February 2026: Delivered significant improvements to the SAT pointing model in simonsobs/sotodlib. Introduced lat_v2 pointing model for better handling of elevation sag and more accurate pointing calculations, and tightened reliability with enhanced error handling and parameter filling from the observation database. Implemented logging for failed observations to speed triage. These changes are supported by two commits: 9c5f0750524a60eb3096f5ac32207dd538023050 (fix: fix pointing model application for SATs in ffp; catch more IO errors) and af9939fe670d45d12cc08f7bb149a08e11cf8e18 (feat: lat v2 pointing model).

January 2026 was focused on delivering a high-impact feature that improves batch job management and scalability in the sotodlib library. Delivered a Bulk Job Submission and Creation Refactor, consolidating job creation paths and enabling multi-job commits to boost throughput while maintaining compatibility. The work includes refactoring, performance improvements, and companion minor documentation updates.

December 2025 monthly summary for sotodlib highlighting delivery and impact of the Enhanced Pointing Model (Version 2). Focused on improving latitude-based azimuth/elevation accuracy with base tilt and elevation sag adjustments, plus comprehensive tests. Also addressed defaults and backward compatibility to reduce configuration drift, improving maintainability and downstream data quality.

November 2025: Focused on robustness of the downsampling path in simonsobs/sotodlib and repo hygiene. Delivered Downsampling Robustness Enhancement to correctly handle standalone ranges, support sparse data, and log unhandled types to improve error visibility and maintainability. Completed Codebase Cleanup by removing a stray unused file, reducing clutter and onboarding friction. These changes collectively improve reliability of the data processing workflow, enable faster debugging, and keep the codebase maintainable for future iterations. Technologies demonstrated include Python data processing, robust exception handling, logging, and disciplined Git-based change management.

October 2025 monthly summary focused on ACU Agent improvements to enhance automation, accuracy, and extendibility of scan workflows. Delivered Type 2 and Type 3 scans support in ACU Agent with new parameters and logic in generate_scan, with variations in azimuth speed and elevation nodding. Refactored scan generation functions to support these new scan types, improving maintainability and future extensibility. Fixed a critical azimuth center calculation bug by using the average of two distinct endpoints, ensuring correct pointing centers across scans.

Month: 2025-08 — Focused on ensuring reliability of the downsampling workflow in simonsobs/sotodlib. Delivered a critical bug fix in Downsampling Observations: corrected the key membership check in fft_resample to ensure proper downsampling of observations in downsample_obs. Implemented in commit 7c05c8ac14ae26309bb604a020ba6821128aa67a. Impact: improved data accuracy and stability for downstream analyses, reducing misalignment risks and data quality issues. Technologies demonstrated include Python, debugging, and Git-based collaboration. Business value: more trustworthy downsampled data products, enabling confident scientific conclusions and smoother maintenance.

Monthly summary for 2025-07 focusing on delivering enhanced LAT pointing model and focal plane data processing capabilities within sotodlib, and fixing a critical gamma calculation bug. The period produced tangible business value through more accurate LAT data products and more robust analysis pipelines, enabling downstream scientists to rely on richer data representations and coordinate transformations. Key outcomes include: - Feature delivery: LAT pointing model and FP data processing enhancements in sotodlib, delivering output of R2 data, an option to remove roll data, and improved handling of LAT data and pointing models to refine coordinate transformations and data representation (commits: 03f4e47ec809276305307204e53b6c185248aede; additional fixes and streamlining features in c05769eaf5a1768db3a4f340b006c348c9aad599). - Bug fix: LAT gamma calculation quaternion rotation bug fix, ensuring correct coordinate transformations and accurate focal plane calculations (commit: 074e13bc944691b30bebf63f7af5b45360f29d1d). - Impact: Increased data quality and reliability of LAT data products, improved downstream analysis capabilities, and enhanced maintainability with clear commit history for traceability. - Technologies/skills demonstrated: Python data processing pipelines, coordinate transformations, quaternion math, LAT data handling, FP container enhancements, and git-based collaboration and traceability (commits referenced above).

June 2025 monthly summary for simonsobs/sotodlib: Delivered targeted performance and robustness improvements to RangesMatrix, including a conditional skip of shape checks during expansion and a backward-compatible skip_shape_check parameter. Also refactored axisman_io RangesMatrix initialization to remove functools.partial and to pass skip_shape_check via a dict for robustness. These changes reduce runtime overhead, minimize initialization fragility, and improve maintainability, aligning with business goals of faster, more reliable data processing and easier future refactors.

In May 2025, delivered two key features in simonsobs/sotodlib that enhance FFT processing efficiency and IIR parameter handling, raising robustness and applicability for observation workflows. The work strengthened performance through caching and reuse of FFT objects, and expanded IIR parameter handling to support broader time-range scenarios, directly improving modeling reliability and throughput.

April 2025 highlights for simonsobs/sotodlib: Delivered robust data processing improvements focused on downsampling and source-position formatting, enhancing reliability, flexibility, and test coverage. The new generic downsampling function supports FFT-based resampling across diverse data types, with improved axis management via OffsetAxis and corrected edge-case handling (robust skip behavior when signals are not present in the FFT list and a fixed string comparison). Also expanded get_source_pos formatting to accept both uppercase/lowercase 'J' and signs for declination ('p', 'm', 'n'), alongside new tests validating the expanded options. These changes reduce downstream data errors and improve cross-data compatibility, enabling more automated analysis pipelines.

February 2025 monthly summary for simonsobs/sotodlib highlighting delivery, reliability, and impact of Jump Detection Module improvements.

In Jan 2025, delivered robust enhancements to jump handling in simonsobs/sotodlib (tod_ops), with configurable cleaning, broader compatibility, and targeted testing. The work focused on making jump-estimation more reliable and maintainable, improving downstream data quality for analysis.

Month: 2024-12 — Summary for simonsobs/sotodlib: Key features delivered include Jump Detection and Jump Height Correction System Modernization, which refactors detection and fixing to boost performance and configurability. Notable changes: removed the thread pool in favor of sequential processing; introduced a downsampling parameter ds to better control noise level estimation; migrated jump detection and correction to the so3g library with block-wise min/max calculations and jump height subtraction to potentially improve accuracy across float32 and float64 data. Major bugs fixed include stabilization and performance improvements from removing concurrency-induced overhead and adopting a unified so3g-based implementation, yielding more consistent jump handling and noise estimation. Overall impact and accomplishments: enhanced runtime efficiency, greater configurability, and improved cross-dtype accuracy, with better maintainability and a cleaner integration path within the so3g-enabled pipeline. Technologies and skills demonstrated: Python refactoring and performance optimization, numerical methods (downsampling, block-wise min/max), library migration and integration (so3g), and cross-dtype data handling (float32/float64).

Month: 2024-11 — Key accomplishments in simonsobs/sotodlib focused on stability and maintainability of jump-detection logic. Added a max_tol parameter to twopi_jumps to cap nsigma-based tolerances, with a default of 0.0314, and applied the cap during atol clipping. This delivers more predictable jump-detection thresholds and reduces sensitivity to tolerance scaling across datasets.

In 2024-08, delivered key feature and critical bug fixes for simonsobs/sotodlib. Improved detector pointing accuracy through refined optics coordinate definitions with unit tests. Fixed robust jump detection in signal processing by correcting 2pi flag handling and indexing, plus threading improvements to boost throughput and data integrity. Ensured traceability via explicit commits.

May 2024 monthly summary focusing on key accomplishments for simonsobs/sotodlib. Delivered significant performance improvements by migrating data processing workloads into the so3g module, optimizing block moments and matched filtering, introducing in-place operations, refining standard deviation estimation, and adding block-moment utilities. These changes reduced processing time and memory usage, enabling faster data throughput and improved scalability for large datasets.

April 2024 monthly summary for simonsobs/sotodlib focused on delivering peakfinding-enhanced jump detection within the matched filter. Implementations included introducing a peakfinding approach, adding a block mean filter, updating jumpfinding logic, and optimizing window size handling to reduce unnecessary computations. These changes improved detection accuracy and processing efficiency, enabling higher-throughput data analysis with more robust results.