The yt Project
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.
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
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 meets the minimal requirement # 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
yt is designed to provide meaningful analysis of data. We have some Quickstart example notebooks in the repository:
- Data Inspection_Data_Inspection.ipynb)
- Simple Visualization_Simple_Visualization.ipynb)
- Data Objects and Time Series_Data_Objects_and_Time_Series.ipynb)
- Derived Fields and Profiles_Derived_Fields_and_Profiles.ipynb)
- Volume Rendering_Volume_Rendering.ipynb)
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.