Use a default fill_value of NaN for floats in Zarr v3 #10757
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Zarr stores written with Xarray now consistently use a default Zarr fill value
of ``NaN`` for float variables, for both Zarr v2 and v3. All
other dtypes still use the Zarr default ``fill_value`` of zero. To customize,
explicitly set encoding in :py:meth:`~Dataset.to_zarr`, e.g.,
``encoding=dict.fromkey(ds.data_vars, {'fill_value': 0})``.
Fixes pydata#10646
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xarray/backends/zarr.py
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| # For floating point data, Xarray defaults to a fill_value | ||
| # of NaN (unlike Zarr, which uses zero): | ||
| # https://github.com/pydata/xarray/issues/10646 | ||
| fill_value = v.dtype.type(np.nan) |
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I'm not sure whether there is any point to use a typed nan, since ultimately it just ends up as JSON. Somewhat related to zarr-developers/zarr-python#3478
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Good point. Probably best to just use plain np.nan then.
| with self.temp_dir() as (d, store): | ||
| inputs.to_zarr(store, compute=False) | ||
| with open_dataset(store) as on_disk: | ||
| assert np.isnan(on_disk.variables["floats"].encoding["_FillValue"]) |
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Not a blocker, but it seems a bit unnecessary to have _FillValue set at all in this scenario, doesn't it? We can just write NaNs directly?
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Agreed. Created #10781 so we don't forget about this.
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Zarr stores written with Xarray now consistently use a default Zarr fill value of
NaNfor float variables, for both Zarr v2 and v3. All other dtypes still use the Zarr defaultfill_valueof zero. To customize, explicitly set encoding inDataset.to_zarr, e.g.,encoding=dict.fromkey(ds.data_vars, {'fill_value': 0}).Fixes #10646
whats-new.rst