AVSLab/basilisk
Sep 2025 – Feb 2026
4 Months active
Languages Used
PythonGitignorerstC++reStructuredText
Technical Skills
Module ManagementPythonAerospace EngineeringBackend DevelopmentData ParsingData Structures
WIN-NTNU-NO\oliveha
PROFILE
Win-ntnu-no\oliveha
Contributed to AVSLab/basilisk by developing and enhancing simulation modules for aerospace and astronomy applications over four months. Work included implementing TLE data handling with a dedicated dataclass, integrating NanoAvionics hardware models, and expanding antenna and sky brightness simulation capabilities. Used Python and C++ to refactor data parsing, improve module management, and introduce robust unit testing. Addressed compatibility issues in core data handling and streamlined repository hygiene with updated .gitignore practices. Enhanced user experience by refining antenna module warnings and maintaining comprehensive documentation. The approach emphasized maintainability, clear release notes, and operational reliability across embedded systems and backend simulation workflows.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
13Total
Bugs
1
Commits
13
Features
8
Lines of code
4,681
Activity Months4
Your Network
26 peopleSame Organization
@ntnu.no5
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for AVSLab/basilisk focusing on user experience improvements and clearer release documentation. The main delivery this month was a UX improvement in the ground-based antenna workflow by suppressing unconnected message warnings in the simpleAntenna module, along with updating release notes to reflect the new behavior. No major bug fixes were identified in this period; the change reduces noise and confusion for users and operators, which can lower support load and improve onboarding.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for AVSLab/basilisk focusing on user experience improvements and clearer release documentation. The main delivery this month was a UX improvement in the ground-based antenna workflow by suppressing unconnected message warnings in the simpleAntenna module, along with updating release notes to reflect the new behavior. No major bug fixes were identified in this period; the change reduces noise and confusion for users and operators, which can lower support load and improve onboarding.
February 2026
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for AVSLab/basilisk focusing on delivering core propulsion, antenna modeling, sky physics, and observability enhancements with strong validation, documentation, and release readiness.
January 2026
7 Commits • 4 Features
Jan 1, 2026January 2026 monthly summary for AVSLab/basilisk focusing on delivering core propulsion, antenna modeling, sky physics, and observability enhancements with strong validation, documentation, and release readiness.
October 2025
4 Commits • 3 Features
Oct 1, 2025For 2025-10, AVSLab/basilisk delivered core data handling improvements and hardware integration, with strong testing and repository hygiene gains that enhance reliability, maintainability, and operational readiness of the simulation.
4 Commits • 3 Features
Oct 1, 2025For 2025-10, AVSLab/basilisk delivered core data handling improvements and hardware integration, with strong testing and repository hygiene gains that enhance reliability, maintainability, and operational readiness of the simulation.
October 2025
September 2025
1 Commits
Sep 1, 2025September 2025: Stabilized core data handling in AVSLab/basilisk by addressing a tabulate namedtuple import compatibility issue. Implemented a direct import via collections.namedtuple and removed an unnecessary conditional import to ensure consistent access across environments. This improves reliability for downstream analytics and reduces maintenance overhead.
September 2025
1 Commits
Sep 1, 2025September 2025: Stabilized core data handling in AVSLab/basilisk by addressing a tabulate namedtuple import compatibility issue. Implemented a direct import via collections.namedtuple and removed an unnecessary conditional import to ensure consistent access across environments. This improves reliability for downstream analytics and reduces maintenance overhead.
Activity
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Quality Metrics
Correctness97.8%
Maintainability93.2%
Architecture93.8%
Performance90.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++GitignorePythonreStructuredTextrst
Technical Skills
Aerospace EngineeringAstronomyBackend DevelopmentC++C++ developmentData ParsingData StructuresData analysisEmbedded SystemsModule ManagementObject-Oriented ProgrammingOrbital MechanicsPythonPython developmentPython programming
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