xarray.DataTree

class xarray.DataTree(dataset=None, children=None, name=None)

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.

__init__(dataset=None, children=None, name=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.

See also

DataTree.from_dict

Methods

__init__([dataset, children, name])	Create a single node of a DataTree.
all([dim, keep_attrs])	Reduce this DataTree's data by applying all along some dimension(s).
any([dim, keep_attrs])	Reduce this DataTree's data by applying any along some dimension(s).
argsort([axis, kind, order, stable, descending])	Returns the indices that would sort this array.
assign([items])	Assign new data variables or child nodes to a DataTree.
chunk([chunks, name_prefix, token, lock, ...])	Coerce all arrays in all groups into dask arrays with the given chunks.
close()	Close any files associated with this tree.
compute(**kwargs)	Trigger loading data into memory and return a new datatree.
conj()	Complex-conjugate all elements.
conjugate(*args, **kwargs)	a.conj()
copy(*[, inherit, deep])	Return a copy of this subtree.
count([dim, keep_attrs])	Reduce this DataTree's data by applying count along some dimension(s).
cumprod([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying cumprod (i.e., cumulative product) along some dimension(s).
cumsum([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying cumsum (i.e., cumulative sum) along some dimension(s).
drop_nodes(names, *[, errors])	Drop child nodes from this node.
equals(other)	Check whether two DataTrees are equal.
filter(filterfunc)	Filter nodes according to a specified condition.
filter_like(other)	Filter a datatree like another datatree.
find_common_ancestor(other)	Find the first common ancestor of two nodes in the same tree.
from_dict([data, coords, name, nested])	Create a datatree from a dictionary of data objects, organised by paths into the tree.
get(key[, default])	Access child nodes, variables, or coordinates stored in this node.
identical(other)	Check whether two DataTrees are identical.
isel([indexers, drop, missing_dims])	Return a new data tree with each array indexed along the given dimension(s).
isomorphic(other)	Check whether two DataTrees are isomorphic.
items()
iter_lineage()	Iterate up the tree, starting from the current node.
keys()
load(**kwargs)	Trigger loading data into memory and return this datatree.
map_over_datasets(func, *args[, kwargs])	Apply a function to every dataset in this subtree, returning a new tree which stores the results.
match(pattern)	Return nodes with paths matching pattern.
max([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying max (i.e., maximum) along some dimension(s).
mean([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying mean along some dimension(s).
median([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying median along some dimension(s).
min([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying min (i.e., minimum) along some dimension(s).
orphan()	Detach this node from its parent.
persist(**kwargs)	Trigger computation, keeping data as chunked arrays.
pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs).
prod([dim, skipna, min_count, keep_attrs])	Reduce this DataTree's data by applying prod (i.e., product) along some dimension(s).
prune([drop_size_zero_vars])	Remove empty nodes from the tree.
reduce(func[, dim, keep_attrs, keepdims, ...])	Reduce this tree by applying func along some dimension(s).
relative_to(other)	Compute the relative path from this node to node other.
round(*args, **kwargs)
same_tree(other)	Return True if other node is in the same tree as this node.
sel([indexers, method, tolerance, drop])	Return a new data tree with each array indexed by tick labels.
set_close(close)	Set the closer for this node.
std([dim, skipna, ddof, keep_attrs])	Reduce this DataTree's data by applying std (i.e., standard deviation) along some dimension(s).
sum([dim, skipna, min_count, keep_attrs])	Reduce this DataTree's data by applying sum along some dimension(s).
to_dataset([inherit])	Return the data in this node as a new xarray.Dataset object.
to_dict([relative])	Create a dictionary mapping of paths to the data contained in those nodes.
to_netcdf([filepath, mode, encoding, ...])	Write datatree contents to a netCDF file.
to_zarr(store[, mode, encoding, ...])	Write datatree contents to a Zarr store.
update(other)	Update this node's children and / or variables.
values()
var([dim, skipna, ddof, keep_attrs])	Reduce this DataTree's data by applying var (i.e., variance) along some dimension(s).

Attributes

ancestors	All parent nodes and their parent nodes, starting with the most distant.
attrs	Dictionary of global attributes on this node object.
children	Child nodes of this node, stored under a mapping via their names.
chunksizes	Mapping from group paths to a mapping of chunksizes.
coords	Dictionary of DataArray objects corresponding to coordinate variables.
data_vars	Dictionary of DataArray objects corresponding to data variables.
dataset	An immutable Dataset-like view onto the data in this node.
depth	Maximum level of this tree.
descendants	Child nodes and all their child nodes.
dims	Mapping from dimension names to lengths.
ds	An immutable Dataset-like view onto the data in this node.
encoding	Dictionary of global encoding attributes on this node object.
groups	Return all groups in the tree, given as a tuple of path-like strings.
has_attrs	Whether or not there are any metadata attributes in this node.
has_data	Whether or not there are any variables in this node.
indexes	Mapping of pandas.Index objects used for label based indexing.
is_empty	False if node contains any data or attrs.
is_hollow	True if only leaf nodes contain data.
is_leaf	Whether this node is a leaf node.
is_root	Whether this node is the tree root.
leaves	All leaf nodes.
level	Level of this node.
lineage	All parent nodes and their parent nodes, starting with the closest.
name	The name of this node.
nbytes
parent	Parent of this node.
parents	All parent nodes and their parent nodes, starting with the closest.
path	Return the file-like path from the root to this node.
root	Root node of the tree.
siblings	Nodes with the same parent as this node.
sizes	Mapping from dimension names to lengths.
subtree	Iterate over all nodes in this tree, including both self and all descendants.
subtree_with_keys	Iterate over relative paths and node pairs for all nodes in this tree.
variables	Low level interface to node contents as dict of Variable objects.
width	Number of nodes at this level in the tree.
xindexes	Mapping of xarray Index objects used for label based indexing.