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feat(data_service): add new linear calibration methods - #14
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What and why
This pull request adds robust calibration methods for sensor data, improving the system's resilience to outlier calibration points. It introduces two robust line-fitting methods—Theil-Sen and Siegel's repeated median—and integrates them into the calibration pipeline. Comprehensive tests are also included to ensure correctness and reliability of the new methods.
Robust calibration methods:
THEIL_SENandREPEATED_MEDIANoptions to theConvertMethodenum inapp/models.py, enabling selection of robust linear fitting methods.theil_sen_fit,repeated_median_fit) and a unifiedrobust_interpolatefunction inapp/services/data_service.py, allowing calibration to ignore outliers and improve accuracy.Testing:
tests/test_robust_calibration.pywith extensive test coverage for the robust calibration methods, including edge cases and integration with the sensor parser.Codebase improvements:
app/services/data_service.pyto support the new calibration methods and maintain clarity.How was this tested?
Hardware impact
hardware-risklabel; a lead must approve!, link the matching PRs:Checklist
dev(ormainfor a release/hotfix, or with a reason given above).envfiles or build output committed