issue_comments

Table actions

6 rows where author_association = "MEMBER" and issue = 222676855 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 3

issue 1

  • Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions · 6 ✖

author_association 1

  • MEMBER · 6 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
367152237 https://github.com/pydata/xarray/issues/1378#issuecomment-367152237 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDM2NzE1MjIzNw== jhamman 2443309 2018-02-20T23:03:54Z 2018-02-20T23:03:54Z MEMBER

@gerritholl - rereading this issue, I don't think we're particularly opposed to supporting duplicate dimensions. We do know there are things that don't work right now and that we don't have test coverage for operations that use duplicate dimensions.

This is marked as a help wanted issue and I suspect that if someone like yourself, who has a use case for this functionality, were to want to work on this issue, we'd be happy to see it move forward. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295791392 https://github.com/pydata/xarray/issues/1378#issuecomment-295791392 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTc5MTM5Mg== shoyer 1217238 2017-04-20T15:59:40Z 2017-04-20T15:59:40Z MEMBER

I cannot see a use case in which repeated dims actually make sense.

Agreed. I would have disallowed them entirely, but sometimes it's useful to allow loading variables with duplicate dimensions, even if the only valid operation you can do is de-duplicate them.

Every routine that looks up dimensions by name should go through the get_axis_num method. That would be a good place to add a check for uniqueness.

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295710929 https://github.com/pydata/xarray/issues/1378#issuecomment-295710929 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTcxMDkyOQ== fmaussion 10050469 2017-04-20T12:11:18Z 2017-04-20T12:11:18Z MEMBER

In my case this situation originates from h5 files which indeed contains repeated dimensions

Yes this happened to me too. First thing I did is converting the files to proper netcdf datasets...

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295614924 https://github.com/pydata/xarray/issues/1378#issuecomment-295614924 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTYxNDkyNA== fmaussion 10050469 2017-04-20T07:47:18Z 2017-04-20T07:47:18Z MEMBER

I guess it would be good to document the expected behaviour with repeated dims somewhere? I.e. what should happen when doing: a = xr.DataArray(eye(3), dims=['dim0', 'dim0']) a.mean(dim='dim0') ?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295431292 https://github.com/pydata/xarray/issues/1378#issuecomment-295431292 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTQzMTI5Mg== shoyer 1217238 2017-04-19T20:39:33Z 2017-04-19T20:40:08Z MEMBER

Indeed, we don't have very good test coverage for operations with repeated dimensions. Fixes would certainly be appreciated, though they might be somewhat tricky. Even failing loudly with ValueError: repeated dimensions not yet supported would be an improvement over the current state.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855
295192317 https://github.com/pydata/xarray/issues/1378#issuecomment-295192317 https://api.github.com/repos/pydata/xarray/issues/1378 MDEyOklzc3VlQ29tbWVudDI5NTE5MjMxNw== fmaussion 10050469 2017-04-19T09:46:37Z 2017-04-19T09:46:37Z MEMBER

Yes, also happening on latest master.

I suspect there are several other things which won't work properly (or at least unexpectedly) when having repeated dims...

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Many methods are broken (e.g., concat/stack/sortby) when using repeated dimensions 222676855

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]);