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        "description": "# The yt Project\n\n[![Users' Mailing List](https://img.shields.io/badge/Users-List-lightgrey.svg)](https://mail.python.org/mm3/archives/list/yt-users@python.org//)\n[![Devel Mailing List](https://img.shields.io/badge/Devel-List-lightgrey.svg)](https://mail.python.org/mm3/archives/list/yt-dev@python.org//)\n[![Build Status](https://img.shields.io/travis/yt-project/yt.svg?branch=master)](https://travis-ci.org/yt-project/yt)\n[![codecov](https://codecov.io/gh/yt-project/yt/branch/master/graph/badge.svg)](https://codecov.io/gh/yt-project/yt)\n[![Latest Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg)](http://yt-project.org/docs/dev/)\n[![Data Hub](https://img.shields.io/badge/data-hub-orange.svg)](https://hub.yt/)\n[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](http://numfocus.org)\n\n<a href=\"http://yt-project.org\"><img src=\"doc/source/_static/yt_logo.png\" width=\"300\"></a>\n\nyt is an open-source, permissively-licensed python package for analyzing and\nvisualizing volumetric data.\n\nyt supports structured, variable-resolution meshes, unstructured meshes, and\ndiscrete or sampled data such as particles. Focused on driving\nphysically-meaningful inquiry, yt has been applied in domains such as\nastrophysics, seismology, nuclear engineering, molecular dynamics, and\noceanography. Composed of a friendly community of users and developers, we want\nto make it easy to use and develop - we'd love it if you got involved!\n\nWe've written a [method\npaper](http://adsabs.harvard.edu/abs/2011ApJS..192....9T) you may be interested\nin; if you use yt in the preparation of a publication, please consider citing\nit.\n\n## Code of Conduct\n\nyt abides by a code of conduct partially modified from the PSF code of conduct,\nand is found [in our contributing\nguide](http://yt-project.org/docs/dev/developing/developing.html#yt-community-code-of-conduct).\n\n## Installation\n\nYou can install the most recent stable version of yt either with conda from\n[conda-forge](http://conda-forge.github.io/):\n\n```\nconda install -c conda-forge yt\n```\n\nor with pip:\n\n```\npip install yt\n```\n\nIf you want the latest nightly build, you can manually install from our\nrepository:\n\n```\nconda install -c yt-project/label/dev yt\n```\n\nTo get set up with a development version, you want to clone this repository:\n\n```\ngit clone https://github.com/yt-project/yt yt-git\ncd yt-git\n```\n\nand work within a conda environment:\n\n```\n# Create a conda environment named yt-git\n$ conda create -n yt-git python=3.6\n# Activate it\n$ source activate yt-git\n# Make sure you run the latest version of conda\n$ conda update conda\n# Install yt's runtime dependencies\n$ conda install -c conda-forge yt --only-deps\n# Install yt's build dependencies\n$ conda install -c conda-forge cython\n# Make sure you run the latest version of pip\n$ pip install --upgrade pip\n$ pip install -v -e .\n# Output installed packages\n$ conda env export\n```\n\nAlternatively, you can install yt in a\n[virtualenv](https://packaging.python.org/installing/#creating-virtual-environments):\n\n```\n# It is conventional to create virtualenvs at ~/.virtualenv/\n$ mkdir -p ~/.virtualenv\n# Assuming your version of Python 3 is 3.4 or higher,\n# create a virtualenv named yt-git\n$ python3 -m venv ~/.virtualenv/yt-git\n# Activate it\n$ source ~/.virtualenv/yt-git/bin/activate\n# Make sure you run the latest version of pip\n$ pip install --upgrade pip\n# Assuming you have cd'd into yt-git\n$ pip install -e .\n# Output installed packages\n$ pip freeze\n```\n\n## Getting Started\n\nyt is designed to provide meaningful analysis of data.  We have some Quickstart\nexample notebooks in the repository:\n\n * [Introduction](doc/source/quickstart/1\\)_Introduction.ipynb)\n * [Data Inspection](doc/source/quickstart/2\\)_Data_Inspection.ipynb)\n * [Simple Visualization](doc/source/quickstart/3\\)_Simple_Visualization.ipynb)\n * [Data Objects and Time Series](doc/source/quickstart/4\\)_Data_Objects_and_Time_Series.ipynb)\n * [Derived Fields and Profiles](doc/source/quickstart/5\\)_Derived_Fields_and_Profiles.ipynb)\n * [Volume Rendering](doc/source/quickstart/6\\)_Volume_Rendering.ipynb)\n\nIf you'd like to try these online, you can visit our [yt Hub](https://hub.yt/)\nand run a notebook next to some of our example data.\n\n## Contributing\n\nWe love contributions!  yt is open source, built on open source, and we'd love\nto have you hang out in our community.\n\nWe have developed some [guidelines](CONTRIBUTING.rst) for contributing to yt.\n\n**Imposter syndrome disclaimer**: We want your help. No, really.\n\nThere may be a little voice inside your head that is telling you that you're not\nready to be an open source contributor; that your skills aren't nearly good\nenough to contribute. What could you possibly offer a project like this one?\n\nWe assure you - the little voice in your head is wrong. If you can write code at\nall, you can contribute code to open source. Contributing to open source\nprojects is a fantastic way to advance one's coding skills. Writing perfect code\nisn't the measure of a good developer (that would disqualify all of us!); it's\ntrying to create something, making mistakes, and learning from those\nmistakes. That's how we all improve, and we are happy to help others learn.\n\nBeing an open source contributor doesn't just mean writing code, either. You can\nhelp out by writing documentation, tests, or even giving feedback about the\nproject (and yes - that includes giving feedback about the contribution\nprocess). Some of these contributions may be the most valuable to the project as\na whole, because you're coming to the project with fresh eyes, so you can see\nthe errors and assumptions that seasoned contributors have glossed over.\n\n(This disclaimer was originally written by\n[Adrienne Lowe](https://github.com/adriennefriend) for a\n[PyCon talk](https://www.youtube.com/watch?v=6Uj746j9Heo), and was adapted by yt\nbased on its use in the README file for the\n[MetPy project](https://github.com/Unidata/MetPy))\n\n## Resources\n\nWe have some community and documentation resources available.\n\n * Our latest documentation is always at http://yt-project.org/docs/dev/ and it\n   includes recipes, tutorials, and API documentation\n * The [discussion mailing\n   list](https://mail.python.org/mm3/archives/list/yt-users@python.org//)\n   should be your first stop for general questions\n * The [development mailing\n   list](https://mail.python.org/mm3/archives/list/yt-dev@python.org//) is\n   better suited for more development issues\n * You can also join us on Slack at yt-project.slack.com ([request an\n   invite](http://yt-project.org/slack.html))\n\n\n",
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