simonsobs/sotodlib
Sep 2025 – Feb 2026
5 Months active
Languages Used
Python
Technical Skills
Data ProcessingSoftware EngineeringTime Series AnalysisConfiguration ManagementDebuggingError Handling
Katie Harrington
PROFILE
Katie Harrington
Katie Harrington enhanced the simonsobs/sotodlib repository by building and refining backend systems for time-series data integrity, packaging, and validation. She implemented Python-based solutions to enforce unique stream identifiers, robust error handling, and strict time synchronization across data streams, addressing issues like dropped samples and incomplete observations. Her work included developing diagnostic tools for timestamp anomalies, improving logging for user feedback, and automating the detection of data quality risks. By focusing on configuration management, data processing, and exception management, Katie delivered deeper reliability and traceability in data pipelines, supporting more accurate analytics and streamlined operational workflows for scientific instrumentation.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
5Total
Bugs
2
Commits
5
Features
3
Lines of code
501
Activity Months5
Your Network
26 peopleShared Repositories
26
Work History
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
Activity
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Quality Metrics
Correctness82.0%
Maintainability80.0%
Architecture80.0%
Performance74.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Configuration ManagementData ProcessingDebuggingError HandlingPython programmingSoftware EngineeringTime Series Analysisbackend developmentdata processingdata validationerror handlingexception managementloggingsoftware development
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