issue_comments: 595403356
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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/3831#issuecomment-595403356 | https://api.github.com/repos/pydata/xarray/issues/3831 | 595403356 | MDEyOklzc3VlQ29tbWVudDU5NTQwMzM1Ng== | 5635139 | 2020-03-05T19:25:40Z | 2020-03-05T19:25:40Z | MEMBER | Not to hijack this specific issue for the general case, but any thoughts on the best way for users & us to identify the appropriate library for users to direct their issues? Is it just the last item in the call stack? Does xarray need to build diagnostics / assertions for highlighting where the problem is? A quick survey of the first two pages of xarray issues yield a bunch of issues which receive no response from us, and those that do are often a decent amount of back & forth: https://github.com/pydata/xarray/issues/3815 (zarr?) https://github.com/pydata/xarray/issues/3781 (scipy? dask?) https://github.com/pydata/xarray/issues/3776 (probably xarray, maybe netcdf) https://github.com/pydata/xarray/issues/3767 (scipy? netcdf? this did get a response, from @dcherian ) https://github.com/pydata/xarray/issues/3754 (pydap. @dcherian worked through this one with some back and forth) |
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