issue_comments: 483348884
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/1850#issuecomment-483348884 | https://api.github.com/repos/pydata/xarray/issues/1850 | 483348884 | MDEyOklzc3VlQ29tbWVudDQ4MzM0ODg4NA== | 1197350 | 2019-04-15T17:40:07Z | 2019-04-15T17:40:07Z | MEMBER | The approach we have been taking is to develop "micro-packages". We currently have three: - xgcm - for finite volume cell operations on top of xarray DataArrays - xrft - for coordinate-aware Fourier transforms of Xarray DataArrays - xhistogram - (this one is brand new) - for multidimensional histograms applied along specified axes These packages share some common design principles. In particular, they are all fully lazy and dask-friendly, meaning that we can apply them to very large datasets (which is the main focus in our group). By keeping the packages small, they are more maintainable. Xgcm and Xrft probably have O(3) active contributors, primarily myself and grad students in my group. Small, but significantly different from 1. We use these packages heavily in everyday scientific work, so I know they are useful. I would love to combine forces on a larger effort. However, we have limited time and effort. For now, however, this situation doesn't seem too bad. It's kind of compatible with what @teoliphant was suggesting in his comment 1 above. I'm not sure that some mega xarray-contrib package would have critical mass to be sustainable either. |
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