html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4156#issuecomment-844090889,https://api.github.com/repos/pydata/xarray/issues/4156,844090889,MDEyOklzc3VlQ29tbWVudDg0NDA5MDg4OQ==,2448579,2021-05-19T13:09:25Z,2021-05-19T13:09:25Z,MEMBER,"There is a more standards-compliant version here:https://github.com/pydata/xarray/issues/1077#issuecomment-644803374

This is still blocked on choosing which CF representation to use for sparse vs which one to use for MultiIndex.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638947370
https://github.com/pydata/xarray/issues/4156#issuecomment-843971807,https://api.github.com/repos/pydata/xarray/issues/4156,843971807,MDEyOklzc3VlQ29tbWVudDg0Mzk3MTgwNw==,5637662,2021-05-19T10:33:08Z,2021-05-19T10:33:08Z,CONTRIBUTOR,"I have hacked something that does support the reading and writing of sparse arrays to a netcdf file, however I didn't know how and where to put this within xarray.

```
def ds_to_netcdf(ds, fn):
    dsorg = ds
    ds = dsorg.copy()
    for v in ds:
        if hasattr(ds[v].data, ""nnz"") and (
            hasattr(ds[v].data, ""to_coo"") or hasattr(ds[v].data, ""linear_loc"")
        ):
            coord = f""_{v}_xarray_index_""
            assert coord not in ds
            data = ds[v].data
            if hasattr(data, ""to_coo""):
                data = data.to_coo()
            ds[coord] = coord, data.linear_loc()
            dims = ds[v].dims
            ds[coord].attrs[""compress""] = "" "".join(dims)
            at = ds[v].attrs
            ds[v] = coord, data.data
            ds[v].attrs = at
            ds[v].attrs[""_fill_value""] = str(data.fill_value)
            for d in dims:
                if d not in ds:
                    ds[f""_len_{d}""] = len(dsorg[d])

    print(ds)
    ds.to_netcdf(fn)
```

```
def xr_open_dataset(fn):
    ds = xr.open_dataset(fn)

    def fromflat(shape, i):
        index = []
        for fac in shape[::-1]:
            index.append(i % fac)
            i //= fac
        return tuple(index[::-1])

    for c in ds.coords:
        if ""compress"" in ds[c].attrs:
            vs = c.split(""_"")
            if len(vs) < 5:
                continue
            if vs[-1] != """" or vs[-2] != ""index"" or vs[-3] != ""xarray"":
                continue
            v = ""_"".join(vs[1:-3])
            at = ds[v].attrs
            dat = ds[v].data
            fill = ds[v].attrs.pop(""_fill_value"", None)
            if fill:
                knownfails = {""nan"": np.nan, ""False"": False, ""True"": True}
                if fill in knownfails:
                    fill = knownfails[fill]
                else:
                    fill = np.fromstring(fill, dtype=dat.dtype)
            dims = ds[c].attrs[""compress""].split()
            shape = []
            for d in dims:
                try:
                    shape.append(len(ds[d]))
                except KeyError:
                    shape.append(int(ds[f""_len_{d}""].data))
                    ds = ds.drop_vars(f""_len_{d}"")

            locs = fromflat(shape, ds[c].data)
            data = sparse.COO(locs, ds[v].data, shape, fill_value=fill)
            ds[v] = dims, data, ds[v].attrs, ds[v].encoding
    print(ds)
    return ds
```

Has there been any progress since last year?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638947370
https://github.com/pydata/xarray/issues/4156#issuecomment-644417331,https://api.github.com/repos/pydata/xarray/issues/4156,644417331,MDEyOklzc3VlQ29tbWVudDY0NDQxNzMzMQ==,6815844,2020-06-15T22:13:50Z,2020-06-15T22:13:50Z,MEMBER,"Do we already have something similar *encoding* (and decoding) scheme to write (and read) data?
(does CFTime use a similar scheme?)
I think we don't have a scheme to save multiindex yet but need to manually convert by `reset_index`.
#1077

Maybe we can decide this encoding-decoding API before #1603.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638947370
https://github.com/pydata/xarray/issues/4156#issuecomment-644372749,https://api.github.com/repos/pydata/xarray/issues/4156,644372749,MDEyOklzc3VlQ29tbWVudDY0NDM3Mjc0OQ==,2448579,2020-06-15T20:32:01Z,2020-06-15T20:32:01Z,MEMBER,"Yes I think we will have to ""encode"" to something like this example
```
dimensions:
  lat=73;
  lon=96;
  landpoint=2381;
  depth=4;
variables:
  int landpoint(landpoint);
    landpoint:compress=""lat lon"";
  float landsoilt(depth,landpoint);
    landsoilt:long_name=""soil temperature"";
    landsoilt:units=""K"";
  float depth(depth);
  float lat(lat);
  float lon(lon);
data:
  landpoint=363, 364, 365, ...;
```

and then write that ""encoded"" dataset to file.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638947370
https://github.com/pydata/xarray/issues/4156#issuecomment-644368878,https://api.github.com/repos/pydata/xarray/issues/4156,644368878,MDEyOklzc3VlQ29tbWVudDY0NDM2ODg3OA==,6815844,2020-06-15T20:27:37Z,2020-06-15T20:27:37Z,MEMBER,"@dcherian 
Though I have no experience with this gather compression, it looks that python-netcdf4 does not have this function impremented.

One thing we can do is
`sparse -> multiindex -> reset_index > netCDF`
or maybe we can even add a function to skip constructing a multiindex but just make flattened index arrays from a sparse array.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,638947370