issue_comments: 380593243
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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 |
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https://github.com/pydata/xarray/pull/1252#issuecomment-380593243 | https://api.github.com/repos/pydata/xarray/issues/1252 | 380593243 | MDEyOklzc3VlQ29tbWVudDM4MDU5MzI0Mw== | 1217238 | 2018-04-11T20:57:37Z | 2018-04-11T20:57:37Z | MEMBER | I think the code is in pretty good shape here. My main concern is about the stability of the As for resampling, this would indeed require custom logic for netcdf datetimes. But I think it would be relatively doable. The key thing would dividing an array of datetimes into frequency groups. Then we could reuse xarray's existing logic for resampling, e.g., https://github.com/pydata/xarray/blob/9b76f219ec314dcb0c9a310c097a34f5c751fdd6/xarray/core/groupby.py#L234-L235 For example, if using Pandas does this logic with offset classes. These are somewhat complex because pandas handles complex business day logic. For netcdftime, we could potentially start from scratch and only handle the important cases for climate science (e.g., round to start for year, quarter, month, day, hour, second). |
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