yt 项目

主 yt 仓库。yt 是一个开源、许可的 python 包,用于分析和可视化立体数据。「Main yt repository. yt is an open-source, permissively-licensed python package for analyzing and visualizing volumetric data.」

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yt 项目(The yt Project)

yt 是一个开源的、许可使用的 python 软件包,用于分析和可视化立体数据。

yt 支持结构化的、可变分辨率的网格、非结构化的网格,以及离散的或采样的数据,如颗粒。yt专注于推动物理意义上的探索,已被应用于天体物理学、地震学、核工程、分子动力学和海洋学等领域。我们由一个友好的用户和开发者社区组成,我们希望使它易于使用和开发--我们很乐意你参与其中。

我们已经写了一篇你可能感兴趣的方法论文;如果你在准备发表文章时使用了 yt,请考虑引用它。

行为准则

yt 遵守的行为准则部分修改自 PSF 的行为准则,可以在我们的贡献指南中找到。

安装

你可以用 conda 从 conda-forge 的 安装最新的稳定版 yt。

conda install -c conda-forge yt

或者使用 pip

pip install yt

要使用开发版本进行设置,您需要克隆此存储库。

git clone https://github.com/yt-project/yt yt-git
cd yt-git

并在 conda 环境下工作。

# Create a conda environment named yt-git
$ conda create -n yt-git python=3.6
# Activate it
$ source activate yt-git
# Make sure you run the latest version of conda
$ conda update conda
# Install yt's runtime dependencies
$ conda install -c conda-forge yt --only-deps
# Install yt's build dependencies
$ conda install -c conda-forge cython
# Make sure you run the latest version of pip
$ pip install --upgrade pip
$ pip install -v -e .
# Output installed packages
$ conda env export

或者,您可以在 virtualenv 中安装 yt

# It is conventional to create virtualenvs at ~/.virtualenv/
$ mkdir -p ~/.virtualenv
# Assuming your version of Python 3 is 3.4 or higher,
# create a virtualenv named yt-git
$ python3 -m venv ~/.virtualenv/yt-git
# Activate it
$ source ~/.virtualenv/yt-git/bin/activate
# Make sure you run the latest version of pip
$ pip install --upgrade pip
# Assuming you have cd'd into yt-git
$ pip install -e .
# Output installed packages
$ pip freeze

入门

yt 的设计是为了提供有意义的数据分析。我们有一些快速入门的例子笔记本在资源库中。

如果你想在线尝试这些,你可以访问我们的 yt Hub,在我们的一些示例数据旁边运行一个笔记本。

贡献

我们喜欢贡献! yt 是开源的,建立在开源的基础上,我们希望你能加入我们的社区。

我们已经制定了一些对 yt 贡献的指导方针

冒名顶替综合症免责声明:我们希望得到你的帮助。不,真的。

在你的脑海中可能有一个小声音告诉你,你还没有准备好成为一个开源贡献者;你的技能还不足以做出贡献。你能为这样的项目提供什么?

我们向你保证--你脑子里的小声音是错的。如果你会写代码,你就可以为开源项目贡献代码。贡献给开源项目是提升自己编码技能的绝妙方法。写出完美的代码并不是衡量一个优秀开发者的标准(那会让我们所有人都失去资格!);而是尝试创造一些东西,犯错误,并从这些错误中学习。我们都是这样改进的,我们也很乐意帮助别人学习。

作为一个开源贡献者,也不仅仅意味着写代码。你可以通过编写文档、测试,甚至是对项目进行反馈(是的--包括对贡献过程进行反馈)来提供帮助。其中一些贡献可能是对整个项目最有价值的,因为你是以全新的眼光来看待这个项目,所以你可以看到经验丰富的贡献者所忽略的错误和假设。

(这个免责声明最初是由 Adrienne LowePyCon talk 的一次演讲所写,并由 yt 根据它在 MetPy 项目 的 README 文件中的使用情况进行了改编)。

资源简介

我们有一些社区和文档资源。

  • 我们最新的文档总是在 http://yt-project.org/docs/dev/,它包括配方、教程和 API 文档。
  • 讨论邮件列表应该是您解决一般问题的第一站。
  • 开发邮件列表更适合处理更多的开发问题。
  • 您也可以在 Slack 上加入我们,网址是 yt-project.slack.com(请求邀请)。

由 NumFOCUS 提供

yt 是 NumFOCUS 的一个财政赞助项目。如果您有兴趣支持这个项目的积极维护和发展,请考虑向该项目捐款


(The first version translated by vz on 2020.09.19)

Overview

Name With Owneryt-project/yt
Primary LanguagePython
Program languagePython (Language Count: 9)
PlatformLinux, Mac, Windows
License:Other
Release Count58
Last Release Nameyt-4.3.1 (Posted on )
First Release Nameyt-2.0 (Posted on )
Created At2017-04-19 18:16:11
Pushed At2024-04-19 11:27:50
Last Commit At
Stargazers Count441
Watchers Count21
Fork Count268
Commits Count33.8k
Has Issues Enabled
Issues Count2340
Issue Open Count350
Pull Requests Count2188
Pull Requests Open Count54
Pull Requests Close Count294
Has Wiki Enabled
Is Archived
Is Fork
Is Locked
Is Mirror
Is Private

The yt Project

Users' Mailing List
Devel Mailing List
Build Status
codecov
Latest Documentation
Data Hub
Powered by NumFOCUS

yt is an open-source, permissively-licensed python package for analyzing and
visualizing volumetric data.

yt supports structured, variable-resolution meshes, unstructured meshes, and
discrete or sampled data such as particles. Focused on driving
physically-meaningful inquiry, yt has been applied in domains such as
astrophysics, seismology, nuclear engineering, molecular dynamics, and
oceanography. Composed of a friendly community of users and developers, we want
to make it easy to use and develop - we'd love it if you got involved!

We've written a method
paper
you may be interested
in; if you use yt in the preparation of a publication, please consider citing
it.

Code of Conduct

yt abides by a code of conduct partially modified from the PSF code of conduct,
and is found in our contributing
guide
.

Installation

You can install the most recent stable version of yt either with conda from
conda-forge:

conda install -c conda-forge yt

or with pip:

pip install yt

If you want the latest nightly build, you can manually install from our
repository:

conda install -c yt-project/label/dev yt

To get set up with a development version, you want to clone this repository:

git clone https://github.com/yt-project/yt yt-git
cd yt-git

and work within a conda environment:

# Create a conda environment named yt-git
$ conda create -n yt-git python=3.6
# Activate it
$ source activate yt-git
# Make sure you run the latest version of conda
$ conda update conda
# Install yt's runtime dependencies
$ conda install -c conda-forge yt --only-deps
# Install yt's build dependencies
$ conda install -c conda-forge cython
# Make sure you run the latest version of pip
$ pip install --upgrade pip
$ pip install -v -e .
# Output installed packages
$ conda env export

Alternatively, you can install yt in a
virtualenv:

# It is conventional to create virtualenvs at ~/.virtualenv/
$ mkdir -p ~/.virtualenv
# Assuming your version of Python 3 is 3.4 or higher,
# create a virtualenv named yt-git
$ python3 -m venv ~/.virtualenv/yt-git
# Activate it
$ source ~/.virtualenv/yt-git/bin/activate
# Make sure you run the latest version of pip
$ pip install --upgrade pip
# Assuming you have cd'd into yt-git
$ pip install -e .
# Output installed packages
$ pip freeze

Getting Started

yt is designed to provide meaningful analysis of data. We have some Quickstart
example notebooks in the repository:

If you'd like to try these online, you can visit our yt Hub
and run a notebook next to some of our example data.

Contributing

We love contributions! yt is open source, built on open source, and we'd love
to have you hang out in our community.

We have developed some guidelines for contributing to yt.

Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not
ready to be an open source contributor; that your skills aren't nearly good
enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at
all, you can contribute code to open source. Contributing to open source
projects is a fantastic way to advance one's coding skills. Writing perfect code
isn't the measure of a good developer (that would disqualify all of us!); it's
trying to create something, making mistakes, and learning from those
mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can
help out by writing documentation, tests, or even giving feedback about the
project (and yes - that includes giving feedback about the contribution
process). Some of these contributions may be the most valuable to the project as
a whole, because you're coming to the project with fresh eyes, so you can see
the errors and assumptions that seasoned contributors have glossed over.

(This disclaimer was originally written by
Adrienne Lowe for a
PyCon talk, and was adapted by yt
based on its use in the README file for the
MetPy project)

Resources

We have some community and documentation resources available.

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