
In April 2025, Gerrit Raschke focused on backend development for the simonsobs/sotodlib repository, addressing a critical bug in focal-plane projection workflows. He resolved a longstanding issue in the get_nearby_sources function by updating the FocalPlane projection signature to use the correct so3g.proj.FocalPlane.from_xieta() data format, ensuring accurate coordinate transformations. This Python-based fix improved the reliability of nearby-source calculations and enhanced data integrity for downstream analyses. Gerrit’s work demonstrated strong debugging skills and a deep understanding of coordinate systems, with careful attention to cross-library API compatibility and regression testing, resulting in a more robust data processing pipeline.

April 2025 focused on stabilizing focal-plane projection workflows in sotodlib. Delivered a critical bug fix for the FocalPlane projection signature in get_nearby_sources by using the correct so3g.proj.FocalPlane.from_xieta() data format, preventing coordinate transformation errors during nearby-source calculations. This addressed a stale signature from an imperfect upgrade and is tracked in commit e73fc8dd36e961fdc2862371a09390dd20feda33. Impact includes more reliable nearby-source computations, improved data quality for downstream analyses, and reduced risk of regression in future upgrades. Key KPIs: faster issue resolution, decreased coordinate-related errors in projections, and clearer upgrade-path traceability. Skills demonstrated include Python debugging, cross-library API compatibility, and regression testing across the data processing pipeline.
April 2025 focused on stabilizing focal-plane projection workflows in sotodlib. Delivered a critical bug fix for the FocalPlane projection signature in get_nearby_sources by using the correct so3g.proj.FocalPlane.from_xieta() data format, preventing coordinate transformation errors during nearby-source calculations. This addressed a stale signature from an imperfect upgrade and is tracked in commit e73fc8dd36e961fdc2862371a09390dd20feda33. Impact includes more reliable nearby-source computations, improved data quality for downstream analyses, and reduced risk of regression in future upgrades. Key KPIs: faster issue resolution, decreased coordinate-related errors in projections, and clearer upgrade-path traceability. Skills demonstrated include Python debugging, cross-library API compatibility, and regression testing across the data processing pipeline.
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