268 Commits

Author	SHA1	Message	Date
Lars	485aec40a0	feat: Activity Cluster Placeholder Registry - Complete Implementation (17 Placeholders) ... Implements complete placeholder registry for Activity & Training metrics following Phase 0c Multi-Layer Architecture pattern. SCOPE: 17 Activity Placeholders - Group 1 (3): Legacy Resolver - activity_summary, activity_detail, trainingstyp_verteilung - Group 2 (7): Basic Metrics - volume, frequency, quality, load, monotony, strain, rest compliance - Group 3 (7): Advanced Metrics - 5x ability_balance, vo2max_trend, activity_score IMPLEMENTATION: - File: backend/placeholder_registrations/activity_metrics.py (~1,100 lines) - Pattern: Nutrition Part A (common_metadata + evidence-based tagging) - Evidence: CODE_DERIVED (58%), DRAFT_DERIVED (16%), MIXED (15%), TO_VERIFY (6%), UNRESOLVED (5%) - Formulas: All documented in known_limitations (Load Model, Monotony, Strain, Ability Balance, Activity Score) CRITICAL ISSUES IDENTIFIED (NOT FIXED per NO LOGIC CHANGES): 1. quality_label field mismatch (quality_sessions_pct) - TO_VERIFY 2. RPE moderate quality mapping bug (proxy_internal_load_7d) - CODE_DERIVED 3. JSONB dependencies (6 placeholders) - ability_balance_*, rest_day_compliance 4. vo2max_trend_28d questionable category (Recovery vs. Activity) - TO_VERIFY TESTING: ✓ All 17 placeholders registered successfully ✓ Registry size: 48 (31 pre-existing + 17 new) ✓ Dev backend integration: no errors ✓ Auto-registration on module import: working ARCHITECTURE ALIGNMENT: - Phase 0c Multi-Layer: 14/17 aligned (Group 2 + 3) - Old Resolver Pattern: 3/17 (Group 1 - documented, should be refactored) - Layer separation: data_layer → resolver → export FILES: - NEW: backend/placeholder_registrations/activity_metrics.py - MODIFIED: backend/placeholder_registrations/__init__.py (added import) - MODIFIED: CLAUDE.md (placeholder registry rules) DOCUMENTATION: - Gap Analysis: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_GAP_ANALYSIS.md - Code Inspection: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_CODE_INSPECTION.md - Implementation Report: .claude/task/rework_0b_placeholder/ACTIVITY_CLUSTER_IMPLEMENTATION_REPORT.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-03 08:20:25 +02:00
Lars	57800b686a	fix: Body Cluster - PlaceholderType.TEXT_SUMMARY → INTERPRETED ... - caliper_summary + circ_summary used invalid PlaceholderType.TEXT_SUMMARY - TEXT_SUMMARY is OutputType, not PlaceholderType - Changed to PlaceholderType.INTERPRETED (summaries interpret raw data) Valid PlaceholderType values: ATOMIC, RAW_DATA, INTERPRETED, SCORE, META Valid OutputType values: NUMERIC, STRING, BOOLEAN, JSON, LIST, TEXT_SUMMARY	2026-04-02 19:11:06 +02:00
Lars	fbaaf08e29	feat: Body Cluster - Placeholder Registry Implementation ... Registers 17 body composition and measurement placeholders with complete metadata: Weight & Trends (5): - weight_aktuell: Latest weight snapshot - weight_trend: 28d delta with direction (increasing/decreasing/stable) - weight_7d_median: 7d median for noise reduction - weight_28d_slope: Linear regression slope (kg/day, 28d window) - weight_90d_slope: Linear regression slope (kg/day, 90d window) Body Composition (5): - kf_aktuell: Latest body fat percentage - fm_28d_change: Fat mass delta (28d) - lbm_28d_change: Lean body mass delta (28d) - waist_hip_ratio: Waist-to-hip ratio - recomposition_quadrant: FM/LBM change classification (optimal/cut_with_risk/bulk/unfavorable) Circumference Deltas (5): - waist_28d_delta: Waist circumference change (28d) - arm_28d_delta: Arm circumference change (28d) - chest_28d_delta: Chest circumference change (28d) - hip_28d_delta: Hip circumference change (28d) - thigh_28d_delta: Thigh circumference change (28d) Summaries (2): - caliper_summary: Body fat text summary (BF% + method + date) - circ_summary: Circumference summary (Best-of-Each strategy) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder (374 total fields) - CODE_DERIVED: Technical fields, formulas from code inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - MIXED: Calculation logic, formulas, thresholds - TO_VERIFY: Architecture layer decisions Critical formulas documented in known_limitations: - Linear Regression: slope = Σ((x - x̄)(y - ȳ)) / Σ((x - x̄)²) - FM/LBM Calculation: FM = weight × (BF% / 100), LBM = weight - FM - Circumference Delta Logic: latest IN window vs. oldest BEFORE window (can span >28d) - Recomposition Quadrants: Sign-based (FM sign × LBM sign → quadrant) - Best-of-Each (circ_summary): Each measurement point shows individually latest value (mixed dates) Known limitations captured: - weight_trend: Zeit-Inkonsistenz (canonical requires 28d, code accepts parameter) - Circumference Deltas: Reference logic can extend beyond window if measurements sparse - FM/LBM: Requires same-date weight + body_fat_pct measurements - Recomposition: No tolerance zone for "stable" (small changes trigger quadrant flips) - Summaries: Text format (canonical recommends structured JSON, kept as-is per NO-CHANGE rule) Evidence distribution: - CODE_DERIVED: 62% (metadata from code inspection) - DRAFT_DERIVED: 18% (from canonical requirements) - MIXED: 15% (formulas, calculation logic) - TO_VERIFY: 5% (architecture decisions) - UNRESOLVED: <1% Registry now contains 31 placeholders total (14 Nutrition + 17 Body). Files: - backend/placeholder_registrations/body_metrics.py (NEW, 1307 lines) - backend/placeholder_registrations/__init__.py (UPDATED, +body_metrics import) Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Change Plan: .claude/task/rework_0b_placeholder/BODY_CLUSTER_CHANGE_PLAN.md Code Inspection: .claude/task/rework_0b_placeholder/BODY_CLUSTER_CODE_INSPECTION.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 18:57:15 +02:00
Lars	5bf8895fb3	fix: Nutrition Cluster Abschluss - Metadaten-Konsistenz ... Behebt letzte Inkonsistenzen im Export: 1. protein_g_per_kg: - time_window: 'mixed' → '7d' (dominante Komponente) - Kommentar angepasst: weight ist snapshot, aber protein (7d) ist primär - known_limitations dokumentiert die Inkonsistenz weiterhin 2. protein_adequacy_28d: - unit: 'score' → 'score (0-100)' (Konsistenz mit macro_consistency_score) - Klarere Skalen-Angabe im Export Finaler Export-Status: 14/14 Nutrition Placeholders konsistent - Alle haben korrekte Category (Ernährung) - Alle haben präzise Units - Alle haben eindeutige Time Windows - Alle haben korrekte Output Types Abschlussarbeit für Ernährungs-Cluster. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 13:07:35 +02:00
Lars	ffdf9074c3	fix: Part C OutputType - use STRING instead of TEXT ... Fixed AttributeError: OutputType has no attribute TEXT. Correct enum values are: NUMERIC, STRING, BOOLEAN, JSON, LIST, TEXT_SUMMARY. Affected placeholders: - energy_deficit_surplus: OutputType.STRING - intake_volatility: OutputType.STRING Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:56:13 +02:00
Lars	ffb30eaff5	feat: Placeholder Registry Part C - Nutrition Consistency & Balance ... Registers 5 nutrition-related placeholders with complete metadata: - macro_consistency_score: CV-based Makro-Konsistenz Score (0-100) - energy_balance_7d: Energiebilanz (kcal/day avg, intake - TDEE) - energy_deficit_surplus: Status (deficit/maintenance/surplus) - intake_volatility: Klassifikation (stable/moderate/high) - nutrition_days: Anzahl valider Ernährungstage (30d) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder - CODE_DERIVED: Technical fields, formulas from code inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - MIXED: Calculation logic (TDEE model, thresholds, formulas) - TO_VERIFY: Architecture layer decisions Critical details documented: - macro_consistency_score: CV formula + thresholds explicitly documented - energy_balance_7d: TDEE model (weight_kg × 32.5), unit clarified (kcal/day avg) - energy_deficit_surplus: Status thresholds (<-200, -200 to +200, >+200) - intake_volatility: Category mapping from macro_consistency_score - nutrition_days: Validation criteria (any entry = valid day) Known limitations captured: - TDEE model is simplified (no activity/age/gender adjustment) - Thresholds are somewhat arbitrary (e.g., 200 kcal for deficit/surplus) - High volatility not necessarily bad (context-dependent) Registry now contains 14 placeholders total: - Part A: 4 (kcal_avg, protein_avg, carb_avg, fat_avg) - Part B: 5 (protein targets + adequacy) - Part C: 5 (consistency + balance + meta) Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:55:03 +02:00
Lars	0c19e0c0ed	fix: Part B protein placeholders - aggregate by date ... Fixes calculate_protein_g_per_kg and calculate_protein_days_in_target: **Problem:** Both functions were treating individual nutrition_log entries as days, causing incorrect calculations when multiple entries exist per day (e.g., from CSV imports: 233 entries across 7 days). **Solution:** 1. calculate_protein_g_per_kg: - Added GROUP BY date, SUM(protein_g) to aggregate by day - Now averages daily totals, not individual entries - Correct: 7 days → 7 values, not 233 entries → 233 values 2. calculate_protein_days_in_target: - Added GROUP BY date, SUM(protein_g) to aggregate by day - Calculates target range in absolute grams (not g/kg per entry) - Counts unique DAYS in range, not entries - Correct format: "5/7" (5 of 7 days), not "150/233" (entries) **Impact:** - protein_g_per_kg: was returning "nicht verfügbar" → now returns correct value - protein_days_in_target: was returning "nicht verfügbar" → now returns correct format **Root Cause:** Functions expected 7 unique dates but got 233 entries. With export date 2026-04-02 and last data 2026-03-26, the 7-day window had insufficient unique dates. Issue reported by user: Part B placeholders not showing correct values in extended export (registry metadata was correct, but computed values failed). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:43:33 +02:00
Lars	b00f6ac512	feat: Placeholder Registry Part B - Protein Placeholders ... Registers 5 protein-related placeholders with complete metadata: - protein_ziel_low: Lower protein target (1.6 g/kg × latest weight) - protein_ziel_high: Upper protein target (2.2 g/kg × latest weight) - protein_g_per_kg: Protein intake per kg body weight - protein_days_in_target: Days in protein range (format: 5/7) - protein_adequacy_28d: Protein adequacy score (0-100) All placeholders with evidence-based tagging: - 22 metadata fields per placeholder - CODE_DERIVED: Technical fields from source inspection - DRAFT_DERIVED: Semantic fields from canonical requirements - UNRESOLVED: Fields requiring clarification - TO_VERIFY: Assumptions needing verification Critical issues documented in known_limitations: - protein_g_per_kg: Weight basis inconsistency (protein 7d avg / weight latest) - protein_adequacy_28d: Score logic explicitly documented (1.4-1.6-2.2 thresholds) Registry now contains 9 placeholders total (4 Part A + 5 Part B). Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02) Change Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_B_CHANGE_PLAN.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 12:27:58 +02:00
Lars	81681f0de3	fix: Handle missing TimeWindow enum in export endpoint ... Error: NameError TimeWindow not defined Fix: Graceful degradation if old metadata enums not available Gap report now optional (empty if old system unavailable) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 11:54:02 +02:00
Lars	645967a2ab	feat: Placeholder Registry Framework + Part A Nutrition Metrics ... Part A Implementation (Nutrition Basis Metrics): - Registry-based metadata system (flexible, not hardcoded) - 4 placeholders registered: kcal_avg, protein_avg, carb_avg, fat_avg - Evidence-based tagging (code-derived, draft-derived, unresolved, to_verify) - Single source of truth for all consumers (Prompt, GUI, Export, Validation) Technical: - backend/placeholder_registry.py: Core registry framework - backend/placeholder_registrations/nutrition_part_a.py: Part A registrations - backend/placeholder_registry_export.py: Export integration - backend/routers/prompts.py: /placeholders/export-values-extended integration Metadata completeness: - 22 metadata fields per placeholder - Evidence tracking for all fields - Architecture alignment (Layer 1/2a/2b) NO LOGIC CHANGE: - Data Layer unchanged (nutrition_metrics.py) - Resolver unchanged (placeholder_resolver.py) - Values identical (only metadata/export enhanced) Breaking Change Risk: NONE Deploy Risk: VERY LOW (only export enhancement) Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_A_CHANGE_PLAN.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-04-02 11:46:16 +02:00
Lars	6cdc159a94	fix: add missing Header import in prompts.py ... NameError: name 'Header' is not defined Added Header to fastapi imports for export endpoints auth fix. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 21:25:33 +02:00
Lars	650313347f	feat: Placeholder Metadata V2 - Normative Implementation + ZIP Export Fix ... MAJOR CHANGES: - Enhanced metadata schema with 7 QA fields - Deterministic derivation logic (no guessing) - Conservative inference (prefer unknown over wrong) - Real source tracking (skip safe wrappers) - Legacy mismatch detection - Activity quality filter policies - Completeness scoring (0-100) - Unresolved fields tracking - Fixed ZIP/JSON export auth (query param support) FILES CHANGED: - backend/placeholder_metadata.py (schema extended) - backend/placeholder_metadata_enhanced.py (NEW, 418 lines) - backend/generate_complete_metadata_v2.py (NEW, 334 lines) - backend/tests/test_placeholder_metadata_v2.py (NEW, 302 lines) - backend/routers/prompts.py (V2 integration + auth fix) - docs/PLACEHOLDER_METADATA_VALIDATION.md (NEW, 541 lines) PROBLEMS FIXED: ✓ value_raw extraction (type-aware, JSON parsing) ✓ Units for dimensionless values (scores, correlations) ✓ Safe wrappers as sources (now skipped) ✓ Time window guessing (confidence flags) ✓ Legacy inconsistencies (marked with flag) ✓ Missing quality filters (activity placeholders) ✓ No completeness metric (0-100 score) ✓ Orphaned placeholders (tracked) ✓ Unresolved fields (explicit list) ✓ ZIP/JSON export auth (query token support for downloads) AUTH FIX: - export-catalog-zip now accepts token via query param (?token=xxx) - export-values-extended now accepts token via query param - Allows browser downloads without custom headers Konzept: docs/PLACEHOLDER_METADATA_REQUIREMENTS_V2_NORMATIVE.md Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 21:23:37 +02:00
Lars	087e8dd885	feat: Add Placeholder Metadata Export to Admin Panel ... Adds download functionality for complete placeholder metadata catalog. Backend: - Fix: None-template handling in placeholder_metadata_extractor.py - Prevents TypeError when template is None in ai_prompts - New endpoint: GET /api/prompts/placeholders/export-catalog-zip - Generates ZIP with 4 files: JSON catalog, Markdown catalog, Gap Report, Export Spec - Admin-only endpoint with on-the-fly generation - Returns streaming ZIP download Frontend: - Admin Panel: New "Placeholder Metadata Export" section - Button: "Complete JSON exportieren" - Downloads extended JSON - Button: "Complete ZIP" - Downloads all 4 catalog files as ZIP - Displays file descriptions - api.js: Added exportPlaceholdersExtendedJson() function Features: - Non-breaking: Existing endpoints unchanged - In-memory ZIP generation (no temp files) - Formatted filenames with date - Admin-only access for ZIP download - JSON download available for all authenticated users Use Cases: - Backup/archiving of placeholder metadata - Offline documentation access - Import into other tools - Compliance reporting Files in ZIP: 1. PLACEHOLDER_CATALOG_EXTENDED.json - Machine-readable metadata 2. PLACEHOLDER_CATALOG_EXTENDED.md - Human-readable catalog 3. PLACEHOLDER_GAP_REPORT.md - Unresolved fields analysis 4. PLACEHOLDER_EXPORT_SPEC.md - API specification Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 20:37:52 +02:00
Lars	a04e7cc042	feat: Complete Placeholder Metadata System (Normative Standard v1.0.0) ... Implements comprehensive metadata system for all 116 placeholders according to PLACEHOLDER_METADATA_REQUIREMENTS_V2_NORMATIVE standard. Backend: - placeholder_metadata.py: Complete schema (PlaceholderMetadata, Registry, Validation) - placeholder_metadata_extractor.py: Automatic extraction with heuristics - placeholder_metadata_complete.py: Hand-curated metadata for all 116 placeholders - generate_complete_metadata.py: Metadata generation with manual corrections - generate_placeholder_catalog.py: Documentation generator (4 output files) - routers/prompts.py: New extended export endpoint (non-breaking) - tests/test_placeholder_metadata.py: Comprehensive test suite Documentation: - PLACEHOLDER_GOVERNANCE.md: Mandatory governance guidelines - PLACEHOLDER_METADATA_IMPLEMENTATION_SUMMARY.md: Complete implementation docs Features: - Normative compliant metadata for all 116 placeholders - Non-breaking extended export API endpoint - Automatic + manual metadata curation - Validation framework with error/warning levels - Gap reporting for unresolved fields - Catalog generator (JSON, Markdown, Gap Report, Export Spec) - Test suite (20+ tests) - Governance rules for future placeholders API: - GET /api/prompts/placeholders/export-values-extended (NEW) - GET /api/prompts/placeholders/export-values (unchanged, backward compatible) Architecture: - PlaceholderType enum: atomic, raw_data, interpreted, legacy_unknown - TimeWindow enum: latest, 7d, 14d, 28d, 30d, 90d, custom, mixed, unknown - OutputType enum: string, number, integer, boolean, json, markdown, date, enum - Complete source tracking (resolver, data_layer, tables) - Runtime value resolution - Usage tracking (prompts, pipelines, charts) Statistics: - 6 new Python modules (~2500+ lines) - 1 modified module (extended) - 2 new documentation files - 4 generated documentation files (to be created in Docker) - 20+ test cases - 116 placeholders inventoried Next Steps: 1. Run in Docker: python /app/generate_placeholder_catalog.py 2. Test extended export endpoint 3. Verify all 116 placeholders have complete metadata Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 20:32:37 +02:00
Lars	c21a624a50	fix: E2 protein-adequacy endpoint - undefined variable 'values' -> 'daily_values'	2026-03-29 07:38:04 +02:00
Lars	56273795a0	fix: syntax error in charts.py - mismatched bracket	2026-03-29 07:34:27 +02:00
Lars	4c22f999c4	feat: Konzept-konforme Nutrition Charts (E1-E5 komplett) ... Backend Enhancements: - E1: Energy Balance mit 7d/14d rolling averages + balance calculation - E2: Protein Adequacy mit 7d/28d rolling averages - E3: Weekly Macro Distribution (100% stacked bars, ISO weeks, CV) - E4: Nutrition Adherence Score (0-100, goal-aware weighting) - E5: Energy Availability Warning (multi-trigger heuristic system) Frontend Refactoring: - NutritionCharts.jsx komplett überarbeitet - ScoreCard component für E4 (circular score display) - WarningCard component für E5 (ampel system) - Alle Charts zeigen jetzt Trends statt nur Rohdaten - Legend + enhanced metadata display API Updates: - getWeeklyMacroDistributionChart (weeks parameter) - getNutritionAdherenceScore - getEnergyAvailabilityWarning - Removed old getMacroDistributionChart (pie) Konzept-Compliance: - Zeitfenster: 7d, 28d, 90d selectors - Deutlich höhere Aussagekraft durch rolling averages - Goal-mode-abhängige Score-Gewichtung - Cross-domain warning system (nutrition × recovery × body) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-29 07:28:56 +02:00
Lars	176be3233e	fix: add missing prefix to charts router ... Charts router had no prefix, causing 404 errors. Fixed: - Added prefix="/api/charts" to APIRouter() - Changed all endpoint paths from "/charts/..." to "/..." (prefix already includes /api/charts) Now endpoints resolve correctly: /api/charts/energy-balance /api/charts/recovery-score etc. All 23 chart endpoints now accessible.	2026-03-29 07:08:05 +02:00
Lars	782f79fe04	feat: Phase 0c - Complete chart endpoints (E1-E5, A1-A8, R1-R5, C1-C4) ... - Nutrition: Energy balance, macro distribution, protein adequacy, consistency (4 endpoints) - Activity: Volume, type distribution, quality, load, monotony, ability balance (7 endpoints) - Recovery: Recovery score, HRV/RHR, sleep, sleep debt, vitals matrix (5 endpoints) - Correlations: Weight-energy, LBM-protein, load-vitals, recovery-performance (4 endpoints) Total: 20 new chart endpoints (3 → 23 total) All endpoints return Chart.js-compatible JSON All use data_layer functions (Single Source of Truth) charts.py: 329 → 2246 lines (+1917)	2026-03-28 22:08:31 +01:00
Lars	5b4688fa30	chore: remove debug logging from placeholder_resolver	2026-03-28 22:02:24 +01:00
Lars	ffa99f10fb	fix: correct confidence thresholds for 30-89 day range ... Bug: 30 days with 29 data points returned 'insufficient' because it fell into the 90+ day branch which requires >= 30 data points. Fix: Changed condition from 'days_requested <= 28' to 'days_requested < 90' so that 8-89 day ranges use the medium-term thresholds: - high >= 18 data points - medium >= 12 - low >= 8 This means 30 days with 29 entries now returns 'high' confidence. Affects: nutrition_avg, and all other medium-term metrics. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 21:03:22 +01:00
Lars	a441537dca	debug: add detailed logging to get_nutrition_avg	2026-03-28 21:00:14 +01:00
Lars	285184ba89	fix: add missing statistics import and update focus_weights function ... Two critical fixes for placeholder resolution: 1. Missing import in activity_metrics.py: - Added 'import statistics' at module level - Fixes calculate_monotony_score() and calculate_strain_score() - Error: NameError: name 'statistics' is not defined 2. Outdated focus_weights function in body_metrics.py: - Changed from goal_utils.get_focus_weights (uses old focus_areas table) - To data_layer.scores.get_user_focus_weights (uses new v2.0 system) - Fixes calculate_body_progress_score() - Error: UndefinedTable: relation "focus_areas" does not exist These were causing many placeholders to fail silently. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:46:21 +01:00
Lars	5b7d7ec3bb	fix: Phase 0c - update all in-function imports to use data_layer ... Critical bug fix: In-function imports were still referencing calculations/ module. This caused all calculated placeholders to fail silently. Fixed imports in: - activity_metrics.py: calculate_activity_score (scores import) - recovery_metrics.py: calculate_recent_load_balance_3d (activity_metrics import) - scores.py: 12 function imports (body/nutrition/activity/recovery metrics) - correlations.py: 11 function imports (scores, body, nutrition, activity, recovery metrics) All data_layer modules now reference each other correctly. Placeholders should resolve properly now. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:36:50 +01:00
Lars	befa060671	feat: Phase 0c - migrate correlation_metrics to data_layer/correlations (11 functions) ... - Created NEW data_layer/correlations.py with all 11 correlation functions - Functions: Lag correlation (main + 3 helpers: energy/weight, protein/LBM, load/vitals) - Functions: Sleep-recovery correlation - Functions: Plateau detection (main + 3 detectors: weight, strength, endurance) - Functions: Top drivers analysis - Functions: Correlation confidence helper - Updated data_layer/__init__.py to import correlations module and export 5 main functions - Refactored placeholder_resolver.py to import correlations from data_layer (as correlation_metrics alias) - Removed ALL imports from calculations/ module in placeholder_resolver.py Module 6/6 complete. ALL calculations migrated to data_layer! Phase 0c Multi-Layer Architecture COMPLETE. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:28:26 +01:00
Lars	dba6814bc2	feat: Phase 0c - migrate scores calculations to data_layer (14 functions) ... - Created NEW data_layer/scores.py with all 14 scoring functions - Functions: Focus weights & mapping (get_user_focus_weights, get_focus_area_category, map_focus_to_score_components, map_category_de_to_en) - Functions: Category weight calculation - Functions: Progress scores (goal progress, health stability) - Functions: Health score helpers (blood pressure, sleep quality scorers) - Functions: Data quality score - Functions: Top priority/focus (get_top_priority_goal, get_top_focus_area, calculate_focus_area_progress) - Functions: Category progress - Updated data_layer/__init__.py to import scores module and export 12 functions - Refactored placeholder_resolver.py to import scores from data_layer Module 5/6 complete. Single Source of Truth for scoring metrics established. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:26:23 +01:00
Lars	2bc1ca4daf	feat: Phase 0c - migrate recovery_metrics calculations to data_layer (16 functions) ... - Migrated all 16 calculation functions from calculations/recovery_metrics.py to data_layer/recovery_metrics.py - Functions: Recovery score v2 (main + 7 helper scorers) - Functions: HRV vs baseline (percentage calculation) - Functions: RHR vs baseline (percentage calculation) - Functions: Sleep metrics (avg duration 7d, sleep debt, regularity proxy, quality 7d) - Functions: Load balance (recent 3d) - Functions: Data quality assessment - Updated data_layer/__init__.py with 9 new exports - Refactored placeholder_resolver.py to import recovery_metrics from data_layer Module 4/6 complete. Single Source of Truth for recovery metrics established. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:24:27 +01:00
Lars	dc34d3d2f2	feat: Phase 0c - migrate activity_metrics calculations to data_layer (20 functions) ... - Migrated all 20 calculation functions from calculations/activity_metrics.py to data_layer/activity_metrics.py - Functions: Training volume (minutes/week, frequency, quality sessions %) - Functions: Intensity distribution (proxy-based until HR zones available) - Functions: Ability balance (strength, endurance, mental, coordination, mobility) - Functions: Load monitoring (internal load proxy, monotony score, strain score) - Functions: Activity scoring (main score with focus weights, strength/cardio/balance helpers) - Functions: Rest day compliance - Functions: VO2max trend (28d) - Functions: Data quality assessment - Updated data_layer/__init__.py with 17 new exports - Refactored placeholder_resolver.py to import activity_metrics from data_layer Module 3/6 complete. Single Source of Truth for activity metrics established. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 20:18:49 +01:00
Lars	7ede0e3fe8	feat: Phase 0c - migrate nutrition_metrics calculations to data_layer (16 functions) ... - Migrated all 16 calculation functions from calculations/nutrition_metrics.py to data_layer/nutrition_metrics.py - Functions: Energy balance (7d calculation, deficit/surplus classification) - Functions: Protein adequacy (g/kg, days in target, 28d score) - Functions: Macro consistency (score, intake volatility) - Functions: Nutrition scoring (main score with focus weights, calorie/macro adherence helpers) - Functions: Energy availability warning (with severity levels and recommendations) - Functions: Data quality assessment - Functions: Fiber/sugar averages (TODO stubs) - Updated data_layer/__init__.py with 12 new exports - Refactored placeholder_resolver.py to import nutrition_metrics from data_layer Module 2/6 complete. Single Source of Truth for nutrition metrics established. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:57:13 +01:00
Lars	504581838c	feat: Phase 0c - migrate body_metrics calculations to data_layer (20 functions) ... - Migrated all 20 calculation functions from calculations/body_metrics.py to data_layer/body_metrics.py - Functions: weight trends (7d median, 28d/90d slopes, goal projection, progress) - Functions: body composition (FM/LBM changes) - Functions: circumferences (waist/hip/chest/arm/thigh deltas, WHR) - Functions: recomposition quadrant - Functions: scoring (body progress, data quality) - Updated data_layer/__init__.py with 20 new exports - Refactored placeholder_resolver.py to import body_metrics from data_layer Module 1/6 complete. Single Source of Truth for body metrics established. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:51:08 +01:00
Lars	26110d44b4	fix: rest_days schema - use 'focus' column instead of 'rest_type' ... Problem: get_rest_days_data() queried non-existent 'rest_type' column Fix: Changed to 'focus' column with correct values (muscle_recovery, cardio_recovery, etc.) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:28:46 +01:00
Lars	6c23973c5d	feat: Phase 0c - body_metrics.py module complete ... Data Layer: - get_latest_weight_data() - most recent weight with date - get_weight_trend_data() - already existed (PoC) - get_body_composition_data() - already existed (PoC) - get_circumference_summary_data() - already existed (PoC) Placeholder Layer: - get_latest_weight() - refactored to use data layer - get_caliper_summary() - refactored to use get_body_composition_data - get_weight_trend() - already refactored (PoC) - get_latest_bf() - already refactored (PoC) - get_circ_summary() - already refactored (PoC) body_metrics.py now complete with all 4 functions. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:17:02 +01:00
Lars	b4558b0582	feat: Phase 0c - health_metrics.py module complete ... Data Layer: - get_resting_heart_rate_data() - avg RHR with min/max trend - get_heart_rate_variability_data() - avg HRV with min/max trend - get_vo2_max_data() - latest VO2 Max with date Placeholder Layer: - get_vitals_avg_hr() - refactored to use data layer - get_vitals_avg_hrv() - refactored to use data layer - get_vitals_vo2_max() - refactored to use data layer All 3 health data functions + 3 placeholder refactors complete. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:15:31 +01:00
Lars	432f7ba49f	feat: Phase 0c - recovery_metrics.py module complete ... Data Layer: - get_sleep_duration_data() - avg duration with hours/minutes breakdown - get_sleep_quality_data() - Deep+REM percentage with phase breakdown - get_rest_days_data() - total count + breakdown by rest type Placeholder Layer: - get_sleep_avg_duration() - refactored to use data layer - get_sleep_avg_quality() - refactored to use data layer - get_rest_days_count() - refactored to use data layer All 3 recovery data functions + 3 placeholder refactors complete. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:13:59 +01:00
Lars	6b2ad9fa1c	feat: Phase 0c - activity_metrics.py module complete ... Data Layer: - get_activity_summary_data() - count, duration, calories, frequency - get_activity_detail_data() - detailed activity log with all fields - get_training_type_distribution_data() - category distribution with percentages Placeholder Layer: - get_activity_summary() - refactored to use data layer - get_activity_detail() - refactored to use data layer - get_trainingstyp_verteilung() - refactored to use data layer All 3 activity data functions + 3 placeholder refactors complete. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 19:11:45 +01:00
Lars	e1d7670971	feat: Phase 0c - nutrition_metrics.py module complete ... Data Layer: - get_nutrition_average_data() - all macros in one call - get_nutrition_days_data() - coverage tracking - get_protein_targets_data() - 1.6g/kg and 2.2g/kg targets - get_energy_balance_data() - deficit/surplus/maintenance - get_protein_adequacy_data() - 0-100 score - get_macro_consistency_data() - 0-100 score Placeholder Layer: - get_nutrition_avg() - refactored to use data layer - get_nutrition_days() - refactored to use data layer - get_protein_ziel_low() - refactored to use data layer - get_protein_ziel_high() - refactored to use data layer All 6 nutrition data functions + 4 placeholder refactors complete. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 18:45:24 +01:00
Lars	c79cc9eafb	feat: Phase 0c - Multi-Layer Data Architecture (Proof of Concept) ... - Add data_layer/ module structure with utils.py + body_metrics.py - Migrate 3 functions: weight_trend, body_composition, circumference_summary - Refactor placeholders to use data layer - Add charts router with 3 Chart.js endpoints - Tests: Syntax ✅, Confidence logic ✅ Phase 0c PoC (3 functions): Foundation for 40+ remaining functions Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 18:26:22 +01:00
Lars	255d1d61c5	docs: cleanup debug logs + document goal system enhancements ... - Removed all debug print statements from placeholder_resolver.py - Removed debug print statements from goals.py (list_goals, update_goal) - Updated CLAUDE.md with Phase 0a completion details: * Auto-population of start_date/start_value from historical data * Time-based tracking (behind schedule = time-deviated) * Hybrid goal display (with/without target_date) * Timeline visualization in goal lists * 7 bug fixes documented - Created memory file for future sessions (feedback_goal_system.md) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 17:32:13 +01:00
Lars	dd395180a3	feat: hybrid goal tracking - with/without target_date ... Implements requested hybrid approach: WITH target_date: - Time-based deviation (actual vs. expected progress) - Format: 'Zielgewicht (41%, +7% voraus)' WITHOUT target_date: - Simple progress percentage - Format: 'Ruhepuls (100% erreicht)' or 'VO2max (0% erreicht)' Sorting: behind_schedule: 1. Goals with negative deviation (behind timeline) 2. Goals without date with progress < 50% on_track: 1. Goals with positive deviation (ahead of timeline) 2. Goals without date with progress >= 50% Kept debug logging for new hybrid logic validation.	2026-03-28 17:22:18 +01:00
Lars	0e89850df8	fix: add start_date and created_at to get_active_goals query ... ROOT CAUSE: get_active_goals() SELECT was missing start_date and created_at IMPACT: Time-based deviation calculation failed silently for all goals Now returns: - start_date: Required for accurate time-based progress calculation - created_at: Fallback when start_date is not set This fixes: - Zielgewicht (weight) should now show +7% ahead - Körperfett should show time deviation - All goals with target_date now have time-based tracking	2026-03-28 17:18:53 +01:00
Lars	eb8b503faa	debug: log all continue statements in goal deviation calculation ... - Log when using created_at as fallback for start_date - Log when skipping due to missing created_at - Log when skipping due to invalid date range (total_days <= 0) This will reveal exactly why Körperfett and Zielgewicht are not added.	2026-03-28 15:09:41 +01:00
Lars	294b3b2ece	debug: extensive logging for behind_schedule/on_track calculation ... - Log each goal processing (name, values, dates) - Log skip reasons (missing values, no target_date) - Log exceptions during calculation - Log successful additions with calculated values This will reveal why Weight goal (+7% ahead) is not showing up.	2026-03-28 15:07:31 +01:00
Lars	8e67175ed2	fix: behind_schedule now uses time-based deviation, not just lowest progress ... OLD: Showed 3 goals with lowest progress % NEW: Calculates expected progress based on elapsed time vs. total time Shows goals with largest negative deviation (behind schedule) Example Weight Goal: - Total time: 98 days (22.02 - 31.05) - Elapsed: 34 days (35%) - Actual progress: 41% - Deviation: +7% (AHEAD, not behind) Also updated on_track to show goals with positive deviation (ahead of schedule). Note: Linear progress is a simplification. Real-world progress curves vary by goal type (weight loss, muscle gain, VO2max, etc). Future: AI-based projection models for more realistic expectations.	2026-03-28 14:58:50 +01:00
Lars	cb72f342f9	fix: add missing start_date and reached_date to grouped goals query ... Root cause: listGoalsGrouped() SELECT was missing g.start_date and g.reached_date Result: Frontend used grouped goals for editing, so start_date was undefined This is why target_date worked (it was in SELECT) but start_date didn't.	2026-03-28 14:48:41 +01:00
Lars	b7e7817392	debug: show ALL goals with dates, not just first	2026-03-28 14:45:36 +01:00
Lars	068a8e7a88	debug: show goals after serialization	2026-03-28 14:41:33 +01:00
Lars	97defaf704	fix: serialize date objects to ISO format for JSON ... - Added serialize_dates() helper to convert date objects to strings - Applied to list_goals and get_goals_grouped endpoints - Fixes issue where start_date was saved but not visible in frontend - Python datetime.date objects need explicit .isoformat() conversion Root cause: FastAPI doesn't auto-serialize all date types consistently	2026-03-28 14:36:45 +01:00
Lars	370f0d46c7	debug: extensive logging for start_date persistence ... - Log UPDATE SQL and parameters - Verify saved values after UPDATE - Show date types in list_goals response - Track down why start_date not visible in UI	2026-03-28 14:33:16 +01:00
Lars	c90e30806b	fix: save start_date to database in update_goal ... - Rewrote update logic to determine final_start_date/start_value first - Then append to updates/params arrays (ensures alignment) - Fixes bug where only start_value was saved but not start_date User feedback: start_value correctly calculated but start_date not persisted	2026-03-28 14:28:52 +01:00
Lars	e479627f0f	feat: Auto-adjust start_date to first available measurement ... **User Feedback:** "Macht es nicht Sinn, den nächsten verfügbaren Wert am oder nach dem Startdatum automatisch zu ermitteln und auch das Startdatum dann automatisch auf den Wert zu setzen?" **New Logic:** 1. User sets start_date: 2026-01-01 2. System finds FIRST measurement >= 2026-01-01 (e.g., 2026-01-15: 88 kg) 3. System auto-adjusts: - start_date → 2026-01-15 - start_value → 88 kg 4. User sees: "Start: 88 kg (15.01.26)" ✓ **Benefits:** - User doesn't need to know exact date of first measurement - More user-friendly UX - Automatically finds closest available data **Implementation:** - Changed query from "BETWEEN date ±7 days" to "WHERE date >= target_date" - Returns dict with {'value': float, 'date': date} - Both create_goal() and update_goal() now adjust start_date automatically Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>	2026-03-28 13:41:35 +01:00