Support uncertainty propagation for NDCube reprojection
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
Research direction
Start with NDCube's reproject function and inspect how it currently handles uncertainty, then review reproject's support for pixel areas or combined-pixel counts. Before coding, resolve whether to depend on upstream support or use the proposed approximation; done means an agreed uncertainty-propagation behavior is implemented and verified by relevant tests.
Written by the indexing model from the issue text.
Description
Describe the feature
During the SunPy Community Meeting on 2025-07-16, we discussed that NDCube's reproject function drops the uncertainty. @DanRyanIrish asked me to summarize here so we can continue the conversation. It would be good to handle uncertainty more robustly, but that requires caution and may require changes in reproject for full support. PUNCH specifically would benefit from propagating uncertainty during reprojection since we do many reprojections and utilize the uncertainty as a weighting factor when combining images.
Proposed solution
There are a couple options:
- The more correct path is to have reproject handle uncertainties or at least supply the information about pixel areas and number of pixels combined for the uncertainty propagation. Then, ndcube could utilize that reproject feature.
- The more hacky way is to simply reproject the uncertainty. It's not strictly correct but it's better than nothing and could be good enough in many uses cases.
- Dominant language
- Python
- Stars
- 49
- Forks
- 56
- Avg merge
- 5h 54m
- Merged PRs (30d)
- 9
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.
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