issue_comments

Table actions

9 rows where user = 7316393 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 1

  • arongergely · 9 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1403706989 https://github.com/pydata/xarray/pull/7391#issuecomment-1403706989 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85Tqt5t arongergely 7316393 2023-01-25T14:27:15Z 2023-01-25T14:27:15Z CONTRIBUTOR

maybe not. I changed it back so you could squash to exclude the noise. The test is adjusted accordingly.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1384325872 https://github.com/pydata/xarray/pull/7391#issuecomment-1384325872 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85SgyLw arongergely 7316393 2023-01-16T16:58:15Z 2023-01-16T16:58:15Z CONTRIBUTOR

Will you cherry pick?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1384253560 https://github.com/pydata/xarray/pull/7391#issuecomment-1384253560 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85Sggh4 arongergely 7316393 2023-01-16T16:02:52Z 2023-01-16T16:03:02Z CONTRIBUTOR

Option 2 seems easier, so I'd suggest we go with that?

Agreed!

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1384142858 https://github.com/pydata/xarray/pull/7391#issuecomment-1384142858 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85SgFgK arongergely 7316393 2023-01-16T14:24:23Z 2023-01-16T14:24:23Z CONTRIBUTOR

Changing the option setters to accept combine_attrs-style strings would open room for silent bugs: Other funcs/operators assume keep_attrs to be boolean so an if keep_attrs would cause trouble.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1384130795 https://github.com/pydata/xarray/pull/7391#issuecomment-1384130795 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85SgCjr arongergely 7316393 2023-01-16T14:16:27Z 2023-01-16T14:16:56Z CONTRIBUTOR

Hit a roadblock.

For binary ops the only way to set keep_attrs is through the options. However we can not set combine_attrs style strings to it due to strict validation. So this would lead to an error: python with xr.set_options(keep_attrs="drop_conflicts"): ds1 + ds2

To cricle around this we could let keep_attrs passed in the operator methods so user could do: python ds1.__add__(ds2, keep_attrs="drop_conflicts") But this isn't "nice".

Or perhaps I missed something? 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1378812265 https://github.com/pydata/xarray/issues/7414#issuecomment-1378812265 https://api.github.com/repos/pydata/xarray/issues/7414 IC_kwDOAMm_X85SLwFp arongergely 7316393 2023-01-11T14:11:41Z 2023-01-11T14:11:41Z CONTRIBUTOR

Looks like this is the culprit: https://github.com/scipy/scipy/issues/17718 to be fixed in scipy 1.10.1

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Error using xarray.interp - function signature does not match with scipy.interpn 1518812301
1378800682 https://github.com/pydata/xarray/issues/7414#issuecomment-1378800682 https://api.github.com/repos/pydata/xarray/issues/7414 IC_kwDOAMm_X85SLtQq arongergely 7316393 2023-01-11T14:04:09Z 2023-01-11T14:04:29Z CONTRIBUTOR

This breaks the documentation build too, it seems!

Sphinx errors out when it tries to parse https://github.com/pydata/xarray/blob/main/doc/user-guide/interpolation.rst

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Error using xarray.interp - function signature does not match with scipy.interpn 1518812301
1378579381 https://github.com/pydata/xarray/pull/7391#issuecomment-1378579381 https://api.github.com/repos/pydata/xarray/issues/7391 IC_kwDOAMm_X85SK3O1 arongergely 7316393 2023-01-11T11:03:40Z 2023-01-11T11:03:40Z CONTRIBUTOR

Thanks for your suggestions @keewis!

If possible I'd like to change the computation of the attrs of the result a bit to allow more than just bool values (see below).

I was puzzled initially. We would introduce the combine_attrs behaviour of xarray.merge() but under keep_attrs. Then I found out this is already a thing in apply_ufunc().

I like the idea, shouldn't we - implement this for xarray.Variable too? - consider this as new feature, not just a bugfix? (for describing in whats-new.rst) - describe in the documentation? (also for apply_ufunc() - it's not yet described there).

could do all these, let me know

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Follow keep_attrs in Dataset binary ops 1503573351
1359023892 https://github.com/pydata/xarray/issues/7377#issuecomment-1359023892 https://api.github.com/repos/pydata/xarray/issues/7377 IC_kwDOAMm_X85RAQ8U arongergely 7316393 2022-12-20T08:53:34Z 2022-12-20T08:57:52Z CONTRIBUTOR

Hi, this is a known issue coming from numpy.nanquantile / numpy.nanpercentile. I had the same problem - AFAIK the workaround is to implement your own nanpercentiles calculation.

If you want to take that route:

There is a blog post about the issue + a numpy workaround for 3D arrays: https://krstn.eu/np.nanpercentile()-there-has-to-be-a-faster-way/

I also turned to the numpy mailing list. Abel Aoun had a suggestion to look into the algo used at the xclim project. See our thread here: https://mail.python.org/archives/list/numpy-discussion@python.org/message/EKQIS4KNOHS6ZAU5OSYTLNOOH7U2Y5TW/

I ended up taking that one and rewrote it to suit my needs. I achieved >100x speedup in my case Good luck!

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Aggregating a dimension using the Quantiles method with `skipna=True` is very slow 1497031605

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]);