Difference between revisions of "Applications/Yt"
From HPC
m |
m |
||
| Line 2: | Line 2: | ||
* Description: yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles. | * Description: yt supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles. | ||
| − | * Version: 3.2.3 | + | * Version: 3.2.3, 3.3.1 and 3.4.0 |
| − | * Modules: yt/3.2.3 | + | * Modules: yt/3.2.3, yt/3.3.1 and yt/3.4.0 |
* Licence: Free and open source | * Licence: Free and open source | ||
Revision as of 16:51, 6 November 2017
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 3.4.0
- Modules: yt/3.2.3, yt/3.3.1 and yt/3.4.0
- 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.
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
|