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- Should the zarr backend support NCZarr conventions? · 5 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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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 |
Can you elaborate? What API are you using to do the write: python, netcdf-c, or what? |
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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 |
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"]. |
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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. |
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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:
|
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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:
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. |
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Should the zarr backend support NCZarr conventions? 1172229856 |
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