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id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
740320785 | https://github.com/pydata/xarray/issues/4630#issuecomment-740320785 | https://api.github.com/repos/pydata/xarray/issues/4630 | MDEyOklzc3VlQ29tbWVudDc0MDMyMDc4NQ== | EricKeenan 44210245 | 2020-12-08T02:32:42Z | 2020-12-08T02:32:42Z | CONTRIBUTOR | Thanks for sharing! I'll give this a first shot before the end of the year. |
{ "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
.sel(...., method='nearest') fails for large requests. 753874419 | |
736869263 | https://github.com/pydata/xarray/issues/4630#issuecomment-736869263 | https://api.github.com/repos/pydata/xarray/issues/4630 | MDEyOklzc3VlQ29tbWVudDczNjg2OTI2Mw== | EricKeenan 44210245 | 2020-12-01T22:48:34Z | 2020-12-01T22:48:34Z | CONTRIBUTOR | I'd be happy to give this a shot. But I'm not sure where to start... Can you point me to an example PR that has done something similar? |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
.sel(...., method='nearest') fails for large requests. 753874419 | |
736758627 | https://github.com/pydata/xarray/issues/4630#issuecomment-736758627 | https://api.github.com/repos/pydata/xarray/issues/4630 | MDEyOklzc3VlQ29tbWVudDczNjc1ODYyNw== | EricKeenan 44210245 | 2020-12-01T19:09:52Z | 2020-12-01T19:09:52Z | CONTRIBUTOR | 👏 👍 I didn't realize I needed to do that. Thanks for letting me know. Problem solved - marking this as closed. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
.sel(...., method='nearest') fails for large requests. 753874419 | |
736752768 | https://github.com/pydata/xarray/issues/4630#issuecomment-736752768 | https://api.github.com/repos/pydata/xarray/issues/4630 | MDEyOklzc3VlQ29tbWVudDczNjc1Mjc2OA== | EricKeenan 44210245 | 2020-12-01T18:59:18Z | 2020-12-01T18:59:18Z | CONTRIBUTOR | @dcherian Thanks for pointing me in the right direction. I'm trying to implement this with vectorized indexing, but it seems that my queries need to exactly match the xarray object lat/lon, which is why I tried |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
.sel(...., method='nearest') fails for large requests. 753874419 |
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