '../../context/ProfileContext'
 import { KPI_KCAL_WINDOW_DEFAULT } from '../../widgetSystem/bodyChartDays'
 import { kpiTileOrderFromConfig } from '../../widgetSystem/kpiBoardTiles'
+import KpiTilesOverview from '../KpiTilesOverview'
 
 const MAX_KPI = 9
 
@@ -113,62 +114,63 @@ export default function PilotKpiBoard({ refreshTick = 0, kpiConfig }) {
     return buildAutoTileIds(refTiles, hasBf, hasKcal)
   }, [manualOrder, refTiles, bf, avgKcal])
 
-  const pushTileForId = useCallback(
+  const kpiTiles = useMemo(() => {
-    (id, out) => {
-      if (id === 'body_fat') {
-        if (!bf) return
-        out.push(
-          <div key="kpi-bf" className="card" style={{ margin: 0, padding: 12, boxSizing: 'border-box' }}>
-            <div style={{ fontSize: 11, fontWeight: 600, color: 'var(--text3)' }}>Körperfett</div>
-            <div style={{ fontSize: 17, fontWeight: 700, marginTop: 4, color: bf.cat?.color || 'var(--text1)' }}>
-              {bf.pct}%
-            </div>
-            <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 4 }}>{bf.cat?.label || 'Caliper'}</div>
-          </div>,
-        )
-        return
-      }
-      if (id === 'avg_kcal') {
-        if (avgKcal == null) return
-        out.push(
-          <div key="kpi-kcal" className="card" style={{ margin: 0, padding: 12, boxSizing: 'border-box' }}>
-            <div style={{ fontSize: 11, fontWeight: 600, color: 'var(--text3)' }}>
-              Ø Kalorien ({KPI_KCAL_WINDOW_DEFAULT}T)
-            </div>
-            <div style={{ fontSize: 17, fontWeight: 700, marginTop: 4, color: '#EF9F27' }}>{avgKcal} kcal</div>
-            <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 4 }}>Ernährung</div>
-          </div>,
-        )
-        return
-      }
-      const tk = parseRefTypeKey(id)
-      if (!tk) return
-      const tile = refByKey.get(tk)
-      if (!tile?.latest) return
-      const l = tile.latest
-      out.push(
-        <div key={`ref-${tk}`} className="card" style={{ margin: 0, padding: 12, boxSizing: 'border-box' }}>
-          <div style={{ fontSize: 11, fontWeight: 600, color: 'var(--text3)' }}>{tile.type_label}</div>
-          <div style={{ fontSize: 17, fontWeight: 700, marginTop: 4 }}>
-            {formatRefVal(l)}
-            {l.unit ? (
-              <span style={{ fontSize: 13, fontWeight: 600, color: 'var(--text2)', marginLeft: 4 }}>{l.unit}</span>
-            ) : null}
-          </div>
-          <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 4 }}>Ref.wert</div>
-        </div>,
-      )
-    },
-    [bf, avgKcal, refByKey],
-  )
-
-  const visibleTiles = useMemo(() => {
     const out = []
     for (const id of orderIds) {
-      pushTileForId(id, out)
+      if (id === 'body_fat') {
+        if (!bf) continue
+        out.push({
+          key: 'kpi-bf',
+          status: 'good',
+          category: 'Körperfett',
+          icon: '🫧',
+          value: `${bf.pct}%`,
+          sublabel: bf.cat?.label || 'Caliper',
+          valueColor: bf.cat?.color,
+          hoverTop: 'Körperfett (Caliper)',
+          hoverBody:
+            `Letzte Messung: ${bf.date ? dayjs(bf.date).format('DD.MM.YYYY') : '—'}.\n` +
+            'Wert aus dem Caliper-Log; die Farbe/Kategorie richtet sich nach Geschlecht und üblicher Spanne.',
+        })
+        continue
+      }
+      if (id === 'avg_kcal') {
+        if (avgKcal == null) continue
+        out.push({
+          key: 'kpi-kcal',
+          status: 'good',
+          category: `Ø Kalorien (${KPI_KCAL_WINDOW_DEFAULT}T)`,
+          icon: '🍽️',
+          value: `${avgKcal} kcal`,
+          sublabel: 'Ernährung',
+          valueColor: '#EF9F27',
+          hoverTop: `Ø Kalorien (${KPI_KCAL_WINDOW_DEFAULT} Tage)`,
+          hoverBody:
+            `Durchschnitt der täglichen Kalorien aus dem Ernährungs-Log über die letzten ${KPI_KCAL_WINDOW_DEFAULT} Tage (Mittel über alle geladenen Tageseinträge im Fenster).`,
+        })
+        continue
+      }
+      const tk = parseRefTypeKey(id)
+      if (!tk) continue
+      const tile = refByKey.get(tk)
+      if (!tile?.latest) continue
+      const l = tile.latest
+      const valStr = formatRefVal(l)
+      const withUnit = l.unit ? `${valStr} ${l.unit}`.trim() : valStr
+      out.push({
+        key: `ref-${tk}`,
+        status: 'good',
+        category: tile.type_label,
+        icon: '📌',
+        value: withUnit,
+        sublabel: 'Ref.wert',
+        hoverTop: tile.type_label,
+        hoverBody:
+          'Persönlicher Referenzwert aus dem Profil. Verwaltung unter Einstellungen → Referenzwerte.',
+      })
     }
     return out
-  }, [orderIds, pushTileForId])
+  }, [orderIds, bf, avgKcal, refByKey])
 
   if (loading) {
     return (
@@ -185,7 +187,7 @@ export default function PilotKpiBoard({ refreshTick = 0, kpiConfig }) {
     )
   }
 
-  if (visibleTiles.length === 0) {
+  if (kpiTiles.length === 0) {
     return (
       <div className="card section-gap">
         <div className="card-title">Kennzahlen</div>
@@ -216,7 +218,13 @@ export default function PilotKpiBoard({ refreshTick = 0, kpiConfig }) {
           ? 'Ausgewählte Kacheln in festgelegter Reihenfolge (ohne Daten werden Kacheln ausgelassen).'
           : `Bis ${MAX_KPI} Kacheln: Referenzwerte, Körperfett, Ø Kalorien (${KPI_KCAL_WINDOW_DEFAULT}T).`}
       </p>
-      <div className="ref-value-tiles-grid">{visibleTiles}</div>
+      <KpiTilesOverview
+        tiles={kpiTiles}
+        heading={null}
+        showTouchHint
+        gridClassName="ref-value-tiles-grid"
+        marginBottom={0}
+      />
     </div>
   )
 }

frontend/src/pages/History.jsx

@@ -11,10 +11,12 @@ import { api } from '../utils/api'
 import { photoMonthKey, photoSortKey, formatPhotoCaption } from '../utils/photoDisplay'
 import { getBfCategory } from '../utils/calc'
 import { getStatusColor, getStatusBg } from '../utils/interpret'
+import { MACRO_CHART, macroFillByName, NUTRITION_MACRO_CHART_BLOCK_PX } from '../utils/macroChartTheme'
 import Markdown from '../utils/Markdown'
 import TrainingTypeDistribution from '../components/TrainingTypeDistribution'
-import NutritionCharts from '../components/NutritionCharts'
+import NutritionCharts, { WeeklyMacroDistributionPanel } from '../components/NutritionCharts'
 import RecoveryCharts from '../components/RecoveryCharts'
+import KpiTilesOverview from '../components/KpiTilesOverview'
 import dayjs from 'dayjs'
 import 'dayjs/locale/de'
 dayjs.locale('de')
@@ -232,39 +234,46 @@ function buildBodyKpiTiles({
   return tiles
 }
 
-/** KPI-Kacheln: Kurzvergleich sichtbar, ausführlicher Text per nativem Hover (`title`). */
+function NutritionGoalsStrip({ grouped }) {
-function BodyKpiOverview({ tiles }) {
+  const nav = useNavigate()
-  if (!tiles?.length) return null
+  const goals = (grouped?.nutrition || []).filter(g => g.status === 'active').slice(0, 4)
-  return (
+  if (!goals.length) return null
-    <div style={{ marginBottom: 12 }}>
-      <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8 }}>Kennzahlen</div>
-      <div className="body-kpi-overview">
-        {tiles.map(t => {
-          const accent = getStatusColor(t.status)
-          const tip = [t.hoverTop, t.hoverBody, t.keys?.length ? `Registry: ${t.keys.join(', ')}` : ''].filter(Boolean).join('\n\n')
   return (
+    <div className="card" style={{ marginBottom: 12 }}>
+      <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center', marginBottom: 8 }}>
+        <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)' }}>Ernährungsbezogene Ziele</div>
+        <button type="button" className="btn btn-secondary" style={{ fontSize: 10, padding: '2px 8px' }} onClick={() => nav('/goals')}>
+          Ziele <ChevronRight size={10} />
+        </button>
+      </div>
+      <div style={{ display: 'flex', flexWrap: 'wrap', gap: 8 }}>
+        {goals.map(g => (
           <div
-              key={t.key}
+            key={g.id}
-              className="body-kpi-card"
+            style={{
-              style={{ borderLeft: `4px solid ${accent}` }}
+              flex: '1 1 140px',
-              title={tip}
+              background: 'var(--surface2)',
+              borderRadius: 8,
+              padding: '8px 10px',
+              borderTop: `3px solid ${g.is_primary ? 'var(--accent)' : 'var(--border2)'}`,
+            }}
           >
-              <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', gap: 6 }}>
+            <div style={{
-                <span style={{ fontSize: 14, lineHeight: 1 }}>{t.icon}</span>
+              fontSize: 11, fontWeight: 600, color: 'var(--text2)',
-                <div style={{ flex: 1, minWidth: 0 }}>
+              whiteSpace: 'nowrap', overflow: 'hidden', textOverflow: 'ellipsis',
-                  <div style={{ fontSize: 9, fontWeight: 600, color: 'var(--text3)', textTransform: 'uppercase', letterSpacing: '0.04em' }}>{t.category}</div>
+            }}>{g.name || g.label_de || g.goal_type}</div>
-                  <div style={{ fontSize: 18, fontWeight: 700, color: t.valueColor || 'var(--text1)', marginTop: 2, lineHeight: 1.2 }}>{t.value}</div>
+            <div style={{ marginTop: 4, height: 6, background: 'var(--border)', borderRadius: 3, overflow: 'hidden' }}>
-                  {t.sublabel && (
+              <div style={{
-                    <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 2, lineHeight: 1.25 }}>{t.sublabel}</div>
+                width: `${Math.min(100, Math.max(0, g.progress_pct ?? 0))}%`,
-                  )}
+                height: '100%',
+                background: 'var(--accent)',
+              }} />
             </div>
-                <div style={{ textAlign: 'right', flexShrink: 0 }}>
+            <div style={{ fontSize: 10, color: 'var(--text3)', marginTop: 4 }}>
-                  <div style={{ fontSize: 10, fontWeight: 700, color: accent, lineHeight: 1.2 }}>{t.verdict}</div>
+              {Math.round(g.progress_pct ?? 0)}% · Ziel {g.target_value}{g.unit ? ` ${g.unit}` : ''}
             </div>
           </div>
-            </div>
+        ))}
-          )
-        })}
       </div>
     </div>
   )
@@ -535,7 +544,7 @@ function BodySection({ profile, insights, onRequest, loadingSlug, filterActiveSl
         </div>
       )}
 
-      <BodyKpiOverview tiles={kpiTiles} />
+      <KpiTilesOverview tiles={kpiTiles} />
 
       {vizLoading && (
         <div style={{ fontSize: 11, color: 'var(--text3)', marginBottom: 8 }}>Aktualisiere…</div>
@@ -690,191 +699,398 @@ function BodySection({ profile, insights, onRequest, loadingSlug, filterActiveSl
     </div>
   )
 }
-// ── Nutrition Section ─────────────────────────────────────────────────────────
+
-function NutritionSection({ nutrition, weights, profile, insights, onRequest, loadingSlug, filterActiveSlugs }) {
+/** TDEE-Linie muss in der kcal-Y-Domain liegen (sonst unsichtbar trotz Legende). */
-  const [period, setPeriod] = useState(30)
+function kcalVsWeightKcalDomain(points, tdeeRef) {
-  if (!nutrition?.length) return (
+  const vals = (points || [])
-    <EmptySection text="Noch keine Ernährungsdaten." to="/nutrition" toLabel="FDDB importieren"/>
+    .map(d => Number(d.kcal_avg))
+    .filter(v => !Number.isNaN(v))
+  if (!vals.length) return ['auto', 'auto']
+  let lo = Math.min(...vals)
+  let hi = Math.max(...vals)
+  const t = tdeeRef != null ? Number(tdeeRef) : NaN
+  if (!Number.isNaN(t)) {
+    lo = Math.min(lo, t)
+    hi = Math.max(hi, t)
+  }
+  const span = hi - lo || 400
+  const pad = Math.max(100, span * 0.1)
+  return [Math.max(0, Math.floor(lo - pad)), Math.ceil(hi + pad)]
+}
+
+const TDEE_REF_LINE_COLOR = '#475569'
+
+/** Legende unter dem Chart: Linien + ggf. TDEE-Referenz (gestrichelt). */
+function KcalVsWeightLegend({ showTdee }) {
+  const line = (color) => ({
+    display: 'inline-block',
+    width: 22,
+    height: 3,
+    background: color,
+    borderRadius: 1,
+    verticalAlign: 'middle',
+    marginRight: 6,
+  })
+  return (
+    <div
+      className="kcal-vs-weight-legend"
+      style={{
+        display: 'flex',
+        flexWrap: 'wrap',
+        justifyContent: 'center',
+        alignItems: 'center',
+        gap: '12px 18px',
+        marginTop: 10,
+        fontSize: 10,
+        color: 'var(--text2)',
+        lineHeight: 1.35,
+      }}
+    >
+      <span style={{ display: 'inline-flex', alignItems: 'center' }}>
+        <span style={line('#EA580C')} />
+        Ø Kalorien (7-Tage-Mittel)
+      </span>
+      <span style={{ display: 'inline-flex', alignItems: 'center' }}>
+        <span
+          style={{
+            display: 'inline-block',
+            width: 9,
+            height: 9,
+            borderRadius: '50%',
+            background: '#2563EB',
+            marginRight: 6,
+            verticalAlign: 'middle',
+          }}
+        />
+        Gewicht (kg)
+      </span>
+      {showTdee ? (
+        <span style={{ display: 'inline-flex', alignItems: 'center' }}>
+          <span
+            style={{
+              display: 'inline-block',
+              width: 22,
+              height: 0,
+              verticalAlign: 'middle',
+              marginRight: 6,
+              borderTop: `2px dashed ${TDEE_REF_LINE_COLOR}`,
+              opacity: 0.95,
+            }}
+          />
+          TDEE-Referenz (geschätzt)
+        </span>
+      ) : null}
+    </div>
   )
+}
 
-  const cutoff  = dayjs().subtract(period,'day').format('YYYY-MM-DD')
+/** Kalorien (Ø 7T) vs. Gewicht — Daten aus Layer-2b-Bundle (nutrition_metrics / TDEE wie Data Layer). */
-  const filtN   = nutrition.filter(d=>period===9999||d.date>=cutoff)
+function KcalVsWeightChart({ vizKcalWeight, corrData: corrRows, profile, cutoffDate, allTime }) {
-  const sorted  = [...filtN].sort((a,b)=>a.date.localeCompare(b.date))
+  if (vizKcalWeight?.points?.length >= 5) {
+    const tdee = vizKcalWeight.tdee_reference_kcal
+    const kcalVsW = vizKcalWeight.points.map(d => ({
+      ...d,
+      date: fmtDate(d.date),
+    }))
+    const n = vizKcalWeight.common_days_count ?? kcalVsW.length
+    const tdeeLabel = tdee != null && tdee > 0 ? Math.round(tdee) : null
+    const kcalDomain = kcalVsWeightKcalDomain(kcalVsW, tdeeLabel)
+    return (
+      <div className="card" style={{ marginBottom: 12 }}>
+        <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+          Kalorien (Ø 7 Tage) vs. Gewicht
+        </div>
+        <div style={{ fontSize: 10, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
+          Nur Tage mit Kalorien- und Gewichtsdaten. Linke Achse: kcal (Ø 7 Tage), rechte Achse: kg.
+        </div>
+        <ResponsiveContainer width="100%" height={200}>
+          <LineChart data={kcalVsW} margin={{ top: 4, right: 8, bottom: 0, left: -16 }}>
+            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
+            <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(kcalVsW.length / 6) - 1)} />
+            <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={kcalDomain} />
+            <YAxis yAxisId="weight" orientation="right" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+            <Tooltip
+              contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
+              formatter={(v, n) => [`${Math.round(v)} ${n === 'weight' ? 'kg' : 'kcal'}`, n === 'kcal_avg' ? 'Ø Kalorien' : 'Gewicht']}
+            />
+            {tdeeLabel != null && (
+              <ReferenceLine
+                yAxisId="kcal"
+                y={tdeeLabel}
+                stroke={TDEE_REF_LINE_COLOR}
+                strokeDasharray="6 5"
+                strokeWidth={2}
+                isFront
+              />
+            )}
+            <Line yAxisId="kcal" type="monotone" dataKey="kcal_avg" stroke="#EA580C" strokeWidth={2.5} dot={false} name="kcal_avg" />
+            <Line yAxisId="weight" type="monotone" dataKey="weight" stroke="#2563EB" strokeWidth={2.5} dot={{ r: 2, fill: '#2563EB' }} name="weight" />
+          </LineChart>
+        </ResponsiveContainer>
+        <KcalVsWeightLegend showTdee={tdeeLabel != null} />
+        <div style={{ fontSize: 10, color: 'var(--text3)', textAlign: 'center', marginTop: 8 }}>
+          {tdeeLabel != null
+            ? `TDEE ~${tdeeLabel} kcal · ${n} gemeinsame Tage`
+            : `Keine TDEE-Referenz (Gewicht/Demografie) · ${n} gemeinsame Tage`}
+        </div>
+      </div>
+    )
+  }
 
-  if (!filtN.length) return (
+  const raw = (corrRows || []).filter(d => {
+    if (!d.kcal || d.weight == null) return false
+    const ds = typeof d.date === 'string' ? d.date.slice(0, 10) : dayjs(d.date).format('YYYY-MM-DD')
+    return allTime || ds >= cutoffDate
+  })
+  if (raw.length < 5) return null
+
+  const sex = profile?.sex || 'm'
+  const height = profile?.height || 178
+  const latestW = raw[raw.length - 1]?.weight || 80
+  const age = profile?.dob ? Math.floor((Date.now() - new Date(profile.dob)) / (365.25 * 24 * 3600 * 1000)) : 35
+  const bmr = sex === 'm' ? 10 * latestW + 6.25 * height - 5 * age + 5 : 10 * latestW + 6.25 * height - 5 * age - 161
+  const tdee = Math.round(bmr * 1.4)
+  const kcalVsW = rollingAvg(raw.map(d => ({ ...d, date: fmtDate(d.date) })), 'kcal')
+  const kcalDomainFb = kcalVsWeightKcalDomain(kcalVsW, tdee)
+
+  return (
+    <div className="card" style={{ marginBottom: 12 }}>
+      <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+        Kalorien (Ø 7 Tage) vs. Gewicht
+      </div>
+      <div style={{ fontSize: 10, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
+        Nur Tage mit Kalorien- und Gewichtsdaten. Linke Achse: kcal (Ø 7 Tage), rechte Achse: kg.
+      </div>
+      <ResponsiveContainer width="100%" height={200}>
+        <LineChart data={kcalVsW} margin={{ top: 4, right: 8, bottom: 0, left: -16 }}>
+          <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
+          <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(kcalVsW.length / 6) - 1)} />
+          <YAxis yAxisId="kcal" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={kcalDomainFb} />
+          <YAxis yAxisId="weight" orientation="right" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} domain={['auto', 'auto']} />
+          <Tooltip
+            contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }}
+            formatter={(v, n) => [`${Math.round(v)} ${n === 'weight' ? 'kg' : 'kcal'}`, n === 'kcal_avg' ? 'Ø Kalorien' : 'Gewicht']}
+          />
+          <ReferenceLine
+            yAxisId="kcal"
+            y={tdee}
+            stroke={TDEE_REF_LINE_COLOR}
+            strokeDasharray="6 5"
+            strokeWidth={2}
+            isFront
+          />
+          <Line yAxisId="kcal" type="monotone" dataKey="kcal_avg" stroke="#EA580C" strokeWidth={2.5} dot={false} name="kcal_avg" />
+          <Line yAxisId="weight" type="monotone" dataKey="weight" stroke="#2563EB" strokeWidth={2.5} dot={{ r: 2, fill: '#2563EB' }} name="weight" />
+        </LineChart>
+      </ResponsiveContainer>
+      <KcalVsWeightLegend showTdee />
+      <div style={{ fontSize: 10, color: 'var(--text3)', textAlign: 'center', marginTop: 8 }}>
+        TDEE ~{tdee} kcal (Fallback Mifflin ×1,4) · {raw.length} gemeinsame Tage
+      </div>
+    </div>
+  )
+}
+
+// ── Nutrition Section ─────────────────────────────────────────────────────────
+/** Layer 2b: Kennzahlen und Reihen nur aus GET /charts/nutrition-history-viz (nutrition_metrics). */
+function NutritionSection({ profile, insights, onRequest, loadingSlug, filterActiveSlugs }) {
+  const [period, setPeriod] = useState(30)
+  const [groupedGoals, setGroupedGoals] = useState(null)
+  const [viz, setViz] = useState(null)
+  const [vizLoad, setVizLoad] = useState(true)
+  const [vizErr, setVizErr] = useState(null)
+
+  useEffect(() => {
+    let cancelled = false
+    api.listGoalsGrouped()
+      .then(g => { if (!cancelled) setGroupedGoals(g) })
+      .catch(() => { if (!cancelled) setGroupedGoals({}) })
+    return () => { cancelled = true }
+  }, [])
+
+  useEffect(() => {
+    let cancelled = false
+    setViz(null)
+    setVizLoad(true)
+    setVizErr(null)
+    const daysReq = period === 9999 ? 9999 : period
+    api.getNutritionHistoryViz(daysReq)
+      .then(v => { if (!cancelled) setViz(v) })
+      .catch(e => { if (!cancelled) setVizErr(e.message || 'Laden fehlgeschlagen') })
+      .finally(() => { if (!cancelled) setVizLoad(false) })
+    return () => { cancelled = true }
+  }, [period])
+
+  if (vizLoad) {
+    return (
       <div>
         <SectionHeader title="🍽️ Ernährung" to="/nutrition" toLabel="Import" />
         <PeriodSelector value={period} onChange={setPeriod} />
+        <div className="spinner" style={{ margin: 24 }} />
+      </div>
+    )
+  }
+
+  if (vizErr) {
+    return (
+      <div>
+        <SectionHeader title="🍽️ Ernährung" to="/nutrition" toLabel="Import" />
+        <div className="card" style={{ color: 'var(--danger)', marginTop: 8 }}>{vizErr}</div>
+      </div>
+    )
+  }
+
+  if (!viz?.has_nutrition_entries) {
+    return (
+      <EmptySection text="Noch keine Ernährungsdaten." to="/nutrition" toLabel="FDDB importieren"/>
+    )
+  }
+
+  const summary = viz.summary || {}
+  const n = Math.max(0, Number(summary.data_points) || 0)
+  const avgKcal = Math.round(Number(summary.kcal_avg) || 0)
+  const ptLow = Math.round(Number(viz.protein_reference_line_g) || 0)
+  const chartDays = viz.nutrition_charts_days || (period === 9999 ? 90 : period)
+  const kpiTiles = (viz.kpi_tiles || []).map(t => ({
+    ...t,
+    sublabel: typeof t.sublabel === 'string' && t.sublabel.length > 36 ? `${t.sublabel.slice(0, 34)}…` : t.sublabel,
+  }))
+  const pieData = viz.donut_avg_pct || []
+  const cdMacro = (viz.daily_macros || []).map(d => ({
+    date: fmtDate(d.date),
+    Protein: d.Protein,
+    KH: d.KH,
+    Fett: d.Fett,
+    kcal: d.kcal,
+  }))
+  const weeklyMacro = viz.weekly_macro_chart
+  const wmLoading = false
+  const wmError = null
+
+  if (!cdMacro.length || n === 0) {
+    return (
+      <div>
+        <SectionHeader title="🍽️ Ernährung" to="/nutrition" toLabel="Import" lastUpdated={viz.last_updated} />
+        <PeriodSelector value={period} onChange={setPeriod}/>
         <EmptySection text="Keine Einträge im gewählten Zeitraum."/>
       </div>
     )
-
+  }
-  const n = filtN.length
-  const avgKcal    = Math.round(filtN.reduce((s,d)=>s+(d.kcal||0),0)/n)
-  const avgProtein = Math.round(filtN.reduce((s,d)=>s+(d.protein_g||0),0)/n*10)/10
-  const avgFat     = Math.round(filtN.reduce((s,d)=>s+(d.fat_g||0),0)/n*10)/10
-  const avgCarbs   = Math.round(filtN.reduce((s,d)=>s+(d.carbs_g||0),0)/n*10)/10
-  const latestW    = weights?.[0]?.weight||80
-  const ptLow      = Math.round(latestW*1.6)
-  const ptHigh     = Math.round(latestW*2.2)
-  const proteinOk  = avgProtein>=ptLow
-
-  // Stacked macro bar (daily)
-  const cdMacro = sorted.map(d=>({
-    date: fmtDate(d.date),
-    Protein: Math.round(d.protein_g||0),
-    KH:      Math.round(d.carbs_g||0),
-    Fett:    Math.round(d.fat_g||0),
-    kcal:    Math.round(d.kcal||0),
-  }))
-
-  // Pie
-  const totalMacroKcal = avgProtein*4+avgCarbs*4+avgFat*9
-  const pieData = [
-    {name:'Protein',value:Math.round(avgProtein*4/totalMacroKcal*100),color:'#1D9E75'},
-    {name:'KH',     value:Math.round(avgCarbs*4/totalMacroKcal*100),  color:'#D4537E'},
-    {name:'Fett',   value:Math.round(avgFat*9/totalMacroKcal*100),    color:'#378ADD'},
-  ]
-
-  // Weekly macro bars
-  const weeklyMap={}
-  filtN.forEach(d=>{
-    const wk=dayjs(d.date).format('YYYY-WW')
-    const weekNum = (() => { const dt=new Date(d.date); dt.setHours(0,0,0,0); dt.setDate(dt.getDate()+4-(dt.getDay()||7)); const y=new Date(dt.getFullYear(),0,1); return Math.ceil(((dt-y)/86400000+1)/7) })()
-    if(!weeklyMap[wk]) weeklyMap[wk]={label:'KW'+weekNum,n:0,protein:0,carbs:0,fat:0,kcal:0}
-    weeklyMap[wk].protein+=d.protein_g||0; weeklyMap[wk].carbs+=d.carbs_g||0
-    weeklyMap[wk].fat+=d.fat_g||0; weeklyMap[wk].kcal+=d.kcal||0; weeklyMap[wk].n++
-  })
-  const weeklyData=Object.values(weeklyMap).slice(-12).map(w=>({
-    label:w.label,
-    Protein:Math.round(w.protein/w.n),
-    KH:Math.round(w.carbs/w.n),
-    Fett:Math.round(w.fat/w.n),
-    kcal:Math.round(w.kcal/w.n),
-  }))
-
-  // Rules
-  const macroRules=[]
-  if(!proteinOk) macroRules.push({status:'bad',icon:'🥩',category:'Protein',
-    title:`Unterversorgung: ${avgProtein}g/Tag (Ziel ${ptLow}–${ptHigh}g)`,
-    detail:`1,6–2,2g/kg KG. Fehlend: ~${ptLow-Math.round(avgProtein)}g täglich. Konsequenz: Muskelverlust bei Defizit.`,
-    value:avgProtein+'g'})
-  else macroRules.push({status:'good',icon:'🥩',category:'Protein',
-    title:`Gut: ${avgProtein}g/Tag (Ziel ${ptLow}–${ptHigh}g)`,
-    detail:`Ausreichend für Muskelerhalt und -aufbau.`,value:avgProtein+'g'})
-  const protPct=Math.round(avgProtein*4/totalMacroKcal*100)
-  if(protPct<20) macroRules.push({status:'warn',icon:'📊',category:'Makro-Anteil',
-    title:`Protein-Anteil niedrig: ${protPct}% der Kalorien`,
-    detail:`Empfehlung: 25–35%. Aktuell: ${protPct}% P / ${Math.round(avgCarbs*4/totalMacroKcal*100)}% KH / ${Math.round(avgFat*9/totalMacroKcal*100)}% F`,
-    value:protPct+'%'})
 
   return (
     <div>
-      <SectionHeader title="🍽️ Ernährung" to="/nutrition" toLabel="Import" lastUpdated={nutrition[0]?.date}/>
+      <SectionHeader title="🍽️ Ernährung" to="/nutrition" toLabel="Import" lastUpdated={viz.last_updated}/>
       <PeriodSelector value={period} onChange={setPeriod}/>
 
-      <div style={{display:'flex',gap:6,marginBottom:12,flexWrap:'wrap'}}>
+      <p style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 10 }}>
-        {[['Ø Kalorien',avgKcal+' kcal','#EF9F27'],['Ø Protein',avgProtein+'g',proteinOk?'#1D9E75':'#D85A30'],
+        Kennzahlen und Charts nutzen dieselbe Berechnung wie die KI-Platzhalter (Ernährungs-Data-Layer).{' '}
-          ['Ø Fett',avgFat+'g','#378ADD'],['Ø KH',avgCarbs+'g','#D4537E'],
+        <strong>Kalorien vs. Gewicht</strong> und TDEE-Referenz entsprechen Mifflin–St Jeor × PAL 1,55 bzw. kg-Fallback (32,5 kcal/kg).
-          ['Einträge',n+' T','var(--text3)']].map(([l,v,c])=>(
+      </p>
-          <div key={l} style={{flex:1,minWidth:60,background:'var(--surface2)',borderRadius:8,
+
-            padding:'8px 6px',textAlign:'center'}}>
+      <NutritionGoalsStrip grouped={groupedGoals} />
-            <div style={{fontSize:13,fontWeight:700,color:c}}>{v}</div>
+
-            <div style={{fontSize:9,color:'var(--text3)'}}>{l}</div>
+      <KpiTilesOverview tiles={kpiTiles} />
-          </div>
+
-        ))}
+      <KcalVsWeightChart
-      </div>
+        vizKcalWeight={viz.kcal_vs_weight}
+        corrData={[]}
+        profile={profile}
+        cutoffDate=""
+        allTime={period === 9999}
+      />
 
-      {/* Stacked macro bars (daily) */}
       <div className="card" style={{ marginBottom: 12 }}>
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>
+        <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
-          Makroverteilung täglich (g) · {sorted[0]?.date?.slice(0,7)} – {sorted[sorted.length-1]?.date?.slice(0,7)}
+          Makroverteilung täglich (g) · Fokus Protein
         </div>
-        <ResponsiveContainer width="100%" height={170}>
+        <div style={{ fontSize: 10, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 8 }}>
-          <BarChart data={cdMacro} margin={{top:4,right:8,bottom:0,left:-20}}>
+          Gestapelte Balken in Gramm; gestrichelte Linie = Protein-Minimum ({ptLow || '—'} g) nach 1,6 g/kg (Referenzgewicht).
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
+        </div>
-            <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
+        <ResponsiveContainer width="100%" height={200}>
-              interval={Math.max(0,Math.floor(cdMacro.length/6)-1)}/>
+          <BarChart data={cdMacro} margin={{ top: 6, right: 8, bottom: 0, left: -18 }}>
+            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" vertical={false} />
+            <XAxis dataKey="date" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={Math.max(0, Math.floor(cdMacro.length / 6) - 1)} />
             <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
-            <ReferenceLine y={ptLow} stroke="#1D9E75" strokeDasharray="4 4" strokeWidth={1.5}
+            {ptLow > 0 && (
-              label={{value:`Ziel ${ptLow}g P`,fontSize:9,fill:'#1D9E75',position:'insideTopRight'}}/>
+              <ReferenceLine y={ptLow} stroke={MACRO_CHART.protein} strokeDasharray="5 4" strokeWidth={2} label={{ value: `${ptLow}g P`, fontSize: 9, fill: MACRO_CHART.protein, position: 'insideTopRight' }} />
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',
-              borderRadius:8,fontSize:11}} formatter={(v,n)=>[`${v}g`,n]}/>
-            <Bar dataKey="Protein" stackId="a" fill="#1D9E7599"/>
-            <Bar dataKey="KH"      stackId="a" fill="#D4537E99"/>
-            <Bar dataKey="Fett"    stackId="a" fill="#378ADD99" radius={[2,2,0,0]}/>
-          </BarChart>
-        </ResponsiveContainer>
-        <div style={{display:'flex',gap:12,justifyContent:'center',marginTop:6,fontSize:10,color:'var(--text3)'}}>
-          <span><span style={{display:'inline-block',width:10,height:10,background:'#1D9E7599',borderRadius:2,verticalAlign:'middle',marginRight:3}}/>Protein</span>
-          <span><span style={{display:'inline-block',width:10,height:10,background:'#D4537E99',borderRadius:2,verticalAlign:'middle',marginRight:3}}/>KH</span>
-          <span><span style={{display:'inline-block',width:10,height:10,background:'#378ADD99',borderRadius:2,verticalAlign:'middle',marginRight:3}}/>Fett</span>
-          <span><span style={{display:'inline-block',width:14,height:2,background:'#1D9E75',verticalAlign:'middle',marginRight:3,borderTop:'2px dashed #1D9E75'}}/>Protein-Ziel</span>
-        </div>
-      </div>
-
-      {/* Pie + macro breakdown */}
-      <div className="card" style={{marginBottom:12}}>
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:10}}>
-          Ø Makroverteilung · {n} Tage ({sorted[0]?.date?.slice(0,10)} – {sorted[sorted.length-1]?.date?.slice(0,10)})
-        </div>
-        <div style={{display:'flex',alignItems:'center',gap:16}}>
-          <PieChart width={110} height={110}>
-            <Pie data={pieData} cx={50} cy={50} innerRadius={32} outerRadius={50}
-              dataKey="value" startAngle={90} endAngle={-270}>
-              {pieData.map((e,i)=><Cell key={i} fill={e.color}/>)}
-            </Pie>
-            <Tooltip formatter={(v,n)=>[`${v}%`,n]}/>
-          </PieChart>
-          <div style={{flex:1}}>
-            {pieData.map(p=>(
-              <div key={p.name} style={{display:'flex',alignItems:'center',gap:8,marginBottom:7}}>
-                <div style={{width:10,height:10,borderRadius:2,background:p.color,flexShrink:0}}/>
-                <div style={{flex:1,fontSize:13}}>{p.name}</div>
-                <div style={{fontSize:13,fontWeight:600,color:p.color}}>{p.value}%</div>
-                <div style={{fontSize:11,color:'var(--text3)'}}>{Math.round(p.name==='Protein'?avgProtein:p.name==='KH'?avgCarbs:avgFat)}g</div>
-                {p.name==='Protein' && <div style={{fontSize:10,color:proteinOk?'var(--accent)':'var(--warn)',marginLeft:2}}>
-                  {proteinOk?'✓':'⚠️'} Ziel {ptLow}g
-                </div>}
-              </div>
-            ))}
-            <div style={{marginTop:6,fontSize:11,color:'var(--text3)',borderTop:'1px solid var(--border)',paddingTop:6}}>
-              Gesamt: {avgKcal} kcal/Tag
-            </div>
-          </div>
-        </div>
-      </div>
-
-      {/* Weekly stacked bars */}
-      {weeklyData.length>=2 && (
-        <div className="card" style={{marginBottom:12}}>
-          <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>Makros pro Woche (Ø g/Tag)</div>
-          <ResponsiveContainer width="100%" height={150}>
-            <BarChart data={weeklyData} margin={{top:4,right:8,bottom:0,left:-20}}>
-              <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
-              <XAxis dataKey="label" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}/>
-              <YAxis tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}/>
-              <ReferenceLine y={ptLow} stroke="#1D9E75" strokeDasharray="4 4" strokeWidth={1.5}/>
-              <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',
-                borderRadius:8,fontSize:11}} formatter={(v,n)=>[`${v}g`,n]}/>
-              <Bar dataKey="Protein" stackId="a" fill="#1D9E7599"/>
-              <Bar dataKey="KH"      stackId="a" fill="#D4537E99"/>
-              <Bar dataKey="Fett"    stackId="a" fill="#378ADD99" radius={[3,3,0,0]}/>
-            </BarChart>
-          </ResponsiveContainer>
-        </div>
             )}
-
+            <Tooltip contentStyle={{ background: 'var(--surface)', border: '1px solid var(--border)', borderRadius: 8, fontSize: 11 }} formatter={(v, name) => [`${v}g`, name]} />
-      <div style={{marginBottom:12}}>
+            <Bar dataKey="Protein" stackId="a" fill={MACRO_CHART.protein} name="Protein" />
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>BEWERTUNG</div>
+            <Bar dataKey="Fett" stackId="a" fill={MACRO_CHART.fat} name="Fett" />
-        {macroRules.map((item,i)=><RuleCard key={i} item={item}/>)}
+            <Bar dataKey="KH" stackId="a" fill={MACRO_CHART.carbs} name="KH" radius={[5, 5, 0, 0]} />
+          </BarChart>
+        </ResponsiveContainer>
+        <div style={{ display: 'flex', gap: 12, justifyContent: 'center', marginTop: 8, fontSize: 10, color: 'var(--text3)', flexWrap: 'wrap' }}>
+          <span><span style={{ display: 'inline-block', width: 10, height: 10, background: MACRO_CHART.protein, borderRadius: 2, verticalAlign: 'middle', marginRight: 4 }} />Protein (unten)</span>
+          <span><span style={{ display: 'inline-block', width: 10, height: 10, background: MACRO_CHART.fat, borderRadius: 2, verticalAlign: 'middle', marginRight: 4 }} />Fett (Mitte)</span>
+          <span><span style={{ display: 'inline-block', width: 10, height: 10, background: MACRO_CHART.carbs, borderRadius: 2, verticalAlign: 'middle', marginRight: 4 }} />KH (oben)</span>
+        </div>
       </div>
 
-      {/* New Nutrition Charts (Phase 0c) */}
+      <div className="nutrition-macro-pair">
-      <div style={{marginTop:16}}>
+        <div className="card nutrition-macro-pair__donut">
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>📊 DETAILLIERTE CHARTS</div>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8 }}>
-        <NutritionCharts days={period === 9999 ? 90 : period} />
+            Ø Makro-Quote ({n} Tage)
           </div>
+          {pieData.length > 0 ? (
+            <div className="nutrition-macro-pair__donut-inner">
+              <div className="nutrition-macro-pair__donut-chart">
+                <ResponsiveContainer width="100%" height={NUTRITION_MACRO_CHART_BLOCK_PX}>
+                  <PieChart>
+                    <Pie
+                      data={pieData}
+                      cx="50%"
+                      cy="50%"
+                      innerRadius="38%"
+                      outerRadius="58%"
+                      dataKey="value"
+                      startAngle={90}
+                      endAngle={-270}
+                      paddingAngle={1}
+                    >
+                      {pieData.map((e, i) => (
+                        <Cell key={i} fill={macroFillByName(e.name)} />
+                      ))}
+                    </Pie>
+                    <Tooltip formatter={(v, name) => [`${v}%`, name]} />
+                  </PieChart>
+                </ResponsiveContainer>
+              </div>
+              <div className="nutrition-macro-pair__legend">
+                {pieData.map(p => {
+                  const fill = macroFillByName(p.name)
+                  return (
+                    <div key={p.name} style={{ display: 'flex', alignItems: 'center', gap: 8, marginBottom: 4 }}>
+                      <div style={{ width: 10, height: 10, borderRadius: 2, background: fill, flexShrink: 0 }} />
+                      <div style={{ flex: 1, fontSize: 13 }}>{p.name}</div>
+                      <div style={{ fontSize: 13, fontWeight: 600, color: fill }}>{p.value}%</div>
+                      <div style={{ fontSize: 11, color: 'var(--text3)' }}>
+                        {p.grams != null ? `${p.grams}g` : '—'}
+                      </div>
+                    </div>
+                  )
+                })}
+                <div style={{ marginTop: 8, fontSize: 11, color: 'var(--text3)', borderTop: '1px solid var(--border)', paddingTop: 8 }}>
+                  Ø {avgKcal} kcal/Tag · Anteil der Makro-Kalorien am Tagesumsatz
+                </div>
+              </div>
+            </div>
+          ) : (
+            <div style={{ fontSize: 12, color: 'var(--text3)' }}>Keine Makro-Mittelwerte im Zeitraum.</div>
+          )}
+        </div>
+        <div className="card nutrition-macro-pair__weekly">
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 4 }}>
+            Wöchentliche Makro-Verteilung (Backend)
+          </div>
+          <WeeklyMacroDistributionPanel macroWeeklyData={weeklyMacro} loading={wmLoading} error={wmError} />
+        </div>
+      </div>
+
+      <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 8, marginTop: 4 }}>
+        Zeitverläufe (Energie & Protein)
+      </div>
+      <NutritionCharts days={chartDays} showWeeklyMacroDistribution={false} hideEnergyAvailabilityCard />
 
       <InsightBox insights={insights} slugs={filterActiveSlugs(['ernaehrung'])} onRequest={onRequest} loading={loadingSlug}/>
     </div>
@@ -1001,10 +1217,7 @@ function CorrelationSection({ corrData, insights, profile, onRequest, loadingSlu
   const bmr    = sex==='m' ? 10*latestW+6.25*height-5*age+5 : 10*latestW+6.25*height-5*age-161
   const tdee   = Math.round(bmr*1.4)  // light activity baseline
 
-  // Chart 1: Kcal vs Weight
+  // Protein vs Lean Mass (only days with both)
-  const kcalVsW = rollingAvg(filtered.map(d=>({...d,date:fmtDate(d.date)})),'kcal')
-
-  // Chart 2: Protein vs Lean Mass (only days with both)
   const protVsLean = filtered.filter(d=>d.protein_g&&d.lean_mass)
     .map(d=>({date:fmtDate(d.date),protein:d.protein_g,lean:d.lean_mass}))
 
@@ -1080,31 +1293,11 @@ function CorrelationSection({ corrData, insights, profile, onRequest, loadingSlu
     <div>
       <SectionHeader title="🔗 Korrelationen"/>
 
-      {/* Chart 1: Kcal vs Weight */}
+      <p style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 12 }}>
-      <div className="card" style={{marginBottom:12}}>
+        Das Diagramm <strong>Kalorien (Ø 7T) vs. Gewicht</strong> liegt unter <strong>Verlauf → Ernährung</strong> (gleiche Datenbasis).
-        <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>
+      </p>
-          📉 Kalorien (Ø 7T) vs. Gewicht
-        </div>
-        <ResponsiveContainer width="100%" height={190}>
-          <LineChart data={kcalVsW} margin={{top:4,right:8,bottom:0,left:-16}}>
-            <CartesianGrid stroke="var(--border)" strokeDasharray="3 3"/>
-            <XAxis dataKey="date" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false}
-              interval={Math.max(0,Math.floor(kcalVsW.length/6)-1)}/>
-            <YAxis yAxisId="kcal"   tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false} domain={['auto','auto']}/>
-            <YAxis yAxisId="weight" orientation="right" tick={{fontSize:9,fill:'var(--text3)'}} tickLine={false} domain={['auto','auto']}/>
-            <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}
-              formatter={(v,n)=>[`${Math.round(v)} ${n==='weight'?'kg':'kcal'}`,n==='kcal_avg'?'Ø Kalorien':'Gewicht']}/>
-            <ReferenceLine yAxisId="kcal" y={tdee} stroke="var(--text3)" strokeDasharray="3 3" strokeWidth={1}/>
-            <Line yAxisId="kcal"   type="monotone" dataKey="kcal_avg" stroke="#EF9F27" strokeWidth={2} dot={false} name="kcal_avg"/>
-            <Line yAxisId="weight" type="monotone" dataKey="weight"   stroke="#378ADD" strokeWidth={2.5} dot={{r:2,fill:'#378ADD'}} name="weight"/>
-          </LineChart>
-        </ResponsiveContainer>
-        <div style={{fontSize:10,color:'var(--text3)',textAlign:'center',marginTop:4}}>
-          Gestrichelt: geschätzter TDEE {tdee} kcal · <span style={{color:'#EF9F27'}}>— Kalorien</span> · <span style={{color:'#378ADD'}}>— Gewicht</span>
-        </div>
-      </div>
 
-      {/* Chart 2: Calorie balance */}
+      {/* Chart: Calorie balance */}
       <div className="card" style={{marginBottom:12}}>
         <div style={{fontSize:12,fontWeight:600,color:'var(--text3)',marginBottom:8}}>
           ⚖️ Kalorienbilanz (Aufnahme − TDEE {tdee} kcal)
@@ -1411,7 +1604,7 @@ export default function History() {
         </nav>
         <div className="history-content">
           {tab==='body'        && <BodySection profile={profile} {...sp}/>}
-          {tab==='nutrition'   && <NutritionSection nutrition={nutrition} weights={weights} profile={profile} {...sp}/>}
+          {tab==='nutrition'   && <NutritionSection profile={profile} {...sp}/>}
           {tab==='activity'    && <ActivitySection  activities={activities} globalQualityLevel={activeProfile?.quality_filter_level} {...sp}/>}
           {tab==='recovery'    && <RecoverySection  {...sp}/>}
           {tab==='correlation' && <CorrelationSection corrData={corrData} profile={profile} {...sp}/>}

frontend/src/pages/NutritionPage.jsx

@@ -5,6 +5,7 @@ import {
   ResponsiveContainer, CartesianGrid, Legend, ReferenceLine, ScatterChart, Scatter
 } from 'recharts'
 import { api as nutritionApi } from '../utils/api'
+import { MACRO_CHART } from '../utils/macroChartTheme'
 import dayjs from 'dayjs'
 import isoWeek from 'dayjs/plugin/isoWeek'
 dayjs.extend(isoWeek)
@@ -709,9 +710,9 @@ function WeeklyMacros({ weekly }) {
         <Tooltip contentStyle={{background:'var(--surface)',border:'1px solid var(--border)',borderRadius:8,fontSize:11}}
           formatter={(v,n)=>[`${Math.round(v)} g`, n]}/>
         <Legend wrapperStyle={{fontSize:11}}/>
-        <Bar dataKey="Protein"       stackId="a" fill="#1D9E75"/>
+        <Bar dataKey="Protein"       stackId="a" fill={MACRO_CHART.protein}/>
-        <Bar dataKey="Fett"          stackId="a" fill="#378ADD"/>
+        <Bar dataKey="Fett"          stackId="a" fill={MACRO_CHART.fat}/>
-        <Bar dataKey="Kohlenhydrate" stackId="a" fill="#D4537E" radius={[3,3,0,0]}/>
+        <Bar dataKey="Kohlenhydrate" stackId="a" fill={MACRO_CHART.carbs} radius={[3,3,0,0]}/>
       </BarChart>
     </ResponsiveContainer>
   )

frontend/src/utils/api.js

@@ -637,6 +637,8 @@ export const api = {
   // Nutrition Charts (E1-E5)
   /** Layer 2b: Verlauf Körper — Charts, Kennzahlen, Bewertung (einheitlich mit Platzhalter-Registry) */
   getBodyHistoryViz:                  (days=90)  => req(`/charts/body-history-viz?days=${days}`),
+  /** Layer 2b: Verlauf Ernährung — Kennzahlen, Reihen, TDEE, Wochen-Chart (nutrition_metrics) */
+  getNutritionHistoryViz:             (days=90)  => req(`/charts/nutrition-history-viz?days=${days}`),
   getEnergyBalanceChart:              (days=28)  => req(`/charts/energy-balance?days=${days}`),
   getProteinAdequacyChart:            (days=28)  => req(`/charts/protein-adequacy?days=${days}`),
   getNutritionConsistencyChart:       (days=28)  => req(`/charts/nutrition-consistency?days=${days}`),

frontend/src/utils/macroChartTheme.js

@@ -0,0 +1,21 @@
+/**
+ * Einheitliche Makro-Farben für Verlauf (Balken, Donut, E3).
+ * Reihenfolge gestapelter Balken (Recharts, unten zuerst): Protein → Fett → Kohlenhydrate.
+ */
+export const MACRO_CHART = {
+  protein: '#4a8f72',
+  fat: '#6e8eb8',
+  carbs: '#c17d45',
+}
+
+/** Einheitliche Höhe Donut-Bereich / E3-Balken (Verlauf) */
+export const NUTRITION_MACRO_CHART_BLOCK_PX = 260
+
+/** Farbe nach Segment-Name (Protein / KH / Fett / englische Keys). */
+export function macroFillByName(name) {
+  const n = String(name || '').toLowerCase()
+  if (n.includes('protein') || n === 'p') return MACRO_CHART.protein
+  if (n.includes('fett') || n.includes('fat')) return MACRO_CHART.fat
+  if (n.includes('kh') || n.includes('kohlenhydrat') || n.includes('carb')) return MACRO_CHART.carbs
+  return MACRO_CHART.carbs
+}