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datatree.py
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datatree.py
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from __future__ import annotations
import functools
import itertools
import textwrap
from collections import ChainMap
from collections.abc import (
Callable,
Hashable,
Iterable,
Iterator,
Mapping,
)
from html import escape
from typing import TYPE_CHECKING, Any, Literal, NoReturn, Union, overload
from xarray.core import utils
from xarray.core._aggregations import DataTreeAggregations
from xarray.core._typed_ops import DataTreeOpsMixin
from xarray.core.alignment import align
from xarray.core.common import TreeAttrAccessMixin, get_chunksizes
from xarray.core.coordinates import Coordinates, DataTreeCoordinates
from xarray.core.dataarray import DataArray
from xarray.core.dataset import Dataset, DataVariables
from xarray.core.datatree_mapping import (
map_over_datasets,
)
from xarray.core.formatting import (
datatree_repr,
diff_treestructure,
dims_and_coords_repr,
)
from xarray.core.formatting_html import (
datatree_repr as datatree_repr_html,
)
from xarray.core.indexes import Index, Indexes
from xarray.core.merge import dataset_update_method
from xarray.core.options import OPTIONS as XR_OPTS
from xarray.core.treenode import NamedNode, NodePath, zip_subtrees
from xarray.core.types import Self
from xarray.core.utils import (
Default,
FilteredMapping,
Frozen,
_default,
drop_dims_from_indexers,
either_dict_or_kwargs,
maybe_wrap_array,
parse_dims_as_set,
)
from xarray.core.variable import Variable
from xarray.namedarray.parallelcompat import get_chunked_array_type
from xarray.namedarray.pycompat import is_chunked_array
try:
from xarray.core.variable import calculate_dimensions
except ImportError:
# for xarray versions 2022.03.0 and earlier
from xarray.core.dataset import calculate_dimensions
if TYPE_CHECKING:
import numpy as np
import pandas as pd
from xarray.core.datatree_io import T_DataTreeNetcdfEngine, T_DataTreeNetcdfTypes
from xarray.core.merge import CoercibleMapping, CoercibleValue
from xarray.core.types import (
Dims,
DtCompatible,
ErrorOptions,
ErrorOptionsWithWarn,
NetcdfWriteModes,
T_ChunkDimFreq,
T_ChunksFreq,
ZarrWriteModes,
)
from xarray.namedarray.parallelcompat import ChunkManagerEntrypoint
# """
# DEVELOPERS' NOTE
# ----------------
# The idea of this module is to create a `DataTree` class which inherits the tree structure from TreeNode, and also copies
# the entire API of `xarray.Dataset`, but with certain methods decorated to instead map the dataset function over every
# node in the tree. As this API is copied without directly subclassing `xarray.Dataset` we instead create various Mixin
# classes (in ops.py) which each define part of `xarray.Dataset`'s extensive API.
#
# Some of these methods must be wrapped to map over all nodes in the subtree. Others are fine to inherit unaltered
# (normally because they (a) only call dataset properties and (b) don't return a dataset that should be nested into a new
# tree) and some will get overridden by the class definition of DataTree.
# """
T_Path = Union[str, NodePath]
def _collect_data_and_coord_variables(
data: Dataset,
) -> tuple[dict[Hashable, Variable], dict[Hashable, Variable]]:
data_variables = {}
coord_variables = {}
for k, v in data.variables.items():
if k in data._coord_names:
coord_variables[k] = v
else:
data_variables[k] = v
return data_variables, coord_variables
def _to_new_dataset(data: Dataset | Coordinates | None) -> Dataset:
if isinstance(data, Dataset):
ds = data.copy(deep=False)
elif isinstance(data, Coordinates):
ds = data.to_dataset()
elif data is None:
ds = Dataset()
else:
raise TypeError(f"data object is not an xarray.Dataset, dict, or None: {data}")
return ds
def _inherited_dataset(ds: Dataset, parent: Dataset) -> Dataset:
return Dataset._construct_direct(
variables=parent._variables | ds._variables,
coord_names=parent._coord_names | ds._coord_names,
dims=parent._dims | ds._dims,
attrs=ds._attrs,
indexes=parent._indexes | ds._indexes,
encoding=ds._encoding,
close=ds._close,
)
def _without_header(text: str) -> str:
return "\n".join(text.split("\n")[1:])
def _indented(text: str) -> str:
return textwrap.indent(text, prefix=" ")
def check_alignment(
path: str,
node_ds: Dataset,
parent_ds: Dataset | None,
children: Mapping[str, DataTree],
) -> None:
if parent_ds is not None:
try:
align(node_ds, parent_ds, join="exact", copy=False)
except ValueError as e:
node_repr = _indented(_without_header(repr(node_ds)))
parent_repr = _indented(dims_and_coords_repr(parent_ds))
raise ValueError(
f"group {path!r} is not aligned with its parents:\n"
f"Group:\n{node_repr}\nFrom parents:\n{parent_repr}"
) from e
if children:
if parent_ds is not None:
base_ds = _inherited_dataset(node_ds, parent_ds)
else:
base_ds = node_ds
for child_name, child in children.items():
child_path = str(NodePath(path) / child_name)
child_ds = child.to_dataset(inherit=False)
check_alignment(child_path, child_ds, base_ds, child.children)
def _deduplicate_inherited_coordinates(child: DataTree, parent: DataTree) -> None:
# This method removes repeated indexes (and corresponding coordinates)
# that are repeated between a DataTree and its parents.
removed_something = False
for name in parent._indexes:
if name in child._node_indexes:
# Indexes on a Dataset always have a corresponding coordinate.
# We already verified that these coordinates match in the
# check_alignment() call from _pre_attach().
del child._node_indexes[name]
del child._node_coord_variables[name]
removed_something = True
if removed_something:
child._node_dims = calculate_dimensions(
child._data_variables | child._node_coord_variables
)
for grandchild in child._children.values():
_deduplicate_inherited_coordinates(grandchild, child)
def _check_for_slashes_in_names(variables: Iterable[Hashable]) -> None:
offending_variable_names = [
name for name in variables if isinstance(name, str) and "/" in name
]
if len(offending_variable_names) > 0:
raise ValueError(
"Given variables have names containing the '/' character: "
f"{offending_variable_names}. "
"Variables stored in DataTree objects cannot have names containing '/' characters, as this would make path-like access to variables ambiguous."
)
class DatasetView(Dataset):
"""
An immutable Dataset-like view onto the data in a single DataTree node.
In-place operations modifying this object should raise an AttributeError.
This requires overriding all inherited constructors.
Operations returning a new result will return a new xarray.Dataset object.
This includes all API on Dataset, which will be inherited.
"""
# TODO what happens if user alters (in-place) a DataArray they extracted from this object?
__slots__ = (
"_attrs",
"_cache", # used by _CachedAccessor
"_coord_names",
"_dims",
"_encoding",
"_close",
"_indexes",
"_variables",
)
def __init__(
self,
data_vars: Mapping[Any, Any] | None = None,
coords: Mapping[Any, Any] | None = None,
attrs: Mapping[Any, Any] | None = None,
):
raise AttributeError("DatasetView objects are not to be initialized directly")
@classmethod
def _constructor(
cls,
variables: dict[Any, Variable],
coord_names: set[Hashable],
dims: dict[Any, int],
attrs: dict | None,
indexes: dict[Any, Index],
encoding: dict | None,
close: Callable[[], None] | None,
) -> DatasetView:
"""Private constructor, from Dataset attributes."""
# We override Dataset._construct_direct below, so we need a new
# constructor for creating DatasetView objects.
obj: DatasetView = object.__new__(cls)
obj._variables = variables
obj._coord_names = coord_names
obj._dims = dims
obj._indexes = indexes
obj._attrs = attrs
obj._close = close
obj._encoding = encoding
return obj
def __setitem__(self, key, val) -> None:
raise AttributeError(
"Mutation of the DatasetView is not allowed, please use `.__setitem__` on the wrapping DataTree node, "
"or use `dt.to_dataset()` if you want a mutable dataset. If calling this from within `map_over_datasets`,"
"use `.copy()` first to get a mutable version of the input dataset."
)
def update(self, other) -> NoReturn:
raise AttributeError(
"Mutation of the DatasetView is not allowed, please use `.update` on the wrapping DataTree node, "
"or use `dt.to_dataset()` if you want a mutable dataset. If calling this from within `map_over_datasets`,"
"use `.copy()` first to get a mutable version of the input dataset."
)
def set_close(self, close: Callable[[], None] | None) -> None:
raise AttributeError("cannot modify a DatasetView()")
def close(self) -> None:
raise AttributeError(
"cannot close a DatasetView(). Close the associated DataTree node "
"instead"
)
# FIXME https://github.com/python/mypy/issues/7328
@overload # type: ignore[override]
def __getitem__(self, key: Mapping) -> Dataset: # type: ignore[overload-overlap]
...
@overload
def __getitem__(self, key: Hashable) -> DataArray: ...
# See: https://github.com/pydata/xarray/issues/8855
@overload
def __getitem__(self, key: Any) -> Dataset: ...
def __getitem__(self, key) -> DataArray | Dataset:
# TODO call the `_get_item` method of DataTree to allow path-like access to contents of other nodes
# For now just call Dataset.__getitem__
return Dataset.__getitem__(self, key)
@classmethod
def _construct_direct( # type: ignore[override]
cls,
variables: dict[Any, Variable],
coord_names: set[Hashable],
dims: dict[Any, int] | None = None,
attrs: dict | None = None,
indexes: dict[Any, Index] | None = None,
encoding: dict | None = None,
close: Callable[[], None] | None = None,
) -> Dataset:
"""
Overriding this method (along with ._replace) and modifying it to return a Dataset object
should hopefully ensure that the return type of any method on this object is a Dataset.
"""
if dims is None:
dims = calculate_dimensions(variables)
if indexes is None:
indexes = {}
obj = object.__new__(Dataset)
obj._variables = variables
obj._coord_names = coord_names
obj._dims = dims
obj._indexes = indexes
obj._attrs = attrs
obj._close = close
obj._encoding = encoding
return obj
def _replace( # type: ignore[override]
self,
variables: dict[Hashable, Variable] | None = None,
coord_names: set[Hashable] | None = None,
dims: dict[Any, int] | None = None,
attrs: dict[Hashable, Any] | None | Default = _default,
indexes: dict[Hashable, Index] | None = None,
encoding: dict | None | Default = _default,
inplace: bool = False,
) -> Dataset:
"""
Overriding this method (along with ._construct_direct) and modifying it to return a Dataset object
should hopefully ensure that the return type of any method on this object is a Dataset.
"""
if inplace:
raise AttributeError("In-place mutation of the DatasetView is not allowed")
return Dataset._replace(
self,
variables=variables,
coord_names=coord_names,
dims=dims,
attrs=attrs,
indexes=indexes,
encoding=encoding,
inplace=inplace,
)
def map( # type: ignore[override]
self,
func: Callable,
keep_attrs: bool | None = None,
args: Iterable[Any] = (),
**kwargs: Any,
) -> Dataset:
"""Apply a function to each data variable in this dataset
Parameters
----------
func : callable
Function which can be called in the form `func(x, *args, **kwargs)`
to transform each DataArray `x` in this dataset into another
DataArray.
keep_attrs : bool | None, optional
If True, both the dataset's and variables' attributes (`attrs`) will be
copied from the original objects to the new ones. If False, the new dataset
and variables will be returned without copying the attributes.
args : iterable, optional
Positional arguments passed on to `func`.
**kwargs : Any
Keyword arguments passed on to `func`.
Returns
-------
applied : Dataset
Resulting dataset from applying ``func`` to each data variable.
Examples
--------
>>> da = xr.DataArray(np.random.randn(2, 3))
>>> ds = xr.Dataset({"foo": da, "bar": ("x", [-1, 2])})
>>> ds
<xarray.Dataset> Size: 64B
Dimensions: (dim_0: 2, dim_1: 3, x: 2)
Dimensions without coordinates: dim_0, dim_1, x
Data variables:
foo (dim_0, dim_1) float64 48B 1.764 0.4002 0.9787 2.241 1.868 -0.9773
bar (x) int64 16B -1 2
>>> ds.map(np.fabs)
<xarray.Dataset> Size: 64B
Dimensions: (dim_0: 2, dim_1: 3, x: 2)
Dimensions without coordinates: dim_0, dim_1, x
Data variables:
foo (dim_0, dim_1) float64 48B 1.764 0.4002 0.9787 2.241 1.868 0.9773
bar (x) float64 16B 1.0 2.0
"""
# Copied from xarray.Dataset so as not to call type(self), which causes problems (see https://github.com/xarray-contrib/datatree/issues/188).
# TODO Refactor xarray upstream to avoid needing to overwrite this.
# TODO This copied version will drop all attrs - the keep_attrs stuff should be re-instated
variables = {
k: maybe_wrap_array(v, func(v, *args, **kwargs))
for k, v in self.data_vars.items()
}
# return type(self)(variables, attrs=attrs)
return Dataset(variables)
class DataTree(
NamedNode["DataTree"],
DataTreeAggregations,
DataTreeOpsMixin,
TreeAttrAccessMixin,
Mapping[str, "DataArray | DataTree"],
):
"""
A tree-like hierarchical collection of xarray objects.
Attempts to present an API like that of xarray.Dataset, but methods are wrapped to also update all the tree's child nodes.
"""
# TODO Some way of sorting children by depth
# TODO do we need a watch out for if methods intended only for root nodes are called on non-root nodes?
# TODO dataset methods which should not or cannot act over the whole tree, such as .to_array
# TODO .loc method
# TODO a lot of properties like .variables could be defined in a DataMapping class which both Dataset and DataTree inherit from
# TODO all groupby classes
# TODO a lot of properties like .variables could be defined in a DataMapping class which both Dataset and DataTree inherit from
# TODO all groupby classes
_name: str | None
_parent: DataTree | None
_children: dict[str, DataTree]
_cache: dict[str, Any] # used by _CachedAccessor
_data_variables: dict[Hashable, Variable]
_node_coord_variables: dict[Hashable, Variable]
_node_dims: dict[Hashable, int]
_node_indexes: dict[Hashable, Index]
_attrs: dict[Hashable, Any] | None
_encoding: dict[Hashable, Any] | None
_close: Callable[[], None] | None
__slots__ = (
"_name",
"_parent",
"_children",
"_cache", # used by _CachedAccessor
"_data_variables",
"_node_coord_variables",
"_node_dims",
"_node_indexes",
"_attrs",
"_encoding",
"_close",
)
def __init__(
self,
dataset: Dataset | Coordinates | None = None,
children: Mapping[str, DataTree] | None = None,
name: str | None = None,
):
"""
Create a single node of a DataTree.
The node may optionally contain data in the form of data and coordinate
variables, stored in the same way as data is stored in an
xarray.Dataset.
Parameters
----------
dataset : Dataset, optional
Data to store directly at this node.
children : Mapping[str, DataTree], optional
Any child nodes of this node.
name : str, optional
Name for this node of the tree.
Returns
-------
DataTree
See Also
--------
DataTree.from_dict
"""
self._set_node_data(_to_new_dataset(dataset))
# comes after setting node data as this will check for clashes between child names and existing variable names
super().__init__(name=name, children=children)
def _set_node_data(self, dataset: Dataset):
_check_for_slashes_in_names(dataset.variables)
data_vars, coord_vars = _collect_data_and_coord_variables(dataset)
self._data_variables = data_vars
self._node_coord_variables = coord_vars
self._node_dims = dataset._dims
self._node_indexes = dataset._indexes
self._encoding = dataset._encoding
self._attrs = dataset._attrs
self._close = dataset._close
def _pre_attach(self: DataTree, parent: DataTree, name: str) -> None:
super()._pre_attach(parent, name)
if name in parent.dataset.variables:
raise KeyError(
f"parent {parent.name} already contains a variable named {name}"
)
path = str(NodePath(parent.path) / name)
node_ds = self.to_dataset(inherit=False)
parent_ds = parent._to_dataset_view(rebuild_dims=False, inherit=True)
check_alignment(path, node_ds, parent_ds, self.children)
_deduplicate_inherited_coordinates(self, parent)
@property
def _node_coord_variables_with_index(self) -> Mapping[Hashable, Variable]:
return FilteredMapping(
keys=self._node_indexes, mapping=self._node_coord_variables
)
@property
def _coord_variables(self) -> ChainMap[Hashable, Variable]:
return ChainMap(
self._node_coord_variables,
*(p._node_coord_variables_with_index for p in self.parents),
)
@property
def _dims(self) -> ChainMap[Hashable, int]:
return ChainMap(self._node_dims, *(p._node_dims for p in self.parents))
@property
def _indexes(self) -> ChainMap[Hashable, Index]:
return ChainMap(self._node_indexes, *(p._node_indexes for p in self.parents))
def _to_dataset_view(self, rebuild_dims: bool, inherit: bool) -> DatasetView:
coord_vars = self._coord_variables if inherit else self._node_coord_variables
variables = dict(self._data_variables)
variables |= coord_vars
if rebuild_dims:
dims = calculate_dimensions(variables)
elif inherit:
# Note: rebuild_dims=False with inherit=True can create
# technically invalid Dataset objects because it still includes
# dimensions that are only defined on parent data variables
# (i.e. not present on any parent coordinate variables).
#
# For example:
# >>> tree = DataTree.from_dict(
# ... {
# ... "/": xr.Dataset({"foo": ("x", [1, 2])}), # x has size 2
# ... "/b": xr.Dataset(),
# ... }
# ... )
# >>> ds = tree["b"]._to_dataset_view(rebuild_dims=False, inherit=True)
# >>> ds
# <xarray.DatasetView> Size: 0B
# Dimensions: (x: 2)
# Dimensions without coordinates: x
# Data variables:
# *empty*
#
# Notice the "x" dimension is still defined, even though there are no variables
# or coordinates.
#
# Normally this is not supposed to be possible in xarray's data model,
# but here it is useful internally for use cases where we
# want to inherit everything from parents nodes, e.g., for align() and repr().
#
# The user should never be able to see this dimension via public API.
dims = dict(self._dims)
else:
dims = dict(self._node_dims)
return DatasetView._constructor(
variables=variables,
coord_names=set(self._coord_variables),
dims=dims,
attrs=self._attrs,
indexes=dict(self._indexes if inherit else self._node_indexes),
encoding=self._encoding,
close=None,
)
@property
def dataset(self) -> DatasetView:
"""
An immutable Dataset-like view onto the data in this node.
Includes inherited coordinates and indexes from parent nodes.
For a mutable Dataset containing the same data as in this node, use
`.to_dataset()` instead.
See Also
--------
DataTree.to_dataset
"""
return self._to_dataset_view(rebuild_dims=True, inherit=True)
@dataset.setter
def dataset(self, data: Dataset | None = None) -> None:
ds = _to_new_dataset(data)
self._replace_node(ds)
# soft-deprecated alias, to facilitate the transition from
# xarray-contrib/datatree
ds = dataset
def to_dataset(self, inherit: bool = True) -> Dataset:
"""
Return the data in this node as a new xarray.Dataset object.
Parameters
----------
inherit : bool, optional
If False, only include coordinates and indexes defined at the level
of this DataTree node, excluding any inherited coordinates and indexes.
See Also
--------
DataTree.dataset
"""
coord_vars = self._coord_variables if inherit else self._node_coord_variables
variables = dict(self._data_variables)
variables |= coord_vars
dims = calculate_dimensions(variables) if inherit else dict(self._node_dims)
return Dataset._construct_direct(
variables,
set(coord_vars),
dims,
None if self._attrs is None else dict(self._attrs),
dict(self._indexes if inherit else self._node_indexes),
None if self._encoding is None else dict(self._encoding),
None,
)
@property
def has_data(self) -> bool:
"""Whether or not there are any variables in this node."""
return bool(self._data_variables or self._node_coord_variables)
@property
def has_attrs(self) -> bool:
"""Whether or not there are any metadata attributes in this node."""
return len(self.attrs.keys()) > 0
@property
def is_empty(self) -> bool:
"""False if node contains any data or attrs. Does not look at children."""
return not (self.has_data or self.has_attrs)
@property
def is_hollow(self) -> bool:
"""True if only leaf nodes contain data."""
return not any(node.has_data for node in self.subtree if not node.is_leaf)
@property
def variables(self) -> Mapping[Hashable, Variable]:
"""Low level interface to node contents as dict of Variable objects.
This dictionary is frozen to prevent mutation that could violate
Dataset invariants. It contains all variable objects constituting this
DataTree node, including both data variables and coordinates.
"""
return Frozen(self._data_variables | self._coord_variables)
@property
def attrs(self) -> dict[Hashable, Any]:
"""Dictionary of global attributes on this node object."""
if self._attrs is None:
self._attrs = {}
return self._attrs
@attrs.setter
def attrs(self, value: Mapping[Any, Any]) -> None:
self._attrs = dict(value)
@property
def encoding(self) -> dict:
"""Dictionary of global encoding attributes on this node object."""
if self._encoding is None:
self._encoding = {}
return self._encoding
@encoding.setter
def encoding(self, value: Mapping) -> None:
self._encoding = dict(value)
@property
def dims(self) -> Mapping[Hashable, int]:
"""Mapping from dimension names to lengths.
Cannot be modified directly, but is updated when adding new variables.
Note that type of this object differs from `DataArray.dims`.
See `DataTree.sizes`, `Dataset.sizes`, and `DataArray.sizes` for consistently named
properties.
"""
return Frozen(self._dims)
@property
def sizes(self) -> Mapping[Hashable, int]:
"""Mapping from dimension names to lengths.
Cannot be modified directly, but is updated when adding new variables.
This is an alias for `DataTree.dims` provided for the benefit of
consistency with `DataArray.sizes`.
See Also
--------
DataArray.sizes
"""
return self.dims
@property
def _attr_sources(self) -> Iterable[Mapping[Hashable, Any]]:
"""Places to look-up items for attribute-style access"""
yield from self._item_sources
yield self.attrs
@property
def _item_sources(self) -> Iterable[Mapping[Any, Any]]:
"""Places to look-up items for key-completion"""
yield self.data_vars
yield FilteredMapping(keys=self._coord_variables, mapping=self.coords)
# virtual coordinates
yield FilteredMapping(keys=self.dims, mapping=self)
# immediate child nodes
yield self.children
def _ipython_key_completions_(self) -> list[str]:
"""Provide method for the key-autocompletions in IPython.
See http://ipython.readthedocs.io/en/stable/config/integrating.html#tab-completion
For the details.
"""
# TODO allow auto-completing relative string paths, e.g. `dt['path/to/../ <tab> node'`
# Would require changes to ipython's autocompleter, see https://github.com/ipython/ipython/issues/12420
# Instead for now we only list direct paths to all node in subtree explicitly
items_on_this_node = self._item_sources
paths_to_all_nodes_in_subtree = {
path: node
for path, node in self.subtree_with_keys
if path != "." # exclude the root node
}
all_item_sources = itertools.chain(
items_on_this_node, [paths_to_all_nodes_in_subtree]
)
items = {
item
for source in all_item_sources
for item in source
if isinstance(item, str)
}
return list(items)
def __contains__(self, key: object) -> bool:
"""The 'in' operator will return true or false depending on whether
'key' is either an array stored in the datatree or a child node, or neither.
"""
return key in self.variables or key in self.children
def __bool__(self) -> bool:
return bool(self._data_variables) or bool(self._children)
def __iter__(self) -> Iterator[str]:
return itertools.chain(self._data_variables, self._children) # type: ignore[arg-type]
def __array__(
self, dtype: np.typing.DTypeLike = None, /, *, copy: bool | None = None
) -> np.ndarray:
raise TypeError(
"cannot directly convert a DataTree into a "
"numpy array. Instead, create an xarray.DataArray "
"first, either with indexing on the DataTree or by "
"invoking the `to_array()` method."
)
def __repr__(self) -> str: # type: ignore[override]
return datatree_repr(self)
def __str__(self) -> str:
return datatree_repr(self)
def _repr_html_(self):
"""Make html representation of datatree object"""
if XR_OPTS["display_style"] == "text":
return f"<pre>{escape(repr(self))}</pre>"
return datatree_repr_html(self)
def __enter__(self) -> Self:
return self
def __exit__(self, exc_type, exc_value, traceback) -> None:
self.close()
# DatasetView does not support close() or set_close(), so we reimplement
# these methods on DataTree.
def _close_node(self) -> None:
if self._close is not None:
self._close()
self._close = None
def close(self) -> None:
"""Close any files associated with this tree."""
for node in self.subtree:
node._close_node()
def set_close(self, close: Callable[[], None] | None) -> None:
"""Set the closer for this node."""
self._close = close
def _replace_node(
self: DataTree,
data: Dataset | Default = _default,
children: dict[str, DataTree] | Default = _default,
) -> None:
ds = self.to_dataset(inherit=False) if data is _default else data
if children is _default:
children = self._children
for child_name in children:
if child_name in ds.variables:
raise ValueError(f"node already contains a variable named {child_name}")
parent_ds = (
self.parent._to_dataset_view(rebuild_dims=False, inherit=True)
if self.parent is not None
else None
)
check_alignment(self.path, ds, parent_ds, children)
if data is not _default:
self._set_node_data(ds)
if self.parent is not None:
_deduplicate_inherited_coordinates(self, self.parent)
self.children = children
def _copy_node(
self, inherit: bool, deep: bool = False, memo: dict[int, Any] | None = None
) -> Self:
"""Copy just one node of a tree."""
new_node = super()._copy_node(inherit=inherit, deep=deep, memo=memo)
data = self._to_dataset_view(rebuild_dims=False, inherit=inherit)._copy(
deep=deep, memo=memo
)
new_node._set_node_data(data)
return new_node
def get( # type: ignore[override]
self: DataTree, key: str, default: DataTree | DataArray | None = None
) -> DataTree | DataArray | None:
"""
Access child nodes, variables, or coordinates stored in this node.
Returned object will be either a DataTree or DataArray object depending on whether the key given points to a
child or variable.
Parameters
----------
key : str
Name of variable / child within this node. Must lie in this immediate node (not elsewhere in the tree).
default : DataTree | DataArray | None, optional
A value to return if the specified key does not exist. Default return value is None.
"""
if key in self.children:
return self.children[key]
elif key in self.dataset:
return self.dataset[key]
else:
return default
def __getitem__(self: DataTree, key: str) -> DataTree | DataArray:
"""
Access child nodes, variables, or coordinates stored anywhere in this tree.
Returned object will be either a DataTree or DataArray object depending on whether the key given points to a
child or variable.
Parameters
----------
key : str
Name of variable / child within this node, or unix-like path to variable / child within another node.
Returns
-------
DataTree | DataArray
"""
# Either:
if utils.is_dict_like(key):
# dict-like indexing
raise NotImplementedError("Should this index over whole tree?")
elif isinstance(key, str):
# TODO should possibly deal with hashables in general?
# path-like: a name of a node/variable, or path to a node/variable
path = NodePath(key)
return self._get_item(path)
elif utils.is_list_like(key):
# iterable of variable names
raise NotImplementedError(
"Selecting via tags is deprecated, and selecting multiple items should be "
"implemented via .subset"
)
else:
raise ValueError(f"Invalid format for key: {key}")
def _set(self, key: str, val: DataTree | CoercibleValue) -> None:
"""
Set the child node or variable with the specified key to value.
Counterpart to the public .get method, and also only works on the immediate node, not other nodes in the tree.
"""
if isinstance(val, DataTree):
# create and assign a shallow copy here so as not to alter original name of node in grafted tree
new_node = val.copy(deep=False)
new_node.name = key
new_node._set_parent(new_parent=self, child_name=key)
else:
if not isinstance(val, DataArray | Variable):
# accommodate other types that can be coerced into Variables
val = DataArray(val)
self.update({key: val})
def __setitem__(
self,
key: str,
value: Any,
) -> None:
"""
Add either a child node or an array to the tree, at any position.
Data can be added anywhere, and new nodes will be created to cross the path to the new location if necessary.
If there is already a node at the given location, then if value is a Node class or Dataset it will overwrite the
data already present at that node, and if value is a single array, it will be merged with it.
"""
# TODO xarray.Dataset accepts other possibilities, how do we exactly replicate all the behaviour?
if utils.is_dict_like(key):
raise NotImplementedError
elif isinstance(key, str):
# TODO should possibly deal with hashables in general?
# path-like: a name of a node/variable, or path to a node/variable
path = NodePath(key)
if isinstance(value, Dataset):
value = DataTree(dataset=value)
return self._set_item(path, value, new_nodes_along_path=True)
else:
raise ValueError("Invalid format for key")
def __delitem__(self, key: str) -> None:
"""Remove a variable or child node from this datatree node."""
if key in self.children:
super().__delitem__(key)
elif key in self._node_coord_variables:
if key in self._node_indexes:
del self._node_indexes[key]
del self._node_coord_variables[key]
self._node_dims = calculate_dimensions(self.variables)
elif key in self._data_variables:
del self._data_variables[key]
self._node_dims = calculate_dimensions(self.variables)
else:
raise KeyError(key)
@overload
def update(self, other: Dataset) -> None: ...
@overload
def update(self, other: Mapping[Hashable, DataArray | Variable]) -> None: ...