xarray-contrib / xarray-contrib/xdggs
Convolution
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- Dominant language
- Python
- Stars
- 138
- Forks
- 25
- Avg merge
- 1d 15h
- Merged PRs (30d)
- 11
Description
One way to achieve convolution is a "map-reduce"-ish two step procedure on a Dataset / DataArray:
- Add one cell "neighbors" dimension
a. get neighbor indices for each grid cell
- Healpix:healpy.pixelfunc.get_all_neighbours()
- H3:h3.grid_disk()(unfortunately not yet vectorized)
b. retrieve (data) variable data along the new neighbor dimension (vectorized indexing, potentially expensive) - Aggregate the neighbors dimension. This step is trivial and can be done with xarray API (e.g.,
.mean(), etc.)
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 proposed Dataset/DataArray two-step procedure and the xarray APIs for vectorized indexing and aggregation. Compare neighbor lookup options using healpy.pixelfunc.get_all_neighbours() and h3.grid_disk(). Done should define and implement a convolution workflow, including neighbor retrieval and aggregation, but the issue does not name files, tests, or acceptance criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100