
Over nine months, Aconcagua contributed to the simonsobs/sotodlib repository by engineering robust data processing and mapmaking pipelines for astronomical observations. They developed modular, YAML-driven workflows that improved reproducibility and configuration management, integrating features like atomic map creation, unit-aware FITS outputs, and machine-learning–enhanced iterative mapmaking. Using Python and MPI, Aconcagua refactored core components for efficiency, implemented database-backed metadata storage, and enhanced error handling to ensure reliability in large-scale, parallel processing. Their work addressed both backend and scientific computing challenges, delivering granular data grouping, improved noise modeling, and seamless integration of meteorological context, reflecting a deep understanding of scientific software engineering.

Monthly summary for 2025-10: Focused improvements to the sotodlib mapmaking workflow, delivering granular grouping, corrected band mapping, and MPI robustness. These changes enhance data fidelity, pipeline reliability, and scalability in production, enabling more accurate analyses and reducing risk of runtime failures on large datasets.
Monthly summary for 2025-10: Focused improvements to the sotodlib mapmaking workflow, delivering granular grouping, corrected band mapping, and MPI robustness. These changes enhance data fidelity, pipeline reliability, and scalability in production, enabling more accurate analyses and reducing risk of runtime failures on large datasets.
Month: 2025-09 — Key features delivered: Implemented AtomicInfo meteorological data support in simonsobs/sotodlib by adding wind_speed and wind_direction fields to AtomicInfo in utils.py to store wind data associated with observations. This schema evolution enables richer meteorological context for observations, improving data quality, downstream analytics, and modeling capabilities.
Month: 2025-09 — Key features delivered: Implemented AtomicInfo meteorological data support in simonsobs/sotodlib by adding wind_speed and wind_direction fields to AtomicInfo in utils.py to store wind data associated with observations. This schema evolution enables richer meteorological context for observations, improving data quality, downstream analytics, and modeling capabilities.
Monthly work summary for 2025-08 (simonsobs/sotodlib). Focused on reliability improvements in data processing pipelines and robust error handling for mapmaker scripts. This period centered on ensuring graceful behavior when no TODs are available, reducing spurious failure signals and preserving workflow continuity.
Monthly work summary for 2025-08 (simonsobs/sotodlib). Focused on reliability improvements in data processing pipelines and robust error handling for mapmaker scripts. This period centered on ensuring graceful behavior when no TODs are available, reducing spurious failure signals and preserving workflow continuity.
July 2025: Public API exposure and default behavior standardization for the demod map component in simonsobs/sotodlib. Exposed setup_demod_map in the public API via __all__ to improve tutorial accessibility, and standardized the default for split_labels to ['full'] in both setup_demod_map and make_demod_map to ensure consistent behavior across usage scenarios. Implemented through demod_mapmaker.py updates associated with commit a22ef140947f3753ad264500f1f57708e93f002e. These changes enhance API discoverability, reproducibility, and onboarding while reducing behavioral ambiguity in analyses that rely on demod maps.
July 2025: Public API exposure and default behavior standardization for the demod map component in simonsobs/sotodlib. Exposed setup_demod_map in the public API via __all__ to improve tutorial accessibility, and standardized the default for split_labels to ['full'] in both setup_demod_map and make_demod_map to ensure consistent behavior across usage scenarios. Implemented through demod_mapmaker.py updates associated with commit a22ef140947f3753ad264500f1f57708e93f002e. These changes enhance API discoverability, reproducibility, and onboarding while reducing behavioral ambiguity in analyses that rely on demod maps.
June 2025 monthly wrap-up for simonsobs/sotodlib. Key features delivered include PSD-based mapmaking weighting, enabling use of PSD from preprocessing as weights in mapmaking; refactor of NmatWhite; updates to DemodMapmaker and related functions to incorporate the use_psd option; and unit tests adjusted for compatibility with the new functionality. Also delivered atomic mapmaking database integration, introducing a new atomic database for storing mapmaking information; refactoring mapmaking processes; enhanced write_demod_maps to manage validity of atomic entries and integrate atomic DB writing into make_demod_map; updated data passing and storage for detector counts and map weights. Additionally fixed a bug: safe file handling in the context module by using with open for safer file operations, improving robustness of configuration file loading.
June 2025 monthly wrap-up for simonsobs/sotodlib. Key features delivered include PSD-based mapmaking weighting, enabling use of PSD from preprocessing as weights in mapmaking; refactor of NmatWhite; updates to DemodMapmaker and related functions to incorporate the use_psd option; and unit tests adjusted for compatibility with the new functionality. Also delivered atomic mapmaking database integration, introducing a new atomic database for storing mapmaking information; refactoring mapmaking processes; enhanced write_demod_maps to manage validity of atomic entries and integrate atomic DB writing into make_demod_map; updated data passing and storage for detector counts and map weights. Additionally fixed a bug: safe file handling in the context module by using with open for safer file operations, improving robustness of configuration file loading.
April 2025 summary for simonsobs/sotodlib focused on delivering high-value, technically robust improvements to mapmaking and data preprocessing. The month emphasized integrating machine-learning–driven enhancements into the TOAST mapmaking workflow, while simultaneously strengthening the data preprocessing pipeline for efficiency and robustness. The work advances multipass mapmaking capabilities, refines noise handling, and improves data reduction with simulated data support.
April 2025 summary for simonsobs/sotodlib focused on delivering high-value, technically robust improvements to mapmaking and data preprocessing. The month emphasized integrating machine-learning–driven enhancements into the TOAST mapmaking workflow, while simultaneously strengthening the data preprocessing pipeline for efficiency and robustness. The work advances multipass mapmaking capabilities, refines noise handling, and improves data reduction with simulated data support.
March 2025 monthly summary for simonsobs/sotodlib focusing on delivering unit-aware FITS map outputs with unit metadata propagation across mapmakers. The work centers on extending write methods to accept unit arguments, propagating unit metadata in FITS files, and updating map-writing paths to pass unit information, thereby improving data interpretability and downstream analysis readiness.
March 2025 monthly summary for simonsobs/sotodlib focusing on delivering unit-aware FITS map outputs with unit metadata propagation across mapmakers. The work centers on extending write methods to accept unit arguments, propagating unit metadata in FITS files, and updating map-writing paths to pass unit information, thereby improving data interpretability and downstream analysis readiness.
December 2024 monthly summary for simonsobs/sotodlib: Delivered targeted improvements to flag handling, pipeline configuration, and connectivity robustness that enhance data quality, reliability, and maintainability. The work aligns processing steps across preprocessing and mapmaker, centralizes configuration through YAML-driven parameters, and hardens connectivity to prevent runtime errors in production processing.
December 2024 monthly summary for simonsobs/sotodlib: Delivered targeted improvements to flag handling, pipeline configuration, and connectivity robustness that enhance data quality, reliability, and maintainability. The work aligns processing steps across preprocessing and mapmaker, centralizes configuration through YAML-driven parameters, and hardens connectivity to prevent runtime errors in production processing.
Nov 2024 monthly summary for simonsobs/sotodlib: Delivered mapmaking pipeline enhancements and refactor consolidating modularization, data-driven configuration of observation lists, and new atomic map creation support to boost robustness and processing efficiency. Implemented case-insensitive tube_flavor handling to prevent mapmaking errors by expanding accepted values and ensuring consistent band list determination. Also integrated data flow improvements by loading wafer lists and frequencies from obsdb and aligning the SAT filter + bin demod mapmaking script with the site-pipeline. These changes reduce configuration errors, improve reproducibility, and support scalable processing for larger surveys.
Nov 2024 monthly summary for simonsobs/sotodlib: Delivered mapmaking pipeline enhancements and refactor consolidating modularization, data-driven configuration of observation lists, and new atomic map creation support to boost robustness and processing efficiency. Implemented case-insensitive tube_flavor handling to prevent mapmaking errors by expanding accepted values and ensuring consistent band list determination. Also integrated data flow improvements by loading wafer lists and frequencies from obsdb and aligning the SAT filter + bin demod mapmaking script with the site-pipeline. These changes reduce configuration errors, improve reproducibility, and support scalable processing for larger surveys.
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