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datatree

(★ 164)

WIP implementation of a tree-like hierarchical data structure for xarray.

파일 탐색기

최종 버전 다운로드 (.zip)

- - main.yaml
  - pypipublish.yaml
- dependabot.yml
- pull_request_template.md
- doc.yml
- environment.yml
- - __init__.py
  - conftest.py
  - test_dataset_api.py
  - test_datatree.py
  - test_extensions.py
  - test_formatting.py
  - test_formatting_html.py
  - test_io.py
  - test_mapping.py
  - test_treenode.py
  - test_version.py
- __init__.py
- common.py
- datatree.py
- extensions.py
- formatting.py
- formatting_html.py
- io.py
- iterators.py
- mapping.py
- ops.py
- py.typed
- render.py
- testing.py
- treenode.py
- - api.rst
  - conf.py
  - contributing.rst
  - data-structures.rst
  - hierarchical-data.rst
  - index.rst
  - installation.rst
  - io.rst
  - quick-overview.rst
  - terminology.rst
  - tutorial.rst
  - whats-new.rst
- make.bat
- Makefile
- README.md
.flake8
.git_archival.txt
.gitignore
.pre-commit-config.yaml
codecov.yml
conftest.py
LICENSE
pyproject.toml
README.md
readthedocs.yml

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/xarray-contrib/datatree

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd datatree

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

Python 3 pip 명령어를 쓰려면 Python이 필요합니다.

pip install xarray-datatree

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Python

쉬움

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install xarray-datatree

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

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# datatree

| CI          | [![GitHub Workflow Status][github-ci-badge]][github-ci-link] [![Code Coverage Status][codecov-badge]][codecov-link] [![pre-commit.ci status][pre-commit.ci-badge]][pre-commit.ci-link] |
| :---------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| **Docs**    |                                                                     [![Documentation Status][rtd-badge]][rtd-link]                                                                     |
| **Package** |                                                          [![Conda][conda-badge]][conda-link] [![PyPI][pypi-badge]][pypi-link]                                                          |
| **License** |                                                                         [![License][license-badge]][repo-link]                                                                         |

**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](https://github.com/pydata/xarray/issues/4118),
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`](https://github.com/pydata/xarray/issues/8572), 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](https://github.com/pydata/xarray/discussions/9680).

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

### Installation
You can install datatree via pip:
```shell
pip install xarray-datatree
```

or via conda-forge
```shell
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**](https://speakerdeck.com/tomnicholas/xarray-datatree-hierarchical-data-structures-for-multi-model-science)

### Features

The approach used here is based on benbovy's [`DatasetNode` example](https://gist.github.com/benbovy/92e7c76220af1aaa4b3a0b65374e233a) - 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](https://github.com/xarray-contrib/datatree/issues/7) 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:
<img src="https://user-images.githubusercontent.com/35968931/130657849-577faa00-1b8b-4e33-a45c-4f389ce325b2.png" alt="drawing" width="500"/>

### 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](https://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](https://docs.xarray.dev/en/stable/contributing.html#backwards-compatibility).

### 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](https://github.com/xarray-contrib/datatree/issues).

[github-ci-badge]: https://img.shields.io/github/actions/workflow/status/xarray-contrib/datatree/main.yaml?branch=main&label=CI&logo=github
[github-ci-link]: https://github.com/xarray-contrib/datatree/actions?query=workflow%3ACI
[codecov-badge]: https://img.shields.io/codecov/c/github/xarray-contrib/datatree.svg?logo=codecov
[codecov-link]: https://codecov.io/gh/xarray-contrib/datatree
[rtd-badge]: https://img.shields.io/readthedocs/xarray-datatree/latest.svg
[rtd-link]: https://xarray-datatree.readthedocs.io/en/latest/?badge=latest
[pypi-badge]: https://img.shields.io/pypi/v/xarray-datatree?logo=pypi
[pypi-link]: https://pypi.org/project/xarray-datatree
[conda-badge]: https://img.shields.io/conda/vn/conda-forge/xarray-datatree?logo=anaconda
[conda-link]: https://anaconda.org/conda-forge/xarray-datatree
[license-badge]: https://img.shields.io/github/license/xarray-contrib/datatree
[repo-link]: https://github.com/xarray-contrib/datatree
[pre-commit.ci-badge]: https://results.pre-commit.ci/badge/github/xarray-contrib/datatree/main.svg
[pre-commit.ci-link]: https://results.pre-commit.ci/latest/github/xarray-contrib/datatree/main