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This commit replaces the simplistic `detect_breakpoints` workflow with a more robust and scientifically grounded `bfast_monitor` implementation in Rust. The new workflow scaffolds the core logic of the BFAST Monitor algorithm, including placeholder functions for harmonic model fitting, prediction, and break detection. It is designed for high performance by processing each pixel's time series in parallel using Rayon, showcasing the strength of Rust for complex, iterative EO analysis. The Python bindings and tests have been updated to reflect these changes.
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Implements the BFAST Monitor algorithm for change detection. This patch replaces the placeholder scaffolding with a complete implementation using Ordinary Least Squares (OLS) to fit a harmonic model and an OLS-MOSUM process to detect breaks. Key changes: - Replaced the `ndarray-linalg` dependency with `nalgebra` to avoid linker issues with OpenBLAS. - Implemented the full OLS harmonic model fitting and prediction logic. - Implemented the OLS-MOSUM change detection process. - Updated the Python wrapper and type stubs to match the new Rust function signature. - Added a comprehensive test case to validate the implementation.
Implements the BFAST Monitor algorithm for change detection. This patch replaces the placeholder scaffolding with a complete implementation using Ordinary Least Squares (OLS) to fit a harmonic model and an OLS-MOSUM process to detect breaks. Key changes: - Replaced the `ndarray-linalg` dependency with `nalgebra` to avoid linker issues with OpenBLAS. - Implemented the full OLS harmonic model fitting and prediction logic. - Implemented the OLS-MOSUM change detection process. - Updated the Python wrapper and type stubs to match the new Rust function signature. - Added a comprehensive test case to validate the implementation. - Fixed clippy warnings for `unnecessary_cast` and `too_many_arguments`.
…1051 Implement BFAST Monitor Workflow
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This change introduces a Rust-based scaffold for the BFAST Monitor time-series analysis workflow, replacing the previous
detect_breakpointsfunction. The new implementation is parallelized for high performance and includes updated Python bindings and unit tests. This serves as a foundation for building out more complex and efficient EO processing capabilities.PR created automatically by Jules for task 10782325817424901614 started by @BnJam