litebird/litebird_sim
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Data ProcessingData SimulationScientific ComputingSignal ProcessingTesting
Silvia Micheli
PROFILE
Silvia Micheli
Silvia Micheli developed a reproducible quadratic non-linearity modeling feature for the litebird_sim repository, focusing on enhancing simulation fidelity for detector data. She implemented a deterministic noise application using a hashing-based source and introduced a per-sample quadratic non-linearity function leveraging NonLinParams and a seeded random number generator. Her work included refactoring to align with the hwp_sys module, ensuring consistent integration and improved maintainability. Using Python and applying skills in data processing, scientific computing, and signal processing, Silvia updated simulation paths and tests to adopt the new approach, enabling more reliable end-to-end validation and deterministic performance assessment for the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
104
Activity Months1
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for litebird_sim focused on delivering reproducible, detector-wide quadratic non-linearity modeling and enhancing simulation fidelity. Key outcomes include deterministic noise application via a hashing-based reproducible source, per-sample quadratic non-linearity application, and alignment with the hwp_sys module. Updated simulations and tests reflect the new approach, enabling more reliable end-to-end validation and performance assessment.
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary for litebird_sim focused on delivering reproducible, detector-wide quadratic non-linearity modeling and enhancing simulation fidelity. Key outcomes include deterministic noise application via a hashing-based reproducible source, per-sample quadratic non-linearity application, and alignment with the hwp_sys module. Updated simulations and tests reflect the new approach, enabling more reliable end-to-end validation and performance assessment.
May 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ProcessingData SimulationScientific ComputingSignal ProcessingTesting
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