from __future__ import annotations import json import os import warnings import numpy as np from xarray import coding, conventions from xarray.backends.common import ( BACKEND_ENTRYPOINTS, AbstractWritableDataStore, BackendArray, BackendEntrypoint, _encode_variable_name, _normalize_path, ) from xarray.backends.store import StoreBackendEntrypoint from xarray.core import indexing from xarray.core.pycompat import integer_types from xarray.core.utils import ( FrozenDict, HiddenKeyDict, close_on_error, module_available, ) from xarray.core.variable import Variable # need some special secret attributes to tell us the dimensions DIMENSION_KEY = "_ARRAY_DIMENSIONS" def encode_zarr_attr_value(value): """ Encode a attribute value as something that can be serialized as json Many xarray datasets / variables have numpy arrays and values. This function handles encoding / decoding of such items. ndarray -> list scalar array -> scalar other -> other (no change) """ if isinstance(value, np.ndarray): encoded = value.tolist() # this checks if it's a scalar number elif isinstance(value, np.generic): encoded = value.item() else: encoded = value return encoded class ZarrArrayWrapper(BackendArray): __slots__ = ("datastore", "dtype", "shape", "variable_name") def __init__(self, variable_name, datastore): self.datastore = datastore self.variable_name = variable_name array = self.get_array() self.shape = array.shape dtype = array.dtype self.dtype = dtype def get_array(self): return self.datastore.zarr_group[self.variable_name] def __getitem__(self, key): array = self.get_array() if isinstance(key, indexing.BasicIndexer): return array[key.tuple] elif isinstance(key, indexing.VectorizedIndexer): return array.vindex[ indexing._arrayize_vectorized_indexer(key, self.shape).tuple ] else: assert isinstance(key, indexing.OuterIndexer) return array.oindex[key.tuple] # if self.ndim == 0: # could possibly have a work-around for 0d data here def _determine_zarr_chunks(enc_chunks, var_chunks, ndim, name, safe_chunks): """ Given encoding chunks (possibly None or []) and variable chunks (possibly None or []). """ # zarr chunk spec: # chunks : int or tuple of ints, optional # Chunk shape. If not provided, will be guessed from shape and dtype. # if there are no chunks in encoding and the variable data is a numpy # array, then we let zarr use its own heuristics to pick the chunks if not var_chunks and not enc_chunks: return None # if there are no chunks in encoding but there are dask chunks, we try to # use the same chunks in zarr # However, zarr chunks needs to be uniform for each array # http://zarr.readthedocs.io/en/latest/spec/v1.html#chunks # while dask chunks can be variable sized # http://dask.pydata.org/en/latest/array-design.html#chunks if var_chunks and not enc_chunks: if any(len(set(chunks[:-1])) > 1 for chunks in var_chunks): raise ValueError( "Zarr requires uniform chunk sizes except for final chunk. " f"Variable named {name!r} has incompatible dask chunks: {var_chunks!r}. " "Consider rechunking using `chunk()`." ) if any((chunks[0] < chunks[-1]) for chunks in var_chunks): raise ValueError( "Final chunk of Zarr array must be the same size or smaller " f"than the first. Variable named {name!r} has incompatible Dask chunks {var_chunks!r}." "Consider either rechunking using `chunk()` or instead deleting " "or modifying `encoding['chunks']`." ) # return the first chunk for each dimension return tuple(chunk[0] for chunk in var_chunks) # from here on, we are dealing with user-specified chunks in encoding # zarr allows chunks to be an integer, in which case it uses the same chunk # size on each dimension. # Here we re-implement this expansion ourselves. That makes the logic of # checking chunk compatibility easier if isinstance(enc_chunks, integer_types): enc_chunks_tuple = ndim * (enc_chunks,) else: enc_chunks_tuple = tuple(enc_chunks) if len(enc_chunks_tuple) != ndim: # throw away encoding chunks, start over return _determine_zarr_chunks(None, var_chunks, ndim, name, safe_chunks) for x in enc_chunks_tuple: if not isinstance(x, int): raise TypeError( "zarr chunk sizes specified in `encoding['chunks']` " "must be an int or a tuple of ints. " f"Instead found encoding['chunks']={enc_chunks_tuple!r} " f"for variable named {name!r}." ) # if there are chunks in encoding and the variable data is a numpy array, # we use the specified chunks if not var_chunks: return enc_chunks_tuple # the hard case # DESIGN CHOICE: do not allow multiple dask chunks on a single zarr chunk # this avoids the need to get involved in zarr synchronization / locking # From zarr docs: # "If each worker in a parallel computation is writing to a separate # region of the array, and if region boundaries are perfectly aligned # with chunk boundaries, then no synchronization is required." # TODO: incorporate synchronizer to allow writes from multiple dask # threads if var_chunks and enc_chunks_tuple: for zchunk, dchunks in zip(enc_chunks_tuple, var_chunks): for dchunk in dchunks[:-1]: if dchunk % zchunk: base_error = ( f"Specified zarr chunks encoding['chunks']={enc_chunks_tuple!r} for " f"variable named {name!r} would overlap multiple dask chunks {var_chunks!r}. " f"Writing this array in parallel with dask could lead to corrupted data." ) if safe_chunks: raise NotImplementedError( base_error + " Consider either rechunking using `chunk()`, deleting " "or modifying `encoding['chunks']`, or specify `safe_chunks=False`." ) return enc_chunks_tuple raise AssertionError("We should never get here. Function logic must be wrong.") def _get_zarr_dims_and_attrs(zarr_obj, dimension_key, try_nczarr): # Zarr arrays do not have dimensions. To get around this problem, we add # an attribute that specifies the dimension. We have to hide this attribute # when we send the attributes to the user. # zarr_obj can be either a zarr group or zarr array try: # Xarray-Zarr dimensions = zarr_obj.attrs[dimension_key] except KeyError as e: if not try_nczarr: raise KeyError( f"Zarr object is missing the attribute `{dimension_key}`, which is " "required for xarray to determine variable dimensions." ) from e # NCZarr defines dimensions through metadata in .zarray zarray_path = os.path.join(zarr_obj.path, ".zarray") zarray = json.loads(zarr_obj.store[zarray_path]) try: # NCZarr uses Fully Qualified Names dimensions = [ os.path.basename(dim) for dim in zarray["_NCZARR_ARRAY"]["dimrefs"] ] except KeyError as e: raise KeyError( f"Zarr object is missing the attribute `{dimension_key}` and the NCZarr metadata, " "which are required for xarray to determine variable dimensions." ) from e nc_attrs = [attr for attr in zarr_obj.attrs if attr.startswith("_NC")] attributes = HiddenKeyDict(zarr_obj.attrs, [dimension_key] + nc_attrs) return dimensions, attributes def extract_zarr_variable_encoding( variable, raise_on_invalid=False, name=None, safe_chunks=True ): """ Extract zarr encoding dictionary from xarray Variable Parameters ---------- variable : Variable raise_on_invalid : bool, optional Returns ------- encoding : dict Zarr encoding for `variable` """ encoding = variable.encoding.copy() valid_encodings = { "chunks", "compressor", "filters", "cache_metadata", "write_empty_chunks", } if raise_on_invalid: invalid = [k for k in encoding if k not in valid_encodings] if invalid: raise ValueError( f"unexpected encoding parameters for zarr backend: {invalid!r}" ) else: for k in list(encoding): if k not in valid_encodings: del encoding[k] chunks = _determine_zarr_chunks( encoding.get("chunks"), variable.chunks, variable.ndim, name, safe_chunks ) encoding["chunks"] = chunks return encoding # Function below is copied from conventions.encode_cf_variable. # The only change is to raise an error for object dtypes. def encode_zarr_variable(var, needs_copy=True, name=None): """ Converts an Variable into an Variable which follows some of the CF conventions: - Nans are masked using _FillValue (or the deprecated missing_value) - Rescaling via: scale_factor and add_offset - datetimes are converted to the CF 'units since time' format - dtype encodings are enforced. Parameters ---------- var : Variable A variable holding un-encoded data. Returns ------- out : Variable A variable which has been encoded as described above. """ var = conventions.encode_cf_variable(var, name=name) # zarr allows unicode, but not variable-length strings, so it's both # simpler and more compact to always encode as UTF-8 explicitly. # TODO: allow toggling this explicitly via dtype in encoding. coder = coding.strings.EncodedStringCoder(allows_unicode=True) var = coder.encode(var, name=name) var = coding.strings.ensure_fixed_length_bytes(var) return var def _validate_existing_dims(var_name, new_var, existing_var, region, append_dim): if new_var.dims != existing_var.dims: raise ValueError( f"variable {var_name!r} already exists with different " f"dimension names {existing_var.dims} != " f"{new_var.dims}, but changing variable " f"dimensions is not supported by to_zarr()." ) existing_sizes = {} for dim, size in existing_var.sizes.items(): if region is not None and dim in region: start, stop, stride = region[dim].indices(size) assert stride == 1 # region was already validated size = stop - start if dim != append_dim: existing_sizes[dim] = size new_sizes = {dim: size for dim, size in new_var.sizes.items() if dim != append_dim} if existing_sizes != new_sizes: raise ValueError( f"variable {var_name!r} already exists with different " f"dimension sizes: {existing_sizes} != {new_sizes}. " f"to_zarr() only supports changing dimension sizes when " f"explicitly appending, but append_dim={append_dim!r}." ) def _put_attrs(zarr_obj, attrs): """Raise a more informative error message for invalid attrs.""" try: zarr_obj.attrs.put(attrs) except TypeError as e: raise TypeError("Invalid attribute in Dataset.attrs.") from e return zarr_obj class ZarrStore(AbstractWritableDataStore): """Store for reading and writing data via zarr""" __slots__ = ( "zarr_group", "_append_dim", "_consolidate_on_close", "_group", "_mode", "_read_only", "_synchronizer", "_write_region", "_safe_chunks", ) @classmethod def open_group( cls, store, mode="r", synchronizer=None, group=None, consolidated=False, consolidate_on_close=False, chunk_store=None, storage_options=None, append_dim=None, write_region=None, safe_chunks=True, stacklevel=2, zarr_version=None, ): import zarr # zarr doesn't support pathlib.Path objects yet. zarr-python#601 if isinstance(store, os.PathLike): store = os.fspath(store) if zarr_version is None: # default to 2 if store doesn't specify it's version (e.g. a path) zarr_version = getattr(store, "_store_version", 2) open_kwargs = dict( mode=mode, synchronizer=synchronizer, path=group, ) open_kwargs["storage_options"] = storage_options if zarr_version > 2: open_kwargs["zarr_version"] = zarr_version if consolidated or consolidate_on_close: raise ValueError( "consolidated metadata has not been implemented for zarr " f"version {zarr_version} yet. Set consolidated=False for " f"zarr version {zarr_version}. See also " "https://github.com/zarr-developers/zarr-specs/issues/136" ) if consolidated is None: consolidated = False if chunk_store: open_kwargs["chunk_store"] = chunk_store if consolidated is None: consolidated = False if consolidated is None: try: zarr_group = zarr.open_consolidated(store, **open_kwargs) except KeyError: try: zarr_group = zarr.open_group(store, **open_kwargs) warnings.warn( "Failed to open Zarr store with consolidated metadata, " "but successfully read with non-consolidated metadata. " "This is typically much slower for opening a dataset. " "To silence this warning, consider:\n" "1. Consolidating metadata in this existing store with " "zarr.consolidate_metadata().\n" "2. Explicitly setting consolidated=False, to avoid trying " "to read consolidate metadata, or\n" "3. Explicitly setting consolidated=True, to raise an " "error in this case instead of falling back to try " "reading non-consolidated metadata.", RuntimeWarning, stacklevel=stacklevel, ) except zarr.errors.GroupNotFoundError: raise FileNotFoundError(f"No such file or directory: '{store}'") elif consolidated: # TODO: an option to pass the metadata_key keyword zarr_group = zarr.open_consolidated(store, **open_kwargs) else: zarr_group = zarr.open_group(store, **open_kwargs) return cls( zarr_group, mode, consolidate_on_close, append_dim, write_region, safe_chunks, ) def __init__( self, zarr_group, mode=None, consolidate_on_close=False, append_dim=None, write_region=None, safe_chunks=True, ): self.zarr_group = zarr_group self._read_only = self.zarr_group.read_only self._synchronizer = self.zarr_group.synchronizer self._group = self.zarr_group.path self._mode = mode self._consolidate_on_close = consolidate_on_close self._append_dim = append_dim self._write_region = write_region self._safe_chunks = safe_chunks @property def ds(self): # TODO: consider deprecating this in favor of zarr_group return self.zarr_group def open_store_variable(self, name, zarr_array): data = indexing.LazilyIndexedArray(ZarrArrayWrapper(name, self)) try_nczarr = self._mode == "r" dimensions, attributes = _get_zarr_dims_and_attrs( zarr_array, DIMENSION_KEY, try_nczarr ) attributes = dict(attributes) # TODO: this should not be needed once # https://github.com/zarr-developers/zarr-python/issues/1269 is resolved. attributes.pop("filters", None) encoding = { "chunks": zarr_array.chunks, "preferred_chunks": dict(zip(dimensions, zarr_array.chunks)), "compressor": zarr_array.compressor, "filters": zarr_array.filters, } # _FillValue needs to be in attributes, not encoding, so it will get # picked up by decode_cf if getattr(zarr_array, "fill_value") is not None: attributes["_FillValue"] = zarr_array.fill_value return Variable(dimensions, data, attributes, encoding) def get_variables(self): return FrozenDict( (k, self.open_store_variable(k, v)) for k, v in self.zarr_group.arrays() ) def get_attrs(self): return { k: v for k, v in self.zarr_group.attrs.asdict().items() if not k.startswith("_NC") } def get_dimensions(self): try_nczarr = self._mode == "r" dimensions = {} for k, v in self.zarr_group.arrays(): dim_names, _ = _get_zarr_dims_and_attrs(v, DIMENSION_KEY, try_nczarr) for d, s in zip(dim_names, v.shape): if d in dimensions and dimensions[d] != s: raise ValueError( f"found conflicting lengths for dimension {d} " f"({s} != {dimensions[d]})" ) dimensions[d] = s return dimensions def set_dimensions(self, variables, unlimited_dims=None): if unlimited_dims is not None: raise NotImplementedError( "Zarr backend doesn't know how to handle unlimited dimensions" ) def set_attributes(self, attributes): _put_attrs(self.zarr_group, attributes) def encode_variable(self, variable): variable = encode_zarr_variable(variable) return variable def encode_attribute(self, a): return encode_zarr_attr_value(a) def store( self, variables, attributes, check_encoding_set=frozenset(), writer=None, unlimited_dims=None, ): """ Top level method for putting data on this store, this method: - encodes variables/attributes - sets dimensions - sets variables Parameters ---------- variables : dict-like Dictionary of key/value (variable name / xr.Variable) pairs attributes : dict-like Dictionary of key/value (attribute name / attribute) pairs check_encoding_set : list-like List of variables that should be checked for invalid encoding values writer : ArrayWriter unlimited_dims : list-like List of dimension names that should be treated as unlimited dimensions. dimension on which the zarray will be appended only needed in append mode """ import zarr existing_variable_names = { vn for vn in variables if _encode_variable_name(vn) in self.zarr_group } new_variables = set(variables) - existing_variable_names variables_without_encoding = {vn: variables[vn] for vn in new_variables} variables_encoded, attributes = self.encode( variables_without_encoding, attributes ) if existing_variable_names: # Decode variables directly, without going via xarray.Dataset to # avoid needing to load index variables into memory. # TODO: consider making loading indexes lazy again? existing_vars, _, _ = conventions.decode_cf_variables( self.get_variables(), self.get_attrs() ) # Modified variables must use the same encoding as the store. vars_with_encoding = {} for vn in existing_variable_names: vars_with_encoding[vn] = variables[vn].copy(deep=False) vars_with_encoding[vn].encoding = existing_vars[vn].encoding vars_with_encoding, _ = self.encode(vars_with_encoding, {}) variables_encoded.update(vars_with_encoding) for var_name in existing_variable_names: new_var = variables_encoded[var_name] existing_var = existing_vars[var_name] _validate_existing_dims( var_name, new_var, existing_var, self._write_region, self._append_dim, ) if self._mode not in ["r", "r+"]: self.set_attributes(attributes) self.set_dimensions(variables_encoded, unlimited_dims=unlimited_dims) self.set_variables( variables_encoded, check_encoding_set, writer, unlimited_dims=unlimited_dims ) if self._consolidate_on_close: zarr.consolidate_metadata(self.zarr_group.store) def sync(self): pass def set_variables(self, variables, check_encoding_set, writer, unlimited_dims=None): """ This provides a centralized method to set the variables on the data store. Parameters ---------- variables : dict-like Dictionary of key/value (variable name / xr.Variable) pairs check_encoding_set : list-like List of variables that should be checked for invalid encoding values writer unlimited_dims : list-like List of dimension names that should be treated as unlimited dimensions. """ for vn, v in variables.items(): name = _encode_variable_name(vn) check = vn in check_encoding_set attrs = v.attrs.copy() dims = v.dims dtype = v.dtype shape = v.shape fill_value = attrs.pop("_FillValue", None) if v.encoding == {"_FillValue": None} and fill_value is None: v.encoding = {} if name in self.zarr_group: # existing variable # TODO: if mode="a", consider overriding the existing variable # metadata. This would need some case work properly with region # and append_dim. zarr_array = self.zarr_group[name] else: # new variable encoding = extract_zarr_variable_encoding( v, raise_on_invalid=check, name=vn, safe_chunks=self._safe_chunks ) encoded_attrs = {} # the magic for storing the hidden dimension data encoded_attrs[DIMENSION_KEY] = dims for k2, v2 in attrs.items(): encoded_attrs[k2] = self.encode_attribute(v2) if coding.strings.check_vlen_dtype(dtype) == str: dtype = str zarr_array = self.zarr_group.create( name, shape=shape, dtype=dtype, fill_value=fill_value, **encoding ) zarr_array = _put_attrs(zarr_array, encoded_attrs) write_region = self._write_region if self._write_region is not None else {} write_region = {dim: write_region.get(dim, slice(None)) for dim in dims} if self._append_dim is not None and self._append_dim in dims: # resize existing variable append_axis = dims.index(self._append_dim) assert write_region[self._append_dim] == slice(None) write_region[self._append_dim] = slice( zarr_array.shape[append_axis], None ) new_shape = list(zarr_array.shape) new_shape[append_axis] += v.shape[append_axis] zarr_array.resize(new_shape) region = tuple(write_region[dim] for dim in dims) writer.add(v.data, zarr_array, region) def close(self): pass def open_zarr( store, group=None, synchronizer=None, chunks="auto", decode_cf=True, mask_and_scale=True, decode_times=True, concat_characters=True, decode_coords=True, drop_variables=None, consolidated=None, overwrite_encoded_chunks=False, chunk_store=None, storage_options=None, decode_timedelta=None, use_cftime=None, zarr_version=None, **kwargs, ): """Load and decode a dataset from a Zarr store. The `store` object should be a valid store for a Zarr group. `store` variables must contain dimension metadata encoded in the `_ARRAY_DIMENSIONS` attribute or must have NCZarr format. Parameters ---------- store : MutableMapping or str A MutableMapping where a Zarr Group has been stored or a path to a directory in file system where a Zarr DirectoryStore has been stored. synchronizer : object, optional Array synchronizer provided to zarr group : str, optional Group path. (a.k.a. `path` in zarr terminology.) chunks : int or dict or tuple or {None, 'auto'}, optional Chunk sizes along each dimension, e.g., ``5`` or ``{'x': 5, 'y': 5}``. If `chunks='auto'`, dask chunks are created based on the variable's zarr chunks. If `chunks=None`, zarr array data will lazily convert to numpy arrays upon access. This accepts all the chunk specifications as Dask does. overwrite_encoded_chunks : bool, optional Whether to drop the zarr chunks encoded for each variable when a dataset is loaded with specified chunk sizes (default: False) decode_cf : bool, optional Whether to decode these variables, assuming they were saved according to CF conventions. mask_and_scale : bool, optional If True, replace array values equal to `_FillValue` with NA and scale values according to the formula `original_values * scale_factor + add_offset`, where `_FillValue`, `scale_factor` and `add_offset` are taken from variable attributes (if they exist). If the `_FillValue` or `missing_value` attribute contains multiple values a warning will be issued and all array values matching one of the multiple values will be replaced by NA. decode_times : bool, optional If True, decode times encoded in the standard NetCDF datetime format into datetime objects. Otherwise, leave them encoded as numbers. concat_characters : bool, optional If True, concatenate along the last dimension of character arrays to form string arrays. Dimensions will only be concatenated over (and removed) if they have no corresponding variable and if they are only used as the last dimension of character arrays. decode_coords : bool, optional If True, decode the 'coordinates' attribute to identify coordinates in the resulting dataset. drop_variables : str or iterable, optional A variable or list of variables to exclude from being parsed from the dataset. This may be useful to drop variables with problems or inconsistent values. consolidated : bool, optional Whether to open the store using zarr's consolidated metadata capability. Only works for stores that have already been consolidated. By default (`consolidate=None`), attempts to read consolidated metadata, falling back to read non-consolidated metadata if that fails. When the experimental ``zarr_version=3``, ``consolidated`` must be either be ``None`` or ``False``. chunk_store : MutableMapping, optional A separate Zarr store only for chunk data. storage_options : dict, optional Any additional parameters for the storage backend (ignored for local paths). decode_timedelta : bool, optional If True, decode variables and coordinates with time units in {'days', 'hours', 'minutes', 'seconds', 'milliseconds', 'microseconds'} into timedelta objects. If False, leave them encoded as numbers. If None (default), assume the same value of decode_time. use_cftime : bool, optional Only relevant if encoded dates come from a standard calendar (e.g. "gregorian", "proleptic_gregorian", "standard", or not specified). If None (default), attempt to decode times to ``np.datetime64[ns]`` objects; if this is not possible, decode times to ``cftime.datetime`` objects. If True, always decode times to ``cftime.datetime`` objects, regardless of whether or not they can be represented using ``np.datetime64[ns]`` objects. If False, always decode times to ``np.datetime64[ns]`` objects; if this is not possible raise an error. zarr_version : int or None, optional The desired zarr spec version to target (currently 2 or 3). The default of None will attempt to determine the zarr version from ``store`` when possible, otherwise defaulting to 2. Returns ------- dataset : Dataset The newly created dataset. See Also -------- open_dataset open_mfdataset References ---------- http://zarr.readthedocs.io/ """ from xarray.backends.api import open_dataset if chunks == "auto": try: import dask.array # noqa chunks = {} except ImportError: chunks = None if kwargs: raise TypeError( "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) ) backend_kwargs = { "synchronizer": synchronizer, "consolidated": consolidated, "overwrite_encoded_chunks": overwrite_encoded_chunks, "chunk_store": chunk_store, "storage_options": storage_options, "stacklevel": 4, "zarr_version": zarr_version, } ds = open_dataset( filename_or_obj=store, group=group, decode_cf=decode_cf, mask_and_scale=mask_and_scale, decode_times=decode_times, concat_characters=concat_characters, decode_coords=decode_coords, engine="zarr", chunks=chunks, drop_variables=drop_variables, backend_kwargs=backend_kwargs, decode_timedelta=decode_timedelta, use_cftime=use_cftime, zarr_version=zarr_version, ) return ds class ZarrBackendEntrypoint(BackendEntrypoint): """ Backend for ".zarr" files based on the zarr package. For more information about the underlying library, visit: https://zarr.readthedocs.io/en/stable See Also -------- backends.ZarrStore """ available = module_available("zarr") description = "Open zarr files (.zarr) using zarr in Xarray" url = "https://docs.xarray.dev/en/stable/generated/xarray.backends.ZarrBackendEntrypoint.html" def guess_can_open(self, filename_or_obj): try: _, ext = os.path.splitext(filename_or_obj) except TypeError: return False return ext in {".zarr"} def open_dataset( self, filename_or_obj, mask_and_scale=True, decode_times=True, concat_characters=True, decode_coords=True, drop_variables=None, use_cftime=None, decode_timedelta=None, group=None, mode="r", synchronizer=None, consolidated=None, chunk_store=None, storage_options=None, stacklevel=3, zarr_version=None, ): filename_or_obj = _normalize_path(filename_or_obj) store = ZarrStore.open_group( filename_or_obj, group=group, mode=mode, synchronizer=synchronizer, consolidated=consolidated, consolidate_on_close=False, chunk_store=chunk_store, storage_options=storage_options, stacklevel=stacklevel + 1, zarr_version=zarr_version, ) store_entrypoint = StoreBackendEntrypoint() with close_on_error(store): ds = store_entrypoint.open_dataset( store, mask_and_scale=mask_and_scale, decode_times=decode_times, concat_characters=concat_characters, decode_coords=decode_coords, drop_variables=drop_variables, use_cftime=use_cftime, decode_timedelta=decode_timedelta, ) return ds BACKEND_ENTRYPOINTS["zarr"] = ZarrBackendEntrypoint