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.

Data Support

yt primarily supports WarpX through plotfiles. There is also support for openPMD HDF5 files in yt (w/o mesh refinement).


From the terminal, install the latest version of yt:

python3 -m pip install cython
python3 -m pip install --upgrade yt

Alternatively, yt can be installed via their installation script, see yt installation web page.

Visualizing the data

Once data (“plotfiles”) 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('./diags/plotfiles/plt00000/')

The list of field data and particle data stored can be seen with:


For a quick start-up, the most useful commands for post-processing can be found in our Jupyter notebook Visualization.ipynb

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', origin='native' )


yt.SlicePlot creates a 2D plot with the same aspect ratio as the physical size of the simulation box. Sometimes this can lead to very elongated plots that are difficult to read. You can modify the aspect ratio with the aspect argument ; for instance:

yt.SlicePlot( ds, 1, 'Ex', aspect=1./10 )

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()

Further information

A lot more information can be obtained from the yt documentation, and the corresponding notebook tutorials here.