Hannah Isaacson developed and maintained the Keck-DataReductionPipelines/KPF-Pipeline, delivering robust data reduction and quality control workflows for astronomical data. She engineered multi-level recipe frameworks, calibration enhancements, and automated FITS file handling using Python and Astropy, focusing on modularity and maintainability. Her work included optimizing I/O, refining configuration management, and implementing error handling to improve pipeline reliability and traceability. By integrating batch metadata updates, stabilizing test suites, and enhancing logging, she addressed both performance and data integrity challenges. The depth of her contributions is reflected in targeted bug fixes, architectural refactoring, and end-to-end validation, supporting reproducible, production-grade scientific processing.
December 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline. Delivered targeted calibration data retrieval enhancements, clarified configuration for spectral extraction and barycentric correction, and aligned master settings with science configurations using a polynomial method (polyorder 5). Introduced plotting gating to only generate plots when a valid plot_path is provided. Fixed core stability and data integrity issues, including wavelength statistics index handling, preventing IMTYPE keyword inheritance via deepcopy, and robust NaN handling in L1 creation during spectral extraction. These efforts improved data accuracy, processing efficiency, robustness, and maintainability of the KPF pipeline across end-to-end data reduction workflows.
December 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline. Delivered targeted calibration data retrieval enhancements, clarified configuration for spectral extraction and barycentric correction, and aligned master settings with science configurations using a polynomial method (polyorder 5). Introduced plotting gating to only generate plots when a valid plot_path is provided. Fixed core stability and data integrity issues, including wavelength statistics index handling, preventing IMTYPE keyword inheritance via deepcopy, and robust NaN handling in L1 creation during spectral extraction. These efforts improved data accuracy, processing efficiency, robustness, and maintainability of the KPF pipeline across end-to-end data reduction workflows.
Monthly summary for 2025-11 focused on Keck-DataReductionPipelines/KPF-Pipeline. Highlights include delivered logging enhancements for better observability and fixed issues in radial velocity processing, driving faster debugging and more robust data validation.
Monthly summary for 2025-11 focused on Keck-DataReductionPipelines/KPF-Pipeline. Highlights include delivered logging enhancements for better observability and fixed issues in radial velocity processing, driving faster debugging and more robust data validation.
For 2025-10, Keck-DataReductionPipelines/KPF-Pipeline delivered targeted improvements across calibration workflows, pipeline integration, and arclamp reliability, with robust fixes enhancing data integrity for large datasets. The month emphasized business value through faster processing, lower overhead, and more dependable data products, enabling smoother operation of high-volume observing campaigns and more reliable scientific output.
For 2025-10, Keck-DataReductionPipelines/KPF-Pipeline delivered targeted improvements across calibration workflows, pipeline integration, and arclamp reliability, with robust fixes enhancing data integrity for large datasets. The month emphasized business value through faster processing, lower overhead, and more dependable data products, enabling smoother operation of high-volume observing campaigns and more reliable scientific output.
September 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline focused on stabilizing the data path, improving reliability and performance, and guiding users through a streamlined workflow. Delivered architectural refactors, critical bug fixes, and targeted optimizations that reduce backend dependencies and improve data quality and processing throughput.
September 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline focused on stabilizing the data path, improving reliability and performance, and guiding users through a streamlined workflow. Delivered architectural refactors, critical bug fixes, and targeted optimizations that reduce backend dependencies and improve data quality and processing throughput.
August 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline focused on increasing reliability, end-to-end run coverage, and maintainability to deliver stable, production-ready pipelines for Keck data. Key achievements: - Test suite stabilization and improvements: stabilized tests, removed redundant tests, handled empty variance, and added error catching for graceful failures (reduces CI flakiness and speeds up feedback). - Config system enhancements for barycorr and L1/L2: moved config to support barycorr in 2D to L1, reintroduced L1 to L2, and improved config behavior for predictable runs. - Pipeline execution completion improvements: ensured science and masters configs and all recipes run to completion, enabling consistent end-to-end results. - Code cleanup and logging improvements: tightened logging, removed debugging statements, and cleaned up recipes for production readiness. - Bug fixes and stability improvements: addressed TSDB error handling and order masking, fixed insidious file renaming, and improved spectral extraction robustness to reduce flaky tests.
August 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline focused on increasing reliability, end-to-end run coverage, and maintainability to deliver stable, production-ready pipelines for Keck data. Key achievements: - Test suite stabilization and improvements: stabilized tests, removed redundant tests, handled empty variance, and added error catching for graceful failures (reduces CI flakiness and speeds up feedback). - Config system enhancements for barycorr and L1/L2: moved config to support barycorr in 2D to L1, reintroduced L1 to L2, and improved config behavior for predictable runs. - Pipeline execution completion improvements: ensured science and masters configs and all recipes run to completion, enabling consistent end-to-end results. - Code cleanup and logging improvements: tightened logging, removed debugging statements, and cleaned up recipes for production readiness. - Bug fixes and stability improvements: addressed TSDB error handling and order masking, fixed insidious file renaming, and improved spectral extraction robustness to reduce flaky tests.
July 2025 — Delivered a robust, configurable multi-level recipe framework for the KPF pipeline (Keck-DataReductionPipelines/KPF-Pipeline) enabling L0-L2 initialization, 2D to L1/L2 transitions, and support for processing via individual recipes with single_recipes config. Migrated to header-only calls and completed cleanup across levels, improving maintainability and modularity. Established end-to-end run readiness across all levels and added targeted test coverage for transitions (L0↔2D, 2D↔L1). Integrated spectral extraction workflow with new import and extraction calls; renamed SpectralExtraction to SpectralExtraction_gjg to avoid ambiguity. Implemented comprehensive code cleanup and refactoring of individual recipes to align with multi-level support; updated documentation accordingly.
July 2025 — Delivered a robust, configurable multi-level recipe framework for the KPF pipeline (Keck-DataReductionPipelines/KPF-Pipeline) enabling L0-L2 initialization, 2D to L1/L2 transitions, and support for processing via individual recipes with single_recipes config. Migrated to header-only calls and completed cleanup across levels, improving maintainability and modularity. Established end-to-end run readiness across all levels and added targeted test coverage for transitions (L0↔2D, 2D↔L1). Integrated spectral extraction workflow with new import and extraction calls; renamed SpectralExtraction to SpectralExtraction_gjg to avoid ambiguity. Implemented comprehensive code cleanup and refactoring of individual recipes to align with multi-level support; updated documentation accordingly.
June 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline highlighting feature delivery, bug fixes, and infrastructure improvements that increased data reliability and build stability. Emphasis on delivering business value through robust drift correction, stable blaze correction, and cleaner codebase.
June 2025 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline highlighting feature delivery, bug fixes, and infrastructure improvements that increased data reliability and build stability. Emphasis on delivering business value through robust drift correction, stable blaze correction, and cleaner codebase.
Monthly performance summary for May 2025 (Keck-DataReductionPipelines/KPF-Pipeline). Delivered a critical fix to the Bad Pixel Mask Path Configuration that ensures the pipeline correctly locates and applies the bad pixel mask data, improving data quality and reducing downstream masking errors. The changes centralized the mask path in the config, added explicit reference to bad_pixel_mask_20240920_2D.fits, and corrected file paths across environments. This work, together with corresponding commits, enhances reproducibility and stability of data products.
Monthly performance summary for May 2025 (Keck-DataReductionPipelines/KPF-Pipeline). Delivered a critical fix to the Bad Pixel Mask Path Configuration that ensures the pipeline correctly locates and applies the bad pixel mask data, improving data quality and reducing downstream masking errors. The changes centralized the mask path in the config, added explicit reference to bad_pixel_mask_20240920_2D.fits, and corrected file paths across environments. This work, together with corresponding commits, enhances reproducibility and stability of data products.
February 2025: Implemented a CSV-driven FITS header updater in KPF-Pipeline enabling batch keyword updates per run with robust logging and missing-file error handling. Addressed stability issues with the header updater (commit 731e57c2f9fd7a22e186491f268538165ad0ea32). Result: faster, more reliable metadata updates, improved traceability and maintainability of the pipeline.
February 2025: Implemented a CSV-driven FITS header updater in KPF-Pipeline enabling batch keyword updates per run with robust logging and missing-file error handling. Addressed stability issues with the header updater (commit 731e57c2f9fd7a22e186491f268538165ad0ea32). Result: faster, more reliable metadata updates, improved traceability and maintainability of the pipeline.
January 2025 — Key outcomes focused on delivering an automated data-management capability within the Keck pipeline and validating its value for data provenance and reproducibility.
January 2025 — Key outcomes focused on delivering an automated data-management capability within the Keck pipeline and validating its value for data provenance and reproducibility.
2024-11 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline. Delivered targeted stellar mask data refinement to improve data reduction accuracy. Removed lines near night sky emission lines and updated mask values in G2.harps.mas and G2.neid.v1.mas. These changes reduce contamination, improve mask reliability, and set the stage for more robust downstream science.
2024-11 monthly summary for Keck-DataReductionPipelines/KPF-Pipeline. Delivered targeted stellar mask data refinement to improve data reduction accuracy. Removed lines near night sky emission lines and updated mask values in G2.harps.mas and G2.neid.v1.mas. These changes reduce contamination, improve mask reliability, and set the stage for more robust downstream science.
Month: 2024-10. Focused on enhancing data quality control for the KPF pipeline. Implemented SNR-based QC tests for LFC frames with a demonstration notebook, introduced a new L0_bad_readout_check QC definition, and fixed a bug in QCL0 exposure time validation. All changes align with the QC framework and improve automated quality assessment and traceability, enabling faster validation of observing data.
Month: 2024-10. Focused on enhancing data quality control for the KPF pipeline. Implemented SNR-based QC tests for LFC frames with a demonstration notebook, introduced a new L0_bad_readout_check QC definition, and fixed a bug in QCL0 exposure time validation. All changes align with the QC framework and improve automated quality assessment and traceability, enabling faster validation of observing data.
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