- Renamed translation function from `t` to `tr_key` for clarity and consistency.
- Updated all instances of translation calls in `90_CI_report_with_kpis_agronomic_support.Rmd` and `91_CI_report_with_kpis_cane_supply.Rmd` to use `tr_key`.
- Introduced a new helper function `get_area_unit_label` to manage area unit preferences across the project.
- Modified area calculations in `91_CI_report_with_kpis_cane_supply.Rmd` to utilize area from analysis data instead of recalculating.
- Added area unit preference setting in `parameters_project.R` to allow for flexible reporting in either hectares or acres.
- Updated `MANUAL_PIPELINE_RUNNER.R` to include language parameter for report generation.
- Adjusted translations in the `translations.xlsx` file to reflect changes in the report structure.
- Update trend categorization thresholds in KPI calculations for clarity.
- Improve comments for better understanding of trend interpretations.
- Refactor report generation to use consistent terminology for trends.
- Add batch pipeline runner for weekly reporting across multiple dates.
- Minor formatting adjustments across various scripts for consistency.
- Changed report date in CI report for cane supply to "2026-02-04".
- Updated output file naming convention for agronomic support report to reflect new report date.
- Enhanced map creation functions to allow customizable legend positions and improved layout settings.
- Adjusted widths for map arrangements to ensure better visual representation.
- Fixed minor issues in ggplot aesthetics for clearer legend positioning and improved readability.
- Corrected field size unit from hectares to acres in KPI summary generation.
- Improved safe_log function to include timestamps and conditional logging
- Added diagnostic messages for field visualization processing
- Updated CI map rendering parameters for consistency
- Refined raster mapping functions in report_utils for clarity
- Added .png files to .gitignore
- Implemented `run_spectral_extraction.py` to batch extract spectral indices (NDVI, BSI, NDWI) from 4-band TIFF files, saving results in a structured CSV format.
- Created `spectral_features.py` for calculating various spectral indices, including NDVI, BSI, NDWI, CI_green, CI_red, GNDVI, SAVI, and EVI2.
- Added Jupyter notebook `02_season_normalization_analysis.ipynb` for analyzing season-length normalization, comparing ESA and Angata spectral indices, and visualizing growth patterns.
- Included functions for computing season age, plotting trajectories, and performing peak and amplitude analysis.
Timon
DimitraVeropoulou