lincc-frameworks/notebooks_lf
Feb 2025 – Feb 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
AstronomyAstropyBenchmarkingDaskData AnalysisJupyter Notebook
Melissa DeLucchi
PROFILE
Melissa Delucchi
Melissa developed an Astronomical Catalog Cross-Matching Benchmarking Framework for the lincc-frameworks/notebooks_lf repository, focusing on performance evaluation and framework development. She designed a reusable benchmarking environment using Python and Jupyter Notebook, enabling systematic analysis of timing results across various cross-matching tools such as LSDB, Astropy, and Smatch. Her approach established reproducible benchmarking practices and provided ready-to-run artifacts for future scalability. By collecting and analyzing performance data, Melissa laid the groundwork for data-driven optimization of catalog cross-matching workflows. The work demonstrated depth in benchmarking methodology and facilitated collaborative development by delivering a robust foundation for ongoing performance improvements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
3,265
Activity Months1
Your Network
14 peopleSame Organization
@epyc.astro.washington.edu1
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on performance and framework development for notebooks_lf. Delivered a reusable Astronomical Catalog Cross-Matching Benchmarking Framework and established the foundation for data-driven optimization across cross-matching tools. The deliverables include a benchmarking environment, Python scripts to run benchmarks, and a Jupyter notebook to analyze and plot timing results, enabling systematic evaluation of configurations for catalog cross-matching workflows.
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary focusing on performance and framework development for notebooks_lf. Delivered a reusable Astronomical Catalog Cross-Matching Benchmarking Framework and established the foundation for data-driven optimization across cross-matching tools. The deliverables include a benchmarking environment, Python scripts to run benchmarks, and a Jupyter notebook to analyze and plot timing results, enabling systematic evaluation of configurations for catalog cross-matching workflows.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AstronomyAstropyBenchmarkingDaskData AnalysisJupyter NotebookLSDBPythonSmatch
Repositories Contributed To
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