QuantEcon / QuantEcon/QuantEcon.py
Refactor simulation methods in the IVP module
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Description
All,
I would like to re-factor the simulation loops (i.e, _integrate_fixed_trajectory and integrate_variable_trajectory) in the quantecon.ivp module in order to speed them up. However I am not exactly sure how to go about it. Would this be a good use case for Numba? If so are there examples in the code base already that I could use as a guide?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the simulation loops _integrate_fixed_trajectory and integrate_variable_trajectory in the quantecon.ivp module, then look for existing Numba examples in the code base. Determine whether Numba fits these loops and establish how to measure the speed improvement. Done means a justified refactor with faster simulation methods.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100