Goals -System refactored - Platzhaltersystem enhanced (als draft) #53

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Lars merged 86 commits from develop into main 2026-03-31 11:46:48 +02:00
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Lars added 86 commits 2026-03-31 11:46:39 +02:00
refactor: split goals.py into 5 modular routers
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12d516c881
Code Splitting Results:
- goals.py: 1339 → 655 lines (-684 lines, -51%)
- Created 4 new routers:
  * goal_types.py (426 lines) - Goal Type Definitions CRUD
  * goal_progress.py (155 lines) - Progress tracking
  * training_phases.py (107 lines) - Training phases
  * fitness_tests.py (94 lines) - Fitness tests

Benefits:
 Improved maintainability (smaller, focused files)
 Better context window efficiency for AI tools
 Clearer separation of concerns
 Easier testing and debugging

All routers registered in main.py.
Backward compatible - no API changes.
chore: remove deprecated vitals.py (-684 lines)
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56933431f6
This file was replaced by the refactored vitals system:
- vitals_baseline.py (morning measurements)
- blood_pressure.py (BP tracking with context)

Migration 015 completed the split in v9d Phase 2d.
File was no longer imported in main.py.

Cleanup result: -684 lines of dead code
- body_metrics.py: K1-K5 calculations (weight trend, FM/LBM, circumferences, recomposition, body score)
- nutrition_metrics.py: E1-E5 calculations (energy balance, protein adequacy, macro consistency, nutrition score)
- activity_metrics.py: A1-A8 calculations (training volume, intensity, quality, ability balance, load monitoring)
- recovery_metrics.py: Improved Recovery Score v2 (HRV, RHR, sleep, regularity, load balance)
- correlation_metrics.py: C1-C7 calculations (lagged correlations, plateau detection, driver panel)
- scores.py: Meta-scores with Dynamic Focus Areas v2.0 integration

All calculations include:
- Data quality assessment
- Confidence levels
- Dynamic weighting by user's focus area priorities
- Support for custom goals via goal_utils integration

Next: Placeholder integration in placeholder_resolver.py
Extended placeholder_resolver.py with:
- 100+ new placeholders across 5 levels (meta-scores, categories, individual metrics, correlations, JSON)
- Safe wrapper functions (_safe_int, _safe_float, _safe_str, _safe_json)
- Integration with calculation engine (body, nutrition, activity, recovery, correlations, scores)
- Dynamic Focus Areas v2.0 support (category progress/weights)
- Top-weighted goals/focus areas (instead of deprecated primary goal)

Placeholder categories:
- Meta Scores: goal_progress_score, body/nutrition/activity/recovery_score (6)
- Top-Weighted: top_goal_*, top_focus_area_* (5)
- Category Scores: focus_cat_*_progress/weight for 7 categories (14)
- Body Metrics: weight trends, FM/LBM changes, circumferences, recomposition (12)
- Nutrition Metrics: energy balance, protein adequacy, macro consistency (7)
- Activity Metrics: training volume, ability balance, load monitoring (13)
- Recovery Metrics: HRV/RHR vs baseline, sleep quality/debt/regularity (7)
- Correlation Metrics: lagged correlations, plateau detection, driver panel (7)
- JSON/Markdown: active_goals, focus_areas, top drivers (8)

TODO: Implement goal_utils extensions for JSON formatters
TODO: Add unit tests for all placeholder functions
docs: Phase 0b Quick Test prompt (Option B)
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4f365e9a69
Compact test prompt for validating calculation engine:
- Tests 25 key placeholders (scores, categories, metrics)
- Covers body, nutrition, activity, recovery calculations
- Documents expected behavior and known limitations
- Step-by-step testing instructions

Use this to validate Phase 0b before implementing JSON formatters.
fix: Update placeholder catalog with Phase 0b placeholders
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7d4f6fe726
Added ~40 Phase 0b placeholders to get_placeholder_catalog():
- Scores (6 new): goal_progress_score, body/nutrition/activity/recovery/data_quality
- Focus Areas (8 new): top focus area, category progress/weights
- Body Metrics (7 new): weight trends, FM/LBM changes, waist, recomposition
- Nutrition (4 new): energy balance, protein g/kg, adequacy, consistency
- Activity (6 new): minutes/week, quality, ability balance, compliance
- Recovery (4 new): sleep duration/debt/regularity/quality
- Vitals (3 new): HRV/RHR vs baseline, VO2max trend

Fixes: Placeholders now visible in Admin UI placeholder list
fix: Add error logging to Phase 0b placeholder calculation wrappers
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6f94154b9e
Problem: All _safe_* functions were silently catching exceptions and returning 'nicht verfügbar',
making it impossible to debug why calculations fail.

Solution: Add detailed error logging with traceback to all 4 wrapper functions:
- _safe_int(): Logs function name, exception type, message, full stack trace
- _safe_float(): Same logging
- _safe_str(): Same logging
- _safe_json(): Same logging

Now when placeholders return 'nicht verfügbar', the backend logs will show:
- Which placeholder function failed
- What exception occurred
- Full stack trace for debugging

Example log output:
[ERROR] _safe_int(goal_progress_score, uuid): ModuleNotFoundError: No module named 'calculations'
Traceback (most recent call last):
  ...

This will help identify if issue is:
- Missing calculations module import
- Missing data in database
- Wrong column names
- Calculation logic errors
fix: SyntaxError in placeholder_resolver.py line 1037
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53969f8768
- Fixed unterminated string literal in get_placeholder_catalog()
- Line 1037 had extra quote: ('quality_sessions_pct', 'Qualitätssessions (%)'),'
- Should be: ('quality_sessions_pct', 'Qualitätssessions (%)'),

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - correct all SQL column names in calculation engine
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4817fd2b29
Schema corrections applied:
- weight_log: weight_kg → weight
- nutrition_log: calories → kcal
- activity_log: duration → duration_min, avg_heart_rate → hr_avg, max_heart_rate → hr_max
- rest_days: rest_type → type (aliased for backward compat)
- vitals_baseline: resting_heart_rate → resting_hr
- sleep_log: total_sleep_min → duration_minutes, deep_min → deep_minutes, rem_min → rem_minutes, waketime → wake_time
- focus_area_definitions: fa.focus_area_id → fa.key (proper join column)

Affected files:
- body_metrics.py: weight column (all queries)
- nutrition_metrics.py: kcal column + weight
- activity_metrics.py: duration_min, hr_avg, hr_max, quality via RPE mapping
- recovery_metrics.py: sleep + vitals columns
- correlation_metrics.py: kcal, weight
- scores.py: focus_area key selection

All 100+ Phase 0b placeholders should now calculate correctly.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - fix remaining calculation errors
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dd3a4111fc
Fixes applied:
1. WHERE clause column names (total_sleep_min → duration_minutes, resting_heart_rate → resting_hr)
2. COUNT() column names (avg_heart_rate → hr_avg, quality_label → rpe)
3. Type errors (Decimal * float) - convert to float before multiplication
4. rest_days table (type column removed in migration 010, now uses rest_config JSONB)
5. c_thigh_l → c_thigh (no separate left/right columns)
6. focus_area_definitions queries (focus_area_id → key, label_de → name_de)

Missing functions implemented:
- goal_utils.get_active_goals() - queries goals table for active goals
- goal_utils.get_goal_by_id() - gets single goal
- calculations.scores.calculate_category_progress() - maps categories to score functions

Changes:
- activity_metrics.py: Fixed Decimal/float type errors, rest_config JSONB, data quality query
- recovery_metrics.py: Fixed all WHERE clause column names
- body_metrics.py: Fixed c_thigh column reference
- scores.py: Fixed focus_area queries, added calculate_category_progress()
- goal_utils.py: Added get_active_goals(), get_goal_by_id()

All calculation functions should now work with correct schema.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - fix remaining calculation bugs from log analysis
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02394ea19c
Bugs fixed based on actual error logs:
1. TypeError: progress_pct None handling - changed .get('progress_pct', 0) to (goal.get('progress_pct') or 0)
2. UUID Error: focus_area_id query - changed WHERE focus_area_id = %s to WHERE key = %s
3. NameError: calculate_recovery_score_v2 - added missing import in calculate_category_progress
4. UndefinedColumn: c_thigh_r - removed left/right separation, only c_thigh exists
5. UndefinedColumn: resting_heart_rate - fixed remaining AVG(resting_heart_rate) to AVG(resting_hr)
6. KeyError: total_sleep_min - changed dict access to duration_minutes

Changes:
- scores.py: Fixed progress_pct None handling, focus_area key query, added recovery import
- body_metrics.py: Fixed thigh_28d_delta to use single c_thigh column
- recovery_metrics.py: Fixed resting_hr SELECT queries, fixed sleep_debt dict access

All errors from logs should now be resolved.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - fix blood_pressure and top_goal_name bugs
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b230a03fdd
Final bug fixes:
1. blood_pressure_log query - changed 'date' column to 'measured_at' (correct column for TIMESTAMP)
2. top_goal_name KeyError - added 'name' to SELECT in get_active_goals()
3. top_goal_name fallback - use goal_type if name is NULL

Changes:
- scores.py: Fixed blood_pressure_log query to use measured_at instead of date
- goal_utils.py: Added 'name' column to get_active_goals() SELECT
- placeholder_resolver.py: Added fallback to goal_type if name is None

These were the last 2 errors showing in logs. All major calculation bugs should now be fixed.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - fix last sleep column names in health_stability_score
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10ea560fcf
Fixed remaining sleep_log column name errors in calculate_health_stability_score:
- SELECT: total_sleep_min, deep_min, rem_min → duration_minutes, deep_minutes, rem_minutes
- _score_sleep_quality: Updated dict access to use new column names

This was blocking goal_progress_score from calculating.

Changes:
- scores.py: Fixed sleep_log SELECT query and _score_sleep_quality dict access

This should be the LAST column name bug! All Phase 0b calculations should now work.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - activity duration column in health_stability_score
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91bafc6af1
fix: Phase 0b - sleep dict access in health_stability_score regularity
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919eae6053
fix: Phase 0b - map_focus_to_score_components English keys
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289b132b8f
fix: Phase 0b - remove orphaned German mapping entries
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e3e635d9f5
fix: Phase 0b - map German to English category names
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43e6c3e7f4
feat: Phase 0b - add nutrition focus areas to score mapping
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9fa6c5dea7
feat: Phase 0b - add avg_per_week_30d aggregation method
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14c4ea13d9
- Calculates average count per week over 30 days
- Use case: Training frequency per week (smoothed)
- Formula: (count in 30 days) / 4.285 weeks
- Documentation: .claude/docs/technical/AGGREGATION_METHODS.md
feat: Phase 0b - add avg_per_week_30d to frontend dropdown
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63bd103b2c
fix: Phase 0b - score functions use English focus area keys
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cc76ae677b
Root cause: All 3 score functions returned None because they queried
German focus area keys that don't exist in database (migration 031
uses English keys).

Changes:
- body_progress_score: körpergewicht/körperfett/muskelmasse
  → weight_loss/muscle_gain/body_recomposition
- nutrition_score: ernährung_basis/proteinzufuhr/kalorienbilanz
  → protein_intake/calorie_balance/macro_consistency/meal_timing/hydration
- activity_score: kraftaufbau/cardio/bewegungsumfang/trainingsqualität
  → strength/aerobic_endurance/flexibility/rhythm/coordination (grouped)

Result: Scores now calculate correctly with existing focus area weights.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - replace non-existent get_goals_by_type import
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202c36fad7
ImportError: cannot import name 'get_goals_by_type' from 'goal_utils'

Changes:
- body_metrics.py: Use get_active_goals() + filter by type_key
- nutrition_metrics.py: Remove unused import (dead code)

Result: Score functions no longer crash on import error.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - body_progress_score uses correct column name
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6f20915d73
Bug: Filtered goals by g.get('type_key') but goals table has 'goal_type' column.
Result: weight_goals was always empty → _score_weight_trend returned None.

Fix: Changed 'type_key' → 'goal_type' (matches goals table schema).

Verified: Migration 022 defines goal_type column, not type_key.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - PostgreSQL Decimal type handling
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78437b649f
TypeError: unsupported operand type(s) for *: 'decimal.Decimal' and 'float'
TypeError: unsupported operand type(s) for -: 'float' and 'decimal.Decimal'

PostgreSQL NUMERIC/DECIMAL columns return decimal.Decimal objects,
not float. These cannot be mixed in arithmetic operations.

Fixed 3 locations:
- Line 62: float(weight_row['weight']) * 32.5
- Line 153: float(weight_row['weight']) for protein_per_kg
- Line 202: float(weight_row['avg_weight']) for adequacy calc

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - complete Decimal/float conversion in nutrition_metrics
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05d15264c8
Previous commit only converted weight values, but missed:
- avg_intake (calories from DB)
- avg_protein (protein_g from DB)
- protein_per_kg calculations in loops

All DB numeric values now converted to float BEFORE arithmetic.

Fixed locations:
- Line 44: avg_intake conversion
- Line 126: avg_protein conversion
- Line 175: protein_per_kg in loop
- Line 213: protein_values list comprehension

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - implement active_goals and focus_areas JSON placeholders
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b09a7b200a
Root cause: Two TODO stubs always returned '[]'

Implemented:
- active_goals_json: Calls get_active_goals() from goal_utils
- focus_areas_weighted_json: Builds weighted list with names/categories

Result:
- active_goals_json now shows actual goals
- body_progress_score should calculate correctly
- top_3_goals placeholders will work

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Phase 0b - body_progress_score + placeholder formatting
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8da577fe58
Fixed remaining placeholder calculation issues:

1. body_progress_score returning 0:
   - When start_value is NULL, query oldest weight from last 90 days
   - Prevents progress = 0% when start equals current

2. focus_areas_weighted_json empty:
   - Changed from goal_utils.get_focus_weights_v2() to scores.get_user_focus_weights()
   - Now uses same function as focus_area_weights_json

3. Implemented 5 TODO markdown formatting functions:
   - _format_goals_as_markdown() - table with progress bars
   - _format_focus_areas_as_markdown() - weighted list
   - _format_top_focus_areas() - top N by weight
   - _format_goals_behind() - lowest progress goals
   - _format_goals_on_track() - goals >= 50% progress

All placeholders should now return proper values.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Convert goal values to float before progress calculation
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112226938d
TypeError: unsupported operand type(s) for -: 'decimal.Decimal' and 'float'

PostgreSQL NUMERIC columns return Decimal objects. Must convert
current_value, target_value, start_value to float before passing
to calculate_goal_progress_pct().

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: focus_areas column name + goal progress calculation
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befc310958
Fixed 2 critical placeholder issues:

1. focus_areas_weighted_json was empty:
   - Query used 'area_key' but column is 'key' in focus_area_definitions
   - Changed to SELECT key, not area_key

2. Goal progress placeholders showed "nicht verfügbar":
   - progress_pct in goals table is NULL (not auto-calculated)
   - Added manual calculation in all 3 formatter functions:
     * _format_goals_as_markdown() - shows % in table
     * _format_goals_behind() - finds lowest progress
     * _format_goals_on_track() - finds >= 50% progress

All placeholders should now return proper values.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Include start_value in get_active_goals query
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a6701bf7b2
Goal progress placeholders were filtering out all goals because
start_value was missing from the SELECT statement.

Added start_value to both:
- get_active_goals() - for placeholder formatters
- get_goal_by_id() - for consistency

This will fix:
- active_goals_md progress column (was all "-")
- top_3_goals_behind_schedule (was "keine Ziele")
- top_3_goals_on_track (was "keine Ziele")

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
feat: Auto-populate goal start_value from historical data
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efde158dd4
**Problem:** Goals created today had start_value = current_value,
showing 0% progress even after months of tracking.

**Solution:**
1. Added start_date and start_value to GoalCreate/GoalUpdate models
2. New function _get_historical_value_for_goal_type():
   - Queries source table for value on specific date
   - ±7 day window for closest match
   - Works with all goal types via goal_type_definitions
3. create_goal() logic:
   - If start_date < today → auto-populate from historical data
   - If start_date = today → use current value
   - User can override start_value manually
4. update_goal() logic:
   - Changing start_date recalculates start_value
   - Can manually override start_value

**Example:**
- Goal created today with start_date = 3 months ago
- System finds weight on that date (88 kg)
- Current weight: 85.2 kg, Target: 82 kg
- Progress: (85.2 - 88) / (82 - 88) = 47% ✓

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
feat: Frontend - Startdatum field in goal form
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327319115d
Added start_date field to goal creation/editing form:

1. New "Startdatum" input field before "Zieldatum"
   - Defaults to today
   - Hint: "Startwert wird automatisch aus historischen Daten ermittelt"

2. Display start_date in goals list
   - Shows next to start_value: "85 kg (01.01.26)"
   - Compact format for better readability

3. Updated formData state
   - Added start_date with today as default
   - API calls automatically include it

User can now:
- Set historical start date (e.g., 3 months ago)
- Backend auto-populates start_value from that date
- See exact start date and value for each goal

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: Include start_date in goal edit form and API call
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1c7b5e0653
**Bug:** start_date was not being loaded into edit form or sent in update request

**Fixed:**
1. handleEditGoal() - Added start_date to formData when editing
2. handleSaveGoal() - Added start_date to API payload for both create and update

Now when editing a goal:
- start_date field is populated with existing value
- Changing start_date triggers backend to recalculate start_value
- Update request includes start_date

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
fix: PostgreSQL date subtraction in historical value query
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7ffa8f039b
**Error:**
function pg_catalog.extract(unknown, integer) does not exist
HINT: No function matches the given name and argument types.

**Problem:**
In PostgreSQL, date - date returns INTEGER (days), not INTERVAL.
EXTRACT(EPOCH FROM integer) fails because EPOCH expects timestamp/interval.

**Solution:**
Changed from:
  ORDER BY ABS(EXTRACT(EPOCH FROM (date - '2026-01-01')))

To:
  ORDER BY ABS(date - '2026-01-01'::date)

This directly uses the day difference (integer) for sorting,
which is exactly what we need to find the closest date.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
debug: Add logging to update_goal to trace start_date issue
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42cc583b9b
debug: Add comprehensive logging to trace historical value lookup
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169dbba092
feat: Auto-adjust start_date to first available measurement
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e479627f0f
**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>
fix: Load actual start_date in edit form + improve timeline display
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3604ebc781
**Problem 1:** Edit form showed today's date instead of stored start_date
- Cause: Fallback logic `goal.start_date || today` always defaulted to today
- Fix: Load actual date or empty string (no fallback)
- Input field: Remove fallback from value binding

**Problem 2:** Timeline only showed target_date, not start_date
- Added dedicated timeline display below values
- Shows: "📅 15.01.26 → 31.05.26"
- Only appears if at least one date exists
- Start date with calendar icon, target date bold

**Result:**
- Editing goals now preserves the start_date ✓
- Timeline clearly shows start → target dates ✓
- No more accidental overwrites with today's date ✓

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
debug: Add console logging to trace start_date loading
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ab29a85903
fix: save start_date to database in update_goal
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c90e30806b
- 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
debug: extensive logging for start_date persistence
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370f0d46c7
- 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
fix: serialize date objects to ISO format for JSON
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97defaf704
- 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
debug: show goals after serialization
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068a8e7a88
debug: show ALL goals with dates, not just first
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b7e7817392
debug: extensive frontend logging for goal dates
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623f34c184
fix: add missing start_date and reached_date to grouped goals query
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cb72f342f9
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.
feat: show target_date in goal list next to target value
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d7aa0eb3af
- Start value already showed start_date in parentheses
- Now target value also shows target_date in parentheses
- Consistent UX: both dates visible at their respective values
fix: behind_schedule now uses time-based deviation, not just lowest progress
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8e67175ed2
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.
debug: extensive logging for behind_schedule/on_track calculation
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294b3b2ece
- 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.
debug: log all continue statements in goal deviation calculation
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eb8b503faa
- 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.
fix: add start_date and created_at to get_active_goals query
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0e89850df8
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
feat: hybrid goal tracking - with/without target_date
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dd395180a3
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.
docs: cleanup debug logs + document goal system enhancements
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255d1d61c5
- 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>
feat: Phase 0c - Multi-Layer Data Architecture (Proof of Concept)
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c79cc9eafb
- 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>
feat: Phase 0c - nutrition_metrics.py module complete
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e1d7670971
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>
feat: Phase 0c - activity_metrics.py module complete
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6b2ad9fa1c
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>
feat: Phase 0c - recovery_metrics.py module complete
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432f7ba49f
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>
feat: Phase 0c - health_metrics.py module complete
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b4558b0582
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>
feat: Phase 0c - body_metrics.py module complete
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6c23973c5d
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>
fix: rest_days schema - use 'focus' column instead of 'rest_type'
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26110d44b4
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>
feat: Phase 0c - migrate body_metrics calculations to data_layer (20 functions)
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504581838c
- 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>
feat: Phase 0c - migrate nutrition_metrics calculations to data_layer (16 functions)
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7ede0e3fe8
- 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>
feat: Phase 0c - migrate activity_metrics calculations to data_layer (20 functions)
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dc34d3d2f2
- 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>
feat: Phase 0c - migrate recovery_metrics calculations to data_layer (16 functions)
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2bc1ca4daf
- 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>
feat: Phase 0c - migrate scores calculations to data_layer (14 functions)
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dba6814bc2
- 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>
feat: Phase 0c - migrate correlation_metrics to data_layer/correlations (11 functions)
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befa060671
- 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>
fix: Phase 0c - update all in-function imports to use data_layer
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5b7d7ec3bb
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>
fix: add missing statistics import and update focus_weights function
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285184ba89
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>
debug: add detailed logging to get_nutrition_avg
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a441537dca
fix: correct confidence thresholds for 30-89 day range
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ffa99f10fb
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>
Neue Docs
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fb6d37ecfd
chore: remove debug logging from placeholder_resolver
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5b4688fa30
feat: Phase 0c - Complete chart endpoints (E1-E5, A1-A8, R1-R5, C1-C4)
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782f79fe04
- 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)
docs: Phase 0c completion + new issue #55
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f81171a1f5
- Mark issue #53 as completed
- Create issue #55: Dynamic Aggregation Methods
- Update CLAUDE.md with Phase 0c achievements
- Document 97 migrated functions + 20 new chart endpoints
feat: Phase 0c Frontend Phase 1 - Nutrition + Recovery Charts
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d4500ca00c
- Create NutritionCharts component (E1-E5)
  - Energy Balance Timeline
  - Macro Distribution (Pie)
  - Protein Adequacy Timeline
  - Nutrition Consistency Score

- Create RecoveryCharts component (R1-R5)
  - Recovery Score Timeline
  - HRV/RHR vs Baseline (dual-axis)
  - Sleep Duration + Quality (dual-axis)
  - Sleep Debt Accumulation
  - Vital Signs Matrix (horizontal bar)

- Add 9 chart API functions to api.js
  - 4 nutrition endpoints (E1-E5)
  - 5 recovery endpoints (R1-R5)

- Integrate into History page
  - Add NutritionCharts to existing Nutrition tab
  - Create new Recovery tab with RecoveryCharts
  - Period selector controls chart timeframe

Charts use Recharts (existing dependency)
All charts display Chart.js-compatible data from backend
Confidence handling: Show 'Nicht genug Daten' message

Files:
+ frontend/src/components/NutritionCharts.jsx (329 lines)
+ frontend/src/components/RecoveryCharts.jsx (342 lines)
M frontend/src/utils/api.js (+14 functions)
M frontend/src/pages/History.jsx (+22 lines, new Recovery tab)
fix: add missing prefix to charts router
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176be3233e
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.
feat: Konzept-konforme Nutrition Charts (E1-E5 komplett)
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4c22f999c4
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>
fix: syntax error in charts.py - mismatched bracket
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56273795a0
fix: E2 protein-adequacy endpoint - undefined variable 'values' -> 'daily_values'
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c21a624a50
feat: Complete Placeholder Metadata System (Normative Standard v1.0.0)
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a04e7cc042
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>
docs: Add placeholder metadata deployment guide
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b7afa98639
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
feat: Add Placeholder Metadata Export to Admin Panel
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087e8dd885
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>
feat: Placeholder Metadata V2 - Normative Implementation + ZIP Export Fix
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650313347f
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>
fix: add missing Header import in prompts.py
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6cdc159a94
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>
Lars merged commit c762495b6f into main 2026-03-31 11:46:48 +02:00
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Reference: Lars/mitai-jinkendo#53
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