CosmoStat / CosmoStat/sp_validation
image simulations validation and diagnostics
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2
- Forks
- 5
- Avg merge
- 1d 14h
- Merged PRs (30d)
- 19
Description
After shapepipe and sp_validation runs on image simulations, we want to perform some tests and diagnostics. Some are already implemented in cosmo_val, e.g. plots of the ellipticity distribution, footprint, additive bias computation, etc. How can we re-use the cosmo_val structure and classes? We have potentially many different output catalogues (e.g. 1<d>2<d>_<type>_<idx> with d=p, m, z (+,- or no shear); type=grid and random, idx a running number). Think about how to implement this, with or without the underlying yaml config.
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 reviewing the existing cosmo_val structure and classes, especially the ellipticity, footprint, and additive-bias diagnostics mentioned in the issue. Then examine how shapepipe and sp_validation produce the different output catalogue names and consider whether the yaml config is needed. Done means a defined, reusable approach for running these diagnostics across the listed image-simulation outputs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, data-visualization, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100