import logging import os import time import traceback from typing import Any, Dict, Tuple, Type, Union import numpy as np from ..conventions import cf_encoder from ..core import indexing from ..core.pycompat import is_duck_dask_array from ..core.utils import FrozenDict, NdimSizeLenMixin, is_remote_uri # 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): 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 _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 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 __array__(self, dtype=None): key = indexing.BasicIndexer((slice(None),) * self.ndim) return np.asarray(self[key], dtype=dtype) 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_duck_dask_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): if self.sources: import dask.array as da # TODO: consider wrapping targets with dask.delayed, if this makes # for any discernable difference in perforance, e.g., # targets = [dask.delayed(t) for t in self.targets] delayed_store = da.store( self.sources, self.targets, lock=self.lock, compute=compute, flush=True, regions=self.regions, ) 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_dataset_parameters: Union[Tuple, None] = None """list of ``open_dataset`` method parameters""" def open_dataset( self, filename_or_obj: str, drop_variables: Tuple[str] = None, **kwargs: Any, ): """ Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`. """ raise NotImplementedError def guess_can_open(self, filename_or_obj): """ Backend open_dataset method used by Xarray in :py:func:`~xarray.open_dataset`. """ return False BACKEND_ENTRYPOINTS: Dict[str, Type[BackendEntrypoint]] = {}