lsst-sitcom/notebooks_vandv
Nov 2024 – Jul 2025
8 Months active
Languages Used
JSONJupyter NotebookPythonMarkdown
Technical Skills
AstropyData AnalysisData VisualizationJupyter NotebookJupyter NotebooksLSST Software Stack
Laura Toribio San Cipriano
PROFILE
Laura Toribio San Cipriano
Laura Toribio developed a suite of data analysis and diagnostics Jupyter notebooks for the lsst-sitcom/notebooks_vandv repository, focusing on telescope performance, star tracker error analysis, and operational telemetry. She applied Python, Pandas, and Matplotlib to build reproducible workflows that ingest, process, and visualize time-series data, supporting tasks such as slew rate evaluation, air compressor diagnostics, and pointing accuracy assessment. Laura emphasized code readability, maintainability, and documentation, refactoring notebooks for clarity and aligning with PEP8 and Ruff standards. Her work enabled faster onboarding, improved data-driven decision-making, and streamlined validation processes for LSST SITCOM engineering and science operations.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
67Total
Bugs
0
Commits
67
Features
21
Lines of code
30,725
Activity Months8
Your Network
18 people
Work History
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 (2025-07) performance summary for lsst-sitcom/notebooks_vandv. Delivered two notebook-based analyses focused on telescope slew performance and Air Compressor behavior, with robust plotting, data filtering, and documentation to support SITCOM data workflows.
3 Commits • 2 Features
Jul 1, 2025July 2025 (2025-07) performance summary for lsst-sitcom/notebooks_vandv. Delivered two notebook-based analyses focused on telescope slew performance and Air Compressor behavior, with robust plotting, data filtering, and documentation to support SITCOM data workflows.
July 2025
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 — Focused on notebook usability and operational diagnostics. Delivered two main features in lsst-sitcom/notebooks_vandv: (1) Notebook Readability and Documentation Enhancements; improved readability by converting a code cell to markdown and removing unnecessary metadata; (2) Air Compressor Diagnostics Notebook; introduced a diagnostics notebook to retrieve telemetry, analyze event and error logs for Air Compressors 1 and 2, identify faults, and visualize behavior to explain why Compressor 2 was off. These efforts reduce onboarding time and accelerate root cause analysis, with commits linked to each feature.
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 — Focused on notebook usability and operational diagnostics. Delivered two main features in lsst-sitcom/notebooks_vandv: (1) Notebook Readability and Documentation Enhancements; improved readability by converting a code cell to markdown and removing unnecessary metadata; (2) Air Compressor Diagnostics Notebook; introduced a diagnostics notebook to retrieve telemetry, analyze event and error logs for Air Compressors 1 and 2, identify faults, and visualize behavior to explain why Compressor 2 was off. These efforts reduce onboarding time and accelerate root cause analysis, with commits linked to each feature.
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary: Delivered two end-to-end analysis notebooks for LSSTCam pointing and ComCam Star Tracker data, plus notebook maintenance and documentation enhancements. Established reproducible workflows (data download, processing, visualization of RA/Dec errors over time) and prepared for future metadata fields; introduced code quality improvements and StarTracker integration tweaks for LSST cam data.
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary: Delivered two end-to-end analysis notebooks for LSSTCam pointing and ComCam Star Tracker data, plus notebook maintenance and documentation enhancements. Established reproducible workflows (data download, processing, visualization of RA/Dec errors over time) and prepared for future metadata fields; introduced code quality improvements and StarTracker integration tweaks for LSST cam data.
May 2025
April 2025
7 Commits • 3 Features
Apr 1, 20252025-04 monthly summary for lsst-sitcom/notebooks_vandv: Delivered three feature notebooks to enhance data validation, performance assessment, and notebook usability. All work focused on business value: reproducibility, faster validation, and clearer guidance for analysts working with campaign data.
April 2025
7 Commits • 3 Features
Apr 1, 20252025-04 monthly summary for lsst-sitcom/notebooks_vandv: Delivered three feature notebooks to enhance data validation, performance assessment, and notebook usability. All work focused on business value: reproducibility, faster validation, and clearer guidance for analysts working with campaign data.
March 2025
18 Commits • 3 Features
Mar 1, 2025March 2025 performance summary for lsst-sitcom/notebooks_vandv focusing on delivering robust data-analysis tooling for star-tracker workflows and maintaining high code quality and reproducibility. Key features delivered: - Star Tracker Error Analysis Notebooks: Enhanced support for ComCam data and introduced azimuth error analysis notebooks for Star Tracker data from Rubin TV, enabling more accurate error characterization and faster troubleshooting. - MT Accelerometers and Encoder/Accelerometer Analysis: Implemented an end-to-end pipeline to retrieve, process, and visualize MT accelerometer and encoder data, improving motion-dynamics insight and enabling richer diagnostics. - Notebook Cleanup and Maintenance: Removed deprecated samples, refactored for readability, and cleaned execution metadata to ensure reliable re-runs and easier onboarding. Major bugs fixed / quality improvements: - Code quality and consistency: applied isort and snake_case conventions, resolved lint issues with ruff checks, and corrected small mistakes with additional comments for clarity. - Notebook hygiene: cleaned cells, standardized cell execution environment, and removed stale or erroneous notebooks to prevent confusion and execution errors. Overall impact and accomplishments: - Accelerated data-analysis workflows for star-tracker data, enabling quicker insight into azimuth errors and motion dynamics, supporting Rubin TV data pipelines. - Improved maintainability, readability, and reproducibility across the notebooks project, reducing onboarding time and risk of regressions in future iterations. - Strengthened collaboration through consistent coding standards and clearer commit history. Technologies/skills demonstrated: - Python data analysis, Jupyter notebooks, data ingestion and visualization. - Data integration: ComCam and Rubin TV data support in Star Tracker analyses; MT accelerometer/encoder data integration. - Code quality practices: isort, snake_case, ruff linting, and thorough code/documentation cleanup. - Version control discipline with clear, descriptive commits.
18 Commits • 3 Features
Mar 1, 2025March 2025 performance summary for lsst-sitcom/notebooks_vandv focusing on delivering robust data-analysis tooling for star-tracker workflows and maintaining high code quality and reproducibility. Key features delivered: - Star Tracker Error Analysis Notebooks: Enhanced support for ComCam data and introduced azimuth error analysis notebooks for Star Tracker data from Rubin TV, enabling more accurate error characterization and faster troubleshooting. - MT Accelerometers and Encoder/Accelerometer Analysis: Implemented an end-to-end pipeline to retrieve, process, and visualize MT accelerometer and encoder data, improving motion-dynamics insight and enabling richer diagnostics. - Notebook Cleanup and Maintenance: Removed deprecated samples, refactored for readability, and cleaned execution metadata to ensure reliable re-runs and easier onboarding. Major bugs fixed / quality improvements: - Code quality and consistency: applied isort and snake_case conventions, resolved lint issues with ruff checks, and corrected small mistakes with additional comments for clarity. - Notebook hygiene: cleaned cells, standardized cell execution environment, and removed stale or erroneous notebooks to prevent confusion and execution errors. Overall impact and accomplishments: - Accelerated data-analysis workflows for star-tracker data, enabling quicker insight into azimuth errors and motion dynamics, supporting Rubin TV data pipelines. - Improved maintainability, readability, and reproducibility across the notebooks project, reducing onboarding time and risk of regressions in future iterations. - Strengthened collaboration through consistent coding standards and clearer commit history. Technologies/skills demonstrated: - Python data analysis, Jupyter notebooks, data ingestion and visualization. - Data integration: ComCam and Rubin TV data support in Star Tracker analyses; MT accelerometer/encoder data integration. - Code quality practices: isort, snake_case, ruff linting, and thorough code/documentation cleanup. - Version control discipline with clear, descriptive commits.
March 2025
February 2025
7 Commits • 4 Features
Feb 1, 2025February 2025: Delivered four notebook-based data analysis enhancements in lsst-sitcom/notebooks_vandv, emphasizing usability, readability, and robustness. Implemented new EFD Data Query notebook, enhanced Anemometer/Accelerometer notebook, standardized Error Trend Plot notebook, and launched Star Tracker error analysis notebook. These changes improve data access, plotting capabilities, and maintainability.
February 2025
7 Commits • 4 Features
Feb 1, 2025February 2025: Delivered four notebook-based data analysis enhancements in lsst-sitcom/notebooks_vandv, emphasizing usability, readability, and robustness. Implemented new EFD Data Query notebook, enhanced Anemometer/Accelerometer notebook, standardized Error Trend Plot notebook, and launched Star Tracker error analysis notebook. These changes improve data access, plotting capabilities, and maintainability.
December 2024
13 Commits • 2 Features
Dec 1, 2024December 2024 highlights two core deliverables in lsst-sitcom/notebooks_vandv, focusing on notebook reproducibility, data visualization quality, and alignment with LSST utility tooling. The work enhances reliability for analyses, accelerates reproducibility across environments, and improves decision-ready plotting.
13 Commits • 2 Features
Dec 1, 2024December 2024 highlights two core deliverables in lsst-sitcom/notebooks_vandv, focusing on notebook reproducibility, data visualization quality, and alignment with LSST utility tooling. The work enhances reliability for analyses, accelerates reproducibility across environments, and improves decision-ready plotting.
December 2024
November 2024
10 Commits • 2 Features
Nov 1, 20242024-11 monthly summary for lsst-sitcom/notebooks_vandv: Delivered two major feature workstreams focusing on SITCOM/ICS analytics notebooks. Key outcomes: (1) M1M3 Inertia Compensation System single-slew analytics notebook providing end-to-end data prep, querying, analysis, and plotting of forces, torques, and velocities; introduced single-slew statistics and improved notebook structure; metadata updates. (2) SITCOM analysis plotting enhancements adding momentum plotting alongside existing metrics, with clearer legends, axes, and improved subplot configuration for better interpretation of slew dynamics. This work improves data-driven decision making for M1M3 performance and SITCOM slew analysis; increased reproducibility and collaboration by sharing notebooks. No major bugs fixed documented this month; focus on feature delivery and visualization quality.
November 2024
10 Commits • 2 Features
Nov 1, 20242024-11 monthly summary for lsst-sitcom/notebooks_vandv: Delivered two major feature workstreams focusing on SITCOM/ICS analytics notebooks. Key outcomes: (1) M1M3 Inertia Compensation System single-slew analytics notebook providing end-to-end data prep, querying, analysis, and plotting of forces, torques, and velocities; introduced single-slew statistics and improved notebook structure; metadata updates. (2) SITCOM analysis plotting enhancements adding momentum plotting alongside existing metrics, with clearer legends, axes, and improved subplot configuration for better interpretation of slew dynamics. This work improves data-driven decision making for M1M3 performance and SITCOM slew analysis; increased reproducibility and collaboration by sharing notebooks. No major bugs fixed documented this month; focus on feature delivery and visualization quality.
Activity
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Quality Metrics
Correctness86.8%
Maintainability88.6%
Architecture82.2%
Performance76.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
Astronomy Data ProcessingAstropyCode CleanupCode DocumentationCode FormattingCode OrganizationCode ReadabilityCode RefactoringCode ReviewData AnalysisData CleaningData VisualizationDatabase QueryingDocumentationEFD Client
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