Smithsonian/layup
Jan 2025 – Oct 2025
7 Months active
Languages Used
MarkdownPythonTOMLYAMLRSTShellINI
Technical Skills
CI/CDDependency ManagementDocumentationGitHub ActionsProject ManagementPull Request Management
Meg Schwamb
PROFILE
Meg Schwamb
Megan Schwamb contributed to the Smithsonian/layup repository by developing and refining scientific computing infrastructure for astronomical data processing. She implemented robust Python backend features, integrating libraries such as numpy and astropy to enable reproducible numerical workflows and precise time conversions. Megan modernized CI/CD pipelines using GitHub Actions, expanded cross-platform testing, and improved configuration management with YAML and TOML. Her work included enhancing command-line interfaces, clarifying documentation, and standardizing code formatting with tools like Black. By addressing dependency management, onboarding, and data updates, Megan delivered maintainable solutions that improved reliability, developer experience, and the accuracy of ephemeris-based calculations throughout the project.
Overall Statistics
Feature vs Bugs
72%Features
Repository Contributions
35Total
Bugs
5
Commits
35
Features
13
Lines of code
274
Activity Months7
Your Network
10 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered a focused UX improvement in Smithsonian/layup by clarifying the CLI sexagesimal flag help text in predict.py. This documentation-only update enhances clarity for users and reduces misunderstanding without touching functional behavior or impacting pipelines.
1 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered a focused UX improvement in Smithsonian/layup by clarifying the CLI sexagesimal flag help text in predict.py. This documentation-only update enhances clarity for users and reduces misunderstanding without touching functional behavior or impacting pipelines.
October 2025
September 2025
1 Commits
Sep 1, 2025Month 2025-09: Focused on stabilizing ephemeris data usage for Smithsonian/layup by updating NAIF data references to the latest Earth ephemeris files and aligning configuration and tests with current data sources. Primary work was a data dependency refresh; no new user-facing features were released this month.
September 2025
1 Commits
Sep 1, 2025Month 2025-09: Focused on stabilizing ephemeris data usage for Smithsonian/layup by updating NAIF data references to the latest Earth ephemeris files and aligning configuration and tests with current data sources. Primary work was a data dependency refresh; no new user-facing features were released this month.
May 2025
6 Commits • 2 Features
May 1, 2025May 2025 monthly summary for Smithsonian/layup focused on delivering reliable data processing enhancements, stabilizing time handling, and improving CLI robustness. Key features delivered included improved orbit data processing, time-accurate predictions, and robust CLI input/output handling. All work was completed with attention to maintainability and clear code hygiene, resulting in reduced risk of runtime errors and easier future enhancements. Overall impact: Increased data accuracy and consistency across prediction and fitter pipelines, improved developer experience through cleaner code and standardized interfaces, and stronger cross-tool reliability for time handling and I/O operations. Technologies/skills demonstrated: Python (orbitfit.py), integration with C++ fitter, UTC/JD TDB time handling, timezone management, cross-tool CLI standardization, linting and code quality practices, and git-based change traceability.
6 Commits • 2 Features
May 1, 2025May 2025 monthly summary for Smithsonian/layup focused on delivering reliable data processing enhancements, stabilizing time handling, and improving CLI robustness. Key features delivered included improved orbit data processing, time-accurate predictions, and robust CLI input/output handling. All work was completed with attention to maintainability and clear code hygiene, resulting in reduced risk of runtime errors and easier future enhancements. Overall impact: Increased data accuracy and consistency across prediction and fitter pipelines, improved developer experience through cleaner code and standardized interfaces, and stronger cross-tool reliability for time handling and I/O operations. Technologies/skills demonstrated: Python (orbitfit.py), integration with C++ fitter, UTC/JD TDB time handling, timezone management, cross-tool CLI standardization, linting and code quality practices, and git-based change traceability.
May 2025
April 2025
3 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Smithsonian/layup: Focused on enhancing usability and maintainability. Delivered key features to clarify converter behavior and standardized code style tooling, improving developer experience and CI reliability.
April 2025
3 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for Smithsonian/layup: Focused on enhancing usability and maintainability. Delivered key features to clarify converter behavior and standardized code style tooling, improving developer experience and CI reliability.
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 (2025-03) for Smithsonian/layup focused on delivering user-visible enhancements, documentation branding, and a robust development workflow. Key outcomes include a versioning capability for the package and CLI, branding and onboarding documentation polish, and stability/quality improvements via CI/test environment enhancements. These investments improve inspectability, onboarding efficiency, and long-term maintainability, enabling more reliable releases and faster feature iteration.
8 Commits • 3 Features
Mar 1, 2025March 2025 (2025-03) for Smithsonian/layup focused on delivering user-visible enhancements, documentation branding, and a robust development workflow. Key outcomes include a versioning capability for the package and CLI, branding and onboarding documentation polish, and stability/quality improvements via CI/test environment enhancements. These investments improve inspectability, onboarding efficiency, and long-term maintainability, enabling more reliable releases and faster feature iteration.
March 2025
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for Smithsonian/layup. Key accomplishments include delivering a structured issue reporting workflow by adding a GitHub issue templates configuration to discourage blank issues and encourage structured bug reporting, clarifying CLI command help text for predict and visualize commands, and stabilizing user experience through precise documentation. The work reduced triage friction, improved bug-report quality, and clarified command behavior for smoother onboarding and faster release cycles.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for Smithsonian/layup. Key accomplishments include delivering a structured issue reporting workflow by adding a GitHub issue templates configuration to discourage blank issues and encourage structured bug reporting, clarifying CLI command help text for predict and visualize commands, and stabilizing user experience through precise documentation. The work reduced triage friction, improved bug-report quality, and clarified command behavior for smoother onboarding and faster release cycles.
January 2025
13 Commits • 3 Features
Jan 1, 2025January 2025 (Smithsonian/layup) monthly summary: - Key features delivered: Added and pinned a scientific computing stack in pyproject.toml to enable numerical, astronomical calculations and data handling (numpy, assist, astropy, rebound, pooch, tqdm). This lays the groundwork for expanded scientific workloads and reproducible environments. Commit: be22fd019a522c390e669acccd9181bdedc7a559. - Major bugs fixed: Corrected a dependency typo in pyproject.toml for the assist package to ensure reliable builds and runtime; removed an unnecessary debug print from orbitfit.py to restore clean runtime output. Commits: 30371b19a789ec99d3cb5e87ac624221ab5f0623; ac0de76f16752e8ebc25bfe27f54bf43ebb061c4. - Documentation and onboarding improvements: Enhanced contributor onboarding and visibility with updated README badges, corrected badge links, PR template updates, and a Read the Docs badge to surface docs build status. Commits: 7082ba96ea73cdbc2c2adb7dcbd3d9ca10f446d7; e1360256a651b6c42a5de5eeead5dc94959df806; 4be4f95b887495d16e398f60f308e358c3edf6ae; 6dadb569222230bb0f73ea0gui318322. - CI/CD modernization and cross-environment testing: Expanded CI to test across macOS and Ubuntu with updated Python versions (including 3.10 and 3.12), adjusted installation steps, and broadened smoke tests to cover multiple OSes. Commits: 4b43af20d6ecc26be6a045a3ea2651eeab0ac169; 379b979309dcf074dc8880c45bf72942d7f85bc1; 8565e23d09df642e24994372f81a3293a6d54e59; 4c0856985f7e7112e19e903c3d912757234b18ab; b36210aa90884ca360a7316cf5776b1d5e9cd996; ae7dd26913c8c13b9f9064cd0b325d0ae85b9a43. - Overall impact: This work increases reliability and broadens platform support, enabling smoother contributor onboarding, reproducible scientific environments, and reduced time-to-production for future features. It sets a strong foundation for scaling scientific workflows in layup while lowering maintenance risk.
13 Commits • 3 Features
Jan 1, 2025January 2025 (Smithsonian/layup) monthly summary: - Key features delivered: Added and pinned a scientific computing stack in pyproject.toml to enable numerical, astronomical calculations and data handling (numpy, assist, astropy, rebound, pooch, tqdm). This lays the groundwork for expanded scientific workloads and reproducible environments. Commit: be22fd019a522c390e669acccd9181bdedc7a559. - Major bugs fixed: Corrected a dependency typo in pyproject.toml for the assist package to ensure reliable builds and runtime; removed an unnecessary debug print from orbitfit.py to restore clean runtime output. Commits: 30371b19a789ec99d3cb5e87ac624221ab5f0623; ac0de76f16752e8ebc25bfe27f54bf43ebb061c4. - Documentation and onboarding improvements: Enhanced contributor onboarding and visibility with updated README badges, corrected badge links, PR template updates, and a Read the Docs badge to surface docs build status. Commits: 7082ba96ea73cdbc2c2adb7dcbd3d9ca10f446d7; e1360256a651b6c42a5de5eeead5dc94959df806; 4be4f95b887495d16e398f60f308e358c3edf6ae; 6dadb569222230bb0f73ea0gui318322. - CI/CD modernization and cross-environment testing: Expanded CI to test across macOS and Ubuntu with updated Python versions (including 3.10 and 3.12), adjusted installation steps, and broadened smoke tests to cover multiple OSes. Commits: 4b43af20d6ecc26be6a045a3ea2651eeab0ac169; 379b979309dcf074dc8880c45bf72942d7f85bc1; 8565e23d09df642e24994372f81a3293a6d54e59; 4c0856985f7e7112e19e903c3d912757234b18ab; b36210aa90884ca360a7316cf5776b1d5e9cd996; ae7dd26913c8c13b9f9064cd0b325d0ae85b9a43. - Overall impact: This work increases reliability and broadens platform support, enabling smoother contributor onboarding, reproducible scientific environments, and reduced time-to-production for future features. It sets a strong foundation for scaling scientific workflows in layup while lowering maintenance risk.
January 2025
Activity
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Quality Metrics
Correctness94.6%
Maintainability93.8%
Architecture91.4%
Performance90.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
INIMarkdownPythonRSTShellTOMLYAML
Technical Skills
Algorithm ImplementationAstronomy Software IntegrationBackend DevelopmentBug FixCI/CDCLI DevelopmentCode FormattingCode RefactoringCommand Line InterfaceConfiguration ManagementData ProcessingData UpdatesDependency ManagementDevOpsDocumentation
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