JuliaDynamics / JuliaDynamics/RecurrenceAnalysis.jl
Speeding up recurrence quantification
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 50
- Forks
- 16
- PR merge metrics
- No merged PRs in 30d
Description
My research has now moved into looking at the evolution of recurrence metrics over a range of ϵs. As such, I have to compute RQA across 100 different values of ϵ, which is pretty slow - some particularly annoying datasets can take up to 10 minutes, even with multithreading.
I am going to look into speeding these computations up in the future, and would appreciate any advice on where to start.
Want to back this issue? Post a bounty on it! We accept bounties via Bountysource.
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
No source file or test is identified in the issue. Start by profiling the RQA computation across the 100 ε values and trace the recurrence-metric entry points; done means reducing the reported runtime while preserving the computed metrics.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- performance
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100