issue_comments
6 rows where author_association = "MEMBER" and issue = 252548859 sorted by updated_at descending
This data as json, CSV (advanced)
Suggested facets: created_at (date), updated_at (date)
issue 1
- (trivial) xarray.quantile silently resolves dask arrays · 6 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
---|---|---|---|---|---|---|---|---|---|---|---|
514002753 | https://github.com/pydata/xarray/issues/1524#issuecomment-514002753 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDUxNDAwMjc1Mw== | shoyer 1217238 | 2019-07-23T00:18:05Z | 2019-07-23T00:18:05Z | MEMBER |
Yes, to some degree. I'm still troubled by that the "default" algorithm (which is selected by default) has no error bounds. It seems a little backwards to me to default to a fast algorithm with unknown accuracy. Also, it still only works on 1D arrays, which would not be terribly useful for us. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 | |
404733610 | https://github.com/pydata/xarray/issues/1524#issuecomment-404733610 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDQwNDczMzYxMA== | shoyer 1217238 | 2018-07-13T05:55:14Z | 2018-07-13T05:55:14Z | MEMBER | { "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 | ||
404733398 | https://github.com/pydata/xarray/issues/1524#issuecomment-404733398 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDQwNDczMzM5OA== | shoyer 1217238 | 2018-07-13T05:53:58Z | 2018-07-13T05:53:58Z | MEMBER | @acrosby if you're at SciPy, I'd be happy to chat about this tomorrow or over the weekend if you're staying for the sprints. This is not an immediate priority for me, but it would be straightforward to make quantile work over non-chunked dimensions by rewriting it to use Approximate quantiles over chunked dimensions could be done by leveraging dask.array.percentile, but that algorithm has some accuracy concerns. See https://github.com/dask/dask/issues/1225 for discussion. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 | |
325252313 | https://github.com/pydata/xarray/issues/1524#issuecomment-325252313 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDMyNTI1MjMxMw== | jhamman 2443309 | 2017-08-28T03:33:39Z | 2017-08-28T03:33:39Z | MEMBER | @crusaderky - thanks for this report. I just opened #1529 which takes care of the trivial part of this issue. If you want to tackle bringing dask.percentile in, that would be awesome. |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 | |
324616689 | https://github.com/pydata/xarray/issues/1524#issuecomment-324616689 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDMyNDYxNjY4OQ== | crusaderky 6213168 | 2017-08-24T12:07:53Z | 2017-08-24T12:07:53Z | MEMBER | Dask only supports 1d. One would first need to expand dask to support N-dimensional arrays like numpy does. I plan to di it if/when I have the time |
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 | |
324607199 | https://github.com/pydata/xarray/issues/1524#issuecomment-324607199 | https://api.github.com/repos/pydata/xarray/issues/1524 | MDEyOklzc3VlQ29tbWVudDMyNDYwNzE5OQ== | rabernat 1197350 | 2017-08-24T11:18:34Z | 2017-08-24T11:18:34Z | MEMBER | Dask implements percentile now http://dask.pydata.org/en/latest/array-api.html#dask.array.percentile So perhaps our version of quantile can be refactored to accommodate actual lazy computation on dask arrays, rather than simply erroring. In any case, I agree that automatic silent eager evaluation of dask arrays is bad. Sent from my iPhone
|
{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 } |
(trivial) xarray.quantile silently resolves dask arrays 252548859 |
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 4