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z)#MN_EX)p(dF?J0$?ExP(QQ>K&gcM@iF1$)x^m@aGe4^Px~bDr9ibTE(G*qs|AAhK z)yN1w(+Hj9)|0Sck>;GOt6@78H&VHbtcr&0oK7uyq@DT?f5g>?v{fBqv?K80o(vq? zy>2!OBou&m=P=;JeuB{kAp_wbBKH7uR-&$qxkJRFa)8D_pOfOWAkBqX1Y$;nQ!MBh z(k8=3O0*aLE|?*c0>W1{Vj>hG&m34+C^+}b=;WSLt2w^prox7GO`>?bSqXocvb+*q z&*z~_m9K+sZnS;8Qx~@IL+v~$MF2#Se;Na9eEy6l(j*p5{KW+8@!jYWrxe#GDLDQ{ zFt*twLotOsJ|QKGgvLuS(6(-JTx>K(msqJ>m#bi!Tm{?bx?RsEc(Robpj3s? 0) and (chunk_spec is not None): - # does this cause any data to be read? - token2 = tokenize(name, var._data) - name2 = "zarr-%s" % token2 - var = var.chunk(chunk_spec, name=name2, lock=None) - if overwrite_encoded_chunks and var.chunks is not None: - var.encoding["chunks"] = tuple(x[0] for x in var.chunks) - return var - else: - return var - - variables = {k: maybe_chunk(k, v, chunks) for k, v in ds.variables.items()} - return ds._replace_vars_and_dims(variables) + ds = open_dataset() + + # if "auto_chunk" in kwargs: + # auto_chunk = kwargs.pop("auto_chunk") + # if auto_chunk: + # chunks = "auto" # maintain backwards compatibility + # else: + # chunks = None + + # warnings.warn( + # "auto_chunk is deprecated. Use chunks='auto' instead.", + # FutureWarning, + # stacklevel=2, + # ) + + # if kwargs: + # raise TypeError( + # "open_zarr() got unexpected keyword arguments " + ",".join(kwargs.keys()) + # ) + + # if not isinstance(chunks, (int, dict)): + # if chunks != "auto" and chunks is not None: + # raise ValueError( + # "chunks must be an int, dict, 'auto', or None. " + # "Instead found %s. " % chunks + # ) + + # if chunks == "auto": + # try: + # import dask.array # noqa + # except ImportError: + # chunks = None + + # if not decode_cf: + # mask_and_scale = False + # decode_times = False + # concat_characters = False + # decode_coords = False + + # def maybe_decode_store(store, lock=False): + # ds = conventions.decode_cf( + # store, + # mask_and_scale=mask_and_scale, + # decode_times=decode_times, + # concat_characters=concat_characters, + # decode_coords=decode_coords, + # drop_variables=drop_variables, + # ) + + # # TODO: this is where we would apply caching + + # return ds + + # # Zarr supports a wide range of access modes, but for now xarray either + # # reads or writes from a store, never both. For open_zarr, we only read + # mode = "r" + # zarr_store = ZarrStore.open_group( + # store, + # mode=mode, + # synchronizer=synchronizer, + # group=group, + # consolidated=consolidated, + # ) + # ds = maybe_decode_store(zarr_store) + + # # auto chunking needs to be here and not in ZarrStore because variable + # # chunks do not survive decode_cf + # # return trivial case + # if not chunks: + # return ds + + # # adapted from Dataset.Chunk() + # if isinstance(chunks, int): + # chunks = dict.fromkeys(ds.dims, chunks) + + # if isinstance(chunks, tuple) and len(chunks) == len(ds.dims): + # chunks = dict(zip(ds.dims, chunks)) + + # def get_chunk(name, var, chunks): + # chunk_spec = dict(zip(var.dims, var.encoding.get("chunks"))) + + # # Coordinate labels aren't chunked + # if var.ndim == 1 and var.dims[0] == name: + # return chunk_spec + + # if chunks == "auto": + # return chunk_spec + + # for dim in var.dims: + # if dim in chunks: + # spec = chunks[dim] + # if isinstance(spec, int): + # spec = (spec,) + # if isinstance(spec, (tuple, list)) and chunk_spec[dim]: + # if any(s % chunk_spec[dim] for s in spec): + # warnings.warn( + # "Specified Dask chunks %r would " + # "separate Zarr chunk shape %r for " + # "dimension %r. This significantly " + # "degrades performance. Consider " + # "rechunking after loading instead." + # % (chunks[dim], chunk_spec[dim], dim), + # stacklevel=2, + # ) + # chunk_spec[dim] = chunks[dim] + # return chunk_spec + + # def maybe_chunk(name, var, chunks): + # from dask.base import tokenize + + # chunk_spec = get_chunk(name, var, chunks) + + # if (var.ndim > 0) and (chunk_spec is not None): + # # does this cause any data to be read? + # token2 = tokenize(name, var._data) + # name2 = "zarr-%s" % token2 + # var = var.chunk(chunk_spec, name=name2, lock=None) + # if overwrite_encoded_chunks and var.chunks is not None: + # var.encoding["chunks"] = tuple(x[0] for x in var.chunks) + # return var + # else: + # return var + + # variables = {k: maybe_chunk(k, v, chunks) for k, v in ds.variables.items()} + # return ds._replace_vars_and_dims(variables) + return ds