issue_comments

Table actions

3 rows where issue = 1336946890 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

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

user 2

author_association 2

issue 1

  • Loading icon-d2 · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1212987160 https://github.com/pydata/xarray/issues/6909#issuecomment-1212987160 https://api.github.com/repos/pydata/xarray/issues/6909 IC_kwDOAMm_X85ITLcY RafaelAbel 21034385 2022-08-12T10:58:15Z 2022-08-12T16:07:36Z NONE

Thanks for your help, Kai. I have a follow-up question:

What about when I have multiple timesteps...

``` import xarray as xr import glob

file_list = glob.glob(target_dir + '*.grib2')

ds = xr.open_mfdataset(file_list, engine='cfgrib', combine="nested", concat_dim="generalVerticalLayer") ```

It does load the data, but it does not get the dimensions right. Note that I attached a new dataset, which in the end should have the dimensions lon, lat, vertical and time.

Thanks for your help!

icon-d2_germany_regular-lat-lon_model-level_u.zip

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Loading icon-d2 1336946890
1213062603 https://github.com/pydata/xarray/issues/6909#issuecomment-1213062603 https://api.github.com/repos/pydata/xarray/issues/6909 IC_kwDOAMm_X85ITd3L kmuehlbauer 5821660 2022-08-12T12:31:04Z 2022-08-12T12:31:04Z MEMBER

This would be not as easy.

  1. If you want to stick with a single line of code you would need to feed a nested list:

[[t0_f0, t0_f1, ...], [t1_f0, t1_f1, ...], [...]].

ds = xr.open_mfdataset(file_list, engine='cfgrib', combine="nested", concat_dim=["time", "generalVerticalLayer"], coords="minimal", compat="override") But note that it will take some coordinates from the first timestep only (eg. valid_time).

This might work without coords="minimal", compat="override", if you provide equivalent layers for each timestep.

2. You can break this into pieces:

python d0 = xr.open_mfdataset(file_list[0], engine='cfgrib', combine="nested", concat_dim="generalVerticalLayer") d1 = xr.open_mfdataset(file_list[1], engine='cfgrib', combine="nested", concat_dim="generalVerticalLayer") ds = xr.concat([ds1, ds2], dim="time")

I'm not sure if there is an incantation to get it work with a oneliner, but others might know.

 
{
    "total_count": 2,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 1,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Loading icon-d2 1336946890
1212900207 https://github.com/pydata/xarray/issues/6909#issuecomment-1212900207 https://api.github.com/repos/pydata/xarray/issues/6909 IC_kwDOAMm_X85IS2Nv kmuehlbauer 5821660 2022-08-12T09:13:10Z 2022-08-12T09:13:10Z MEMBER

ds = xr.open_mfdataset(file_list, engine='cfgrib', combine="nested", concat_dim="generalVerticalLayer")

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Loading icon-d2 1336946890

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]);