- Refactored the `calculate_proxy_internal_load_7d` function to `calculate_proxy_internal_load_window`, allowing for dynamic day range input.
- Introduced new functions for calculating training volume deltas and building fitness progress insights, enhancing user feedback on training metrics.
- Updated the fitness dashboard to include new charts for quality sessions and load monitoring, improving data visualization.
- Integrated these new metrics into the fitness dashboard overview, providing users with comprehensive insights into their training performance.
- Streamlined the router to utilize the new chart-building functions, ensuring consistency and maintainability across the application.
- Added new functions to build fitness dashboard visualizations, including weekly training volume and training type distribution charts.
- Updated the `charts.py` router to include a new endpoint for the fitness dashboard, integrating data from activity metrics.
- Refactored existing activity-related functions to improve modularity and maintainability.
- Updated frontend components to reflect the new fitness terminology and integrate the fitness dashboard overview, enhancing user experience.
- Integrated compact JSON payload generation for session metrics in `get_training_sessions_recent_weeks_data`.
- Updated the registration of activity session insights to reflect the new compact format for session metrics.
- Improved documentation to clarify the structure and semantics of the session metrics in the JSON output.
- Added normalization for prompt numbers to ensure consistent formatting in the metrics.
- Introduced new fields for descriptions in training parameters, improving clarity for AI context in `training_sessions_recent_json`.
- Added a glossary placeholder `{{training_parameters_glossary_md}}` to provide a Markdown table of active training parameters, including names and descriptions.
- Updated the `placeholder_resolver.py` and `activity_metrics.py` to support the new glossary functionality.
- Enhanced the `AdminActivityAttributeProfilesPage` to allow input for descriptions in both German and English, ensuring better context for metrics.
- Revised tests to validate the inclusion of description fields in parameter schema merges and metrics handling.
- Updated the `get_activity_detail` function to include session metrics in the activity detail output, allowing for enriched data representation.
- Refactored the activity import logic to streamline the process of inserting and updating activity records, utilizing new helper functions for better maintainability.
- Improved the handling of duplicate activity entries by implementing a more robust identification mechanism.
- Enhanced the metadata for activity detail registration to reflect the inclusion of EAV metrics and updated source tables.
- Introduced Activity Session Metrics for enhanced tracking of session data.
- Updated backend to support new API endpoints for managing session metrics.
- Added new Pydantic models for activity metrics and replaced metrics functionality.
- Enhanced data layer to include session metrics in recent training session data.
- Updated documentation to reflect changes in session metrics handling.
- Updated the Gitea issues index to reflect the latest state as of 2026-04-11, adding issue #76 to the list.
- Refined data handling in `activity_metrics.py`, `body_metrics.py`, `nutrition_metrics.py`, and `scores.py` to ensure consistent float conversions for calculations, improving accuracy in metric evaluations.
- Enhanced the calculation logic for various metrics to handle potential None values more robustly, ensuring smoother data processing and improved reliability across the application.
These changes improve the clarity of the Gitea issues documentation and enhance the overall accuracy and reliability of health and fitness metrics.
- Updated `get_sleep_avg_duration` and `get_sleep_avg_quality` functions in `placeholder_resolver.py` to provide clearer error messages when data is unavailable.
- Enhanced sleep quality calculations in `recovery_metrics.py` to handle cases with insufficient data more robustly.
- Improved data handling in various metrics files (`activity_metrics.py`, `body_metrics.py`, `nutrition_metrics.py`, `recovery_metrics.py`, and `scores.py`) to ensure consistent float conversions for calculations.
- Added utility functions in `recovery_metrics.py` for parsing and normalizing sleep segment data, enhancing the accuracy of sleep quality assessments.
These changes improve the reliability and clarity of sleep-related metrics and enhance overall data handling across the application.
- Added new functions for BMI and goal weight/body fat percentage retrieval in `body_metrics.py`.
- Introduced training frequency and inter-session gap calculations in `activity_metrics.py`.
- Updated placeholder registrations to include new metrics for nutrition and activity.
- Improved data handling in `placeholder_resolver.py` for better integration of new metrics.
- Enhanced documentation across modules to reflect the new functionalities.
These updates improve the accuracy and comprehensiveness of health and fitness assessments within the application.
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>
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>
