Add support for netCDF4.EnumType

doc/whats-new.rst

@@ -221,6 +221,10 @@ New Features
 
 - Use `opt_einsum <https://optimized-einsum.readthedocs.io/en/stable/>`_ for :py:func:`xarray.dot` by default if installed.
   By `Deepak Cherian <https://github.com/dcherian>`_. (:issue:`7764`, :pull:`8373`).
+- Decode/Encode netCDF4 enums and store the enum definition in dataarrays' dtype metadata.
+  If multiple variables share the same enum in netCDF4, each dataarray will have its own
+  enum definition in their respective dtype metadata.
+  By `Abel Aoun <https://github.com/bzah>_`(:issue:`8144`, :pull:`8147`)
 - Add ``DataArray.dt.total_seconds()`` method to match the Pandas API. (:pull:`8435`).
   By `Ben Mares <https://github.com/maresb>`_.
 - Allow passing ``region="auto"`` in  :py:meth:`Dataset.to_zarr` to automatically infer the

xarray/backends/netCDF4_.py

@@ -49,7 +49,6 @@
 # string used by netCDF4.
 _endian_lookup = {"=": "native", ">": "big", "<": "little", "|": "native"}
 
-
 NETCDF4_PYTHON_LOCK = combine_locks([NETCDFC_LOCK, HDF5_LOCK])
 
 
@@ -141,7 +140,9 @@ def _check_encoding_dtype_is_vlen_string(dtype):
         )
 
 
-def _get_datatype(var, nc_format="NETCDF4", raise_on_invalid_encoding=False):
+def _get_datatype(
+    var, nc_format="NETCDF4", raise_on_invalid_encoding=False
+) -> np.dtype:
     if nc_format == "NETCDF4":
         return _nc4_dtype(var)
     if "dtype" in var.encoding:
@@ -234,13 +235,13 @@ def _force_native_endianness(var):
 
 
 def _extract_nc4_variable_encoding(
-    variable,
+    variable: Variable,
     raise_on_invalid=False,
     lsd_okay=True,
     h5py_okay=False,
     backend="netCDF4",
     unlimited_dims=None,
-):
+) -> dict[str, Any]:
     if unlimited_dims is None:
         unlimited_dims = ()
 
@@ -308,7 +309,7 @@ def _extract_nc4_variable_encoding(
     return encoding
 
 
-def _is_list_of_strings(value):
+def _is_list_of_strings(value) -> bool:
     arr = np.asarray(value)
     return arr.dtype.kind in ["U", "S"] and arr.size > 1
 
@@ -414,13 +415,25 @@ def _acquire(self, needs_lock=True):
     def ds(self):
         return self._acquire()
 
-    def open_store_variable(self, name, var):
+    def open_store_variable(self, name: str, var):
+        import netCDF4
+
         dimensions = var.dimensions
-        data = indexing.LazilyIndexedArray(NetCDF4ArrayWrapper(name, self))
         attributes = {k: var.getncattr(k) for k in var.ncattrs()}
+        data = indexing.LazilyIndexedArray(NetCDF4ArrayWrapper(name, self))
+        encoding: dict[str, Any] = {}
+        if isinstance(var.datatype, netCDF4.EnumType):
+            encoding["dtype"] = np.dtype(
+                data.dtype,
+                metadata={
+                    "enum": var.datatype.enum_dict,
+                    "enum_name": var.datatype.name,
+                },
+            )
+        else:
+            encoding["dtype"] = var.dtype
         _ensure_fill_value_valid(data, attributes)
         # netCDF4 specific encoding; save _FillValue for later
-        encoding = {}
         filters = var.filters()
         if filters is not None:
             encoding.update(filters)
@@ -440,7 +453,6 @@ def open_store_variable(self, name, var):
         # save source so __repr__ can detect if it's local or not
         encoding["source"] = self._filename
         encoding["original_shape"] = var.shape
-        encoding["dtype"] = var.dtype
 
         return Variable(dimensions, data, attributes, encoding)
 
@@ -485,21 +497,24 @@ def encode_variable(self, variable):
         return variable
 
     def prepare_variable(
-        self, name, variable, check_encoding=False, unlimited_dims=None
+        self, name, variable: Variable, check_encoding=False, unlimited_dims=None
     ):
         _ensure_no_forward_slash_in_name(name)
-
+        attrs = variable.attrs.copy()
+        fill_value = attrs.pop("_FillValue", None)
         datatype = _get_datatype(
             variable, self.format, raise_on_invalid_encoding=check_encoding
         )
-        attrs = variable.attrs.copy()
-
-        fill_value = attrs.pop("_FillValue", None)
-
+        # check enum metadata and use netCDF4.EnumType
+        if (
+            (meta := np.dtype(datatype).metadata)
+            and (e_name := meta.get("enum_name"))
+            and (e_dict := meta.get("enum"))
+        ):
+            datatype = self._build_and_get_enum(name, datatype, e_name, e_dict)
         encoding = _extract_nc4_variable_encoding(
             variable, raise_on_invalid=check_encoding, unlimited_dims=unlimited_dims
         )
-
         if name in self.ds.variables:
             nc4_var = self.ds.variables[name]
         else:
@@ -527,6 +542,33 @@ def prepare_variable(
 
         return target, variable.data
 
+    def _build_and_get_enum(
+        self, var_name: str, dtype: np.dtype, enum_name: str, enum_dict: dict[str, int]
+    ) -> Any:
+        """
+        Add or get the netCDF4 Enum based on the dtype in encoding.
+        The return type should be ``netCDF4.EnumType``,
+        but we avoid importing netCDF4 globally for performances.
+        """
+        if enum_name not in self.ds.enumtypes:
+            return self.ds.createEnumType(
+                dtype,
+                enum_name,
+                enum_dict,
+            )
+        datatype = self.ds.enumtypes[enum_name]
+        if datatype.enum_dict != enum_dict:
+            error_msg = (
+                f"Cannot save variable `{var_name}` because an enum"
+                f" `{enum_name}` already exists in the Dataset but have"
+                " a different definition. To fix this error, make sure"
+                " each variable have a uniquely named enum in their"
+                " `encoding['dtype'].metadata` or, if they should share same"
+                " the same enum type, make sure the enums are identical."
+            )
+            raise ValueError(error_msg)
+        return datatype
+
     def sync(self):
         self.ds.sync()
 

xarray/coding/variables.py

@@ -566,11 +566,30 @@ def decode(self):
 
 class ObjectVLenStringCoder(VariableCoder):
     def encode(self):
-        return NotImplementedError
+        raise NotImplementedError
 
     def decode(self, variable: Variable, name: T_Name = None) -> Variable:
         if variable.dtype == object and variable.encoding.get("dtype", False) == str:
             variable = variable.astype(variable.encoding["dtype"])
             return variable
         else:
             return variable
+
+
+class NativeEnumCoder(VariableCoder):
+    """Encode Enum into variable dtype metadata."""
+
+    def encode(self, variable: Variable, name: T_Name = None) -> Variable:
+        if (
+            "dtype" in variable.encoding
+            and np.dtype(variable.encoding["dtype"]).metadata
+            and "enum" in variable.encoding["dtype"].metadata
+        ):
+            dims, data, attrs, encoding = unpack_for_encoding(variable)
+            data = data.astype(dtype=variable.encoding.pop("dtype"))
+            return Variable(dims, data, attrs, encoding, fastpath=True)
+        else:
+            return variable
+
+    def decode(self, variable: Variable, name: T_Name = None) -> Variable:
+        raise NotImplementedError()

xarray/conventions.py

@@ -48,10 +48,6 @@
     T_DatasetOrAbstractstore = Union[Dataset, AbstractDataStore]
 
 
-def _var_as_tuple(var: Variable) -> T_VarTuple:
-    return var.dims, var.data, var.attrs.copy(), var.encoding.copy()
-
-
 def _infer_dtype(array, name=None):
     """Given an object array with no missing values, infer its dtype from all elements."""
     if array.dtype.kind != "O":
@@ -111,7 +107,7 @@ def _copy_with_dtype(data, dtype: np.typing.DTypeLike):
 def ensure_dtype_not_object(var: Variable, name: T_Name = None) -> Variable:
     # TODO: move this from conventions to backends? (it's not CF related)
     if var.dtype.kind == "O":
-        dims, data, attrs, encoding = _var_as_tuple(var)
+        dims, data, attrs, encoding = variables.unpack_for_encoding(var)
 
         # leave vlen dtypes unchanged
         if strings.check_vlen_dtype(data.dtype) is not None:
@@ -162,7 +158,7 @@ def encode_cf_variable(
     var: Variable, needs_copy: bool = True, name: T_Name = None
 ) -> Variable:
     """
-    Converts an Variable into an Variable which follows some
+    Converts a Variable into a Variable which follows some
     of the CF conventions:
 
         - Nans are masked using _FillValue (or the deprecated missing_value)
@@ -188,6 +184,7 @@ def encode_cf_variable(
         variables.CFScaleOffsetCoder(),
         variables.CFMaskCoder(),
         variables.UnsignedIntegerCoder(),
+        variables.NativeEnumCoder(),
         variables.NonStringCoder(),
         variables.DefaultFillvalueCoder(),
         variables.BooleanCoder(),
@@ -447,7 +444,7 @@ def stackable(dim: Hashable) -> bool:
                 decode_timedelta=decode_timedelta,
             )
         except Exception as e:
-            raise type(e)(f"Failed to decode variable {k!r}: {e}")
+            raise type(e)(f"Failed to decode variable {k!r}: {e}") from e
         if decode_coords in [True, "coordinates", "all"]:
             var_attrs = new_vars[k].attrs
             if "coordinates" in var_attrs:
@@ -633,7 +630,11 @@ def cf_decoder(
     decode_cf_variable
     """
     variables, attributes, _ = decode_cf_variables(
-        variables, attributes, concat_characters, mask_and_scale, decode_times
+        variables,
+        attributes,
+        concat_characters,
+        mask_and_scale,
+        decode_times,
     )
     return variables, attributes
 

xarray/core/dataarray.py

@@ -4069,6 +4069,9 @@ def to_netcdf(
         name is the same as a coordinate name, then it is given the name
         ``"__xarray_dataarray_variable__"``.
 
+        [netCDF4 backend only] netCDF4 enums are decoded into the
+        dataarray dtype metadata.
+
         See Also
         --------
         Dataset.to_netcdf

xarray/tests/test_backends.py

@@ -1704,6 +1704,126 @@ def test_raise_on_forward_slashes_in_names(self) -> None:
                 with self.roundtrip(ds):
                     pass
 
+    @requires_netCDF4
+    def test_encoding_enum__no_fill_value(self):
+        with create_tmp_file() as tmp_file:
+            cloud_type_dict = {"clear": 0, "cloudy": 1}
+            with nc4.Dataset(tmp_file, mode="w") as nc:
+                nc.createDimension("time", size=2)
+                cloud_type = nc.createEnumType("u1", "cloud_type", cloud_type_dict)
+                v = nc.createVariable(
+                    "clouds",
+                    cloud_type,
+                    "time",
+                    fill_value=None,
+                )
+                v[:] = 1
+            with open_dataset(tmp_file) as original:
+                save_kwargs = {}
+                if self.engine == "h5netcdf":
+                    save_kwargs["invalid_netcdf"] = True
+                with self.roundtrip(original, save_kwargs=save_kwargs) as actual:
+                    assert_equal(original, actual)
+                    assert (
+                        actual.clouds.encoding["dtype"].metadata["enum"]
+                        == cloud_type_dict
+                    )
+                    if self.engine != "h5netcdf":
+                        # not implemented in h5netcdf yet
+                        assert (
+                            actual.clouds.encoding["dtype"].metadata["enum_name"]
+                            == "cloud_type"
+                        )
+
+    @requires_netCDF4
+    def test_encoding_enum__multiple_variable_with_enum(self):
+        with create_tmp_file() as tmp_file:
+            cloud_type_dict = {"clear": 0, "cloudy": 1, "missing": 255}
+            with nc4.Dataset(tmp_file, mode="w") as nc:
+                nc.createDimension("time", size=2)
+                cloud_type = nc.createEnumType("u1", "cloud_type", cloud_type_dict)
+                nc.createVariable(
+                    "clouds",
+                    cloud_type,
+                    "time",
+                    fill_value=255,
+                )
+                nc.createVariable(
+                    "tifa",
+                    cloud_type,
+                    "time",
+                    fill_value=255,
+                )
+            with open_dataset(tmp_file) as original:
+                save_kwargs = {}
+                if self.engine == "h5netcdf":
+                    save_kwargs["invalid_netcdf"] = True
+                with self.roundtrip(original, save_kwargs=save_kwargs) as actual:
+                    assert_equal(original, actual)
+                    assert (
+                        actual.clouds.encoding["dtype"] == actual.tifa.encoding["dtype"]
+                    )
+                    assert (
+                        actual.clouds.encoding["dtype"].metadata
+                        == actual.tifa.encoding["dtype"].metadata
+                    )
+                    assert (
+                        actual.clouds.encoding["dtype"].metadata["enum"]
+                        == cloud_type_dict
+                    )
+                    if self.engine != "h5netcdf":
+                        # not implemented in h5netcdf yet
+                        assert (
+                            actual.clouds.encoding["dtype"].metadata["enum_name"]
+                            == "cloud_type"
+                        )
+
+    @requires_netCDF4
+    def test_encoding_enum__error_multiple_variable_with_changing_enum(self):
+        """
+        Given 2 variables, if they share the same enum type,
+        the 2 enum definition should be identical.
+        """
+        with create_tmp_file() as tmp_file:
+            cloud_type_dict = {"clear": 0, "cloudy": 1, "missing": 255}
+            with nc4.Dataset(tmp_file, mode="w") as nc:
+                nc.createDimension("time", size=2)
+                cloud_type = nc.createEnumType("u1", "cloud_type", cloud_type_dict)
+                nc.createVariable(
+                    "clouds",
+                    cloud_type,
+                    "time",
+                    fill_value=255,
+                )
+                nc.createVariable(
+                    "tifa",
+                    cloud_type,
+                    "time",
+                    fill_value=255,
+                )
+            with open_dataset(tmp_file) as original:
+                assert (
+                    original.clouds.encoding["dtype"].metadata
+                    == original.tifa.encoding["dtype"].metadata
+                )
+                modified_enum = original.clouds.encoding["dtype"].metadata["enum"]
+                modified_enum.update({"neblig": 2})
+                original.clouds.encoding["dtype"] = np.dtype(
+                    "u1",
+                    metadata={"enum": modified_enum, "enum_name": "cloud_type"},
+                )
+                if self.engine != "h5netcdf":
+                    # not implemented yet in h5netcdf
+                    with pytest.raises(
+                        ValueError,
+                        match=(
+                            "Cannot save variable .*"
+                            " because an enum `cloud_type` already exists in the Dataset .*"
+                        ),
+                    ):
+                        with self.roundtrip(original):
+                            pass
+
 
 @requires_netCDF4
 class TestNetCDF4Data(NetCDF4Base):