issue_comments
1 row where author_association = "NONE" and issue = 910844095 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- xarray.open_rasterio · 1 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 863119738 | https://github.com/pydata/xarray/issues/5434#issuecomment-863119738 | https://api.github.com/repos/pydata/xarray/issues/5434 | MDEyOklzc3VlQ29tbWVudDg2MzExOTczOA== | ghost 10137 | 2021-06-17T10:20:46Z | 2021-06-17T10:26:12Z | NONE | Sorry for late response. I was trying to read a big geotif file as follows. import xarray as xr xds = xr.open_rasterio(geotif_file) My task was to array indexing and to save output into disk. columns = [8,9,7,100,1050,......, 9000] rows = [18,19,17,1100,1105,......, 9100] data = xds.isel(x=xr.DataArray(columns), y=xr.DataArray(rows)) np.save('output.npy', data) Unfortunately, the performance in terms of time requirement seems quite unsatisfactory. When I saw docs on I look forward to see it as |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
xarray.open_rasterio 910844095 |
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