Harmonize encoding from h5netcdf with netcdf4

We were trying to figure why certain compression features were not
included in our h5netcdf backend.

This PR to h5netcdf should provide the filters variable, but I
understand that it is too bleeding edge for you all to pin to
h5netcdf/h5netcdf#307

Author: hmaarrfk

xarray/backends/h5netcdf_.py

@@ -266,41 +266,8 @@ def open_store_variable(self, name, var):
         dimensions = var.dimensions
         data = indexing.LazilyIndexedArray(H5NetCDFArrayWrapper(name, self))
         attrs = _read_attributes(var)
-
-        # netCDF4 specific encoding
-        encoding = {
-            "chunksizes": var.chunks,
-            "fletcher32": var.fletcher32,
-            "shuffle": var.shuffle,
-        }
-        if var.chunks:
-            encoding["preferred_chunks"] = dict(
-                zip(var.dimensions, var.chunks, strict=True)
-            )
-        # Convert h5py-style compression options to NetCDF4-Python
-        # style, if possible
-        if var.compression == "gzip":
-            encoding["zlib"] = True
-            encoding["complevel"] = var.compression_opts
-        elif var.compression is not None:
-            encoding["compression"] = var.compression
-            encoding["compression_opts"] = var.compression_opts
-
-        # save source so __repr__ can detect if it's local or not
-        encoding["source"] = self._filename
-        encoding["original_shape"] = data.shape
-
-        vlen_dtype = h5py.check_dtype(vlen=var.dtype)
-        if vlen_dtype is str:
-            encoding["dtype"] = str
-        elif vlen_dtype is not None:  # pragma: no cover
-            # xarray doesn't support writing arbitrary vlen dtypes yet.
-            pass
-        # just check if datatype is available and create dtype
-        # this check can be removed if h5netcdf >= 1.4.0 for any environment
-        elif (datatype := getattr(var, "datatype", None)) and isinstance(
-            datatype, h5netcdf.core.EnumType
-        ):
+        encoding: dict[str, Any] = {}
+        if (datatype := var.datatype) and isinstance(datatype, h5netcdf.core.EnumType):
             encoding["dtype"] = np.dtype(
                 data.dtype,
                 metadata={
@@ -309,7 +276,48 @@ def open_store_variable(self, name, var):
                 },
             )
         else:
-            encoding["dtype"] = var.dtype
+            vlen_dtype = h5py.check_dtype(vlen=var.dtype)
+            if vlen_dtype is str:
+                encoding["dtype"] = str
+            elif vlen_dtype is not None:  # pragma: no cover
+                # xarray doesn't support writing arbitrary vlen dtypes yet.
+                encoding["dtype"] = var.dtype
+            else:
+                encoding["dtype"] = var.dtype
+
+        if var.chunks:
+            encoding["contiguous"] = False
+            encoding["chunksizes"] = var.chunks
+            encoding["preferred_chunks"] = dict(
+                zip(var.dimensions, var.chunks, strict=True)
+            )
+        else:
+            encoding["contiguous"] = True
+            encoding["chunksizes"] = None
+
+        # filters only exists in an unreleased version of h5netcdf for now
+        if hasattr(var, "filters"):
+            filters = var.filters()
+            if filters is not None:
+                encoding.update(filters)
+        else:
+            # Continue with the old path before the filters() method existed
+            encoding |= {
+                "chunksizes": var.chunks,
+                "fletcher32": var.fletcher32,
+                "shuffle": var.shuffle,
+            }
+            if var.compression == "gzip":
+                encoding["zlib"] = True
+                encoding["complevel"] = var.compression_opts
+            # I'm pretty sure compression is always None if it is not gzip
+            # The filters() method returns more information
+            elif var.compression is not None:
+                encoding["compression"] = var.compression
+                encoding["compression_opts"] = var.compression_opts
+
+        encoding["source"] = self._filename
+        encoding["original_shape"] = data.shape
 
         return Variable(dimensions, data, attrs, encoding)