Merge pull request 'feat: Update Gitea issues index and enhance data layer metrics' (#78) from develop into main

Reviewed-on: #78
2026-04-11 22:17:18 +02:00 · 2026-04-11 22:17:18 +02:00 · 4d81ea2cf3
commit 4d81ea2cf3
parent caeed3fbaa d7cefdd9e9
7 changed files with 121 additions and 50 deletions
--- a/.claude/docs/GITEA_ISSUES_INDEX.md
+++ b/.claude/docs/GITEA_ISSUES_INDEX.md
@ -1,6 +1,6 @@
 # Gitea Issues – Landkarte (Auswertung)

-**Quelle:** Gitea `Lars/mitai-jinkendo`, Stand **2026-04-09** (Abfrage `state=all`, ergänzt: #71).  
+**Quelle:** Gitea `Lars/mitai-jinkendo`, Stand **2026-04-11** (Abfrage `state=all`, ergänzt: #71, #76).  
 **URL:** http://192.168.2.144:3000/Lars/mitai-jinkendo/issues  

 Dieses Dokument ist ein **Orientierungs-Index** für Agenten und Entwickler. Verbindliches Tracking bleibt **in Gitea**; hier: Kategorien, Dubletten-Hinweise, grobe Prioritätseinschätzung.
@ -88,6 +88,7 @@ Dieses Dokument ist ein **Orientierungs-Index** für Agenten und Entwickler. Ver
 | # | Titel |
 |---|--------|
 | 15 | [FEAT-002] Quality-Filter für KI-Auswertungen & Charts integrieren |
+| 76 | Trainings-Qualität: zielbezogene Logik + Listen-Filter statt globalem „Hochwertig“-Hide |
 | 36 | BUG-009: Trainingstyp-Erstellung führt zu Internal Server Error |

 ---
--- a/backend/data_layer/activity_metrics.py
+++ b/backend/data_layer/activity_metrics.py
@ -604,26 +604,23 @@ def calculate_activity_score(profile_id: str, focus_weights: Optional[Dict] = No
        from data_layer.scores import get_user_focus_weights
        focus_weights = get_user_focus_weights(profile_id)

-    # Activity-related focus areas (English keys from DB)
-    # Strength training
-    strength = focus_weights.get('strength', 0)
-    strength_endurance = focus_weights.get('strength_endurance', 0)
-    power = focus_weights.get('power', 0)
+    # Activity-related focus areas (English keys from DB); Gewichte float (kein Decimal×float)
+    strength = float(focus_weights.get('strength', 0) or 0)
+    strength_endurance = float(focus_weights.get('strength_endurance', 0) or 0)
+    power = float(focus_weights.get('power', 0) or 0)
    total_strength = strength + strength_endurance + power

-    # Endurance training
-    aerobic = focus_weights.get('aerobic_endurance', 0)
-    anaerobic = focus_weights.get('anaerobic_endurance', 0)
-    cardiovascular = focus_weights.get('cardiovascular_health', 0)
+    aerobic = float(focus_weights.get('aerobic_endurance', 0) or 0)
+    anaerobic = float(focus_weights.get('anaerobic_endurance', 0) or 0)
+    cardiovascular = float(focus_weights.get('cardiovascular_health', 0) or 0)
    total_cardio = aerobic + anaerobic + cardiovascular

-    # Mobility/Coordination
-    flexibility = focus_weights.get('flexibility', 0)
-    mobility = focus_weights.get('mobility', 0)
-    balance = focus_weights.get('balance', 0)
-    reaction = focus_weights.get('reaction', 0)
-    rhythm = focus_weights.get('rhythm', 0)
-    coordination = focus_weights.get('coordination', 0)
+    flexibility = float(focus_weights.get('flexibility', 0) or 0)
+    mobility = float(focus_weights.get('mobility', 0) or 0)
+    balance = float(focus_weights.get('balance', 0) or 0)
+    reaction = float(focus_weights.get('reaction', 0) or 0)
+    rhythm = float(focus_weights.get('rhythm', 0) or 0)
+    coordination = float(focus_weights.get('coordination', 0) or 0)
    total_ability = flexibility + mobility + balance + reaction + rhythm + coordination

    total_activity_weight = total_strength + total_cardio + total_ability
--- a/backend/data_layer/body_metrics.py
+++ b/backend/data_layer/body_metrics.py
@ -445,12 +445,16 @@ def calculate_weight_7d_median(profile_id: str) -> Optional[float]:
            ORDER BY date DESC
        """, (profile_id,))

-        weights = [row['weight'] for row in cur.fetchall()]
+        weights = [
+            safe_float(row['weight'])
+            for row in cur.fetchall()
+            if row['weight'] is not None
+        ]

        if len(weights) < 4:  # Need at least 4 measurements
            return None

-        return round(statistics.median(weights), 1)
+        return round(float(statistics.median(weights)), 1)


 def calculate_weight_28d_slope(profile_id: str) -> Optional[float]:
@ -478,7 +482,11 @@ def _calculate_weight_slope(profile_id: str, days: int) -> Optional[float]:
            ORDER BY date
        """, (profile_id, days))

-        data = [(row['date'], row['weight']) for row in cur.fetchall()]
+        data = [
+            (row['date'], safe_float(row['weight']))
+            for row in cur.fetchall()
+            if row['weight'] is not None
+        ]

        # Need minimum data points based on period
        min_points = max(18, int(days * 0.6))  # 60% coverage
@ -488,21 +496,21 @@ def _calculate_weight_slope(profile_id: str, days: int) -> Optional[float]:
        # Convert dates to days since start
        start_date = data[0][0]
        x_values = [(date - start_date).days for date, _ in data]
-        y_values = [weight for _, weight in data]
+        y_values = [w for _, w in data]

-        # Linear regression
+        # Linear regression (alles float: PostgreSQL numeric → Decimal in Python)
        n = len(data)
-        x_mean = sum(x_values) / n
-        y_mean = sum(y_values) / n
+        x_mean = float(sum(x_values)) / n
+        y_mean = float(sum(y_values)) / n

-        numerator = sum((x - x_mean) * (y - y_mean) for x, y in zip(x_values, y_values))
-        denominator = sum((x - x_mean) ** 2 for x in x_values)
+        numerator = sum(float(x - x_mean) * float(y - y_mean) for x, y in zip(x_values, y_values))
+        denominator = float(sum((x - x_mean) ** 2 for x in x_values))

        if denominator == 0:
            return None

        slope = numerator / denominator
-        return round(slope, 4)  # kg/day
+        return round(float(slope), 4)  # kg/day


 def calculate_goal_projection_date(profile_id: str, goal_id: str) -> Optional[str]:
@ -594,19 +602,24 @@ def _calculate_body_composition_change(profile_id: str, metric: str, days: int)
        recent = data[0]
        oldest = data[-1]

-        # Calculate FM and LBM
-        recent_fm = recent['weight'] * (recent['bf_pct'] / 100)
-        recent_lbm = recent['weight'] - recent_fm
+        # Calculate FM and LBM (DB numeric → Decimal; für Regression/Scores nur float)
+        rw = float(safe_float(recent['weight']) or 0)
+        ob = float(safe_float(recent['bf_pct']) or 0)
+        ow = float(safe_float(oldest['weight']) or 0)
+        obf = float(safe_float(oldest['bf_pct']) or 0)

-        oldest_fm = oldest['weight'] * (oldest['bf_pct'] / 100)
-        oldest_lbm = oldest['weight'] - oldest_fm
+        recent_fm = rw * (ob / 100)
+        recent_lbm = rw - recent_fm
+
+        oldest_fm = ow * (obf / 100)
+        oldest_lbm = ow - oldest_fm

        if metric == 'fm':
            change = recent_fm - oldest_fm
        else:
            change = recent_lbm - oldest_lbm

-        return round(change, 2)
+        return round(float(change), 2)


 # ── Circumference Calculations ──────────────────────────────────────────────
@ -731,9 +744,9 @@ def calculate_body_progress_score(profile_id: str, focus_weights: Optional[Dict]
        from data_layer.scores import get_user_focus_weights
        focus_weights = get_user_focus_weights(profile_id)

-    weight_loss = focus_weights.get('weight_loss', 0)
-    muscle_gain = focus_weights.get('muscle_gain', 0)
-    body_recomp = focus_weights.get('body_recomposition', 0)
+    weight_loss = float(focus_weights.get('weight_loss', 0) or 0)
+    muscle_gain = float(focus_weights.get('muscle_gain', 0) or 0)
+    body_recomp = float(focus_weights.get('body_recomposition', 0) or 0)

    total_body_weight = weight_loss + muscle_gain + body_recomp

--- a/backend/data_layer/nutrition_metrics.py
+++ b/backend/data_layer/nutrition_metrics.py
@ -844,14 +844,16 @@ def calculate_nutrition_score(profile_id: str, focus_weights: Optional[Dict] = N
        from data_layer.scores import get_user_focus_weights
        focus_weights = get_user_focus_weights(profile_id)

-    # Nutrition-related focus areas (English keys from DB)
-    protein_intake = focus_weights.get('protein_intake', 0)
-    calorie_balance = focus_weights.get('calorie_balance', 0)
-    macro_consistency = focus_weights.get('macro_consistency', 0)
-    meal_timing = focus_weights.get('meal_timing', 0)
-    hydration = focus_weights.get('hydration', 0)
+    # Nutrition-related focus areas (English keys from DB; Gewichte immer float)
+    protein_intake = float(focus_weights.get('protein_intake', 0) or 0)
+    calorie_balance = float(focus_weights.get('calorie_balance', 0) or 0)
+    macro_consistency = float(focus_weights.get('macro_consistency', 0) or 0)
+    meal_timing = float(focus_weights.get('meal_timing', 0) or 0)
+    hydration = float(focus_weights.get('hydration', 0) or 0)

-    total_nutrition_weight = protein_intake + calorie_balance + macro_consistency + meal_timing + hydration
+    total_nutrition_weight = (
+        protein_intake + calorie_balance + macro_consistency + meal_timing + hydration
+    )

    if total_nutrition_weight == 0:
        return None  # No nutrition goals
--- a/backend/data_layer/scores.py
+++ b/backend/data_layer/scores.py
@ -224,8 +224,8 @@ def calculate_goal_progress_score(profile_id: str) -> Optional[int]:
    if total_weight == 0:
        return None

-    # Normalize to 0-100
-    final_score = total_score / total_weight
+    # Normalize to 0-100 (Explizit float: Zwischensummen können Decimal aus DB sein)
+    final_score = float(total_score) / float(total_weight)

    return int(final_score)

@ -533,11 +533,19 @@ def calculate_focus_area_progress(profile_id: str, focus_area_id: str) -> Option
        if not goals:
            return None

-        # Weighted average by contribution_weight (Numeric → float)
-        total_progress = sum(
-            float(g['progress_pct']) * float(g['contribution_weight']) for g in goals
-        )
-        total_weight = sum(float(g['contribution_weight']) for g in goals)
+        # Weighted average; progress_pct darf NULL sein (Ziele ohne quantitative Berechnung)
+        parts: List[tuple] = []
+        for g in goals:
+            pct = g['progress_pct']
+            if pct is None:
+                continue
+            parts.append((float(pct), float(g['contribution_weight'])))
+
+        if not parts:
+            return None
+
+        total_progress = sum(p * w for p, w in parts)
+        total_weight = sum(w for _, w in parts)

        return int(total_progress / total_weight) if total_weight > 0 else None

--- a/docs/README.md
+++ b/docs/README.md
@ -29,6 +29,7 @@ Dieser Ordner ist **immer mit Git versioniert**. Er ergänzt **`.claude/docs/`**
 | `issue-53-phase-0c-multi-layer-architecture.md` |
 | `issue-54-dynamic-placeholder-system.md` |
 | `issue-55-dynamic-aggregation-methods.md` |
+| `issue-76-training-quality-goal-list-filter.md` |
 | `PHASE_PLAN_RESPONSIVE_UI.md` |
 | `REVIEW_OPEN_ISSUES_2026-04-04.md` |
 | `UMSETZUNGSPLAN_TRAININGSPROFILE_SPORTSPEZIFISCH_2026-04-05.md` |
--- a/docs/issues/issue-76-training-quality-goal-list-filter.md
+++ b/docs/issues/issue-76-training-quality-goal-list-filter.md
@ -0,0 +1,49 @@
+# Issue #76: Trainings-Qualität – Zielbezug + Listen-Filter
+
+**Gitea:** http://192.168.2.144:3000/Lars/mitai-jinkendo/issues/76  
+**Status:** OFFEN  
+**Erstellt:** 11.04.2026  
+**Typ:** Enhancement (Backend, Frontend, Produkt)  
+**Verwandt:** #31 (globaler Quality-Filter), #15 (Quality-Filter KI/Charts – historisch)
+
+---
+
+## Kurzfassung
+
+Die heutige **`quality_label` / `quality_filter_level`**-Logik soll überdacht werden: nicht nur **Akzeptanz** (formales Label), sondern langfristig **Nutzen für die Zielerreichung**. Parallel: der **globale Profil-Filter** in der **Aktivitätsliste** versteckt u. a. **fehlende Einstufungen** (`NULL`) – das erschwert Datenpflege und Transparenz.
+
+---
+
+## Ist-Zustand (technisch)
+
+| Bereich | Filter nach `quality_filter_level`? |
+|---------|--------------------------------------|
+| `GET` Aktivitätsliste (`routers/activity.py`) | Ja (`get_quality_filter_sql`) |
+| KI-Rohkontext (`routers/insights.py`, `_get_profile_data`) | Ja (letzte 90 Zeilen `activity_log`) |
+| Data-Layer / Platzhalter / Chart-Aggregate (`data_layer/activity_metrics.py`) | Nein – alle Einträge im Zeitfenster |
+
+Bei Stufen ≠ `all` schließt SQL-`IN` typischerweise **`NULL`** und nicht erlaubte Labels aus.
+
+---
+
+## Ziele
+
+1. **Konzept „Qualität“:** Richtung Ziel-/Focus-/Phasenbezug; Umsetzung kann in Folge-Issues zerlegt werden.
+2. **Listendarstellung:** Standard soll **alle** relevanten Einträge zeigen; **optionale lokale** Filter (inkl. „ohne Einstufung“), kein unbemerktes globales Ausblenden.
+3. **KI/Insights:** Klarstellen, ob gefilterte vs. vollständige Aktivitätsmenge; ggf. konfigurierbar oder im Prompt kenntlich.
+4. **Dokumentation:** Kurz festhalten, wo `get_quality_filter_sql` greift und wie das zu den Aggregationen passt.
+
+---
+
+## Code-Referenzen
+
+- `backend/quality_filter.py`
+- `backend/routers/activity.py` (`list_activity`)
+- `backend/routers/insights.py` (`_get_profile_data`)
+- `backend/data_layer/activity_metrics.py`
+
+---
+
+## Akzeptanz (aus Gitea, Checkliste)
+
+Siehe Issue-Body in Gitea #76; bei Umsetzung dort Häkchen setzen und diese Datei ggf. um **Umsetzungsnotizen / Abnahme** ergänzen.