home / github

Menu
  • GraphQL API
  • Search all tables

issue_comments

Table actions
  • GraphQL API for issue_comments

5 rows where issue = 1172229856 and user = 905179 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

  • DennisHeimbigner · 5 ✖

issue 1

  • Should the zarr backend support NCZarr conventions? · 5 ✖

author_association 1

  • NONE 5
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1090483461 https://github.com/pydata/xarray/issues/6374#issuecomment-1090483461 https://api.github.com/repos/pydata/xarray/issues/6374 IC_kwDOAMm_X85A_3UF DennisHeimbigner 905179 2022-04-06T16:46:32Z 2022-04-06T16:46:32Z NONE

As it is currently it is also not possible to write a zarr which follows the GDAL ZARR driver conventions. Writing the _CRS attribute also results in a TypeError:

Can you elaborate? What API are you using to do the write: python, netcdf-c, or what?

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Should the zarr backend support NCZarr conventions? 1172229856
1081236360 https://github.com/pydata/xarray/issues/6374#issuecomment-1081236360 https://api.github.com/repos/pydata/xarray/issues/6374 IC_kwDOAMm_X85AcluI DennisHeimbigner 905179 2022-03-28T23:05:51Z 2022-03-28T23:05:51Z NONE

dimension names stored by xarray in .zattrs["_ARRAY_DIMENSIONS"] are stored by NCZarr in .zarray["_NCZARR_ARRAY"]["dimrefs"]

I made a recent change to this so that where possible, all NCZarr files contain the xarray _ARRAY_ATTRIBUTE. By "where possible" I mean that the array is in the root group and the dimensions it references are "defined" in the root group (i.e. they have the simple FQN "/XXX" where XXX is the dim name. This means that there is sometimes a duplication of information between _ARRAY_ATTRIBUTE and ".zarray["_NCZARR_ARRAY"]["dimrefs"].

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Should the zarr backend support NCZarr conventions? 1172229856
1076821132 https://github.com/pydata/xarray/issues/6374#issuecomment-1076821132 https://api.github.com/repos/pydata/xarray/issues/6374 IC_kwDOAMm_X85ALvyM DennisHeimbigner 905179 2022-03-23T21:07:01Z 2022-03-23T21:07:01Z NONE

I guess I was not clear. If you are willing to lose netcdf specific metadata, then I believe any xarray or zarr implementation should be able to read nczarr written data with no changes needed.

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Should the zarr backend support NCZarr conventions? 1172229856
1076777717 https://github.com/pydata/xarray/issues/6374#issuecomment-1076777717 https://api.github.com/repos/pydata/xarray/issues/6374 IC_kwDOAMm_X85ALlL1 DennisHeimbigner 905179 2022-03-23T20:15:18Z 2022-03-23T20:15:18Z NONE

As the moment, NCzarr format files (as opposed to pure Zarr format files produced by NCZarr) do not include the Xarray _ARRAY_DIMENSIONS attribute. Now that I think about it, there is no reason not to include that attribute where it is meaningful, so I will make that change. After that change, the situation should be as follows:

Xarray can read any nczarr format file subject to the following conditions:
1. xarray attempts to read only the root group and ignores subgroups
    * this is because xarray cannot handle subgroups.
2. the xarray implementation ignores extra dictionary keys in e.g. .zarray and .zattr
   that it does not recognize
    * this should already be the case under the principle of "read broadly, write narrowly".
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Should the zarr backend support NCZarr conventions? 1172229856
1071720621 https://github.com/pydata/xarray/issues/6374#issuecomment-1071720621 https://api.github.com/repos/pydata/xarray/issues/6374 IC_kwDOAMm_X84_4Sit DennisHeimbigner 905179 2022-03-17T22:47:59Z 2022-03-17T22:47:59Z NONE

For Unidata and netcdf, I think the situation is briefly this.

In netcdf-4, dimensions are named objects that can "reside" inside groups. So for example we might have this: netcdf example { dimensions: x=1; y=10; z=20; group g1 { dimensions: a=1; y=10; z=5; variables: float v(/x, /g1/y, /z); } } So base dimension names (e.g. "z") can occur in different groups and can represent different dimension objects (with different sizes).

It is possible to reference any dimension using fully-qualified-names (FQNs) such as "/g1/y". This capability is important so that, for example, related dimensions can be isolated with a group.

NCZarr captures this information by recording fully qualified names as special keys. This differs from XArray where fully qualified names are not supported. From the netcdf point of view, it is as if all dimension objects were declared in the root group.

If XArray is to be extended to support the equivalent of groups and distinct sets of dimensions are going to be supported in different groups, then some equivalent of the netcdf FQN is going to be needed.

One final note. In netcdf, the dimension size is declared once and associated with a name. In zarr/xarray, the size occurs in multiple places (via the "shape" key) and the name-size associated is also declared multlple times via the _ARRAY_DIMENSIONS attribute.

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
  Should the zarr backend support NCZarr conventions? 1172229856

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]);
Powered by Datasette · Queries took 13.323ms · About: xarray-datasette