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Enzo 2.0 and Inline yt
Enzo 2.0 has just been released to its new Google Code website. This release features preliminary support for inline Python analysis, using yt.
In the Enzo documentation there’s a brief section on how to use yt for inline analysis. As it stands, many features are not fully functional, but things like phase plots, profiles, derived quantities and slices all work. This functionality is currently untested at large (> 128) processors, but for small runs – particularly debugging runs!
kD-Tree Rendering Improvements
Hi all,
Just sharing a video here that showcases some improvements I’ve made to the kD-tree rendering that will be making its way to yt for the 2.0 release. You can download it render_movie.3gp|here
Just to be clear this is showing the rendering of a cosmology simulation with a 64^3 root grid + 6 AMR levels in real time on 8 processors. The script is run in parallel, with the root processor displaying the results once each frame is finished.
Improvements to Parallelism
The last few days I’ve spent some time looking at how parallelism in yt performs. I conducted two different tests, both of which operated on the 512^3, 7 level ‘Santa Fe Light Cone’ dataset RD0036. This dataset has 5.5e8 total cells and in the neighborhood of 380,000 grids. I ran four different tests: a 1D profile of the entire dataset, a 2D profile of the entire dataset, and projections of both ‘Density’ (requires IO) and ‘Ones’ (doesn’t require IO).
Quad Tree Projections
The current method for projections in yt is based on a home-grown algorithm for calculating grid overlap and joining points. I’ve always been pretty proud of it – it gave good results, and it succeeded at the project-once-make-many-images philosophy that went into its design. Rather than storing a 2D array of pixels, projections and slices in yt store flat arrays of image plane coordinates and cell widths. This means that there’s an additional step of pixelization to create an image, but it also means that arbitrary images can be made from a single projection or slice operation.
How the Merger Tree Sped Up SQLite Database UPDATEs
The Parallel Merger Tree in yt, like most of the code in yt, has a rich history of changes and upgrades. One of the most significant upgrades was a change in the way the SQLite database file is updated during the course of building the merger tree. Briefly, the database contains all the information about the merger tree of the halos, as well as the specifics of each halo, such as the mass, position or bulk velocity.
yt has moved to mercurial!
For about a year and a half now, most of the unstable development of yt has occurred inside a mercurial repo. Mercurial is a distributed version control system, not unlike git or bzr, where each checkout brings with it the entire history of the repository and enables full development. Each individual can commit changes to their own repository, while still accepting changes from others. It also makes it much easier to submit patches upstream.
Figuring Out Stereo Volume Rendering
Last week I was approached by a friend and collaborator to prepare some large volume renderings using the software volume renderer in yt. In the past we’ve successfully made very, very large image renderings using yt – Sam’s even made one at 8192^2, although at extremely high resolution like that sometimes the lack of fidelity in the underlying volume renderer shows up; sometimes even artifacts in the AMR grid boundaries, but that’s less common.
Volume Rendering with a kD-Tree Decomposition
Let’s talk about the progress we’ve made on a kD-Tree decomposition.
Aboout the yt Project
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Check out our documentation here
The yt-project homepage
Our quickstart notebooks on getting started
The yt source repository
yt extension modules
yt has many extension packages to help you in your scientific workflow! Check these out, or create your own.
ytini
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
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
pyXSIM is a Python package for simulating X-ray observations from astrophysical sources.
ytree
Analyze merger tree data from multiple sources. It’s yt for merger trees!
yt_idv
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
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
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
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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.