simonsobs/sotodlib
Oct 2024 – Feb 2026
15 Months active
Languages Used
PythonNumPyC++TOML
Technical Skills
Data ProcessingScientific ComputingSignal ProcessingSoftware DevelopmentTestingCode Refactoring
Michael McCrackan
PROFILE
Michael Mccrackan
Mike McCrackan engineered robust data processing and quality assurance pipelines for the simonsobs/sotodlib repository, focusing on reliability, maintainability, and scientific accuracy. Over 15 months, he delivered features such as modular preprocessing, chained Fourier filtering, and enhanced reporting, while addressing edge-case bugs and improving data integrity. His technical approach emphasized Python and NumPy for numerical computing, with careful configuration management and error handling to ensure reproducible results. By integrating AxisManager for structured data handling and developing utilities for HDF5 file management, Mike enabled scalable, testable workflows. His work demonstrated depth in backend development, scientific computing, and pipeline optimization for astronomy data.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
81Total
Bugs
19
Commits
81
Features
32
Lines of code
10,411
Activity Months15
Your Network
26 peopleShared Repositories
26
Work History
February 2026
5 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for simonsobs/sotodlib: Implemented targeted UFM selection for update_det_match, renamed QA metric NET to noise, delivered a suite of quality report enhancements, and fixed a preprocessing bug to robustly handle non-scalar PCFG references. Result: improved update precision, clearer QA signals, richer diagnostics, and more robust preprocessing, enabling faster, more reliable data processing and QA workflows.
5 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for simonsobs/sotodlib: Implemented targeted UFM selection for update_det_match, renamed QA metric NET to noise, delivered a suite of quality report enhancements, and fixed a preprocessing bug to robustly handle non-scalar PCFG references. Result: improved update precision, clearer QA signals, richer diagnostics, and more robust preprocessing, enabling faster, more reliable data processing and QA workflows.
February 2026
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 — Monthly summary for simonsobs/sotodlib. Focused on reliability and data integrity across preprocessing and quality-report workflows. Delivered key features, fixed critical issues, and demonstrated strong technical skills with measurable business value.
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 — Monthly summary for simonsobs/sotodlib. Focused on reliability and data integrity across preprocessing and quality-report workflows. Delivered key features, fixed critical issues, and demonstrated strong technical skills with measurable business value.
December 2025
2 Commits • 1 Features
Dec 1, 2025December 2025: Reliability and data integrity improvements in simonsobs/sotodlib. Implemented H5ContextManager to manage HDF5 I/O with retry logic and direct file opening, and fixed NaN handling in HWP mean rate calculations. These changes improve robustness of file operations, prevent downstream errors, and strengthen code quality through focused testing and collaboration.
2 Commits • 1 Features
Dec 1, 2025December 2025: Reliability and data integrity improvements in simonsobs/sotodlib. Implemented H5ContextManager to manage HDF5 I/O with retry logic and direct file opening, and fixed NaN handling in HWP mean rate calculations. These changes improve robustness of file operations, prevent downstream errors, and strengthen code quality through focused testing and collaboration.
December 2025
November 2025
8 Commits • 3 Features
Nov 1, 2025In November 2025, delivered substantive robustness and data-management improvements for simonsobs/sotodlib. Focused on reliability of multilayer processing, enhanced preprocessing data handling and archiving, and code quality improvements that reduce warnings and future maintenance burden. These changes deliver tangible business value by increasing pipeline reliability, enabling reproducible results, and reducing manual intervention in preprocessing and plotting workflows.
November 2025
8 Commits • 3 Features
Nov 1, 2025In November 2025, delivered substantive robustness and data-management improvements for simonsobs/sotodlib. Focused on reliability of multilayer processing, enhanced preprocessing data handling and archiving, and code quality improvements that reduce warnings and future maintenance burden. These changes deliver tangible business value by increasing pipeline reliability, enabling reproducible results, and reducing manual intervention in preprocessing and plotting workflows.
October 2025
5 Commits
Oct 1, 2025Monthly work summary for 2025-10 focusing on delivering robust data processing enhancements in simonsobs/sotodlib, stabilizing data quality workflows, and improving reporting context. The month’s work delivered concrete, business-value improvements across the preprocessing pipeline, quality data handling, reporting, and glitch detection.
5 Commits
Oct 1, 2025Monthly work summary for 2025-10 focusing on delivering robust data processing enhancements in simonsobs/sotodlib, stabilizing data quality workflows, and improving reporting context. The month’s work delivered concrete, business-value improvements across the preprocessing pipeline, quality data handling, reporting, and glitch detection.
October 2025
September 2025
6 Commits • 4 Features
Sep 1, 2025During Sep 2025, the Sotodlib preprocessing suite received targeted reliability, quality, and documentation improvements, focused on AxisManager handling, data integrity, and metrics tagging. Deliberate changes reduced edge-case failures, improved data provenance, and strengthened testability across the preprocessing pipeline.
September 2025
6 Commits • 4 Features
Sep 1, 2025During Sep 2025, the Sotodlib preprocessing suite received targeted reliability, quality, and documentation improvements, focused on AxisManager handling, data integrity, and metrics tagging. Deliberate changes reduced edge-case failures, improved data provenance, and strengthened testability across the preprocessing pipeline.
August 2025
10 Commits • 4 Features
Aug 1, 2025August 2025 (simonsobs/sotodlib) highlights: delivered feature improvements and robustness fixes that enhance report timeliness, usability, and data reliability. Key features delivered include Enhanced Report Generation and Accessibility (extra target plotting, navigation bar reordering, shorter generation buffers), Internal Logging Simplification (removal of explicit logger configuration in favor of module-level loggers), Preprocess Context Retrieval Enhancement (get_preprocess_context now also searches for a preprocessing label), and Unit Scaling for QA Metrics (introduced unit factor for nets and applied to white_noise). Major bugs fixed include Demodulate Preprocess Robustness (proper management of frequency cutoffs in proc_aman), Demod_tod Signal Handling Robustness (use of a copied signal to avoid unintended mutation during filtering), QA Metrics Data Validation and Safe Calculations (checks for wafer and band existence before calculations), Noise Model Fitting Robustness (robust handling of warnings and data masking to stabilize estimation), and Database Cleanup Resource Management (explicitly close preprocess DB after use). Overall impact: faster, more reliable report generation; simplified configuration and logging; more flexible preprocessing workflows; strengthened data integrity and resource hygiene, enabling scalable analyses. Technologies/skills demonstrated: Python data processing, signal processing safeguards, robust error handling, modular logging architecture, QA metrics engineering, and resource management.
10 Commits • 4 Features
Aug 1, 2025August 2025 (simonsobs/sotodlib) highlights: delivered feature improvements and robustness fixes that enhance report timeliness, usability, and data reliability. Key features delivered include Enhanced Report Generation and Accessibility (extra target plotting, navigation bar reordering, shorter generation buffers), Internal Logging Simplification (removal of explicit logger configuration in favor of module-level loggers), Preprocess Context Retrieval Enhancement (get_preprocess_context now also searches for a preprocessing label), and Unit Scaling for QA Metrics (introduced unit factor for nets and applied to white_noise). Major bugs fixed include Demodulate Preprocess Robustness (proper management of frequency cutoffs in proc_aman), Demod_tod Signal Handling Robustness (use of a copied signal to avoid unintended mutation during filtering), QA Metrics Data Validation and Safe Calculations (checks for wafer and band existence before calculations), Noise Model Fitting Robustness (robust handling of warnings and data masking to stabilize estimation), and Database Cleanup Resource Management (explicitly close preprocess DB after use). Overall impact: faster, more reliable report generation; simplified configuration and logging; more flexible preprocessing workflows; strengthened data integrity and resource hygiene, enabling scalable analyses. Technologies/skills demonstrated: Python data processing, signal processing safeguards, robust error handling, modular logging architecture, QA metrics engineering, and resource management.
August 2025
July 2025
10 Commits • 3 Features
Jul 1, 2025July 2025 monthly performance summary for simonsobs/sotodlib. Focused on delivering tangible business value through QA/reporting enhancements, robust noise-analysis controls, and signal-processing robustness improvements. The work enhances data quality oversight, ensures physically consistent preprocessing, and speeds up analysis pipelines for large data volumes.
July 2025
10 Commits • 3 Features
Jul 1, 2025July 2025 monthly performance summary for simonsobs/sotodlib. Focused on delivering tangible business value through QA/reporting enhancements, robust noise-analysis controls, and signal-processing robustness improvements. The work enhances data quality oversight, ensures physically consistent preprocessing, and speeds up analysis pipelines for large data volumes.
June 2025
6 Commits • 4 Features
Jun 1, 2025June 2025 performance summary for simonsobs/sotodlib: Delivered core features to improve data quality, processing flexibility, and observability; strengthened configuration validation; and improved runtime efficiency. Key features delivered include backwards-compatible SourceFlags inputs with DetcalNanCuts data cleaning; a chained Fourier filtering framework; a QA metric for detector white-noise median exposed to InfluxDB; and a performance optimization for get_trending_flags. Major bug fix: robust AxisManager config comparison now validates both reference and loaded configs as AxisManager objects before recursive comparison, reducing false positives. Overall impact: higher data quality, more reliable preprocessing, improved observability, and better memory efficiency. Technologies demonstrated: Python, data validation patterns, modular filtering pipelines, and instrumentation.
6 Commits • 4 Features
Jun 1, 2025June 2025 performance summary for simonsobs/sotodlib: Delivered core features to improve data quality, processing flexibility, and observability; strengthened configuration validation; and improved runtime efficiency. Key features delivered include backwards-compatible SourceFlags inputs with DetcalNanCuts data cleaning; a chained Fourier filtering framework; a QA metric for detector white-noise median exposed to InfluxDB; and a performance optimization for get_trending_flags. Major bug fix: robust AxisManager config comparison now validates both reference and loaded configs as AxisManager objects before recursive comparison, reducing false positives. Overall impact: higher data quality, more reliable preprocessing, improved observability, and better memory efficiency. Technologies demonstrated: Python, data validation patterns, modular filtering pipelines, and instrumentation.
June 2025
May 2025
9 Commits • 1 Features
May 1, 2025May 2025 Sotodlib monthly summary: Delivered a more robust and configurable preprocessing pipeline, strengthened data management to prevent cleanup failures, and improved LAT detector parameter handling. These changes reduce runtime failures, increase user control, and enhance reliability in processing pipelines across simulations and data archives.
May 2025
9 Commits • 1 Features
May 1, 2025May 2025 Sotodlib monthly summary: Delivered a more robust and configurable preprocessing pipeline, strengthened data management to prevent cleanup failures, and improved LAT detector parameter handling. These changes reduce runtime failures, increase user control, and enhance reliability in processing pipelines across simulations and data archives.
April 2025
1 Commits
Apr 1, 2025April 2025 — sotodlib (simonsobs): Delivered a critical bug fix to ensure a deep copy of the noisy_subscan flags configuration before iteration, preventing unintended modifications across loop iterations and improving the reliability of flag generation. The fix (commit 04e6702d38c2dcc467c710d5d29243b2e422f9cd) enhances stability in batch processing and reduces debugging time. Key technologies include Python data structures, deep copy semantics, configuration management, and robust testing.
1 Commits
Apr 1, 2025April 2025 — sotodlib (simonsobs): Delivered a critical bug fix to ensure a deep copy of the noisy_subscan flags configuration before iteration, preventing unintended modifications across loop iterations and improving the reliability of flag generation. The fix (commit 04e6702d38c2dcc467c710d5d29243b2e422f9cd) enhances stability in batch processing and reduces debugging time. Key technologies include Python data structures, deep copy semantics, configuration management, and robust testing.
April 2025
January 2025
7 Commits • 2 Features
Jan 1, 2025January 2025 — simonsobs/sotodlib: Delivered significant reliability and capability gains in preprocessing, metadata handling, and source flag processing, with improvements in logging and safe cleanup to support scalable pipelines and reduced operational risk.
January 2025
7 Commits • 2 Features
Jan 1, 2025January 2025 — simonsobs/sotodlib: Delivered significant reliability and capability gains in preprocessing, metadata handling, and source flag processing, with improvements in logging and safe cleanup to support scalable pipelines and reduced operational risk.
December 2024
2 Commits
Dec 1, 2024December 2024 monthly summary for simonsobs/sotodlib. Focused on improving preprocessing robustness and reliability in the Demodulate pipeline and cleanup workflow, with targeted bug fixes to prevent data integrity issues and pipeline failures.
2 Commits
Dec 1, 2024December 2024 monthly summary for simonsobs/sotodlib. Focused on improving preprocessing robustness and reliability in the Demodulate pipeline and cleanup workflow, with targeted bug fixes to prevent data integrity issues and pipeline failures.
December 2024
November 2024
5 Commits • 3 Features
Nov 1, 2024In 2024-11, focused on stabilizing and streamlining data preprocessing and PSD handling in simonsobs/sotodlib to improve reliability, throughput, and maintainability. Delivered a Robust Preprocessing Module that consolidates utilities, ensures consistent logger initialization, and fixes missing logger and H5 import issues, reducing preprocessing failures and improving manifest updates. Implemented PCA Relcal data handling with a low-pass filter to minimize unnecessary data copying and hardened configuration/trimming logic. Consolidated PSD calculation into a dedicated calc_and_save path, simplifying configuration references and reducing processing steps. These changes improve data quality, pipeline predictability, and onboarding efficiency, aligning with business goals of reliable data delivery and faster insight generation.
November 2024
5 Commits • 3 Features
Nov 1, 2024In 2024-11, focused on stabilizing and streamlining data preprocessing and PSD handling in simonsobs/sotodlib to improve reliability, throughput, and maintainability. Delivered a Robust Preprocessing Module that consolidates utilities, ensures consistent logger initialization, and fixes missing logger and H5 import issues, reducing preprocessing failures and improving manifest updates. Implemented PCA Relcal data handling with a low-pass filter to minimize unnecessary data copying and hardened configuration/trimming logic. Consolidated PSD calculation into a dedicated calc_and_save path, simplifying configuration references and reducing processing steps. These changes improve data quality, pipeline predictability, and onboarding efficiency, aligning with business goals of reliable data delivery and faster insight generation.
October 2024
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Focused delivery on Sotodlib robustness enhancements in simonsobs/sotodlib. Implemented dependency updates and data processing enhancements (fill_glitches, trending flags), and improved the T2P leakage coefficient calculation to increase robustness and accuracy of data analysis pipelines. Work anchored by commit 21d7e84c68b073dcd001992fef1669ddb32b0119 (20240819 satp3 catchup).
1 Commits • 1 Features
Oct 1, 2024Month: 2024-10 — Focused delivery on Sotodlib robustness enhancements in simonsobs/sotodlib. Implemented dependency updates and data processing enhancements (fill_glitches, trending flags), and improved the T2P leakage coefficient calculation to increase robustness and accuracy of data analysis pipelines. Work anchored by commit 21d7e84c68b073dcd001992fef1669ddb32b0119 (20240819 satp3 catchup).
October 2024
Activity
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Quality Metrics
Correctness85.0%
Maintainability84.2%
Architecture80.6%
Performance74.4%
AI Usage21.4%
Skills & Technologies
Programming Languages
C++NumPyPythonTOML
Technical Skills
Array ManipulationAstronomy SoftwareBackend DevelopmentBackwards CompatibilityBug FixingCode ExamplesCode FormattingCode RefactoringCommand-line Interface DevelopmentConfiguration ManagementCoordinate SystemsData AnalysisData CleaningData HandlingData Management
Repositories Contributed To
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