
Worked on stabilizing focal-plane projection workflows in the simonsobs/sotodlib repository, focusing on backend development and coordinate systems. Addressed a critical bug by correcting the use of the so3g.proj.FocalPlane.from_xieta() signature in the get_nearby_sources function, ensuring the projection was initialized with the expected data format. This fix resolved coordinate transformation errors that arose from a previous upgrade, improving the reliability of nearby-source calculations and data integrity for downstream analyses. Utilized Python for debugging and regression testing, emphasizing cross-library API compatibility and clear documentation to support future upgrades and reduce the risk of similar issues recurring in 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.
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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