QuantEcon/lecture-python.myst
Jul 2025 – Jul 2025
1 Month active
Languages Used
JAXPython
Technical Skills
JAXModel ImplementationNumerical ComputationScientific Computing
John Stachurski
PROFILE
John Stachurski
Developed a JAX-based backend for the McCall model in the QuantEcon/lecture-python.myst repository, focusing on migrating numerical computations from Numba to JAX to enable just-in-time compilation and vectorized operations. The work introduced a Model NamedTuple to encapsulate parameters and state, streamlining model usage and future extensions. Refactoring the solve_model and update functions to leverage JAX array operations improved both numerical stability and maintainability. This update delivered faster simulations and enhanced reliability for downstream analyses and teaching materials. The project utilized Python and JAX, emphasizing model implementation and scientific computing within a robust, reproducible research and teaching environment.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
234
Activity Months1
Your Network
9 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Delivered a JAX-based McCall model backend for QuantEcon/lecture-python.myst, refactoring the model to use a Model NamedTuple for parameter encapsulation and updating solve_model and update to leverage JAX jit and vectorized array operations. This work migrates from Numba to JAX, enabling faster simulations, improved numerical stability, and a more reliable interface for downstream analyses and teaching materials.
1 Commits • 1 Features
Jul 1, 2025July 2025: Delivered a JAX-based McCall model backend for QuantEcon/lecture-python.myst, refactoring the model to use a Model NamedTuple for parameter encapsulation and updating solve_model and update to leverage JAX jit and vectorized array operations. This work migrates from Numba to JAX, enabling faster simulations, improved numerical stability, and a more reliable interface for downstream analyses and teaching materials.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JAXPython
Technical Skills
JAXModel ImplementationNumerical ComputationScientific Computing
Repositories Contributed To
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