- Introduced `c_arm_relaxed` to the CircumferenceEntry model for tracking relaxed arm measurements.
- Updated database schema to include `c_arm_relaxed` in the circumference_log table.
- Implemented calculation for 28-day relaxed arm circumference change with `calculate_arm_relaxed_28d_delta`.
- Enhanced placeholder resolver and registration to support new relaxed arm measurement.
- Updated frontend components to accommodate the new measurement, including forms and CSV exports.
- Improved documentation and guide data to reflect the addition of relaxed arm measurements.
- Updated `_normalize_metric_value_for_read` to compact numeric strings and ensure consistent formatting for string data types.
- Enhanced `normalize_prompt_number` to handle numeric strings and non-finite float values effectively.
- Improved unit tests to validate the new normalization behavior for session metrics and scalar formatting.
- Introduced `format_scalar_for_prompt_text` function to standardize the representation of scalar values in activity summaries and details.
- Updated `get_activity_summary` and `get_activity_detail` functions to utilize the new formatting for improved readability.
- Added normalization for float values in session metrics to prevent excessively long representations.
- Enhanced unit tests to verify the new formatting and normalization behavior.
- 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.
- Updated `get_activity_session_logical_unit` to support optional parameters for form training context, allowing for more flexible schema resolution.
- Introduced a new endpoint `/attribute-schema` to fetch activity attribute schemas without an existing session, improving manual data entry capabilities.
- Enhanced the `getActivitySession` API method to accept query parameters for training category and type, facilitating dynamic schema retrieval.
- Updated the frontend `ActivityPage` to utilize the new schema fetching logic, ensuring a smoother user experience when managing activity sessions and 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.
- Enhanced the `merge_column_backed_and_eav_metrics` function to ensure that when both legacy columns and EAV values are present, the legacy column takes precedence.
- Revised documentation in `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` and `ACTIVITY_SCALAR_KANON_TABLE.md` to reflect the new reading logic and clarify the handling of metrics.
- Updated the `activity_data_canon.py` to specify the new merge behavior, ensuring consistency in data retrieval.
- Added unit tests to validate the new logic, confirming that legacy columns are preferred when available.
- Added new updates for Phase A and Phase B in `CLAUDE.md`, detailing the completion of Phase A and the introduction of enriched session metrics in the API response for `GET /api/activity`.
- Enhanced the README to include references to new documentation files for scalar canon and composite metrics implementation.
- Updated `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` to reflect the current status of phases and added navigation rules for data access.
- Improved `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE.md` with links to new implementation concepts for composite metrics.
- Refactored the activity router to integrate enriched session metrics into the activity list responses, ensuring a more comprehensive data presentation.
- Updated the `upsert_session_metrics_from_csv_mapped` function to ensure EAV writes are only skipped when the `source_field` is populated, preventing duplicate storage.
- Enhanced unit tests to cover scenarios where the `source_field` is set but the corresponding column is empty, ensuring correct EAV behavior.
- Adjusted test cases to reflect changes in the handling of `hr_avg` and other metrics, improving overall test coverage and accuracy.
- Revised the `upsert_session_metrics_from_csv_mapped` function to clarify EAV writing conditions, ensuring only relevant parameters are processed.
- Enhanced the `merge_column_backed_and_eav_metrics` function to exclude EAV rows for parameters not present in the schema, improving data integrity.
- Updated unit tests to reflect changes in EAV handling and ensure correct functionality when parameters are mapped or not mapped in the profile schema.
- Improved frontend logic to prevent duplicate display of metrics already handled in the entry form, enhancing user experience.
- Eliminated checks for `source_field` in the `replace_activity_session_metrics` function to streamline EAV row replacement, ensuring consistency with existing logic.
- Updated frontend logic to simplify the filtering of metrics during payload construction, enhancing maintainability.
- Removed outdated unit tests related to `source_field` handling, reflecting the updated logic in the codebase.
- Modified the `replace_activity_session_metrics` function to skip EAV inserts for parameters with a defined `source_field`, aligning with the existing logic in `upsert_session_metrics_from_csv_mapped`.
- Enhanced the frontend logic to filter out metrics associated with `source_field` during payload construction, improving data integrity and user experience.
- Added unit tests to validate the new behavior, ensuring that metrics with `source_field` are correctly excluded from inserts.
- Enhanced the `upsert_session_metrics_from_csv_mapped` function to skip EAV writes when a `source_field` is provided, ensuring canonical handling of activity logs.
- Updated the docstring to clarify the behavior regarding `source_field` and EAV logic.
- Added unit tests to validate the new behavior, ensuring correct functionality when `source_field` is set or not set during CSV imports.
- Replaced hardcoded keys in `activity_data_canon.py` with a dynamic retrieval method from the module registry, ensuring that `ACTIVITY_MODULE_REGISTRY_FIELD_KEYS` reflects the current configuration.
- Updated `activity_persistence_orchestrator.py` to utilize the new dynamic field retrieval function, enhancing consistency across the data layer.
- Modified `activity_session_metrics.py` to reference the dynamic field keys, improving maintainability and reducing redundancy in the codebase.
Reverts cd29c7d..026c51b per git revert. Alle zwischenliegenden Commits bleiben in Gitea sichtbar; der Arbeitsbaum entspricht wieder dem Stand von ca8cee9.
Made-with: Cursor
- Introduced `resolve_activity_attribute_schema_for_csv_import` to enhance the handling of training parameters during CSV imports, allowing for the inclusion of active parameters not present in the category/type profile.
- Updated `apply_activity_mapped_column_aliases` and `upsert_session_metrics_from_csv_mapped` to utilize the new CSV import function, ensuring comprehensive mapping and insertion of metrics.
- Added unit tests to validate the new functionality and ensure correct behavior when handling mapped training parameters during CSV imports.
- Updated the `_import_activity` function to include new metrics: duration_min, kcal_active, kcal_resting, hr_avg, hr_max, and distance_km during CSV imports.
- Modified the `insert_activity_csv_minimal` function to accept and store these additional metrics in the activity log.
- Enhanced the `run_activity_post_write_hooks_import` function to utilize the new metrics for auto-evaluation after activity imports.
- Updated the activity import router to pass the new metrics from the CSV file to the database functions, ensuring comprehensive data handling.
- Improved frontend handling of activity entry forms to accommodate the new metrics, enhancing user experience during activity log edits.
- Updated `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` to clarify the derivation of `ACTIVITY_MODULE_REGISTRY_FIELD_KEYS` from `csv_parser.module_registry`.
- Enhanced `activity_data_canon.py` to eliminate hardcoded key lists, ensuring all registry fields are derived dynamically.
- Refactored the `_import_activity` function to remove redundant parameters and streamline the import process.
- Improved the `insert_activity_csv_minimal` function to handle metrics exclusively through `update_activity_columns`, preventing hardcoded values.
- Updated frontend components to manage editable activity log fields more effectively, ensuring proper handling of metrics during CSV imports.
- Added unit tests to validate the new logic and ensure consistency in activity session metrics handling.
- Updated the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with new details on CSV import processes and EAV handling, improving documentation clarity.
- Refactored the `_import_activity` function to utilize `apply_activity_mapped_column_aliases`, ensuring proper mapping of training parameters and reducing redundancy.
- Introduced validation for numeric bounds in the `activity_csv_registry_updates_from_mapped` function, enhancing data integrity during CSV imports.
- Added new utility functions to manage column aliasing and streamline the upsert process for session metrics, preventing duplicate entries.
- Implemented unit tests to validate the new aliasing logic and ensure correct behavior during session metrics updates.
- Updated the README to include new activity production architecture and phases, improving clarity on the development roadmap.
- Enhanced the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with details on the target architecture and phase plan for production readiness.
- Introduced a new function `merge_column_backed_and_eav_metrics` to streamline the merging of metrics from column-backed and EAV sources, ensuring data integrity and reducing duplication.
- Refactored session metrics handling to eliminate deprecated synchronization methods, improving the overall efficiency of data processing.
- Added unit tests for the new merging logic, ensuring robust validation of metrics handling.
- Replaced the deprecated `resolve_activity_log_column_patch_from_csv` function with `activity_csv_registry_updates_from_mapped` to streamline updates from CSV mappings.
- Updated the `_import_activity` function to utilize the new registry updates, improving data integrity during activity imports.
- Enhanced the activity module registry by adding German labels for various fields, improving localization support.
- Refactored the session metrics handling to ensure only relevant fields are processed, enhancing the overall robustness of CSV imports.
- Enhanced the docstring for `upsert_session_metrics_from_csv_mapped` to clarify the handling of schema parameters and EAV logic.
- Modified the condition for skipping updates based on `source_field` to ensure only patchable columns are processed, improving data integrity during session metrics upsert operations.
- Updated the `ACTIVITY_LOG_PATCHABLE_COLUMNS` and `ACTIVITY_LOG_PATCH_FORBIDDEN` sets to improve validation of CSV imports, ensuring only allowed fields are patched.
- Refactored the `_coerce_raw_value_for_parameter` function to handle string inputs for integer and float types, enhancing data coercion accuracy.
- Modified the `SessionMetricsFields` component to display orphan metrics that do not match the current schema, improving user visibility of imported data discrepancies.
- Enhanced the frontend to handle and display additional metrics, ensuring a more comprehensive representation of session data.
- Updated the CSV import logic to merge active training parameters with static fields for the activity module, improving field mapping accuracy.
- Enhanced validation functions to incorporate dynamic field definitions based on active training parameters, ensuring better data integrity during imports.
- Refactored related functions to streamline the process of handling CSV templates and field mappings, improving maintainability and clarity.
- Added new utility functions for resolving activity log column patches and upserting session metrics from CSV, enhancing the overall import functionality.
- 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.
- Updated the `replace_activity_session_metrics` function to improve validation logic and error handling for required fields.
- Enhanced the activity listing query to order results by date, start time, and ID, ensuring consistent output.
- Modified the frontend to handle null values in metrics payload and improved the display of activity statistics, including total entries in profile and sample size.
- Added new fields to the ActivityEntry model for improved tracking: hr_min, pace_min_per_km, cadence, avg_power, elevation_gain, temperature_celsius, humidity_percent, avg_hr_percent, and kcal_per_km.
- Updated the create_activity function to accommodate the new fields in the activity log.
- Modified session metrics handling to ensure accurate data retrieval and merging based on the updated schema.
- Added a new function to synchronize session metrics with activity log entries, ensuring data consistency.
- Updated the create and update activity endpoints to call the synchronization function after inserting or modifying activity logs.
- Introduced a set of allowed keys for activity log payloads to streamline data handling in the frontend.
- Improved data coercion logic for various data types in the frontend to ensure accurate data submission.
- 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.
- Adjusted the total number of placeholders from 116 to 114 across various documentation and code files to reflect the current state of the system.
- Enhanced TDEE calculation logic in `nutrition_metrics.py` to prioritize Mifflin–St Jeor BMR with PAL when demographic data is available, with a fallback to a weight-based estimate.
- Updated placeholder registrations to ensure consistency with the new metadata structure and improved data handling.
- Revised documentation to clarify the authoritative source of placeholder metadata and the implications of the changes on existing functionalities.
These updates improve the accuracy and consistency of the placeholder system and enhance the nutritional assessment capabilities within 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.
- 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.
- Bumped version of reference_values module to 1.3.0.
- Added new imports and functionality for reference values in the backend, enhancing data retrieval.
- Introduced a new PilotVizPage in the frontend for visualizing data, linked from the SettingsPage for easy access.
- Updated routing in App.jsx to include the new pilot visualization route.
- Introduced new routes and API endpoints for managing personal reference values.
- Updated the SettingsPage to include a section for reference values with navigation to manage them.
- Enhanced the backend to support reference values in the data layer and versioning.
- Added necessary imports and UI components for a seamless user experience.
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>
