issue_comments

Table actions

7 rows where issue = 40395257 and user = 1217238 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

user 1

  • shoyer · 7 ✖

issue 1

  • Pointwise indexing -- something like sel_points · 7 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
235742423 https://github.com/pydata/xarray/issues/214#issuecomment-235742423 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDIzNTc0MjQyMw== shoyer 1217238 2016-07-27T22:34:12Z 2016-07-27T22:34:12Z MEMBER 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
58566084 https://github.com/pydata/xarray/issues/214#issuecomment-58566084 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDU4NTY2MDg0 shoyer 1217238 2014-10-09T19:44:25Z 2014-10-09T19:44:25Z MEMBER

What do you mean by "dependent coordinates"?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
58554847 https://github.com/pydata/xarray/issues/214#issuecomment-58554847 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDU4NTU0ODQ3 shoyer 1217238 2014-10-09T18:27:11Z 2014-10-09T18:27:18Z MEMBER

The main logic there -- it looks like this is a routine for broadcasting data arrays? I have something similar, but not exactly the same, in xray.core.variable.broadcast_variables. It's also very similar to the logic in xray.Dataset.to_dataframe, e.g., right now I think you could do the broadcasting by doing xray.Dataset.from_dataframe(spat_only.to_dataframe()).

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
58553960 https://github.com/pydata/xarray/issues/214#issuecomment-58553960 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDU4NTUzOTYw shoyer 1217238 2014-10-09T18:21:24Z 2014-10-09T18:21:24Z MEMBER

The only part that wouldn't work for a Dataset is spat_only.shape. On a dataset, you can get that information from the values of the dims dictionary (the difference between dims on a dataset and dataarray is definitely an ugly corner of the API).

Also, you probably want to use c.ravel() instead of c.flatten(), because the later always makes a copy.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
57863711 https://github.com/pydata/xarray/issues/214#issuecomment-57863711 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDU3ODYzNzEx shoyer 1217238 2014-10-03T21:19:57Z 2014-10-03T21:19:57Z MEMBER

@WeatherGod Very nice! I'm not entirely sure why you have to reverse y and x at the end, either -- what is the order of the dimensions on mod['longitude']?

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
57849594 https://github.com/pydata/xarray/issues/214#issuecomment-57849594 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDU3ODQ5NTk0 shoyer 1217238 2014-10-03T20:03:53Z 2014-10-03T20:03:53Z MEMBER

@WeatherGod You are totally correct. The last dataset on which I have needed to do this was an unprojected grid with a constant increment of 0.5 degrees between points, so finding nearest neighbors was easy.

If you have a lot of points to select at, finding nearest neighbor points could be done efficiently with a tree, e.g., scipy.spatial.cKDTree: http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html#scipy.spatial.KDTree.query

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257
52872417 https://github.com/pydata/xarray/issues/214#issuecomment-52872417 https://api.github.com/repos/pydata/xarray/issues/214 MDEyOklzc3VlQ29tbWVudDUyODcyNDE3 shoyer 1217238 2014-08-21T02:48:07Z 2014-08-21T02:48:07Z MEMBER

This operation is actually sort of like reindexing. So perhaps this should be spelled ds.reindex_like(other) or ds.reindex(other.coords). With labeled dimensions and variables there is enough metadata to make the reshaping unambiguous.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Pointwise indexing -- something like sel_points 40395257

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]);