simonsobs/sotodlib
Sep 2025 – Jun 2026
9 Months active
Languages Used
Python
Technical Skills
Data ProcessingSoftware EngineeringTime Series AnalysisConfiguration ManagementDebuggingError Handling
Katie Harrington
PROFILE
Katie Harrington
Over nine months, contributed to the simonsobs/sotodlib repository by building and refining backend data processing workflows focused on time-series integrity, data packaging, and pipeline reliability. Leveraged Python and scientific computing techniques to implement robust error handling, parallel processing, and validation logic that improved data quality and operational observability. Enhanced features included automated correction of timing anomalies, streamlined downsampling, and performance optimizations for book binding and finalization. Strengthened database management and cleanup routines, ensuring traceable, reliable data delivery for downstream analytics. The work emphasized maintainable code, clear logging, and resilient exception management, supporting scalable, high-quality scientific data pipelines.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
16Total
Bugs
3
Commits
16
Features
9
Lines of code
1,511
Activity Months9
Your Network
27 peopleShared Repositories
27
Work History
June 2026
6 Commits • 2 Features
Jun 1, 2026June 2026 focused on fortifying the sotodlib data pipeline by delivering robust data packaging, database initialization, and Level 2 data cleanup enhancements. Key features delivered include Data Packaging and Database Robustness Improvements and Level 2 Data Cleanup Enhancements, each bringing improved logging, time handling, error management, and safer cleanup workflows. These changes reduce data integrity risks, improve restart resilience, and provide better observability, paving the way for reliable data delivery and scalable maintenance.
6 Commits • 2 Features
Jun 1, 2026June 2026 focused on fortifying the sotodlib data pipeline by delivering robust data packaging, database initialization, and Level 2 data cleanup enhancements. Key features delivered include Data Packaging and Database Robustness Improvements and Level 2 Data Cleanup Enhancements, each bringing improved logging, time handling, error management, and safer cleanup workflows. These changes reduce data integrity risks, improve restart resilience, and provide better observability, paving the way for reliable data delivery and scalable maintenance.
June 2026
May 2026
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary for simonsobs/sotodlib. Focused on performance optimization, reliability, and observability in data packaging, finalization, and CLI workflows. Key work includes: 1) Book Binding Performance Optimization delivering parallel binding with an optional non-parallel path; 2) G3tSmurf Finalization Performance and Logging Enhancements improving logging, profiling readiness, and query efficiency; 3) Dropped Mount Data Robustness in CLI improving error handling and message parsing. Commits underpinning these changes include e5636986a61bb1ca72fe93b4fe28b23f9bd8c47b; efe86f76e6b975f7101595f449fec0fad4ce75e6; 59321502ca30e9aac51efc9443133ad5aeead843.
May 2026
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary for simonsobs/sotodlib. Focused on performance optimization, reliability, and observability in data packaging, finalization, and CLI workflows. Key work includes: 1) Book Binding Performance Optimization delivering parallel binding with an optional non-parallel path; 2) G3tSmurf Finalization Performance and Logging Enhancements improving logging, profiling readiness, and query efficiency; 3) Dropped Mount Data Robustness in CLI improving error handling and message parsing. Commits underpinning these changes include e5636986a61bb1ca72fe93b4fe28b23f9bd8c47b; efe86f76e6b975f7101595f449fec0fad4ce75e6; 59321502ca30e9aac51efc9443133ad5aeead843.
April 2026
1 Commits • 1 Features
Apr 1, 20262026-04 monthly summary: focused on delivering a robustness enhancement in Sotodlib's downsampling workflow by standardizing axis handling across data types, with a concrete commit and integration effort. This improvements reduce axis misalignment risks and improve reliability of downstream analyses.
1 Commits • 1 Features
Apr 1, 20262026-04 monthly summary: focused on delivering a robustness enhancement in Sotodlib's downsampling workflow by standardizing axis handling across data types, with a concrete commit and integration effort. This improvements reduce axis misalignment risks and improve reliability of downstream analyses.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for simonsobs/sotodlib: HWP Angle Model Caching and Performance Optimization. Added a check to run the HWP angle model only if it does not already exist in the data structure, preventing redundant calculations and improving efficiency. This aligns with the ongoing effort to optimize runtime for large datasets and to improve pipeline throughput.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for simonsobs/sotodlib: HWP Angle Model Caching and Performance Optimization. Added a check to run the HWP angle model only if it does not already exist in the data structure, preventing redundant calculations and improving efficiency. This aligns with the ongoing effort to optimize runtime for large datasets and to improve pipeline throughput.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 (Month: 2026-02) focused on strengthening data packaging reliability and integrity in simonsobs/sotodlib. Implemented stream_id uniqueness, correct wafer-slot ordering, and robust handling of streaming data to prevent incomplete observations from persisting. These changes improve data quality, traceability, and readiness for downstream analytics and instrument workflows (ASO/LF).
1 Commits • 1 Features
Feb 1, 2026February 2026 (Month: 2026-02) focused on strengthening data packaging reliability and integrity in simonsobs/sotodlib. Implemented stream_id uniqueness, correct wafer-slot ordering, and robust handling of streaming data to prevent incomplete observations from persisting. These changes improve data quality, traceability, and readiness for downstream analytics and instrument workflows (ASO/LF).
February 2026
December 2025
1 Commits
Dec 1, 2025December 2025: Strengthened data integrity in sotodlib by fixing observation binding validation and timing counter error handling. The changes prevent binding of very short observation books, introduce detection and flagging of bad timing counters, fix a boolean logic error in error handling, and improve error message parsing to aid operators. These improvements reduce downstream processing failures, improve data quality, and support faster diagnosis and resolution of issues.
December 2025
1 Commits
Dec 1, 2025December 2025: Strengthened data integrity in sotodlib by fixing observation binding validation and timing counter error handling. The changes prevent binding of very short observation books, introduce detection and flagging of bad timing counters, fix a boolean logic error in error handling, and improve error message parsing to aid operators. These improvements reduce downstream processing failures, improve data quality, and support faster diagnosis and resolution of issues.
November 2025
1 Commits • 1 Features
Nov 1, 20252025-11 monthly summary for simonsobs/sotodlib focused on strengthening data integrity, user feedback, and observability. Delivered ACU Data Integrity and User Feedback Enhancements, including flags for missing ACU data, improved error handling for dropped mount data, and an adjusted logging level to improve clarity around fixed tones. The change consolidates data quality checks and enhances user-visible feedback, contributing to more reliable data pipelines and quicker operator guidance. This milestone centers on commit 1bf101d46f36e58847512a1e144acde2e381105a, which also adds mount data dropping to the imprinter CLI and normalizes log level now that fixed tones are the default. Impact includes reduced data quality risk, clearer run-time feedback for end-users, and a stronger foundation for automated monitoring and alerting. Technologies/skills demonstrated include Python-based data integrity checks, robust error handling, CLI tooling enhancements, and observability improvements.
1 Commits • 1 Features
Nov 1, 20252025-11 monthly summary for simonsobs/sotodlib focused on strengthening data integrity, user feedback, and observability. Delivered ACU Data Integrity and User Feedback Enhancements, including flags for missing ACU data, improved error handling for dropped mount data, and an adjusted logging level to improve clarity around fixed tones. The change consolidates data quality checks and enhances user-visible feedback, contributing to more reliable data pipelines and quicker operator guidance. This milestone centers on commit 1bf101d46f36e58847512a1e144acde2e381105a, which also adds mount data dropping to the imprinter CLI and normalizes log level now that fixed tones are the default. Impact includes reduced data quality risk, clearer run-time feedback for end-users, and a stronger foundation for automated monitoring and alerting. Technologies/skills demonstrated include Python-based data integrity checks, robust error handling, CLI tooling enhancements, and observability improvements.
November 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered major feature enhancements to sotodlib focused on data packaging robustness and cross-stream timing synchronization. Implemented advanced autofix for BadTimeSamples, refactored preprocessing to improve time alignment across streams, and added diagnostic capabilities for SMURF and ACU timestamp issues. These changes reduce data binding errors, improve data quality for downstream analytics, and enable earlier detection of timing anomalies.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered major feature enhancements to sotodlib focused on data packaging robustness and cross-stream timing synchronization. Implemented advanced autofix for BadTimeSamples, refactored preprocessing to improve time alignment across streams, and added diagnostic capabilities for SMURF and ACU timestamp issues. These changes reduce data binding errors, improve data quality for downstream analytics, and enable earlier detection of timing anomalies.
September 2025
1 Commits
Sep 1, 2025September 2025: Key deliverable focused on hardening time-series data integrity in the Bookbinder Time Sampling Validation for simonsobs/sotodlib. Implemented a critical bug fix to enforce maximum ctime limits, increased the maximum dropped samples, and ensured time samples are strictly increasing within the ctime range, reducing data corruption and boosting reliability of time-series processing. This enhances data quality for downstream analytics and operational reporting.
1 Commits
Sep 1, 2025September 2025: Key deliverable focused on hardening time-series data integrity in the Bookbinder Time Sampling Validation for simonsobs/sotodlib. Implemented a critical bug fix to enforce maximum ctime limits, increased the maximum dropped samples, and ensured time samples are strictly increasing within the ctime range, reducing data corruption and boosting reliability of time-series processing. This enhances data quality for downstream analytics and operational reporting.
September 2025
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Quality Metrics
Correctness81.8%
Maintainability80.0%
Architecture80.0%
Performance80.6%
AI Usage27.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Configuration ManagementData ProcessingDebuggingError HandlingPythonPython programmingPython scriptingSoftware EngineeringTime Series Analysisbackend developmentcommand line interfacedata handlingdata managementdata processingdata validation
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