issue_comments
1 row where author_association = "MEMBER", issue = 216689747 and user = 1217238 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- extract values at nearest point with multidimensional latitude and longitude field · 1 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
289164300 | https://github.com/pydata/xarray/issues/1325#issuecomment-289164300 | https://api.github.com/repos/pydata/xarray/issues/1325 | MDEyOklzc3VlQ29tbWVudDI4OTE2NDMwMA== | shoyer 1217238 | 2017-03-24T23:10:39Z | 2017-03-24T23:10:39Z | MEMBER | The right solution is to use a KDTree. We've discussed possible syntax for this in https://github.com/pydata/xarray/issues/475 but nobody has implemented it yet. Russ Rew wrote a nice overview article on using KDTree in http://www.unidata.ucar.edu/blogs/developer/entry/accessing_netcdf_data_by_coordinates |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
extract values at nearest point with multidimensional latitude and longitude field 216689747 |
Advanced export
JSON shape: default, array, newline-delimited, object
CREATE TABLE [issue_comments] ( [html_url] TEXT, [issue_url] TEXT, [id] INTEGER PRIMARY KEY, [node_id] TEXT, [user] INTEGER REFERENCES [users]([id]), [created_at] TEXT, [updated_at] TEXT, [author_association] TEXT, [body] TEXT, [reactions] TEXT, [performed_via_github_app] TEXT, [issue] INTEGER REFERENCES [issues]([id]) ); CREATE INDEX [idx_issue_comments_issue] ON [issue_comments] ([issue]); CREATE INDEX [idx_issue_comments_user] ON [issue_comments] ([user]);
user 1