
Worked on the Keck-DataReductionPipelines/KPF-Pipeline repository, delivering new features and reliability improvements for time-series data analysis. Developed a method to visualize radial velocity time-series per fiber across green and red chips, incorporating wavelength metadata for more granular diagnostics. Enhanced the robustness of configuration handling by refactoring parameter access and improving plotting label logic. Introduced an automated tool to check WLS file date consistency, supporting both file and directory processing and triggering downstream automation on mismatches. Utilized Python for backend development, data validation, and visualization, with a focus on scripting, file processing, and time-series analysis to streamline scientific workflows.
April 2025 — Keck-DataReductionPipelines/KPF-Pipeline (performance-review ready) 1) Key features delivered - AnalyzeTimeSeries: Added plot_rv_per_fiber_wavelength to visualize RV time-series per fiber (SCI1/SCI2/SCI3) across green/red chips with wavelength information. Refactored panel dictionary parameter access to .get() and improved object-like handling and plotting label robustness. Commits: 916d92687ea392eae867a87a4e8230cccd3c3d0e; 7d9eaf6130cc36056d7e16f46ffbd3e7e996bf66 - WLS file date consistency checker: check_wls_files_match.py introduced to verify date consistency between WLSFILE headers; supports processing files or directories, adds WLSMATCH header, and can trigger kpf_slowtouch.sh on mismatches. Commit: ec43fb01571d5614f48054388ae5792351337893 2) Major bugs fixed - AnalyzeTimeSeries: Fixed object_like query formatting to ensure proper quoting and escaping for LIKE clause filtering, improving accuracy of time-series filtering. Commit: 6a3f9c2477c42f73bb52fd6c7151cfe9667a1289 3) Overall impact and accomplishments - Improved reliability and interpretability of RV analyses through per-fiber visualizations and consistent metadata checks, enabling faster troubleshooting and higher confidence in results across SCI1/SCI2/SCI3 and green/red chips. - Automated data quality gate for WLS files, reducing manual inspection and enabling smoother downstream processing with automated mismatch handling. 4) Technologies/skills demonstrated - Python-based data analysis tooling, plotting, and data-quality automation. - Refactoring for robust configuration access (.get()), enhanced object-like handling, and plotting labels. - File/directory processing, header-level consistency checks, and automation hooks (kpf_slowtouch.sh). Top 3-5 achievements (business value oriented): - Delivered per-fiber RV visualization across chips enabling granular time-series diagnostics (commit 916d9268..., 7d9eaf61...). - Strengthened data filtering accuracy with object_like query formatting fix (commit 6a3f9c24...). - Introduced automated WLS date consistency checks with mismatch-triggered automation (commit ec43fb01...).
April 2025 — Keck-DataReductionPipelines/KPF-Pipeline (performance-review ready) 1) Key features delivered - AnalyzeTimeSeries: Added plot_rv_per_fiber_wavelength to visualize RV time-series per fiber (SCI1/SCI2/SCI3) across green/red chips with wavelength information. Refactored panel dictionary parameter access to .get() and improved object-like handling and plotting label robustness. Commits: 916d92687ea392eae867a87a4e8230cccd3c3d0e; 7d9eaf6130cc36056d7e16f46ffbd3e7e996bf66 - WLS file date consistency checker: check_wls_files_match.py introduced to verify date consistency between WLSFILE headers; supports processing files or directories, adds WLSMATCH header, and can trigger kpf_slowtouch.sh on mismatches. Commit: ec43fb01571d5614f48054388ae5792351337893 2) Major bugs fixed - AnalyzeTimeSeries: Fixed object_like query formatting to ensure proper quoting and escaping for LIKE clause filtering, improving accuracy of time-series filtering. Commit: 6a3f9c2477c42f73bb52fd6c7151cfe9667a1289 3) Overall impact and accomplishments - Improved reliability and interpretability of RV analyses through per-fiber visualizations and consistent metadata checks, enabling faster troubleshooting and higher confidence in results across SCI1/SCI2/SCI3 and green/red chips. - Automated data quality gate for WLS files, reducing manual inspection and enabling smoother downstream processing with automated mismatch handling. 4) Technologies/skills demonstrated - Python-based data analysis tooling, plotting, and data-quality automation. - Refactoring for robust configuration access (.get()), enhanced object-like handling, and plotting labels. - File/directory processing, header-level consistency checks, and automation hooks (kpf_slowtouch.sh). Top 3-5 achievements (business value oriented): - Delivered per-fiber RV visualization across chips enabling granular time-series diagnostics (commit 916d9268..., 7d9eaf61...). - Strengthened data filtering accuracy with object_like query formatting fix (commit 6a3f9c24...). - Introduced automated WLS date consistency checks with mismatch-triggered automation (commit ec43fb01...).

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