simonsobs/sotodlib
Sep 2025 – Oct 2025
2 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 focusing on time-series data integrity and robust data packaging for scientific workflows. She implemented a Python-based validation system that enforced strict ctime limits and ensured time samples increased monotonically, reducing data corruption risks in Bookbinder’s time sampling. In the following month, Katie delivered an autofix feature for BadTimeSamples, refactored preprocessing logic to improve time synchronization across data streams, and introduced diagnostics for timestamp anomalies. Leveraging skills in data processing, configuration management, and error handling, her work addressed complex timing issues and improved the reliability and quality of time-series data pipelines.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
208
Activity Months2
Work History
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.
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
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.
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.
Activity
Loading activity data...
Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance65.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Configuration ManagementData ProcessingDebuggingError HandlingSoftware EngineeringTime Series Analysis
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline