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>
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>
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 Outdated focus_weights function in body_metrics.py: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>
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>
- 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>
- 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>
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>
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>
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>
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>
