feat: Refactor sleep metrics calculations and improve error handling

- Updated `get_sleep_avg_duration` and `get_sleep_avg_quality` functions in `placeholder_resolver.py` to provide clearer error messages when data is unavailable.
- Enhanced sleep quality calculations in `recovery_metrics.py` to handle cases with insufficient data more robustly.
- Improved data handling in various metrics files (`activity_metrics.py`, `body_metrics.py`, `nutrition_metrics.py`, `recovery_metrics.py`, and `scores.py`) to ensure consistent float conversions for calculations.
- Added utility functions in `recovery_metrics.py` for parsing and normalizing sleep segment data, enhancing the accuracy of sleep quality assessments.

These changes improve the reliability and clarity of sleep-related metrics and enhance overall data handling across the application.

backend/calculations/recovery_metrics.py

@@ -509,8 +509,15 @@ def calculate_sleep_quality_7d(profile_id: str) -> Optional[int]:
 
         quality_scores = []
         for s in sleep_data:
-            if s['deep_minutes'] and s['rem_minutes']:
+            dur = s["duration_minutes"]
-                quality_pct = ((s['deep_minutes'] + s['rem_minutes']) / s['duration_minutes']) * 100
+            if not dur or dur <= 0:
+                continue
+            d = s["deep_minutes"]
+            r = s["rem_minutes"]
+            if d is None and r is None:
+                continue
+            di, ri = (d or 0), (r or 0)
+            quality_pct = ((di + ri) / dur) * 100
             # 40-60% deep+REM is good
             if quality_pct >= 45:
                 quality_scores.append(100)

backend/data_layer/activity_metrics.py

@@ -674,9 +674,9 @@ def calculate_activity_score(profile_id: str, focus_weights: Optional[Dict] = No
     if not components:
         return None
 
-    # Weighted average
+    # Weighted average (float: DB-Aggregate können Decimal sein)
-    total_score = sum(score * weight for _, score, weight in components)
+    total_score = sum(float(score) * float(weight) for _, score, weight in components)
-    total_weight = sum(weight for _, _, weight in components)
+    total_weight = sum(float(weight) for _, _, weight in components)
 
     return int(total_score / total_weight)
 
@@ -728,12 +728,13 @@ def _score_cardio_presence(profile_id: str) -> Optional[int]:
         if not row:
             return None
 
-        cardio_days = row['cardio_days']
+        # psycopg2: SUM() → oft Decimal — vor Mix mit float konvertieren
-        cardio_minutes = row['cardio_minutes'] or 0
+        cardio_days = int(row['cardio_days'] or 0)
+        cardio_minutes = float(row['cardio_minutes'] or 0)
 
         # Target: 3-5 days/week, 150+ minutes
-        day_score = min(100, (cardio_days / 4) * 100)
+        day_score = min(100.0, (cardio_days / 4) * 100)
-        minute_score = min(100, (cardio_minutes / 150) * 100)
+        minute_score = min(100.0, (cardio_minutes / 150) * 100)
 
         return int((day_score + minute_score) / 2)
 

backend/data_layer/body_metrics.py

@@ -760,8 +760,8 @@ def calculate_body_progress_score(profile_id: str, focus_weights: Optional[Dict]
     if not components:
         return None
 
-    total_score = sum(score * weight for _, score, weight in components)
+    total_score = sum(float(score) * float(weight) for _, score, weight in components)
-    total_weight = sum(weight for _, _, weight in components)
+    total_weight = sum(float(weight) for _, _, weight in components)
 
     return int(total_score / total_weight)
 

backend/data_layer/nutrition_metrics.py

@@ -886,9 +886,9 @@ def calculate_nutrition_score(profile_id: str, focus_weights: Optional[Dict] = N
     if not components:
         return None
 
-    # Weighted average
+    # Weighted average (float: DB-Werte können Decimal sein)
-    total_score = sum(score * weight for _, score, weight in components)
+    total_score = sum(float(score) * float(weight) for _, score, weight in components)
-    total_weight = sum(weight for _, _, weight in components)
+    total_weight = sum(float(weight) for _, _, weight in components)
 
     return int(total_score / total_weight)
 

backend/data_layer/recovery_metrics.py

@@ -15,12 +15,50 @@ Phase 0c: Multi-Layer Architecture
 Version: 1.0
 """
 
-from typing import Dict, List, Optional
+import json
+from typing import Dict, List, Optional, Any
 from datetime import datetime, timedelta, date
-from db import get_db, get_cursor, r2d
+from db import get_db, get_cursor
 from data_layer.utils import calculate_confidence, safe_float, safe_int
 
 
+def _parse_sleep_segments(raw: Any) -> Optional[List[dict]]:
+    """JSONB kann dict/list/str sein; ungültig → None."""
+    if raw is None:
+        return None
+    if isinstance(raw, str):
+        try:
+            raw = json.loads(raw)
+        except (json.JSONDecodeError, TypeError):
+            return None
+    if not isinstance(raw, list):
+        return None
+    return raw
+
+
+def _segment_minutes(seg: Any) -> int:
+    if not isinstance(seg, dict):
+        return 0
+    for key in ("duration_min", "duration_minutes", "minutes"):
+        v = seg.get(key)
+        if v is not None:
+            return max(0, safe_int(v))
+    return 0
+
+
+def _normalize_sleep_phase(seg: dict) -> str:
+    """Kleinbuchstaben; Apple „Core“-Schlaf wird wie light gewertet."""
+    if not isinstance(seg, dict):
+        return ""
+    p = seg.get("phase")
+    if p is None:
+        return ""
+    s = str(p).strip().lower()
+    if s in ("core", "asleep"):
+        return "light"
+    return s
+
+
 def get_sleep_duration_data(
     profile_id: str,
     days: int = 7
@@ -51,7 +89,7 @@ def get_sleep_duration_data(
         cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
 
         cur.execute(
-            """SELECT sleep_segments FROM sleep_log
+            """SELECT sleep_segments, duration_minutes FROM sleep_log
                WHERE profile_id=%s AND date >= %s
                ORDER BY date DESC""",
             (profile_id, cutoff)
@@ -72,9 +110,14 @@ def get_sleep_duration_data(
         nights_with_data = 0
 
         for row in rows:
-            segments = row['sleep_segments']
+            night_minutes = 0
+            segments = _parse_sleep_segments(row.get("sleep_segments"))
             if segments:
-                night_minutes = sum(seg.get('duration_min', 0) for seg in segments)
+                night_minutes = sum(_segment_minutes(seg) for seg in segments)
+            if night_minutes <= 0:
+                dm = row.get("duration_minutes")
+                if dm is not None:
+                    night_minutes = max(0, safe_int(dm))
             if night_minutes > 0:
                 total_minutes += night_minutes
                 nights_with_data += 1
@@ -136,7 +179,9 @@ def get_sleep_quality_data(
         cutoff = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
 
         cur.execute(
-            """SELECT sleep_segments FROM sleep_log
+            """SELECT sleep_segments, duration_minutes, deep_minutes, rem_minutes,
+                      light_minutes, awake_minutes
+               FROM sleep_log
                WHERE profile_id=%s AND date >= %s
                ORDER BY date DESC""",
             (profile_id, cutoff)
@@ -163,15 +208,29 @@ def get_sleep_quality_data(
         count = 0
 
         for row in rows:
-            segments = row['sleep_segments']
+            deep_rem_min = light_min = awake_min = 0
-            if segments:
+            total_min = 0
-                # Note: segments use 'phase' key, stored lowercase (deep, rem, light, awake)
+            used_segments = False
-                deep_rem_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') in ['deep', 'rem'])
-                light_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') == 'light')
-                awake_min = sum(s.get('duration_min', 0) for s in segments if s.get('phase') == 'awake')
-                total_min = sum(s.get('duration_min', 0) for s in segments)
 
+            segments = _parse_sleep_segments(row.get("sleep_segments"))
+            if segments:
+                total_min = sum(_segment_minutes(s) for s in segments)
                 if total_min > 0:
+                    deep_rem_min = sum(
+                        _segment_minutes(s)
+                        for s in segments
+                        if _normalize_sleep_phase(s) in ("deep", "rem")
+                    )
+                    light_min = sum(
+                        _segment_minutes(s)
+                        for s in segments
+                        if _normalize_sleep_phase(s) == "light"
+                    )
+                    awake_min = sum(
+                        _segment_minutes(s)
+                        for s in segments
+                        if _normalize_sleep_phase(s) == "awake"
+                    )
                     quality_pct = (deep_rem_min / total_min) * 100
                     total_quality += quality_pct
                     total_deep_rem += deep_rem_min
@@ -179,6 +238,28 @@ def get_sleep_quality_data(
                     total_awake += awake_min
                     total_all += total_min
                     count += 1
+                    used_segments = True
+
+            if not used_segments:
+                d, r, l, a = (
+                    row.get("deep_minutes"),
+                    row.get("rem_minutes"),
+                    row.get("light_minutes"),
+                    row.get("awake_minutes"),
+                )
+                if d is not None or r is not None or l is not None:
+                    di, ri, li = (d or 0), (r or 0), (l or 0)
+                    phase_sum = di + ri + li
+                    ai = (a or 0) if a is not None else 0
+                    total_min = phase_sum + ai
+                    if total_min > 0 and phase_sum > 0:
+                        quality_pct = ((di + ri) / total_min) * 100
+                        total_quality += quality_pct
+                        total_deep_rem += di + ri
+                        total_light += li
+                        total_awake += ai
+                        total_all += total_min
+                        count += 1
 
         if count == 0:
             return {
@@ -351,8 +432,8 @@ def calculate_recovery_score_v2(profile_id: str) -> Optional[int]:
         return None
 
     # Weighted average
-    total_score = sum(score * weight for _, score, weight in components)
+    total_score = sum(float(score) * float(weight) for _, score, weight in components)
-    total_weight = sum(weight for _, _, weight in components)
+    total_weight = sum(float(weight) for _, _, weight in components)
 
     final_score = int(total_score / total_weight)
 
@@ -783,8 +864,15 @@ def calculate_sleep_quality_7d(profile_id: str) -> Optional[int]:
 
         quality_scores = []
         for s in sleep_data:
-            if s['deep_minutes'] and s['rem_minutes']:
+            dur = s["duration_minutes"]
-                quality_pct = ((s['deep_minutes'] + s['rem_minutes']) / s['duration_minutes']) * 100
+            if not dur or dur <= 0:
+                continue
+            d = s["deep_minutes"]
+            r = s["rem_minutes"]
+            if d is None and r is None:
+                continue
+            di, ri = (d or 0), (r or 0)
+            quality_pct = ((di + ri) / dur) * 100
             # 40-60% deep+REM is good
             if quality_pct >= 45:
                 quality_scores.append(100)

backend/data_layer/scores.py

@@ -202,23 +202,24 @@ def calculate_goal_progress_score(profile_id: str) -> Optional[int]:
     total_weight = 0.0
 
     for focus_area_id, weight in focus_weights.items():
+        w = float(weight)
         component = focus_to_component.get(focus_area_id)
 
         if component == 'body' and body_score is not None:
-            total_score += body_score * weight
+            total_score += float(body_score) * w
-            total_weight += weight
+            total_weight += w
         elif component == 'nutrition' and nutrition_score is not None:
-            total_score += nutrition_score * weight
+            total_score += float(nutrition_score) * w
-            total_weight += weight
+            total_weight += w
         elif component == 'activity' and activity_score is not None:
-            total_score += activity_score * weight
+            total_score += float(activity_score) * w
-            total_weight += weight
+            total_weight += w
         elif component == 'recovery' and recovery_score is not None:
-            total_score += recovery_score * weight
+            total_score += float(recovery_score) * w
-            total_weight += weight
+            total_weight += w
         elif component == 'health' and health_risk_score is not None:
-            total_score += health_risk_score * weight
+            total_score += float(health_risk_score) * w
-            total_weight += weight
+            total_weight += w
 
     if total_weight == 0:
         return None
@@ -282,9 +283,9 @@ def calculate_health_stability_score(profile_id: str) -> Optional[int]:
 
         activities = cur.fetchall()
         if activities:
-            total_minutes = sum(a['duration_min'] for a in activities)
+            total_minutes = float(sum(float(a['duration_min'] or 0) for a in activities))
             # WHO recommends 150-300 min/week moderate activity
-            movement_score = min(100, (total_minutes / 150) * 100)
+            movement_score = min(100.0, (total_minutes / 150) * 100)
             components.append(('movement', movement_score, 20))
 
         # 4. Waist circumference risk (15%)
@@ -328,8 +329,8 @@ def calculate_health_stability_score(profile_id: str) -> Optional[int]:
         return None
 
     # Weighted average
-    total_score = sum(score * weight for _, score, weight in components)
+    total_score = sum(float(score) * float(weight) for _, score, weight in components)
-    total_weight = sum(weight for _, _, weight in components)
+    total_weight = sum(float(weight) for _, _, weight in components)
 
     return int(total_score / total_weight)
 
@@ -532,9 +533,11 @@ def calculate_focus_area_progress(profile_id: str, focus_area_id: str) -> Option
         if not goals:
             return None
 
-        # Weighted average by contribution_weight
+        # Weighted average by contribution_weight (Numeric → float)
-        total_progress = sum(g['progress_pct'] * g['contribution_weight'] for g in goals)
+        total_progress = sum(
-        total_weight = sum(g['contribution_weight'] for g in goals)
+            float(g['progress_pct']) * float(g['contribution_weight']) for g in goals
+        )
+        total_weight = sum(float(g['contribution_weight']) for g in goals)
 
         return int(total_progress / total_weight) if total_weight > 0 else None
 

backend/placeholder_resolver.py

@@ -469,10 +469,10 @@ def get_sleep_avg_duration(profile_id: str, days: int = 7) -> str:
 
     if data['confidence'] == 'insufficient':
         return pv_unavailable(
-            "Schlafdauer (formatiert) nicht aus sleep_segments ableitbar",
+            "Schlafdauer nicht ermittelbar",
             f"confidence={data.get('confidence')}, "
-            f"nächte_mit_segmenten={data.get('nights_with_data', 0)}/{data.get('days_analyzed', days)}; "
+            f"nächte_mit_wert={data.get('nights_with_data', 0)}/{data.get('days_analyzed', days)} "
-            f"Hinweis: {{sleep_avg_duration_7d}} nutzt duration_minutes und kann trotzdem Werte liefern",
+            f"(Quellen: sleep_segments und/oder sleep_log.duration_minutes)",
         )
 
     return f"{data['avg_duration_hours']:.1f}h"
@@ -489,9 +489,10 @@ def get_sleep_avg_quality(profile_id: str, days: int = 7) -> str:
 
     if data['confidence'] == 'insufficient':
         return pv_unavailable(
-            "Schlafqualität (Deep+REM) nicht aus sleep_segments ableitbar",
+            "Schlafqualität (Deep+REM-Anteil) nicht ermittelbar",
             f"confidence={data.get('confidence')}, "
-            f"nächte_analysiert={data.get('nights_analyzed', 0)}/{data.get('days_analyzed', days)}",
+            f"nächte_analysiert={data.get('nights_analyzed', 0)}/{data.get('days_analyzed', days)} "
+            f"(Quellen: sleep_segments oder Spalten deep/rem/light/awake_minutes)",
         )
 
     return f"{data['quality_score']:.0f}% (Deep+REM)"