issue_comments

Table actions

1 row where issue = 638909879 and user = 1053153 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

  • chrisroat · 1 ✖

issue 1

  • Implement interp for interpolating between chunks of data (dask) · 1 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
667255046 https://github.com/pydata/xarray/pull/4155#issuecomment-667255046 https://api.github.com/repos/pydata/xarray/issues/4155 MDEyOklzc3VlQ29tbWVudDY2NzI1NTA0Ng== chrisroat 1053153 2020-07-31T17:56:15Z 2020-07-31T17:56:15Z CONTRIBUTOR

Hi! This work is interesting to me, as I was implementing in dask an image processing algo which needs an intermediate 1-d linear interpolation step. This bottlenecks the calculation through a single node. Your work here on distributed interpolation is intriguing, and I'm wondering if it would be useful in my work and if it could possibly become part of dask itself.

Here is the particular function, which you'll note has a dask.delayed wrapper around np.interp.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Implement interp for interpolating between chunks of data (dask) 638909879

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]);