Visualization with yt¶
yt is a Python package that can help in analyzing and visualizing WarpX data (among other data formats). It is convenient to use yt within a Jupyter notebook.
Installation¶
From the terminal:
pip install yt jupyter
or with the Anaconda distribution of python (recommended):
conda install -c atmyers yt
Visualizing the data¶
Once data (“plot files”) has been created by the simulation, open a Jupyter notebook from the terminal:
jupyter notebook
Then use the following commands in the first cell of the notebook to import yt and load the first plot file:
import yt
ds = yt.load('./plt00000/')
Field data¶
Field data can be visualized using yt.SlicePlot (see the docstring of
this function here)
For instance, in order to plot the field Ex in a slice orthogonal to y (1):
yt.SlicePlot( ds, 1, 'Ex' )
Alternatively, the data can be obtained as a numpy array.
For instance, in order to obtain the field jz (on level 0) as a numpy array:
ad0 = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)
jz_array = ad0['jz'].to_ndarray()
Particle data¶
Particle data can be visualized using yt.ParticlePhasePlot (see the docstring
here).
For instance, in order to plot the particles’ x and y positions:
yt.ParticlePhasePlot( ds.all_data(), 'particle_position_x', 'particle_position_y', 'particle_weight')
Alternatively, the data can be obtained as a numpy array.
For instance, in order to obtain the array of position x as a numpy array:
ad = ds.all_data()
x = ad['particle_position_x'].to_ndarray()