issue_comments
4 rows where author_association = "CONTRIBUTOR" and issue = 593825520 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- Element wise dataArray generation · 4 ✖
| id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 610167347 | https://github.com/pydata/xarray/issues/3932#issuecomment-610167347 | https://api.github.com/repos/pydata/xarray/issues/3932 | MDEyOklzc3VlQ29tbWVudDYxMDE2NzM0Nw== | apatlpo 11750960 | 2020-04-07T04:32:12Z | 2020-04-07T04:32:12Z | CONTRIBUTOR | I'll close this for now as there doesn't seem to be other ideas about this |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Element wise dataArray generation 593825520 | |
| 609605285 | https://github.com/pydata/xarray/issues/3932#issuecomment-609605285 | https://api.github.com/repos/pydata/xarray/issues/3932 | MDEyOklzc3VlQ29tbWVudDYwOTYwNTI4NQ== | apatlpo 11750960 | 2020-04-06T07:08:19Z | 2020-04-06T07:08:19Z | CONTRIBUTOR | This sounds like method 1 (with dask delayed) to me. There may be no faster option, thanks for giving it a thought @fujiisoup |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Element wise dataArray generation 593825520 | |
| 609407162 | https://github.com/pydata/xarray/issues/3932#issuecomment-609407162 | https://api.github.com/repos/pydata/xarray/issues/3932 | MDEyOklzc3VlQ29tbWVudDYwOTQwNzE2Mg== | apatlpo 11750960 | 2020-04-05T12:17:15Z | 2020-04-05T12:17:47Z | CONTRIBUTOR | thanks a lot @fujiisoup, your suggestion does help getting rid of the necessity to build the ds = xr.Dataset(coords={'x': x, 'y': y}) ds = ds.chunk({'x': 1, 'y':1}) # does not change anythinglet's say each experiment outputs 5 statistical diagnosticsNstats = 5 some_exp = lambda x, y: np.ones((Nstats,)) out = xr.apply_ufunc(some_exp, ds.x, ds.y, dask='parallelized', vectorize=True, output_dtypes=[float], output_sizes={'stats': Nstats}, output_core_dims=[['stats']]) ``` An inspection of the dask dashboard indicates that the computation is not distributed among workers though. How could I make sure this happens? |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Element wise dataArray generation 593825520 | |
| 609407192 | https://github.com/pydata/xarray/issues/3932#issuecomment-609407192 | https://api.github.com/repos/pydata/xarray/issues/3932 | MDEyOklzc3VlQ29tbWVudDYwOTQwNzE5Mg== | apatlpo 11750960 | 2020-04-05T12:17:26Z | 2020-04-05T12:17:26Z | CONTRIBUTOR | sorry closed by accident |
{
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
} |
Element wise dataArray generation 593825520 |
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