issue_comments

Table actions

4 rows where author_association = "NONE", issue = 470024896 and user = 3019665 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: created_at (date), updated_at (date)

user 1

  • jakirkham · 4 ✖

issue 1

  • Implementing map_blocks and map_overlap · 4 ✖

author_association 1

  • NONE · 4 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1100938882 https://github.com/pydata/xarray/issues/3147#issuecomment-1100938882 https://api.github.com/repos/pydata/xarray/issues/3147 IC_kwDOAMm_X85Bnv6C jakirkham 3019665 2022-04-17T19:44:03Z 2022-04-17T19:44:03Z NONE

Would be good to keep this open

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implementing map_blocks and map_overlap 470024896
668263428 https://github.com/pydata/xarray/issues/3147#issuecomment-668263428 https://api.github.com/repos/pydata/xarray/issues/3147 MDEyOklzc3VlQ29tbWVudDY2ODI2MzQyOA== jakirkham 3019665 2020-08-03T22:02:22Z 2020-08-03T22:02:22Z NONE

Yeah +1 for using pad instead. Had tried to get rid of map_overlap's padding and use da.pad in Dask as well ( https://github.com/dask/dask/pull/5052 ), but haven't had time to get back to that.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implementing map_blocks and map_overlap 470024896
513044413 https://github.com/pydata/xarray/issues/3147#issuecomment-513044413 https://api.github.com/repos/pydata/xarray/issues/3147 MDEyOklzc3VlQ29tbWVudDUxMzA0NDQxMw== jakirkham 3019665 2019-07-19T00:33:55Z 2019-07-19T00:42:03Z NONE

Another approach for the split_by_chunks implementation would be...

python def split_by_chunks(a): for sl in da.core.slices_from_chunks(a.chunks): yield (sl, a[sl])

While a little bit more cumbersome to write, this could be implemented with .blocks and may be a bit more performant.

python def split_by_chunks(a): for i, sl in zip(np.ndindex(a.numblocks), da.core.slices_from_chunks(a.chunks)): yield (sl, a.blocks[i])

If the slices are not strictly needed, this could be simplified a bit more.

python def split_by_chunks(a): for i in np.ndindex(a.numblocks): yield a.blocks[i]

Admittedly slices_from_chunks is an internal utility function. Though it is unlikely to change. We could consider exposing it as part of the API if that is useful.

We could consider other things like making .blocks iterable, which could make this more friendly as well. Raised issue ( https://github.com/dask/dask/issues/5117 ) on this point.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implementing map_blocks and map_overlap 470024896
513029753 https://github.com/pydata/xarray/issues/3147#issuecomment-513029753 https://api.github.com/repos/pydata/xarray/issues/3147 MDEyOklzc3VlQ29tbWVudDUxMzAyOTc1Mw== jakirkham 3019665 2019-07-18T23:22:11Z 2019-07-18T23:22:11Z NONE

That sounds somewhat similar to .blocks accessor in Dask Array. ( https://github.com/dask/dask/pull/3689 ) Maybe we should align on that as well?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implementing map_blocks and map_overlap 470024896

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

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]);