hiddenSymmetries/simsopt
May 2025 – Jan 2026
3 Months active
Languages Used
PythonRSTFortranCMakeDockerfileJSONYAML
Technical Skills
DocumentationNumerical OptimizationNumerical SimulationPythonRandom Number GenerationScientific Computing
Chris
PROFILE
Chris
Christopher Smiet contributed to the hiddenSymmetries/simsopt repository by developing robust data access pathways, enhancing simulation reliability, and standardizing core components. He implemented QUASR integration for efficient data retrieval, expanded test coverage with both Python and C++ tests, and improved caching mechanisms to ensure reproducibility and performance. His work included overhauling the random number generation system using numpy’s PCG64DXSM, updating documentation for MHD integration, and refining simulation defaults for SPEC to address convergence issues. Leveraging skills in Python, CMake, and Docker, Christopher’s engineering focused on maintainability, cross-library consistency, and stable CI/CD pipelines, resulting in deeper reliability across scientific workflows.
Overall Statistics
Feature vs Bugs
42%Features
Repository Contributions
31Total
Bugs
7
Commits
31
Features
5
Lines of code
546,119
Activity Months3
Your Network
135 peopleShared Repositories
19
Work History
January 2026
21 Commits • 3 Features
Jan 1, 2026In January 2026, the team delivered a robust QUASR data access pathway for simsopt, strengthened test coverage across get_data and related components, stabilized core behaviors, expanded the test suite to include C++ tests, and aligned CI with Python 3.13. These efforts improve data reliability, reproducibility, and performance, enabling safer production deployments and faster development cycles.
21 Commits • 3 Features
Jan 1, 2026In January 2026, the team delivered a robust QUASR data access pathway for simsopt, strengthened test coverage across get_data and related components, stabilized core behaviors, expanded the test suite to include C++ tests, and aligned CI with Python 3.13. These efforts improve data reliability, reproducibility, and performance, enabling safer production deployments and faster development cycles.
January 2026
July 2025
1 Commits
Jul 1, 2025July 2025 monthly summary for the hiddenSymmetries/simsopt repository highlighting stability improvements to SPEC Simulation defaults and the associated convergence fix for low-resolution runs. The changes improve reliability of default simulations and reproducibility for downstream analyses, aligned with upstream SPEC updates.
July 2025
1 Commits
Jul 1, 2025July 2025 monthly summary for the hiddenSymmetries/simsopt repository highlighting stability improvements to SPEC Simulation defaults and the associated convergence fix for low-resolution runs. The changes improve reliability of default simulations and reproducibility for downstream analyses, aligned with upstream SPEC updates.
May 2025
9 Commits • 2 Features
May 1, 2025May 2025 monthly performance for hiddenSymmetries/simsopt focused on improving usability, reliability, and reproducibility. Delivered two major feature initiatives with clear business value: (1) Documentation updates for MHD integration and interfaces (VMEC/SPEC), installation guidance, examples, diagnostics, and general readability improvements across MHD-related documentation; (2) RNG overhaul transitioning to numpy PCG64DXSM, wrapped in a Generator class, with a standardized RNG API and accompanying test and documentation updates. These efforts reduce onboarding time, minimize configuration errors, and strengthen cross-library consistency for stochastic components.
9 Commits • 2 Features
May 1, 2025May 2025 monthly performance for hiddenSymmetries/simsopt focused on improving usability, reliability, and reproducibility. Delivered two major feature initiatives with clear business value: (1) Documentation updates for MHD integration and interfaces (VMEC/SPEC), installation guidance, examples, diagnostics, and general readability improvements across MHD-related documentation; (2) RNG overhaul transitioning to numpy PCG64DXSM, wrapped in a Generator class, with a standardized RNG API and accompanying test and documentation updates. These efforts reduce onboarding time, minimize configuration errors, and strengthen cross-library consistency for stochastic components.
May 2025
Activity
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Quality Metrics
Correctness93.0%
Maintainability89.0%
Architecture87.8%
Performance87.8%
AI Usage21.4%
Skills & Technologies
Programming Languages
CMakeDockerfileFortranJSONPythonRSTYAML
Technical Skills
API integrationC++ integrationCI/CDCMakeContinuous IntegrationDevOpsDockerDocumentationImport ManagementNumerical OptimizationNumerical SimulationPlasma Physics SimulationPythonPython DevelopmentPython programming
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