mitai-jinkendo/docs/phase-0c-placeholder-migration-analysis.md

Phase 0c: Placeholder Migration Analysis

Erstellt: 28. März 2026 Zweck: Analyse welche Platzhalter zu Data Layer migriert werden müssen

---

Gesamt-Übersicht

Aktuelle Platzhalter: 116 Nach Phase 0c Migration:

• ✅ Bleiben einfach (kein Data Layer): 8 Platzhalter
• 🔄 Gehen zu Data Layer: 108 Platzhalter

---

Kategorisierung: BLEIBEN EINFACH (8 Platzhalter)

Diese Platzhalter bleiben im KI Layer (placeholder_resolver.py) weil sie:

• Keine Berechnungen durchführen
• Keine Daten-Aggregation benötigen
• Einfache Getter oder Konstanten sind

Zeitraum (4 Platzhalter)

'{{datum_heute}}': lambda pid: datetime.now().strftime('%d.%m.%Y')
'{{zeitraum_7d}}': lambda pid: 'letzte 7 Tage'
'{{zeitraum_30d}}': lambda pid: 'letzte 30 Tage'
'{{zeitraum_90d}}': lambda pid: 'letzte 90 Tage'

Begründung: Konstanten oder einfache Datum-Formatierung. Kein Data Layer nötig.

Profil - Basis (4 Platzhalter)

'{{name}}': lambda pid: get_profile_data(pid).get('name', 'Nutzer')
'{{age}}': lambda pid: calculate_age(get_profile_data(pid).get('dob'))
'{{height}}': lambda pid: str(get_profile_data(pid).get('height', 'unbekannt'))
'{{geschlecht}}': lambda pid: 'männlich' if get_profile_data(pid).get('sex') == 'm' else 'weiblich'

Begründung: Direkte Getter aus profiles Tabelle. Keine Aggregation.

---

GEHEN ZU DATA LAYER (108 Platzhalter)

1. Körper (20 Platzhalter) → data_layer.body_metrics

Basis-Metriken (8):

'{{weight_aktuell}}'        → get_weight_trend_data()['last_value']
'{{weight_trend}}'          → get_weight_trend_data() (formatiert)
'{{kf_aktuell}}'            → get_body_composition_data()['body_fat_pct'][-1]
'{{bmi}}'                   → get_body_composition_data() (berechnet)
'{{caliper_summary}}'       → get_caliper_summary_data()
'{{circ_summary}}'          → get_circumference_summary()
'{{goal_weight}}'           → get_active_goals() (filtered)
'{{goal_bf_pct}}'           → get_active_goals() (filtered)

Phase 0b - Advanced Body (12):

'{{weight_7d_median}}'      → get_weight_trend_data()['rolling_median_7d'][-1]
'{{weight_28d_slope}}'      → get_weight_trend_data()['slope_28d']
'{{weight_90d_slope}}'      → get_weight_trend_data()['slope_90d']
'{{fm_28d_change}}'         → get_body_composition_data()['fm_delta_28d']
'{{lbm_28d_change}}'        → get_body_composition_data()['lbm_delta_28d']
'{{waist_28d_delta}}'       → get_circumference_summary()['changes']['waist_28d']
'{{hip_28d_delta}}'         → get_circumference_summary()['changes']['hip_28d']
'{{chest_28d_delta}}'       → get_circumference_summary()['changes']['chest_28d']
'{{arm_28d_delta}}'         → get_circumference_summary()['changes']['arm_28d']
'{{thigh_28d_delta}}'       → get_circumference_summary()['changes']['thigh_28d']
'{{waist_hip_ratio}}'       → get_circumference_summary()['ratios']['waist_to_hip']
'{{recomposition_quadrant}}'→ get_body_composition_data()['recomposition_score']

Data Layer Funktionen benötigt:

• get_weight_trend_data(profile_id, days=90)
• get_body_composition_data(profile_id, days=90)
• get_circumference_summary(profile_id, days=90)
• get_caliper_summary_data(profile_id, days=90)

---

2. Ernährung (14 Platzhalter) → data_layer.nutrition_metrics

Basis-Metriken (7):

'{{kcal_avg}}'              → get_energy_balance_data()['avg_intake']
'{{protein_avg}}'           → get_protein_adequacy_data()['avg_protein_g']
'{{carb_avg}}'              → get_macro_distribution_data()['avg_carbs_g']
'{{fat_avg}}'               → get_macro_distribution_data()['avg_fat_g']
'{{nutrition_days}}'        → get_energy_balance_data()['data_points']
'{{protein_ziel_low}}'      → get_protein_adequacy_data()['target_protein_g'] (low)
'{{protein_ziel_high}}'     → get_protein_adequacy_data()['target_protein_g'] (high)

Phase 0b - Advanced Nutrition (7):

'{{energy_balance_7d}}'     → get_energy_balance_data()['avg_net']
'{{energy_deficit_surplus}}'→ get_energy_balance_data()['deficit_surplus_avg']
'{{protein_g_per_kg}}'      → get_protein_adequacy_data()['avg_protein_per_kg']
'{{protein_days_in_target}}'→ get_protein_adequacy_data()['adherence_pct']
'{{protein_adequacy_28d}}'  → get_protein_adequacy_data()['adherence_score']
'{{macro_consistency_score}}'→ get_macro_distribution_data()['balance_score']
'{{intake_volatility}}'     → get_macro_distribution_data()['variability']

Data Layer Funktionen benötigt:

• get_protein_adequacy_data(profile_id, days=28, goal_mode=None)
• get_energy_balance_data(profile_id, days=28)
• get_macro_distribution_data(profile_id, days=28)

---

3. Training (16 Platzhalter) → data_layer.activity_metrics

Basis-Metriken (3):

'{{activity_summary}}'      → get_training_volume_data()['weekly_totals'] (formatted)
'{{activity_detail}}'       → get_training_volume_data()['by_type'] (formatted)
'{{trainingstyp_verteilung}}'→ get_activity_quality_distribution()

Phase 0b - Advanced Activity (13):

'{{training_minutes_week}}' → get_training_volume_data()['weekly_totals'][0]['duration_min']
'{{training_frequency_7d}}' → get_training_volume_data()['weekly_totals'][0]['sessions']
'{{quality_sessions_pct}}'  → get_activity_quality_distribution()['high_quality_pct']
'{{ability_balance_strength}}' → get_ability_balance_data()['abilities']['strength']
'{{ability_balance_endurance}}'→ get_ability_balance_data()['abilities']['cardio']
'{{ability_balance_mental}}'   → get_ability_balance_data()['abilities']['mental']
'{{ability_balance_coordination}}'→ get_ability_balance_data()['abilities']['coordination']
'{{ability_balance_mobility}}'  → get_ability_balance_data()['abilities']['mobility']
'{{proxy_internal_load_7d}}'→ get_training_volume_data()['strain']
'{{monotony_score}}'        → get_training_volume_data()['monotony']
'{{strain_score}}'          → get_training_volume_data()['strain']
'{{rest_day_compliance}}'   → get_recovery_score_data()['components']['rest_compliance']['score']
'{{vo2max_trend_28d}}'      → get_vitals_baseline_data()['vo2_max']['trend']

Data Layer Funktionen benötigt:

• get_training_volume_data(profile_id, weeks=4)
• get_activity_quality_distribution(profile_id, days=28)
• get_ability_balance_data(profile_id, weeks=4)

---

4. Schlaf & Erholung (10 Platzhalter) → data_layer.recovery_metrics

Basis-Metriken (3):

'{{sleep_avg_duration}}'    → get_sleep_regularity_data()['avg_duration_h']
'{{sleep_avg_quality}}'     → get_sleep_regularity_data()['avg_quality']
'{{rest_days_count}}'       → get_recovery_score_data()['components']['rest_compliance']['rest_days']

Phase 0b - Advanced Recovery (7):

'{{hrv_vs_baseline_pct}}'   → get_vitals_baseline_data()['hrv']['deviation_pct']
'{{rhr_vs_baseline_pct}}'   → get_vitals_baseline_data()['rhr']['deviation_pct']
'{{sleep_avg_duration_7d}}' → get_sleep_regularity_data()['avg_duration_h']
'{{sleep_debt_hours}}'      → get_sleep_regularity_data()['sleep_debt_h']
'{{sleep_regularity_proxy}}'→ get_sleep_regularity_data()['regularity_score']
'{{recent_load_balance_3d}}'→ get_recovery_score_data()['load_balance']
'{{sleep_quality_7d}}'      → get_sleep_regularity_data()['avg_quality']

Data Layer Funktionen benötigt:

• get_recovery_score_data(profile_id, days=7)
• get_sleep_regularity_data(profile_id, days=28)
• get_vitals_baseline_data(profile_id, days=7)

---

5. Vitalwerte (3 Platzhalter) → data_layer.health_metrics

'{{vitals_avg_hr}}'         → get_vitals_baseline_data()['rhr']['current']
'{{vitals_avg_hrv}}'        → get_vitals_baseline_data()['hrv']['current']
'{{vitals_vo2_max}}'        → get_vitals_baseline_data()['vo2_max']['current']

Data Layer Funktionen benötigt:

• get_vitals_baseline_data(profile_id, days=7) (bereits in recovery)

---

6. Scores (6 Platzhalter) → Diverse Module

'{{goal_progress_score}}'   → get_goal_progress_data() → goals.py
'{{body_progress_score}}'   → get_body_composition_data() → body_metrics.py
'{{nutrition_score}}'       → get_protein_adequacy_data() → nutrition_metrics.py
'{{activity_score}}'        → get_training_volume_data() → activity_metrics.py
'{{recovery_score}}'        → get_recovery_score_data()['score'] → recovery_metrics.py
'{{data_quality_score}}'    → get_data_quality_score() → utils.py (NEW)

Hinweis: Scores nutzen bestehende Data Layer Funktionen, nur Formatierung nötig.

---

7. Top Goals/Focus (5 Platzhalter) → data_layer.goals

'{{top_goal_name}}'         → get_active_goals()[0]['name']
'{{top_goal_progress_pct}}' → get_active_goals()[0]['progress_pct']
'{{top_goal_status}}'       → get_active_goals()[0]['status']
'{{top_focus_area_name}}'   → get_weighted_focus_areas()[0]['name']
'{{top_focus_area_progress}}'→ get_weighted_focus_areas()[0]['progress']

Data Layer Funktionen benötigt:

• get_active_goals(profile_id) (already exists from Phase 0b)
• get_weighted_focus_areas(profile_id) (already exists from Phase 0b)

---

8. Category Scores (14 Platzhalter) → Formatierung nur

'{{focus_cat_körper_progress}}'     → _format_from_aggregated_data()
'{{focus_cat_körper_weight}}'       → _format_from_aggregated_data()
'{{focus_cat_ernährung_progress}}'  → _format_from_aggregated_data()
'{{focus_cat_ernährung_weight}}'    → _format_from_aggregated_data()
# ... (7 Kategorien × 2 = 14 total)

Hinweis: Diese nutzen bereits aggregierte Daten aus Phase 0b. Migration: Nur KI Layer Formatierung, Data Layer nicht nötig (Daten kommen aus anderen Funktionen).

---

9. Korrelationen (7 Platzhalter) → data_layer.correlations

'{{correlation_energy_weight_lag}}'  → get_correlation_data(pid, 'energy', 'weight')
'{{correlation_protein_lbm}}'        → get_correlation_data(pid, 'protein', 'lbm')
'{{correlation_load_hrv}}'           → get_correlation_data(pid, 'load', 'hrv')
'{{correlation_load_rhr}}'           → get_correlation_data(pid, 'load', 'rhr')
'{{correlation_sleep_recovery}}'     → get_correlation_data(pid, 'sleep', 'recovery')
'{{plateau_detected}}'               → detect_plateau(pid, 'weight')
'{{top_drivers}}'                    → get_top_drivers(pid)

Data Layer Funktionen benötigt:

• get_correlation_data(profile_id, metric_a, metric_b, days=90, max_lag=7)
• detect_plateau(profile_id, metric, days=28)
• get_top_drivers(profile_id) (NEW - identifies top correlations)

---

10. JSON/Markdown (8 Platzhalter) → Formatierung nur

'{{active_goals_json}}'             → json.dumps(get_active_goals(pid))
'{{active_goals_md}}'               → format_as_markdown(get_active_goals(pid))
'{{focus_areas_weighted_json}}'     → json.dumps(get_weighted_focus_areas(pid))
'{{focus_areas_weighted_md}}'       → format_as_markdown(get_weighted_focus_areas(pid))
'{{focus_area_weights_json}}'       → json.dumps(get_focus_area_weights(pid))
'{{top_3_focus_areas}}'             → format_top_3(get_weighted_focus_areas(pid))
'{{top_3_goals_behind_schedule}}'   → format_goals_behind(get_active_goals(pid))
'{{top_3_goals_on_track}}'          → format_goals_on_track(get_active_goals(pid))

Hinweis: Diese nutzen bereits existierende Data Layer Funktionen. Migration: Nur KI Layer Formatierung (json.dumps, markdown, etc.).

---

Data Layer Funktionen - Zusammenfassung

Neue Funktionen zu erstellen (Phase 0c):

body_metrics.py (4 Funktionen):

• ✅ get_weight_trend_data()
• ✅ get_body_composition_data()
• ✅ get_circumference_summary()
• ✅ get_caliper_summary_data()

nutrition_metrics.py (3 Funktionen):

• ✅ get_protein_adequacy_data()
• ✅ get_energy_balance_data()
• ✅ get_macro_distribution_data()

activity_metrics.py (3 Funktionen):

• ✅ get_training_volume_data()
• ✅ get_activity_quality_distribution()
• ✅ get_ability_balance_data()

recovery_metrics.py (2 Funktionen):

• ✅ get_recovery_score_data()
• ✅ get_sleep_regularity_data()

health_metrics.py (2 Funktionen):

• ✅ get_vitals_baseline_data()
• ✅ get_blood_pressure_data() (aus Spec)

goals.py (3 Funktionen):

• ✅ get_active_goals() (exists from Phase 0b)
• ✅ get_weighted_focus_areas() (exists from Phase 0b)
• ✅ get_goal_progress_data() (aus Spec)

correlations.py (3 Funktionen):

• ✅ get_correlation_data()
• ✅ detect_plateau()
• 🆕 get_top_drivers() (NEW - not in spec)

utils.py (Shared):

• ✅ calculate_confidence()
• ✅ calculate_baseline()
• ✅ detect_outliers()
• ✅ aggregate_data()
• ✅ serialize_dates()
• 🆕 get_data_quality_score() (NEW)

Total neue Funktionen: 20 (aus Spec) + 2 (zusätzlich) = 22 Data Layer Funktionen

---

Migration-Aufwand pro Kategorie

Kategorie	Platzhalter	Data Layer Funcs	Aufwand	Priorität
Körper	20	4	3-4h	High
Ernährung	14	3	2-3h	High
Training	16	3	3-4h	Medium
Recovery	10	2	2-3h	Medium
Vitalwerte	3	1 (shared)	0.5h	Low
Scores	6	0 (use others)	1h	Low
Goals/Focus	5	0 (exists)	0.5h	Low
Categories	14	0 (formatting)	1h	Low
Korrelationen	7	3	2-3h	Medium
JSON/Markdown	8	0 (formatting)	0.5h	Low
TOTAL	108	22	16-22h	-

---

KI Layer Refactoring-Muster

VORHER (Phase 0b):

def get_latest_weight(profile_id: str) -> str:
    """Returns latest weight with SQL + formatting"""
    with get_db() as conn:
        cur = get_cursor(conn)
        cur.execute("""
            SELECT weight FROM weight_log
            WHERE profile_id = %s
            ORDER BY date DESC LIMIT 1
        """, (profile_id,))
        row = cur.fetchone()
        if not row:
            return "nicht verfügbar"
        return f"{row['weight']:.1f} kg"

PLACEHOLDER_MAP = {
    '{{weight_aktuell}}': get_latest_weight,
}

NACHHER (Phase 0c):

from data_layer.body_metrics import get_weight_trend_data

def resolve_weight_aktuell(profile_id: str) -> str:
    """Returns latest weight (formatted for KI)"""
    data = get_weight_trend_data(profile_id, days=7)

    if data['confidence'] == 'insufficient':
        return "nicht verfügbar"

    return f"{data['last_value']:.1f} kg"

PLACEHOLDER_MAP = {
    '{{weight_aktuell}}': resolve_weight_aktuell,
}

Reduzierung: Von ~15 Zeilen (SQL + Logic) zu ~7 Zeilen (Call + Format)

---

Erwartetes Ergebnis nach Phase 0c

Zeilen-Reduktion:

• placeholder_resolver.py:
  • Vorher: ~1200 Zeilen
  • Nachher: ~400 Zeilen (67% Reduktion)

Code-Qualität:

• ✅ Keine SQL queries in placeholder_resolver.py
• ✅ Keine Berechnungslogik in placeholder_resolver.py
• ✅ Nur Formatierung für KI-Consumption

Wiederverwendbarkeit:

• ✅ 22 Data Layer Funktionen nutzbar für:
  • KI Layer (108 Platzhalter)
  • Charts Layer (10+ Charts)
  • API Endpoints (beliebig erweiterbar)

---

Checkliste: Migration pro Platzhalter

Für jeden der 108 Platzhalter:

[ ] Data Layer Funktion existiert
[ ] KI Layer ruft Data Layer Funktion auf
[ ] Formatierung für KI korrekt
[ ] Fehlerbehandlung (insufficient data)
[ ] Test: Platzhalter liefert gleichen Output wie vorher
[ ] In PLACEHOLDER_MAP registriert
[ ] Dokumentiert

---

Erstellt: 28. März 2026 Status: Ready for Phase 0c Implementation Nächster Schritt: Data Layer Funktionen implementieren (Start mit utils.py)