# ytree
[](https://circleci.com/gh/ytree-project/ytree/tree/main)
[](https://codecov.io/gh/ytree-project/ytree)
[](http://ytree.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/ytree)
[](https://anaconda.org/conda-forge/ytree)
[](https://doi.org/10.21105/joss.01881)
[![yt-project](https://img.shields.io/static/v1?label="works%20with"&message="yt"&color="blueviolet")](https://yt-project.org)
This is `ytree`, a [yt](https://github.com/yt-project/yt) extension for
working with tree data and especially merger trees.
Generally speaking, tree data defines a one-directional relationship
between a series of objects. In trees, objects can have one or more
ancestors, but only a single descendent.
Structure formation in cosmology proceeds in a hierarchical fashion,
where dark matter halos grow via mergers with other halos. This type
of evolution can be conceptualized as a tree, with small branches
connecting to successively larger ones, and finally to the trunk. A
merger tree describes the growth of halos in a cosmological
simulation by linking a halo appearing in a given snapshot to its
direct ancestors in a previous snapshot and its descendent in the next
snapshot.
Merger trees are computationally expensive to generate and a great
number of codes exist for computing them. However, each of these codes
saves the resulting data to a different format. `ytree` is Python
package for reading and working with merger tree data from multiple
formats. If you are already familiar with using
[yt](https://github.com/yt-project/yt) to analyze snapshots from
cosmological simulations, then think of `ytree` as the `yt` of merger
trees.
To load a merger tree data set with `ytree` and print the masses of
all the halos in a single tree, one could do:
```
>>> import ytree
>>> a = ytree.load('tree_0_0_0.dat')
>>> my_tree = a[0]
>>> print(my_tree['tree', 'mass'].to('Msun'))
[6.57410072e+14 6.57410072e+14 6.53956835e+14 6.50071942e+14 ...
2.60575540e+12 2.17122302e+12 2.17122302e+12] Msun
```
A list of all currently supported formats can be found in the online
[documentation](https://ytree.readthedocs.io/en/latest/Arbor.html#loading-merger-tree-data). If
you would like to see support added for another format, we would be
happy to work with you to make it happen. In principle, any type of
tree-like data where an object has one or more ancestors and a single
descendent can be supported.
## Installation
`ytree` can be installed with pip or conda:
### pip installation
```
pip install ytree
```
### conda installation
```
conda install -c conda-forge ytree
```
### source installation
To get the development version, clone this repository and install like this:
```
git clone https://github.com/ytree-project/ytree
cd ytree
pip install -e .
```
## Getting Started
The [ytree documentation](https://ytree.readthedocs.io) will walk you
through installation, get you started analyzing merger trees, and help
you become a contributor to the project. Have a look!
## Sample Data
Sample data for all merger tree formats supported by `ytree` is available on the
[yt Hub](https://girder.hub.yt/) in the
[ytree data](https://girder.hub.yt/#collection/59835a1ee2a67400016a2cda) collection.
## Contributing
`ytree` would be much better with your contribution! As an extension of
[the yt Project](https://yt-project.org/), we follow the yt
[guidelines for contributing](https://github.com/yt-project/yt#contributing).
## Citing `ytree`
If you use `ytree` in your work, please cite the following:
```
Smith et al., (2019). ytree: A Python package for analyzing merger trees.
Journal of Open Source Software, 4(44), 1881,
https://doi.org/10.21105/joss.01881
```
For BibTeX users:
```
@article{ytree,
doi = {10.21105/joss.01881},
url = {https://doi.org/10.21105/joss.01881},
year = {2019},
month = {dec},
publisher = {The Open Journal},
volume = {4},
number = {44},
pages = {1881},
author = {Britton D. Smith and Meagan Lang},
title = {ytree: A Python package for analyzing merger trees},
journal = {Journal of Open Source Software}
}
```
If you would like to also cite the specific version of `ytree` used in
your work, include the following reference:
```
@software{britton_smith_2023_8349044,
author = {Britton Smith and
Meagan Lang and
John Wise and
Juanjo Bazán},
title = {ytree-project/ytree: ytree 3.2.1 Release},
month = sep,
year = 2023,
publisher = {Zenodo},
version = {ytree-3.2.1},
doi = {10.5281/zenodo.8349044},
url = {https://doi.org/10.5281/zenodo.8349044}
}
```
## Resources
* The latest documentation can be found at
https://ytree.readthedocs.io.
* The [ytree
paper](https://joss.theoj.org/papers/10.21105/joss.01881) in the
[Journal of Open Source Software](https://joss.theoj.org/).
* `ytree` is an extension of [the yt
Project](https://yt-project.org/). The [yt-project community
resources](https://github.com/yt-project/yt#resources) can be used
for ytree-related communication. The `ytree` developers can usually
be found on the yt project Slack channel.
Raw data
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"description": "# ytree\n\n[](https://circleci.com/gh/ytree-project/ytree/tree/main)\n[](https://codecov.io/gh/ytree-project/ytree)\n[](http://ytree.readthedocs.io/en/latest/?badge=latest)\n[](https://badge.fury.io/py/ytree)\n[](https://anaconda.org/conda-forge/ytree)\n[](https://doi.org/10.21105/joss.01881)\n[![yt-project](https://img.shields.io/static/v1?label=\"works%20with\"&message=\"yt\"&color=\"blueviolet\")](https://yt-project.org)\n\nThis is `ytree`, a [yt](https://github.com/yt-project/yt) extension for\nworking with tree data and especially merger trees.\n\nGenerally speaking, tree data defines a one-directional relationship\nbetween a series of objects. In trees, objects can have one or more\nancestors, but only a single descendent.\n\nStructure formation in cosmology proceeds in a hierarchical fashion,\nwhere dark matter halos grow via mergers with other halos. 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If you are already familiar with using\n[yt](https://github.com/yt-project/yt) to analyze snapshots from\ncosmological simulations, then think of `ytree` as the `yt` of merger\ntrees.\n\nTo load a merger tree data set with `ytree` and print the masses of\nall the halos in a single tree, one could do:\n\n```\n>>> import ytree\n>>> a = ytree.load('tree_0_0_0.dat')\n>>> my_tree = a[0]\n>>> print(my_tree['tree', 'mass'].to('Msun'))\n[6.57410072e+14 6.57410072e+14 6.53956835e+14 6.50071942e+14 ...\n 2.60575540e+12 2.17122302e+12 2.17122302e+12] Msun\n```\n\nA list of all currently supported formats can be found in the online\n[documentation](https://ytree.readthedocs.io/en/latest/Arbor.html#loading-merger-tree-data). If\nyou would like to see support added for another format, we would be\nhappy to work with you to make it happen. In principle, any type of\ntree-like data where an object has one or more ancestors and a single\ndescendent can be supported.\n\n## Installation\n\n`ytree` can be installed with pip or conda:\n\n### pip installation\n\n```\npip install ytree\n```\n\n### conda installation\n\n```\nconda install -c conda-forge ytree\n```\n\n### source installation\n\nTo get the development version, clone this repository and install like this:\n\n```\ngit clone https://github.com/ytree-project/ytree\ncd ytree\npip install -e .\n```\n\n## Getting Started\n\nThe [ytree documentation](https://ytree.readthedocs.io) will walk you\nthrough installation, get you started analyzing merger trees, and help\nyou become a contributor to the project. 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