
Andrea developed an end-to-end diagnostic notebook for the lsst-sitcom/notebooks_vandv repository, focusing on M2 Hexapod fault analysis with respect to elevation. Leveraging Python, SQL, and Jupyter Notebooks, Andrea implemented EFD data querying to correlate fault events with elevation angles and visualize fault patterns through plots and histograms. The work included actuator position analysis to support root cause assessment, enabling faster fault isolation and data-driven maintenance. In addition to feature development, Andrea restructured repository directories and enhanced documentation, improving maintainability and onboarding. The contributions demonstrated depth in data analysis, workflow clarity, and sustainable engineering practices over the two-month period.
August 2025 monthly summary for lsst-sitcom/notebooks_vandv focused on repository organization and documentation improvements to improve maintainability, onboarding, and clarity in data workflows. No functional code changes were released this month; the work emphasizes sustainable scaffolding for future development and analysis tasks.
August 2025 monthly summary for lsst-sitcom/notebooks_vandv focused on repository organization and documentation improvements to improve maintainability, onboarding, and clarity in data workflows. No functional code changes were released this month; the work emphasizes sustainable scaffolding for future development and analysis tasks.
July 2025 — Notebooks_VandV: Delivered an end-to-end diagnostic notebook for M2 Hexapod fault analysis with respect to elevation. The notebook queries EFD data, correlates fault occurrences with elevation, and provides plots and histograms to visualize fault patterns and actuator positions during fault events. This initial implementation, captured in commit 447c3b11745a98bd0466de5725f5377ffec3de7b, establishes a foundation for rapid fault diagnosis and data-driven maintenance. No major bugs fixed this cycle; focus was on feature delivery and validation. Business impact: enables faster fault isolation, improved reliability, and better data-driven decision-making. Technologies demonstrated: Python data analysis, Jupyter notebooks, data querying (EFD), visualization, actuator position analysis, and Git version control.
July 2025 — Notebooks_VandV: Delivered an end-to-end diagnostic notebook for M2 Hexapod fault analysis with respect to elevation. The notebook queries EFD data, correlates fault occurrences with elevation, and provides plots and histograms to visualize fault patterns and actuator positions during fault events. This initial implementation, captured in commit 447c3b11745a98bd0466de5725f5377ffec3de7b, establishes a foundation for rapid fault diagnosis and data-driven maintenance. No major bugs fixed this cycle; focus was on feature delivery and validation. Business impact: enables faster fault isolation, improved reliability, and better data-driven decision-making. Technologies demonstrated: Python data analysis, Jupyter notebooks, data querying (EFD), visualization, actuator position analysis, and Git version control.

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