from __future__ import annotations import logging import os import time import traceback from collections.abc import Iterable from glob import glob from typing import TYPE_CHECKING, Any, ClassVar import numpy as np from xarray.conventions import cf_encoder from xarray.core import indexing from xarray.core.utils import FrozenDict, NdimSizeLenMixin, is_remote_uri from xarray.namedarray.parallelcompat import get_chunked_array_type from xarray.namedarray.pycompat import is_chunked_array if TYPE_CHECKING: from io import BufferedIOBase from xarray.core.dataset import Dataset from xarray.core.datatree import DataTree from xarray.core.types import NestedSequence # Create a logger object, but don't add any handlers. Leave that to user code. logger = logging.getLogger(__name__) NONE_VAR_NAME = "__values__" def _normalize_path(path): """ Normalize pathlikes to string. Parameters ---------- path : Path to file. Examples -------- >>> from pathlib import Path >>> directory = Path(xr.backends.common.__file__).parent >>> paths_path = Path(directory).joinpath("comm*n.py") >>> paths_str = xr.backends.common._normalize_path(paths_path) >>> print([type(p) for p in (paths_str,)]) [] """ if isinstance(path, os.PathLike): path = os.fspath(path) if isinstance(path, str) and not is_remote_uri(path): path = os.path.abspath(os.path.expanduser(path)) return path def _find_absolute_paths( paths: str | os.PathLike | NestedSequence[str | os.PathLike], **kwargs ) -> list[str]: """ Find absolute paths from the pattern. Parameters ---------- paths : Path(s) to file(s). Can include wildcards like * . **kwargs : Extra kwargs. Mainly for fsspec. Examples -------- >>> from pathlib import Path >>> directory = Path(xr.backends.common.__file__).parent >>> paths = str(Path(directory).joinpath("comm*n.py")) # Find common with wildcard >>> paths = xr.backends.common._find_absolute_paths(paths) >>> [Path(p).name for p in paths] ['common.py'] """ if isinstance(paths, str): if is_remote_uri(paths) and kwargs.get("engine", None) == "zarr": try: from fsspec.core import get_fs_token_paths except ImportError as e: raise ImportError( "The use of remote URLs for opening zarr requires the package fsspec" ) from e fs, _, _ = get_fs_token_paths( paths, mode="rb", storage_options=kwargs.get("backend_kwargs", {}).get( "storage_options", {} ), expand=False, ) tmp_paths = fs.glob(fs._strip_protocol(paths)) # finds directories paths = [fs.get_mapper(path) for path in tmp_paths] elif is_remote_uri(paths): raise ValueError( "cannot do wild-card matching for paths that are remote URLs " f"unless engine='zarr' is specified. Got paths: {paths}. " "Instead, supply paths as an explicit list of strings." ) else: paths = sorted(glob(_normalize_path(paths))) elif isinstance(paths, os.PathLike): paths = [os.fspath(paths)] else: paths = [os.fspath(p) if isinstance(p, os.PathLike) else p for p in paths] return paths def _encode_variable_name(name): if name is None: name = NONE_VAR_NAME return name def _decode_variable_name(name): if name == NONE_VAR_NAME: name = None return name def _iter_nc_groups(root, parent="/"): from xarray.core.treenode import NodePath parent = NodePath(parent) for path, group in root.groups.items(): gpath = parent / path yield str(gpath) yield from _iter_nc_groups(group, parent=gpath) def find_root_and_group(ds): """Find the root and group name of a netCDF4/h5netcdf dataset.""" hierarchy = () while ds.parent is not None: hierarchy = (ds.name.split("/")[-1],) + hierarchy ds = ds.parent group = "/" + "/".join(hierarchy) return ds, group def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500): """ Robustly index an array, using retry logic with exponential backoff if any of the errors ``catch`` are raised. The initial_delay is measured in ms. With the default settings, the maximum delay will be in the range of 32-64 seconds. """ assert max_retries >= 0 for n in range(max_retries + 1): try: return array[key] except catch: if n == max_retries: raise base_delay = initial_delay * 2**n next_delay = base_delay + np.random.randint(base_delay) msg = ( f"getitem failed, waiting {next_delay} ms before trying again " f"({max_retries - n} tries remaining). Full traceback: {traceback.format_exc()}" ) logger.debug(msg) time.sleep(1e-3 * next_delay) class BackendArray(NdimSizeLenMixin, indexing.ExplicitlyIndexed): __slots__ = () def get_duck_array(self, dtype: np.typing.DTypeLike = None): key = indexing.BasicIndexer((slice(None),) * self.ndim) return self[key] # type: ignore [index] class AbstractDataStore: __slots__ = () def get_dimensions(self): # pragma: no cover raise NotImplementedError() def get_attrs(self): # pragma: no cover raise NotImplementedError() def get_variables(self): # pragma: no cover raise NotImplementedError() def get_encoding(self): return {} def load(self): """ This loads the variables and attributes simultaneously. A centralized loading function makes it easier to create data stores that do automatic encoding/decoding. For example:: class SuffixAppendingDataStore(AbstractDataStore): def load(self): variables, attributes = AbstractDataStore.load(self) variables = {'%s_suffix' % k: v for k, v in variables.items()} attributes = {'%s_suffix' % k: v for k, v in attributes.items()} return variables, attributes This function will be called anytime variables or attributes are requested, so care should be taken to make sure its fast. """ variables = FrozenDict( (_decode_variable_name(k), v) for k, v in self.get_variables().items() ) attributes = FrozenDict(self.get_attrs()) return variables, attributes def close(self): pass def __enter__(self): return self def __exit__(self, exception_type, exception_value, traceback): self.close() class ArrayWriter: __slots__ = ("sources", "targets", "regions", "lock") def __init__(self, lock=None): self.sources = [] self.targets = [] self.regions = [] self.lock = lock def add(self, source, target, region=None): if is_chunked_array(source): self.sources.append(source) self.targets.append(target) self.regions.append(region) else: if region: target[region] = source else: target[...] = source def sync(self, compute=True, chunkmanager_store_kwargs=None): if self.sources: chunkmanager = get_chunked_array_type(*self.sources) # TODO: consider wrapping targets with dask.delayed, if this makes # for any discernible difference in performance, e.g., # targets = [dask.delayed(t) for t in self.targets] if chunkmanager_store_kwargs is None: chunkmanager_store_kwargs = {} delayed_store = chunkmanager.store( self.sources, self.targets, lock=self.lock, compute=compute, flush=True, regions=self.regions, **chunkmanager_store_kwargs, ) self.sources = [] self.targets = [] self.regions = [] return delayed_store class AbstractWritableDataStore(AbstractDataStore): __slots__ = () def encode(self, variables, attributes): """ Encode the variables and attributes in this store Parameters ---------- variables : dict-like Dictionary of key/value (variable name / xr.Variable) pairs attributes : dict-like Dictionary of key/value (attribute name / attribute) pairs Returns ------- variables : dict-like attributes : dict-like """ variables = {k: self.encode_variable(v) for k, v in variables.items()} attributes = {k: self.encode_attribute(v) for k, v in attributes.items()} return variables, attributes def encode_variable(self, v): """encode one variable""" return v def encode_attribute(self, a): """encode one attribute""" return a def set_dimension(self, dim, length): # pragma: no cover raise NotImplementedError() def set_attribute(self, k, v): # pragma: no cover raise NotImplementedError() def set_variable(self, k, v): # pragma: no cover raise NotImplementedError() def store_dataset(self, dataset): """ in stores, variables are all variables AND coordinates in xarray.Dataset variables are variables NOT coordinates, so here we pass the whole dataset in instead of doing dataset.variables """ self.store(dataset, dataset.attrs) 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. """ if writer is None: writer = ArrayWriter() variables, attributes = self.encode(variables, attributes) self.set_attributes(attributes) self.set_dimensions(variables, unlimited_dims=unlimited_dims) self.set_variables( variables, check_encoding_set, writer, unlimited_dims=unlimited_dims ) def set_attributes(self, attributes): """ This provides a centralized method to set the dataset attributes on the data store. Parameters ---------- attributes : dict-like Dictionary of key/value (attribute name / attribute) pairs """ for k, v in attributes.items(): self.set_attribute(k, v) 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 : ArrayWriter 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 target, source = self.prepare_variable( name, v, check, unlimited_dims=unlimited_dims ) writer.add(source, target) def set_dimensions(self, variables, unlimited_dims=None): """ This provides a centralized method to set the dimensions on the data store. Parameters ---------- variables : dict-like Dictionary of key/value (variable name / xr.Variable) pairs unlimited_dims : list-like List of dimension names that should be treated as unlimited dimensions. """ if unlimited_dims is None: unlimited_dims = set() existing_dims = self.get_dimensions() dims = {} for v in unlimited_dims: # put unlimited_dims first dims[v] = None for v in variables.values(): dims.update(dict(zip(v.dims, v.shape))) for dim, length in dims.items(): if dim in existing_dims and length != existing_dims[dim]: raise ValueError( "Unable to update size for existing dimension" f"{dim!r} ({length} != {existing_dims[dim]})" ) elif dim not in existing_dims: is_unlimited = dim in unlimited_dims self.set_dimension(dim, length, is_unlimited) class WritableCFDataStore(AbstractWritableDataStore): __slots__ = () def encode(self, variables, attributes): # All NetCDF files get CF encoded by default, without this attempting # to write times, for example, would fail. variables, attributes = cf_encoder(variables, attributes) variables = {k: self.encode_variable(v) for k, v in variables.items()} attributes = {k: self.encode_attribute(v) for k, v in attributes.items()} return variables, attributes class BackendEntrypoint: """ ``BackendEntrypoint`` is a class container and it is the main interface for the backend plugins, see :ref:`RST backend_entrypoint`. It shall implement: - ``open_dataset`` method: it shall implement reading from file, variables decoding and it returns an instance of :py:class:`~xarray.Dataset`. It shall take in input at least ``filename_or_obj`` argument and ``drop_variables`` keyword argument. For more details see :ref:`RST open_dataset`. - ``guess_can_open`` method: it shall return ``True`` if the backend is able to open ``filename_or_obj``, ``False`` otherwise. The implementation of this method is not mandatory. - ``open_datatree`` method: it shall implement reading from file, variables decoding and it returns an instance of :py:class:`~datatree.DataTree`. It shall take in input at least ``filename_or_obj`` argument. The implementation of this method is not mandatory. For more details see . Attributes ---------- open_dataset_parameters : tuple, default: None A list of ``open_dataset`` method parameters. The setting of this attribute is not mandatory. description : str, default: "" A short string describing the engine. The setting of this attribute is not mandatory. url : str, default: "" A string with the URL to the backend's documentation. The setting of this attribute is not mandatory. """ open_dataset_parameters: ClassVar[tuple | None] = None description: ClassVar[str] = "" url: ClassVar[str] = "" def __repr__(self) -> str: txt = f"<{type(self).__name__}>" if self.description: txt += f"\n {self.description}" if self.url: txt += f"\n Learn more at {self.url}" return txt def open_dataset( self, filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore, *, drop_variables: str | Iterable[str] | None = None, **kwargs: Any, ) -> Dataset: """ Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`. """ raise NotImplementedError() def guess_can_open( self, filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore, ) -> bool: """ Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`. """ return False def open_datatree( self, filename_or_obj: str | os.PathLike[Any] | BufferedIOBase | AbstractDataStore, **kwargs: Any, ) -> DataTree: """ Backend open_datatree method used by Xarray in :py:func:`~xarray.open_datatree`. """ raise NotImplementedError() # mapping of engine name to (module name, BackendEntrypoint Class) BACKEND_ENTRYPOINTS: dict[str, tuple[str | None, type[BackendEntrypoint]]] = {}