Revision as of 11:13, 24 May 2019

Application Details

• Description: yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles.
• Version: 3.2.3, 3.3.1 and 3.4.1
• Modules: yt/3.2.3, yt/3.3.1, yt/3.4.0 and yt/3.4.1
• Licence: Free and open source

Usage Examples

• yt is a community-developed analysis and visualization toolkit for volumetric data. yt has been applied mostly to astrophysical simulation data, but it can be applied to many different types of data including seismology, radio telescope data, weather simulations, and nuclear engineering simulations. yt is developed in Python under the open-source model.
• Rockstar is also installed on the 3.4.1 version. To run the Rockstar Halo finding, you must launch python with MPI and parallelization enabled. While Rockstar itself does not require MPI to run, the MPI libraries allow yt to distribute particle information across multiple nodes.

A code snippet is shown below, using yt as an import

#!usr/bin/env python

import yt
import numpy as np

arr = np.random.random(size=(64,64,64))

data = dict(density = (arr, "g/cm**3"))
bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])
ds = yt.load_uniform_grid(data, arr.shape, length_unit="Mpc", bbox=bbox, nprocs=64)

slc = yt.SlicePlot(ds, "z", ["density"])
slc.set_cmap("density", "Blues")
slc.annotate_grids(cmap=None)
slc.show()

Further Information

• yt project

• Rockstar

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