pyro-kinetics/pyrokinetics
Jul 2025 – Jul 2025
1 Month active
Languages Used
Python
Technical Skills
Backend DevelopmentData InterpolationData ParsingData ProcessingEquilibrium PhysicsFile I/O
Daniel Kennedy
PROFILE
Daniel Kennedy
Daniel Kennedy developed robust data ingestion capabilities for the pyro-kinetics/pyrokinetics repository, focusing on ELITEINP equilibrium and kinetics modeling from .eqin files. He implemented an inline equilibrium reader with 2D psi(R,Z) interpolation and improved error handling and unit consistency, leveraging Python and scientific computing techniques. Daniel extended the kinetics reader to support impurity-aware plasma species by parsing charge, mass, and density, enhancing the fidelity of plasma simulations. His work emphasized backend development, data parsing, and numerical analysis, resulting in more reliable and accurate simulation inputs. The depth of implementation supports informed design decisions and accelerates simulation readiness for users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
2
Lines of code
883
Activity Months1
Your Network
20 people
Work History
July 2025
7 Commits • 2 Features
Jul 1, 2025July 2025 — Pyrokinetics: Delivered ELITEINP data ingestion capabilities to support robust equilibrium and kinetics modeling from .eqin formats. Implemented an inline .eqin reader, 2D psi(R,Z) interpolation, and improved error handling and unit consistency. Expanded kinetics reader to include impurities (charge, mass, density) for plasmas, enabling impurity-aware species construction. The work improves data reliability, accelerates simulation readiness, and enhances modeling fidelity for design and analysis.
7 Commits • 2 Features
Jul 1, 2025July 2025 — Pyrokinetics: Delivered ELITEINP data ingestion capabilities to support robust equilibrium and kinetics modeling from .eqin formats. Implemented an inline .eqin reader, 2D psi(R,Z) interpolation, and improved error handling and unit consistency. Expanded kinetics reader to include impurities (charge, mass, density) for plasmas, enabling impurity-aware species construction. The work improves data reliability, accelerates simulation readiness, and enhances modeling fidelity for design and analysis.
July 2025
Activity
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Quality Metrics
Correctness84.2%
Maintainability80.0%
Architecture82.8%
Performance67.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentData InterpolationData ParsingData ProcessingEquilibrium PhysicsFile I/OFile ParsingNumerical AnalysisObject-Oriented ProgrammingPhysics SimulationPlasma PhysicsPythonScientific ComputingUnit Handling
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