
Worked on the igmhub/picca repository to enhance quasar pair filtering and forest-pair redshift-cut computations, focusing on improving analysis accuracy and developer usability. Refactored the neighbor-finding logic in Python to correctly apply both angular and velocity cuts, and introduced a renamed CLI flag for redshift error cuts to clarify user options. Added redshift error-based cuts to forest pair calculations, updating both xi and dmat computations for more robust cosmological inferences. Improved documentation and command line interface help messages, refreshed docstrings, and clarified XCF steps, supporting reproducibility and onboarding. Emphasized scientific computing, code refactoring, and data analysis throughout.
December 2024: Delivered precision improvements to quasar pair filtering and forest-pair redshift-cut computations, with targeted CLI UX and documentation enhancements. Key changes include a CLI flag rename for redshift error cuts, refactoring the neighbor-finding logic to apply angular and velocity cuts correctly, and introducing redshift error-based cuts in forest pair calculations (xi and dmat) with updated help messages. A stability bug in dmat computation was fixed, accompanied by docstring updates and clearer XCF documentation steps. These changes improve analysis accuracy, reliability, and developer usability, enabling more robust cosmological inferences and faster researcher adoption.
December 2024: Delivered precision improvements to quasar pair filtering and forest-pair redshift-cut computations, with targeted CLI UX and documentation enhancements. Key changes include a CLI flag rename for redshift error cuts, refactoring the neighbor-finding logic to apply angular and velocity cuts correctly, and introducing redshift error-based cuts in forest pair calculations (xi and dmat) with updated help messages. A stability bug in dmat computation was fixed, accompanied by docstring updates and clearer XCF documentation steps. These changes improve analysis accuracy, reliability, and developer usability, enabling more robust cosmological inferences and faster researcher adoption.

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