datatree

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Datatree is a prototype implementation of a tree-like hierarchical data structure for xarray.

Datatree was born after the xarray team recognised a need for a new hierarchical data structure, that was more flexible than a single xarray.Dataset object. The initial motivation was to represent netCDF files / Zarr stores with multiple nested groups in a single in-memory object, but datatree.DataTree objects have many other uses.

:rotating_light: :bell: :warning: NO LONGER MAINTAINED :warning: :bell: :rotating_light:

This repository has been archived and the code is no longer maintained!

Datatree has been merged upstream into pydata/xarray, and released as of xarray version 2024.10.0.

There will be no further bugfixes or feature additions to this respository.

Users of this repository should migrate to using xarray.DataTree instead, following the Migration Guide.

The information below is all outdated, and is left only for historical interest.

Installation

You can install datatree via pip:

pip install xarray-datatree

or via conda-forge

conda install -c conda-forge xarray-datatree

Why Datatree?

You might want to use datatree for:

• Organising many related datasets, e.g. results of the same experiment with different parameters, or simulations of the same system using different models,
• Analysing similar data at multiple resolutions simultaneously, such as when doing a convergence study,
• Comparing heterogenous but related data, such as experimental and theoretical data,
• I/O with nested data formats such as netCDF / Zarr groups.

Talk slides on Datatree from AMS-python 2023

Features

The approach used here is based on benbovy's DatasetNode example - the basic idea is that each tree node wraps a up to a single xarray.Dataset. The differences are that this effort:

• Uses a node structure inspired by anytree for the tree,
• Implements path-like getting and setting,
• Has functions for mapping user-supplied functions over every node in the tree,
• Automatically dispatches some of xarray.Dataset's API over every node in the tree (such as .isel),
• Has a bunch of tests,
• Has a printable representation that currently looks like this:

Get Started

You can create a DataTree object in 3 ways:

1. Load from a netCDF file (or Zarr store) that has groups via open_datatree().
2. Using the init method of DataTree, which creates an individual node. You can then specify the nodes' relationships to one other, either by setting .parent and .children attributes, or through __get/setitem__ access, e.g. dt['path/to/node'] = DataTree().
3. Create a tree from a dictionary of paths to datasets using DataTree.from_dict().

Development Roadmap

Datatree currently lives in a separate repository to the main xarray package. This allows the datatree developers to make changes to it, experiment, and improve it faster.

Eventually we plan to fully integrate datatree upstream into xarray's main codebase, at which point the github.com/xarray-contrib/datatree repository will be archived. This should not cause much disruption to code that depends on datatree - you will likely only have to change the import line (i.e. from from datatree import DataTree to from xarray import DataTree).

However, until this full integration occurs, datatree's API should not be considered to have the same level of stability as xarray's.

User Feedback

We really really really want to hear your opinions on datatree! At this point in development, user feedback is critical to help us create something that will suit everyone's needs. Please raise any thoughts, issues, suggestions or bugs, no matter how small or large, on the github issue tracker.