Updates from yt-napari
@ Chris Havlin | Friday, Oct 6, 2023 | 3 minute read | Update at Friday, Oct 6, 2023

yt-napari has seen a number of new features, performance improvements and new documentation in the past year or so. Read on to find out more!

Long form video walkthroughs

One of the main efforts of the past year has been improving documentation. This included not just new static documentation (e.g., new example notebooks), but also the yt-napari Tutorial Series. The series starts with an introduction to napari for yt users, moves on to how to use the yt-napari plugin to load data into napari from yt and ends with some videos showing examples of using other napari plugins for analysis and visualization of yt data in napari. The final videos are particularly fun as they apply image analysis methods used by the bio-imaging community to segment yt-data. For example, here’s a screenshot of using a watershed transformation via the napari-simpleitk-image-processing plugin to identify 3D density voids in enzo_tiny_cosmology and then the napari-clusters-plotter to interactively visualize mean field values within the domains:

Or, check out this short clip from a longer video in which the napari-clusters-plotter plugin is used to interactively visualize how field intensities relate spatially after having used napari-clusters-plotter to run a kmeans classification.

Additionally, you can also use standard napari shapes layers to interactively sample at points or along paths and then use plugins like napari-line-plot or napari-properties-plotter to visualize how data data varies along the path (screen shot from the Introduction to other plugins video):

Check out either of the following to access all the videos:

New Feature: Sampling Timeseries

One of the more exciting new features of recent releases is the ability to sample and load timeseries for both slices and 3D regions from the napari GUI, a jupyter notebook or via JSON file. This allows you to interactively visualize time-dependent behavior. For example, the following shows a small 3D region centered on the final max density of the enzo_tiny_cosmology dataset:

For higher resolution sampling, you can work from a notebook and leverage dask to lazily-sample 3D regions across a timeseries so that you can load the current timestep on demand:

Check out the sample notebooks and videos for loading timeseries (notebook, video) and using dask with timeseries (notebook, video)

The future of yt-napari

One of the exciting features in development upstream in napari is improved on-demand loading of multi-resolution data. Once that work is completed, it will open up the possibility of progressive sampling in yt-napari! Check out this video for a preview of what this might enable in yt-napari: progressive of the DeeplyNestedZoom dataset. There’s also plenty to do with yt-napari as it is now, check out the Issues page to get involved!

yt extension modules

yt has many extension packages to help you in your scientific workflow! Check these out, or create your own.


ytini is set of tools and tutorials for using yt as a tool inside the 3D visual effects software Houdini or a data pre-processor externally to Houdini.


Trident is a full-featured tool that projects arbitrary sightlines through astrophysical hydrodynamics simulations for generating mock spectral observations of the IGM and CGM.


pyXSIM is a Python package for simulating X-ray observations from astrophysical sources.


Analyze merger tree data from multiple sources. It’s yt for merger trees!


yt_idv is a package for interactive volume rendering with yt! It provides interactive visualization using OpenGL for datasets loaded in yt. It is written to provide both scripting and interactive access.


widgyts is a jupyter widgets extension for yt, backed by rust/webassembly to allow for browser-based, interactive exploration of data from yt.


yt_astro_analysis is the yt extension package for astrophysical analysis.

Make your own!!

Finally, check out our development docs on writing your own yt extensions!

Contributing to the Blog

Are you interested in contributing to the yt blog?

Check out our post on contributing to the blog for a guide!

We welcome contributions from all members of the yt community. Feel free to reach out if you need any help.

the yt data hub

The yt hub at https://girder.hub.yt/ has a ton of resources to check out, whether you have yt installed or not.

The collections host all sorts of data that can be loaded with yt. Some have been used in publications, and others are used as sample frontend data for yt. Maybe there’s data from your simulation software?

The rafts host the yt quickstart notebooks, where you can interact with yt in the browser, without needing to install it locally. Check out some of the other rafts too, like the widgyts release notebooks – a demo of the widgyts yt extension pacakge; or the notebooks from the CCA workshop – a user’s workshop on using yt.

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