qubicsoft/qubic
Sep 2025 – Oct 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
AstrophysicsData AnalysisData VisualizationJupyter NotebooksMachine LearningPython
Leonora
PROFILE
Leonora
Leonora Kardum enhanced the qubicsoft/qubic repository by developing and refining Planck data acquisition and map-making workflows over a two-month period. She introduced notebook-based tools in Python and Jupyter to enable rapid testing of PlanckAcquisition configurations, improving reproducibility and validation of astrophysical data processing. Her work included robust noise generation, refined covariance calculations, and seamless switching between legacy and new implementations, which increased numerical stability and maintainability. Leonora also integrated external Planck data into multi-frequency QUBIC simulations, implementing advanced weighting and preconditioning techniques using scientific computing and signal processing skills to improve accuracy, convergence, and compatibility with external datasets.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
2
Lines of code
16,283
Activity Months2
Work History
October 2025
4 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for qubicsoft/qubic focused on Planck data integration and map-making enhancements across the FMM, CMM, QCMMPipeline, and the QUBIC simulation. Implemented Planck data weighting and integration with external Planck data, adjusted initial guesses for PCG, and added Planck-aware updates to initial maps and components. Improved Planck noise handling in multi-frequency QUBIC simulations to boost accuracy, convergence, and data compatibility with external datasets. The work also consolidated improvements to multi-frequency data weighting and integration to enable more robust sky maps and higher-fidelity simulations.
4 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for qubicsoft/qubic focused on Planck data integration and map-making enhancements across the FMM, CMM, QCMMPipeline, and the QUBIC simulation. Implemented Planck data weighting and integration with external Planck data, adjusted initial guesses for PCG, and added Planck-aware updates to initial maps and components. Improved Planck noise handling in multi-frequency QUBIC simulations to boost accuracy, convergence, and data compatibility with external datasets. The work also consolidated improvements to multi-frequency data weighting and integration to enable more robust sky maps and higher-fidelity simulations.
October 2025
September 2025
4 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated for qubic project. Key achievements: - Notebook-based Planck acquisition enhancements: introduced a testing notebook to try different PlanckAcquisition configurations, enabling rapid validation of new settings and configurations. - Noise generation improvements: simplified and robustified noise generation; refined noise and inverse-noise covariance calculations, weighting, and initialization. - Implementation switching: enabled seamless switching between old and new Planck acquisition implementations to support migration and rollback. - Configuration testing and reproducibility: added notebooks to test PlanckAcquisition under various configurations, improving reproducibility of results. - Bug fix for analytical solution and merge conflicts: resolved merge-conflict issues and corrected the analytical solution for TOD_to_freq_reversed_mono notebook (commit 5275e38edcef6480f8a5576e7772f2e78bc7c2dd). Impact and value: - Increased reliability and reproducibility of Planck data processing; faster validation of acquisition configurations; improved numerical stability of noise modeling, leading to more accurate parameter inference. - Improved maintainability and collaboration through merge conflict resolution and clearer commit history. Technologies and skills demonstrated: - Python notebooks and scripting for PlanckAcquisition workflow, noise modeling, and covariance math. - Version control hygiene with targeted commits addressing configuration, noise, and bug fixes. - End-to-end validation of acquisition configurations, contributing to data quality and project cadence.
September 2025
4 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated for qubic project. Key achievements: - Notebook-based Planck acquisition enhancements: introduced a testing notebook to try different PlanckAcquisition configurations, enabling rapid validation of new settings and configurations. - Noise generation improvements: simplified and robustified noise generation; refined noise and inverse-noise covariance calculations, weighting, and initialization. - Implementation switching: enabled seamless switching between old and new Planck acquisition implementations to support migration and rollback. - Configuration testing and reproducibility: added notebooks to test PlanckAcquisition under various configurations, improving reproducibility of results. - Bug fix for analytical solution and merge conflicts: resolved merge-conflict issues and corrected the analytical solution for TOD_to_freq_reversed_mono notebook (commit 5275e38edcef6480f8a5576e7772f2e78bc7c2dd). Impact and value: - Increased reliability and reproducibility of Planck data processing; faster validation of acquisition configurations; improved numerical stability of noise modeling, leading to more accurate parameter inference. - Improved maintainability and collaboration through merge conflict resolution and clearer commit history. Technologies and skills demonstrated: - Python notebooks and scripting for PlanckAcquisition workflow, noise modeling, and covariance math. - Version control hygiene with targeted commits addressing configuration, noise, and bug fixes. - End-to-end validation of acquisition configurations, contributing to data quality and project cadence.
Activity
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Quality Metrics
Correctness82.6%
Maintainability81.2%
Architecture78.8%
Performance72.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Algorithm ImplementationAstrophysicsData AnalysisData ProcessingData VisualizationJupyter NotebooksLinear AlgebraMachine LearningMap MakingNumerical MethodsNumerical SimulationPythonScientific ComputingSignal ProcessingSoftware Engineering
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