Updated Dewar API methods to use protected endpoints for enhanced security and consistency. Added `pgroups` handling in various frontend components and modified the LogisticsView contact field for clarity. Simplified backend router imports for better readability.
Replaced usage of "ContactPerson" with "Contact" for consistency across the codebase. Updated related component props, state variables, API calls, and database queries to align with the new model. Also enhanced backend functionality with stricter validations and added support for handling active pgroups in contact management.
Upgraded multiple dependencies across Babel, Emotion, ESLint, and DevExpress packages in `package-lock.json` to their latest versions. These updates ensure compatibility, fix minor issues, and improve overall performance and security.
Updated the `number_of_pucks` and `number_of_samples` fields in the `schemas.py` to be optional for greater flexibility. Simplified the test Jupyter Notebook by restructuring imports and consolidating function calls for better readability and maintainability.
Add duplicate detection for spreadsheet data processing
Implemented logic to detect and handle duplicate 'positioninpuck' entries within the same puck during spreadsheet processing. Updated backend to validate duplicates and provide detailed error messages. Enhanced frontend to visually highlight duplicate errors and allow better user feedback during cell editing.
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Improved the backend's value cleaning to differentiate between corrections and defaults, logging metadata for clearer traceability. Updated frontend to display corrected/defaulted fields with visual cues and tooltips for better user feedback. Enhanced data models and response structures to support this richer metadata.
Enhanced value cleaning and validation for spreadsheet data with dynamic handling of columns and corrections. Improved feedback for users with detailed error messages and visual indicators for corrected or defaulted values. Simplified backend and frontend logic for better maintainability and usability.
Introduced serialization for `data_collection_parameters` in backend models and processing. Added logic to parse and attach data collection parameters in the frontend. This ensures consistent handling and storage of these parameters throughout the application.
Introduced a backend mapping for column expected types, improving validation and error handling. Updated UI to highlight default and corrected values, with additional detailed validation for data collection parameters.
Introduced a backend mapping for column expected types, improving validation and error handling. Updated UI to highlight default and corrected values, with additional detailed validation for data collection parameters.
Implemented a toggleable spreadsheet UI component for sample data, added fields such as priority and comments, and improved backend validation. Default values for "directory" are now assigned when missing, with feedback highlighted in green on the front end.
