- Updated `calculate_lag_correlation` to include detailed interpretations and lag details for energy balance vs. weight change, protein vs. lean mass, and load vs. vital metrics.
- Improved handling of insufficient data scenarios in correlation charts, providing clearer messages and metadata for user insights.
- Refactored chart functions to utilize best lag values and correlation data more effectively, enhancing the visualization of relationships between metrics.
- Bumped application version to 0.9t and updated changelog with new features.
- Integrated new chart payloads for energy balance, protein adequacy, and nutrition adherence to optimize data retrieval and reduce HTTP requests.
- Updated NutritionCharts component to utilize prefetched chart payloads, improving loading efficiency and user experience.
- Refactored History page to pass chart payloads, enhancing the visualization of nutrition trends without additional requests.
- Bumped application version to 0.9r and updated build date to 2026-04-20.
- Added a new endpoint `/activity-last-updated` to retrieve the last activity date for a user, optimizing data retrieval for activity history.
- Updated the frontend to utilize the new endpoint, enhancing the ActivitySection with the last activity date display.
- Refactored the History component to streamline data loading and improve user experience with activity insights.
- Refactored the `calculate_lag_correlation` function to normalize lag payloads and improve correlation calculations for various nutrition metrics.
- Introduced a new function `build_nutrition_correlation_heuristic_items` to generate heuristic insights based on merged nutrition data, enhancing user understanding of dietary impacts on weight and body composition.
- Updated the `get_nutrition_history_viz_bundle` function to include daily calorie balance and protein vs. lean mass data, providing a comprehensive view of nutrition trends.
- Enhanced the frontend to visualize calorie balance and protein vs. lean mass insights, improving the user experience with clear graphical representations of dietary correlations.
- Updated the `build_vital_signs_matrix_chart_payload` function to accept optional keys for omitting specific snapshot data, improving flexibility in data presentation.
- Enhanced the `build_recovery_dashboard_kpi_tiles` function to conditionally merge heart and autonomic tiles based on new parameters, refining the dashboard's insights.
- Integrated new analytics features in the `RecoveryDashboardOverview` component, including consolidated paragraphs for better narrative context and visual representation of trends.
- Improved the handling of vital signs data in the frontend, ensuring clearer messaging and enhanced user experience when displaying vital metrics.
- Introduced new functions to handle vital signs data retrieval and processing, including fallback mechanisms for missing values.
- Updated SQL queries in `build_vital_signs_matrix_chart_payload` to improve date filtering and data accuracy.
- Enhanced the frontend `RecoveryDashboardOverview` component to display vital signs with contextual coloring based on health tones.
- Adjusted the data structure for chart rendering, ensuring a more informative and visually appealing representation of vital metrics.
- Added a new endpoint for the recovery dashboard visualization in `charts.py`, integrating multiple recovery metrics and insights.
- Implemented the `get_recovery_dashboard_viz` function to streamline data retrieval for recovery-related charts.
- Refactored the `RecoveryCharts` component to utilize the new `RecoveryDashboardOverview`, simplifying the component structure and enhancing maintainability.
- Updated the `RecoveryChartsPanelWidget` and `History` page to reflect the new recovery dashboard, improving user navigation and experience.
- Deprecated the old recovery charts component, encouraging the use of the new overview for better data presentation.
- 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.
- Added `get_energy_availability_warning_payload` function to assess energy availability and provide contextual warnings based on multiple health indicators.
- Integrated energy availability KPI tile into the nutrition history visualization, enhancing user insights on energy balance.
- Updated frontend components to conditionally display the energy availability warning, improving user experience and data interpretation.
- Refactored existing logic in `charts.py` to utilize the new energy availability functionality, streamlining data handling.
- Added a new `nutrition_interpretation.py` file to handle KPI tile generation for nutrition history.
- Introduced `nutrition_viz.py` to create a visualization bundle for nutrition data, integrating metrics and historical analysis.
- Implemented `get_nutrition_history_viz` endpoint in `charts.py` to serve the new visualization data.
- Updated frontend components to fetch and display nutrition history data, enhancing user experience with detailed insights.
- Refactored existing logic to streamline data handling and improve overall performance.
- Introduced a new API endpoint `/body-history-viz` to retrieve body history visualization data.
- Updated the frontend to fetch and display body history data in the `BodySection` component.
- Enhanced the `EvaluationTileGrid` to include related placeholder keys for improved data interpretation.
- Refactored existing logic to streamline data handling and improve user experience.
- 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.
- Introduced a single TDEE calculation based on current weight, replacing the fixed 2500 kcal value.
- Updated `get_energy_balance_data` to use daily totals for intake calculations and improved energy balance logic.
- Enhanced `get_nutrition_average_data` to calculate averages over calendar days instead of raw log entries.
- Adjusted placeholder resolution to ensure consistent metadata usage across requests.
- Fixed issues in the charts router to reflect the new energy balance logic and TDEE calculations.
These changes improve the accuracy of nutritional assessments and streamline data handling in the application.
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.
