Difference between revisions of "Applications/Yt"
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* Licence: Free and open source | * Licence: Free and open source | ||
| − | '''Important''' : It is now encouraged to use a virtual python environment and install your own required version of yt within it - see [[ | + | '''Important''' : It is now encouraged to use a virtual python environment and install your own required version of yt within it - see [[Applications/Miniconda|Miniconda]] for more details. |
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* [http://yt-project.org/doc/analyzing/analysis_modules/halo_catalogs.html Rockstar] | * [http://yt-project.org/doc/analyzing/analysis_modules/halo_catalogs.html Rockstar] | ||
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Latest revision as of 11:01, 16 November 2022
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, 3.4.0 and 3.4.1
- Modules: yt/3.2.3, yt/3.3.1, yt/3.4.0 and yt/3.4.1 (with SZpack v1.1.1)
- Licence: Free and open source
Important : It is now encouraged to use a virtual python environment and install your own required version of yt within it - see Miniconda for more details.
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()
SZpack
Version yt/3.4.1 has the additional package of SZpack installed:
[pysdlb@login01 ~]$ interactive salloc: Granted job allocation 2164599 Job ID 2164599 connecting to c128, please wait... [pysdlb@c128 ~]$ module add yt/3.4.1 [pysdlb@c128 ~]$ run_SZpack 5D ========================================================================================== || Carrying out 5D integral ========================================================================================== x= 0.1 -4.76326e-06 -0.00128709 x= 0.105931 -5.34365e-06 -0.00144392 x= 0.112213 -5.99458e-06 -0.00161981 x= 0.118868 -6.72457e-06 -0.00181706 x= 0.125917 -7.54317e-06 -0.00203826 x= 0.133385 -8.46106e-06 -0.00228628