- 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.
- 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 contextual hints to KPI tiles in the nutrition interpretation to provide users with actionable insights regarding protein intake and weight assessment.
- Updated the KpiTilesOverview component to display these hints, improving user understanding of nutrition metrics.
- Introduced a new KcalVsWeightLegend component to clarify chart data representation, enhancing the overall user experience in the history visualization.
- Changed color codes for macro nutrients in the nutrition interpretation and metrics files to improve visual consistency.
- Added new CSS styles for uniform chart height and layout adjustments in the frontend components, enhancing the overall user experience.
- Refactored the NutritionCharts component to utilize the new macro chart theme for better maintainability and readability.
- 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.
