DataTree

Creating a DataTree

Methods of creating a DataTree.

DataTree([dataset, children, name])	A tree-like hierarchical collection of xarray objects.
DataTree.from_dict(d, /[, name])	Create a datatree from a dictionary of data objects, organised by paths into the tree.

Tree Attributes

Attributes relating to the recursive tree-like structure of a DataTree.

DataTree.parent	Parent of this node.
DataTree.children	Child nodes of this node, stored under a mapping via their names.
DataTree.name	The name of this node.
DataTree.path	Return the file-like path from the root to this node.
DataTree.root	Root node of the tree
DataTree.is_root	Whether this node is the tree root.
DataTree.is_leaf	Whether this node is a leaf node.
DataTree.leaves	All leaf nodes.
DataTree.level	Level of this node.
DataTree.depth	Maximum level of this tree.
DataTree.width	Number of nodes at this level in the tree.
DataTree.subtree	Iterate over all nodes in this tree, including both self and all descendants.
DataTree.subtree_with_keys	Iterate over relative paths and node pairs for all nodes in this tree.
DataTree.descendants	Child nodes and all their child nodes.
DataTree.siblings	Nodes with the same parent as this node.
DataTree.lineage	All parent nodes and their parent nodes, starting with the closest.
DataTree.parents	All parent nodes and their parent nodes, starting with the closest.
DataTree.ancestors	All parent nodes and their parent nodes, starting with the most distant.
DataTree.groups	Return all groups in the tree, given as a tuple of path-like strings.
DataTree.xindexes	Mapping of xarray Index objects used for label based indexing.

Data Contents

Interface to the data objects (optionally) stored inside a single DataTree node. This interface echoes that of xarray.Dataset.

DataTree.dims	Mapping from dimension names to lengths.
DataTree.sizes	Mapping from dimension names to lengths.
DataTree.data_vars	Dictionary of DataArray objects corresponding to data variables
DataTree.ds	An immutable Dataset-like view onto the data in this node.
DataTree.coords	Dictionary of xarray.DataArray objects corresponding to coordinate variables
DataTree.attrs	Dictionary of global attributes on this node object.
DataTree.encoding	Dictionary of global encoding attributes on this node object.
DataTree.indexes	Mapping of pandas.Index objects used for label based indexing.
DataTree.nbytes
DataTree.dataset	An immutable Dataset-like view onto the data in this node.
DataTree.to_dataset([inherit])	Return the data in this node as a new xarray.Dataset object.
DataTree.has_data	Whether or not there are any variables in this node.
DataTree.has_attrs	Whether or not there are any metadata attributes in this node.
DataTree.is_empty	False if node contains any data or attrs.
DataTree.is_hollow	True if only leaf nodes contain data.
DataTree.chunksizes	Mapping from group paths to a mapping of chunksizes.

Dictionary Interface

DataTree objects also have a dict-like interface mapping keys to either xarray.DataArrays or to child DataTree nodes.

DataTree.__getitem__(key)	Access child nodes, variables, or coordinates stored anywhere in this tree.
DataTree.__setitem__(key, value)	Add either a child node or an array to the tree, at any position.
DataTree.__delitem__(key)	Remove a variable or child node from this datatree node.
DataTree.update(other)	Update this node's children and / or variables.
DataTree.get(key[, default])	Access child nodes, variables, or coordinates stored in this node.
DataTree.items()
DataTree.keys()
DataTree.values()

Tree Manipulation

For manipulating, traversing, navigating, or mapping over the tree structure.

DataTree.orphan()	Detach this node from its parent.
DataTree.same_tree(other)	True if other node is in the same tree as this node.
DataTree.relative_to(other)	Compute the relative path from this node to node other.
DataTree.iter_lineage()	Iterate up the tree, starting from the current node.
DataTree.find_common_ancestor(other)	Find the first common ancestor of two nodes in the same tree.
DataTree.map_over_datasets(func, *args[, kwargs])	Apply a function to every dataset in this subtree, returning a new tree which stores the results.
DataTree.pipe(func, *args, **kwargs)	Apply func(self, *args, **kwargs)
DataTree.match(pattern)	Return nodes with paths matching pattern.
DataTree.filter(filterfunc)	Filter nodes according to a specified condition.
DataTree.filter_like(other)	Filter a datatree like another datatree.

Pathlib-like Interface

DataTree objects deliberately echo some of the API of pathlib.PurePath.

DataTree.name	The name of this node.
DataTree.parent	Parent of this node.
DataTree.parents	All parent nodes and their parent nodes, starting with the closest.
DataTree.relative_to(other)	Compute the relative path from this node to node other.

DataTree Contents

Manipulate the contents of all nodes in a DataTree simultaneously.

DataTree.copy(*[, inherit, deep])

Returns a copy of this subtree.

DataTree Node Contents

Manipulate the contents of a single DataTree node.

DataTree.assign([items])	Assign new data variables or child nodes to a DataTree, returning a new object with all the original items in addition to the new ones.
DataTree.drop_nodes(names, *[, errors])	Drop child nodes from this node.

DataTree Operations

Apply operations over multiple DataTree objects.

map_over_datasets(func, *args[, kwargs])	Applies a function to every dataset in one or more DataTree objects with the same structure (ie.., that are isomorphic), returning new trees which store the results.
group_subtrees(*trees)	Iterate over subtrees grouped by relative paths in breadth-first order.

Comparisons

Compare one DataTree object to another.

DataTree.isomorphic(other)	Two DataTrees are considered isomorphic if the set of paths to their descendent nodes are the same.
DataTree.equals(other)	Two DataTrees are equal if they have isomorphic node structures, with matching node names, and if they have matching variables and coordinates, all of which are equal.
DataTree.identical(other)	Like equals, but also checks attributes on all datasets, variables and coordinates, and requires that any inherited coordinates at the tree root are also inherited on the other tree.

Indexing

Index into all nodes in the subtree simultaneously.

DataTree.isel([indexers, drop, missing_dims])	Returns a new data tree with each array indexed along the specified dimension(s).
DataTree.sel([indexers, method, tolerance, drop])	Returns a new data tree with each array indexed by tick labels along the specified dimension(s).

Aggregation

Aggregate data in all nodes in the subtree simultaneously.

DataTree.all([dim, keep_attrs])	Reduce this DataTree's data by applying all along some dimension(s).
DataTree.any([dim, keep_attrs])	Reduce this DataTree's data by applying any along some dimension(s).
DataTree.max([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying max along some dimension(s).
DataTree.min([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying min along some dimension(s).
DataTree.mean([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying mean along some dimension(s).
DataTree.median([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying median along some dimension(s).
DataTree.prod([dim, skipna, min_count, ...])	Reduce this DataTree's data by applying prod along some dimension(s).
DataTree.sum([dim, skipna, min_count, ...])	Reduce this DataTree's data by applying sum along some dimension(s).
DataTree.std([dim, skipna, ddof, keep_attrs])	Reduce this DataTree's data by applying std along some dimension(s).
DataTree.var([dim, skipna, ddof, keep_attrs])	Reduce this DataTree's data by applying var along some dimension(s).
DataTree.cumsum([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying cumsum along some dimension(s).
DataTree.cumprod([dim, skipna, keep_attrs])	Reduce this DataTree's data by applying cumprod along some dimension(s).

ndarray methods

Methods copied from numpy.ndarray objects, here applying to the data in all nodes in the subtree.

DataTree.argsort([axis, kind, order])	Returns the indices that would sort this array.
DataTree.conj()	Complex-conjugate all elements.
DataTree.conjugate(*args, **kwargs)	a.conj()
DataTree.round(*args, **kwargs)