issue_comments

Table actions

2 rows where author_association = "CONTRIBUTOR" and issue = 330918967 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

issue 1

  • DataArray.interp() : poor performance · 2 ✖

author_association 1

  • CONTRIBUTOR · 2 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
396050734 https://github.com/pydata/xarray/issues/2223#issuecomment-396050734 https://api.github.com/repos/pydata/xarray/issues/2223 MDEyOklzc3VlQ29tbWVudDM5NjA1MDczNA== e-roux 15956441 2018-06-10T13:53:25Z 2018-06-10T13:53:25Z CONTRIBUTOR

Ok, thank you the information. I first worked with the API Clearly documented in the link you provided.

I noticed the attrs get lost after orthogonal interpolation (see the first/second plots of arr in mybinder, I might open a new issue for that

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   DataArray.interp() : poor performance 330918967
396043207 https://github.com/pydata/xarray/issues/2223#issuecomment-396043207 https://api.github.com/repos/pydata/xarray/issues/2223 MDEyOklzc3VlQ29tbWVudDM5NjA0MzIwNw== e-roux 15956441 2018-06-10T11:55:54Z 2018-06-10T11:55:54Z CONTRIBUTOR

Thanks for your comment

had a deeper look at DataArray.interp and it computes a new DataArray with new coords given by the dict. I first thought i'll get a 1D array which is not the case (this is often the behavior I want). I then compared first 10_000 interpolations on sdf against 100_000_000 in xarray! It explains the gap.

Updated my comparison with the same behavior with scipy and sdf in a jupyter notebook on mybinder

It seems everything's well with xarray.

Extrapolation (linear first) would be a good feature too, I put an example at the end of the notebook about sdf interpolation/extrapolation possibilites (work for nd-arrays of dim 32 as with numpy) 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   DataArray.interp() : poor performance 330918967

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]);