from __future__ import annotations
import logging
import os
import time
import traceback
from collections.abc import Hashable, Iterable, Mapping, Sequence
from glob import glob
from typing import TYPE_CHECKING, Any, ClassVar, TypeVar, Union, overload
import numpy as np
import pandas as pd
from xarray.coding import strings, variables
from xarray.coding.variables import SerializationWarning
from xarray.conventions import cf_encoder
from xarray.core import indexing
from xarray.core.datatree import DataTree, Variable
from xarray.core.types import ReadBuffer
from xarray.core.utils import (
FrozenDict,
NdimSizeLenMixin,
attempt_import,
emit_user_level_warning,
is_remote_uri,
)
from xarray.namedarray.parallelcompat import get_chunked_array_type
from xarray.namedarray.pycompat import is_chunked_array
from xarray.namedarray.utils import is_duck_dask_array
if TYPE_CHECKING:
from xarray.core.dataset import Dataset
from xarray.core.types import NestedSequence
T_Name = Union[Hashable, None]
# Create a logger object, but don't add any handlers. Leave that to user code.
logger = logging.getLogger(__name__)
NONE_VAR_NAME = "__values__"
T = TypeVar("T")
@overload
def _normalize_path(path: str | os.PathLike) -> str: ...
@overload
def _normalize_path(path: T) -> T: ...
def _normalize_path(path: str | os.PathLike | T) -> str | T:
"""
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,)])
[<class '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 # type:ignore [return-value]
@overload
def _find_absolute_paths(
paths: str | os.PathLike | Sequence[str | os.PathLike],
**kwargs,
) -> list[str]: ...
@overload
def _find_absolute_paths(
paths: ReadBuffer | Sequence[ReadBuffer],
**kwargs,
) -> list[ReadBuffer]: ...
@overload
def _find_absolute_paths(
paths: NestedSequence[str | os.PathLike], **kwargs
) -> NestedSequence[str]: ...
@overload
def _find_absolute_paths(
paths: NestedSequence[ReadBuffer], **kwargs
) -> NestedSequence[ReadBuffer]: ...
@overload
def _find_absolute_paths(
paths: str
| os.PathLike
| ReadBuffer
| NestedSequence[str | os.PathLike | ReadBuffer],
**kwargs,
) -> NestedSequence[str | ReadBuffer]: ...
def _find_absolute_paths(
paths: str
| os.PathLike
| ReadBuffer
| NestedSequence[str | os.PathLike | ReadBuffer],
**kwargs,
) -> NestedSequence[str | ReadBuffer]:
"""
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") == "zarr":
if TYPE_CHECKING:
import fsspec
else:
fsspec = attempt_import("fsspec")
fs, _, _ = fsspec.core.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
return [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:
return sorted(glob(_normalize_path(paths)))
elif isinstance(paths, os.PathLike):
return [_normalize_path(paths)]
elif isinstance(paths, ReadBuffer):
return [paths]
def _normalize_path_list(
lpaths: NestedSequence[str | os.PathLike | ReadBuffer],
) -> NestedSequence[str | ReadBuffer]:
paths = []
for p in lpaths:
if isinstance(p, str | os.PathLike):
paths.append(_normalize_path(p))
elif isinstance(p, list):
paths.append(_normalize_path_list(p)) # type: ignore[arg-type]
else:
paths.append(p) # type: ignore[arg-type]
return paths
return _normalize_path_list(paths)
def _open_remote_file(file, mode, storage_options=None):
import fsspec
fs, _, paths = fsspec.get_fs_token_paths(
file, mode=mode, storage_options=storage_options
)
return fs.open(paths[0], mode=mode)
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)
yield str(parent)
for path, group in root.groups.items():
gpath = parent / path
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 datatree_from_dict_with_io_cleanup(groups_dict: Mapping[str, Dataset]) -> DataTree:
"""DataTree.from_dict with file clean-up."""
try:
tree = DataTree.from_dict(groups_dict)
except Exception:
for ds in groups_dict.values():
ds.close()
raise
for path, ds in groups_dict.items():
tree[path].set_close(ds._close)
return tree
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__ = ("lock", "regions", "sources", "targets")
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, strict=True)))
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)
def _infer_dtype(array, name=None):
"""Given an object array with no missing values, infer its dtype from all elements."""
if array.dtype.kind != "O":
raise TypeError("infer_type must be called on a dtype=object array")
if array.size == 0:
return np.dtype(float)
native_dtypes = set(np.vectorize(type, otypes=[object])(array.ravel()))
if len(native_dtypes) > 1 and native_dtypes != {bytes, str}:
raise ValueError(
"unable to infer dtype on variable {!r}; object array "
"contains mixed native types: {}".format(
name, ", ".join(x.__name__ for x in native_dtypes)
)
)
element = array[(0,) * array.ndim]
# We use the base types to avoid subclasses of bytes and str (which might
# not play nice with e.g. hdf5 datatypes), such as those from numpy
if isinstance(element, bytes):
return strings.create_vlen_dtype(bytes)
elif isinstance(element, str):
return strings.create_vlen_dtype(str)
dtype = np.array(element).dtype
if dtype.kind != "O":
return dtype
raise ValueError(
f"unable to infer dtype on variable {name!r}; xarray "
"cannot serialize arbitrary Python objects"
)
def _copy_with_dtype(data, dtype: np.typing.DTypeLike):
"""Create a copy of an array with the given dtype.
We use this instead of np.array() to ensure that custom object dtypes end
up on the resulting array.
"""
result = np.empty(data.shape, dtype)
result[...] = data
return result
def ensure_dtype_not_object(var: Variable, name: T_Name = None) -> Variable:
if var.dtype.kind == "O":
dims, data, attrs, encoding = variables.unpack_for_encoding(var)
# leave vlen dtypes unchanged
if strings.check_vlen_dtype(data.dtype) is not None:
return var
if is_duck_dask_array(data):
emit_user_level_warning(
f"variable {name} has data in the form of a dask array with "
"dtype=object, which means it is being loaded into memory "
"to determine a data type that can be safely stored on disk. "
"To avoid this, coerce this variable to a fixed-size dtype "
"with astype() before saving it.",
category=SerializationWarning,
)
data = data.compute()
missing = pd.isnull(data)
if missing.any():
# nb. this will fail for dask.array data
non_missing_values = data[~missing]
inferred_dtype = _infer_dtype(non_missing_values, name)
# There is no safe bit-pattern for NA in typical binary string
# formats, we so can't set a fill_value. Unfortunately, this means
# we can't distinguish between missing values and empty strings.
fill_value: bytes | str
if strings.is_bytes_dtype(inferred_dtype):
fill_value = b""
elif strings.is_unicode_dtype(inferred_dtype):
fill_value = ""
else:
# insist on using float for numeric values
if not np.issubdtype(inferred_dtype, np.floating):
inferred_dtype = np.dtype(float)
fill_value = inferred_dtype.type(np.nan)
data = _copy_with_dtype(data, dtype=inferred_dtype)
data[missing] = fill_value
else:
data = _copy_with_dtype(data, dtype=_infer_dtype(data, name))
assert data.dtype.kind != "O" or data.dtype.metadata
var = Variable(dims, data, attrs, encoding, fastpath=True)
return var
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: ensure_dtype_not_object(v, name=k) for k, v in variables.items()
}
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
<reference to open_datatree documentation>.
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] | ReadBuffer | AbstractDataStore,
*,
drop_variables: str | Iterable[str] | None = None,
) -> 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] | ReadBuffer | 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] | ReadBuffer | AbstractDataStore,
*,
drop_variables: str | Iterable[str] | None = None,
) -> DataTree:
"""
Backend open_datatree method used by Xarray in :py:func:`~xarray.open_datatree`.
"""
raise NotImplementedError()
def open_groups_as_dict(
self,
filename_or_obj: str | os.PathLike[Any] | ReadBuffer | AbstractDataStore,
*,
drop_variables: str | Iterable[str] | None = None,
) -> dict[str, Dataset]:
"""
Opens a dictionary mapping from group names to Datasets.
Called by :py:func:`~xarray.open_groups`.
This function exists to provide a universal way to open all groups in a file,
before applying any additional consistency checks or requirements necessary
to create a `DataTree` object (typically done using :py:meth:`~xarray.DataTree.from_dict`).
"""
raise NotImplementedError()
# mapping of engine name to (module name, BackendEntrypoint Class)
BACKEND_ENTRYPOINTS: dict[str, tuple[str | None, type[BackendEntrypoint]]] = {}