conda-forge/staged-recipes
Oct 2025 – Mar 2026
3 Months active
Languages Used
PythonYAML
Technical Skills
CLI developmentDevOpsPackage ManagementPackage managementPythonPython development
Amber
PROFILE
Amber
Amber Malpas developed and maintained packaging recipes for scientific Python tools in the conda-forge/staged-recipes repository, focusing on microlens-submit and VBMicrolensing. She engineered robust, cross-platform installation scripts and metadata configurations using Python and YAML, emphasizing maintainability and compatibility across Windows and Unix environments. Her work included enforcing minimum Python versions, aligning package metadata with PyPI standards, and integrating Ninja-based build systems to improve build speed and reproducibility. By refining script logic and packaging metadata, Amber reduced maintenance overhead and improved validation coverage, enabling more reliable deployments for users and expanding the conda-forge ecosystem’s support for astrophysics research tools.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
4
Lines of code
337
Activity Months3
Your Network
742 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for conda-forge/staged-recipes: Delivered a new gravitational microlensing package (VBMicrolensing) with contour integration, plus a Unix-compatible build recipe using Ninja. Packaging updates included a license file and removal of a local-only osx_arm64 sysroot tweak, with the Ninja generator forced for VBMicrolensing builds and Ninja used on Unix for the conda build to improve speed and reliability. No major bug fixes were reported this month; focus was on delivering a robust, reproducible build and expanding astrophysics tooling in the conda-forge ecosystem. This work enhances capability for scientists to perform efficient microlensing analyses and improves cross-platform build reliability and reproducibility across environments.
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for conda-forge/staged-recipes: Delivered a new gravitational microlensing package (VBMicrolensing) with contour integration, plus a Unix-compatible build recipe using Ninja. Packaging updates included a license file and removal of a local-only osx_arm64 sysroot tweak, with the Ninja generator forced for VBMicrolensing builds and Ninja used on Unix for the conda build to improve speed and reliability. No major bug fixes were reported this month; focus was on delivering a robust, reproducible build and expanding astrophysics tooling in the conda-forge ecosystem. This work enhances capability for scientists to perform efficient microlensing analyses and improves cross-platform build reliability and reproducibility across environments.
March 2026
November 2025
4 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Conda-forge/staged-recipes: Packaging metadata enhancements for microlens-submit delivered. Four commits implemented comprehensive metadata improvements to improve build reliability and downstream validation. Key changes include enforcing Python 3.10 minimum, preserving a static version string in the recipe, aligning the filename format with PyPI expectations (underscores), and adding pip as a requirement to enable validation on linux_64. These changes were reviewed and validated via CI with no regressions. Impact: Improved metadata quality and validation coverage reduces build failures, improves consistency with PyPI distributions, and enhances downstream compatibility for users deploying microlens-submit via conda. Commits included: - 528607df26399c979d27107d146d22ebf7abdb4c: Update Python minimum version to 3.10 and improve source URL format in meta.yaml per reviewer comments - 52797d1a1a4df2a9c63741129c657c2374422de7: keep version string in the recipe itself - e8905b6974c7f21d7e7aba99915e43b304a84f27: changed "-" to "_" in the filename, so it matches PyPI - beda41e32efcade67c5978de8fc4b2463d840c69: add pip so pip check can run on linux_64
November 2025
4 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Conda-forge/staged-recipes: Packaging metadata enhancements for microlens-submit delivered. Four commits implemented comprehensive metadata improvements to improve build reliability and downstream validation. Key changes include enforcing Python 3.10 minimum, preserving a static version string in the recipe, aligning the filename format with PyPI expectations (underscores), and adding pip as a requirement to enable validation on linux_64. These changes were reviewed and validated via CI with no regressions. Impact: Improved metadata quality and validation coverage reduces build failures, improves consistency with PyPI distributions, and enhances downstream compatibility for users deploying microlens-submit via conda. Commits included: - 528607df26399c979d27107d146d22ebf7abdb4c: Update Python minimum version to 3.10 and improve source URL format in meta.yaml per reviewer comments - 52797d1a1a4df2a9c63741129c657c2374422de7: keep version string in the recipe itself - e8905b6974c7f21d7e7aba99915e43b304a84f27: changed "-" to "_" in the filename, so it matches PyPI - beda41e32efcade67c5978de8fc4b2463d840c69: add pip so pip check can run on linux_64
October 2025
11 Commits • 2 Features
Oct 1, 2025October 2025 focused on stabilizing and improving microlens-submit packaging for conda-forge/staged-recipes, with an emphasis on reliability, cross-platform installation, and future maintainability. The work delivered a robust Microlens-submit recipe alongside Windows-specific installation enhancements, accompanied by targeted fixes to syntax and script handling that reduce maintenance overhead and user friction.
11 Commits • 2 Features
Oct 1, 2025October 2025 focused on stabilizing and improving microlens-submit packaging for conda-forge/staged-recipes, with an emphasis on reliability, cross-platform installation, and future maintainability. The work delivered a robust Microlens-submit recipe alongside Windows-specific installation enhancements, accompanied by targeted fixes to syntax and script handling that reduce maintenance overhead and user friction.
October 2025
Activity
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Quality Metrics
Correctness97.6%
Maintainability97.6%
Architecture97.6%
Performance97.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
CLI developmentDevOpsPackage ManagementPackage managementPythonPython developmentPython packagingScriptingWindows batch scriptingWindows scriptingYAMLYAML configurationconda packagingconda recipescross-platform development
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